Advances in mechanically active materials for soft wearable electronics

Abstract

Soft wearable electronics provide a seamless interface between the human body and electronic systems to support real-time, continuous, long-term monitoring in healthcare and other applications. Incorporating mechanically active materials to these soft electronic systems can further expand sensing modalities, enhance sensing performances, and/or enable new functions that are challenging to achieve with physically static electronic devices. A key property of such mechanically active materials is that their shapes can change upon various external stimuli. This review highlights recent advances in this type of material, with a focus on discussing their integration with soft wearable devices and the resulting impact on the performances. Specifically, the content ranges from piezoelectric materials that generate ultrasound and surface acoustic waves, to magnetic materials that allow for new sensing modalities and haptic feedback, and to elastomeric materials that facilitate pneumatic and hydraulic actuation—all designed for soft wearable devices. The review concludes with an analysis of the key challenges and future opportunities for mechanically active materials.

---

Kedong Wu

Kedong Wu received his BS degree in theoretical and applied mechanics (biomedical engineering) from Peking University in 2025. He is currently a PhD candidate of biomedical engineering at Peking University. His undergraduate and PhD research emphases are on flexible and soft electronics for health monitoring based on magnetic material, and their translation in clinical applications.

Weixiang He

Weixiang He is currently an undergraduate student majoring in biomedical engineering at the college of engineering, Peking University. His research interests focus on wireless sensing based on magnetic materials and their clinical applications.

Mengdi Han

Dr Mengdi Han is an Assistant Professor in the Department of Biomedical Engineering, College of Future Technology, Peking University. He received his BS degree in Huazhong University of Science and Technology in 2012 and PhD degree in Peking University in 2017. He was a visiting PhD student at the Department of Materials Science and Engineering, University of Illinois Urbana-Champaign from 2015 to 2017. He worked as a postdoctoral fellow at Querrey Simpson Institute for Bioelectronics, Northwestern University from 2017 to 2020. His research group aims to develop advanced micromechanical bioelectronics for electronic skins, wireless biosensors and microrobotics.

---

Wider impact

In recent years, flexible wearable electronics have attracted great interest in the fields of healthcare and human–machine interfaces due to their flexibility, stretchability and portability. However, the passive and static properties of traditional flexible materials restrict their adaptability in dynamic, interactive applications. Mechanically active materials—including piezoelectric materials, magnetic materials, and pneumatic/hydraulic actuated elastomers—could address this limitation by enabling bidirectional energy conversion between mechanical deformation and external stimuli such as electrical, magnetic, or pressure signals. These materials enhance device responsiveness, thereby enabling advanced functionalities such as multimodal sensing and haptic feedback. This review systematically discusses the working principles, optimization strategies, and emerging applications of mechanically active materials, with particular emphasis on their unique advantages in wearable sensors and actuators. Progress in this interdisciplinary field demands joint efforts across multiple domains, including functional materials, advanced manufacturing, biomedical devices, and clinical translation. The successful integration of mechanically active materials into wearable systems promises to enable transformative applications ranging from continuous health monitoring and precise diagnostics to intuitive human–machine interfaces and intelligent rehabilitation devices.

1. Introduction

Recent advances in materials science, device-level mechanics, and manufacturing technologies have catalysed the emergence of soft wearable electronics that combine physiological conformity with clinical-grade sensing capabilities.^1–3 Unlike conventional rigid devices, these systems achieve mechanical compatibility with biological tissues through engineered flexibility and stretchability.^4–6 These features enable continuous monitoring of vital biomarkers such as electrophysiology, biomechanics and fluid chemistry.^7–9

However, the passive and static nature of soft materials imposes some limitations on wearable electronics. A notable example is that these systems lack responsive interactions, which limits sensing modalities and impedes therapeutic interventions.^10,11 Mechanically active materials emerge as a potential solution by converting external stimuli into mechanical outputs or vice versa. Such stimulus-responsive features could enable a variety of applications in sensing and actuation. For example, a high-frequency vibration (100 kHz to 10 MHz)^12,13 of the mechanically active materials (e.g. piezoelectric ceramics) can generate ultrasound waves that either propagate horizontally across the skin or penetrate vertically to deep tissues.^14–17 Analysing these ultrasound waves allows for sensing and/or imaging of deep tissues.^18–20 Alternatively, mechanical deformations at lower frequencies (50 to ∼200 Hz), in the forms of vibration, shear or indentation, can induce sensitive feedback to mechanoreceptors in the skin.^21–28 Mechanically active materials that operate in this frequency band, therefore, provide opportunities for haptic interface with further applications in virtual reality, rehabilitation, human–machine interfaces and prosthetic sensory restoration.^29–33 In some other cases, quasi-static mechanical deformations can also be useful for wearable electronics. Materials adopted for this purpose involve elastomers, actuated through pneumatic and hydraulic mechanisms. Typical applications involve artificial muscles, kinesthetic feedback systems and smart tactile sensors.^34–40

A range of dynamic solutions, including shape memory alloys, electroactive polymers, and triboelectric materials, have demonstrated potential for various applications,^41–49 each presenting unique advantages. For instance, shape memory alloys are popular in artificial muscle design due to their high energy density,⁵⁰ whereas piezoelectric materials are particularly useful in ultrasound sensing because of their high-frequency response.⁵¹ This review will focus on piezoelectric, magnetic, and elastomeric materials. These three classes are currently among the most frequently utilized and versatile in addressing the challenges of wearable electronics. This is likely due to a combination of factors, including their versatility in achieving both sensing and actuation in flexible forms, relatively mature integration techniques for on-body applications, or a favourable balance of overall performance characteristics for a broad range of wearable scenarios. Given the unique features of wearable electronics enabled by mechanically active materials, this review highlights recent advancements in this field, with a focus on how these materials enhance the functionalities and their corresponding clinical applications. The review begins with mechanisms of different mechanically active materials, followed by their applications in wearable electronics. Specifically, the content involves piezoelectric materials that generate ultrasound or surface acoustic waves for non-invasive physiological monitoring; magnetic materials that can either enable emerging sensing modalities or provide haptic feedback through programmable deformation; and elastomeric materials that facilitate pneumatic and hydraulic actuation with precise force control and anatomical adaptability. Each class of material is evaluated in terms of its mechanical responsiveness, integration strategies, and potential for improving the performance and/or function wearable device. The review concludes with an assessment of key challenges, as well as future opportunities for mechanically active materials in this rapidly evolving field of wearable electronics.

2. Mechanisms of different mechanically active materials

2.1 Fundamental principles of piezoelectric activation

Piezoelectric materials are a series of materials with non-centrosymmetric crystal structures or anisotropic dipoles. An external pressure applied to the surface of such materials induces lattice deformations, thereby separating the geometric center of cations and anions. This polarization leads to an electric potential difference across different surfaces of the material. This effect, called the piezoelectric effect, is often used to construct sensors that generate electric signals under mechanical stimuli.^52,53 In addition, the piezoelectric effect can operate in reverse—converting electrical potential into mechanical deformation—as well (Fig. 1B). This behavior enables piezoelectric materials to function as mechanically active components under alternating electric input. This feature is important for the development of wearable devices with actuation capabilities, thereby creating potential applications in fields such as healthcare, robotics, and human–computer interaction.^54–57

Fig. 1 Overview of soft wearable electronics based on mechanically active materials. (A) Schematic illustrations of mechanically active materials in wearable applications. Reproduced with permission.⁵⁸ Copyright 2022, American Association for the Advancement of Science. Reproduced with permission.⁵⁹ Copyright 2023, Springer Nature. Reproduced with permission.⁶⁰ Copyright 2023, American Association for the Advancement of Science. Reproduced with permission.⁶¹ Copyright 2022, Springer Nature. Reproduced under the terms of the Creative Commons CC BY 4.0 license.⁶² Reproduced with permission.⁶³ Copyright 2023, The American Association for the Advancement of Science. (B) Mechanism of piezoelectric, magnetic, and elastomeric materials.

Typically, piezoelectric materials can be categorized into two classes: inorganic piezoelectric materials and organic piezoelectric materials. Inorganic piezoelectric materials include piezoelectric crystals (e.g. quartz and LiNbO₃), and piezoelectric ceramics (e.g. lead zirconate titanate (PZT) and barium titanate (BaTiO₃)). Organic piezoelectric materials are represented by polyvinylidene fluoride (PVDF) and poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)). Key metrics to evaluate the performance of a piezoelectric material include (1) the piezoelectric charge constant, defined as the amount of charge generated by unit stress along the direction of polarization, and (2) the electromechanical coupling coefficient (K²) (an index reflecting the transforming efficiency between electric energy and mechanical energy. Piezoelectric crystals usually have high mechanical quality factors, but exhibit relatively low electromechanical coupling coefficients, limiting their applications in energy harvesting devices, self-powered devices or actuators. By contrast, ceramics have a high piezoelectric charge constant, and can convert a large portion of input energy into ultrasound, making them the core elements for ultrasound transducers.¹⁴ However, their brittle structure impedes their integration with soft biological tissues, and the toxicity related to lead limits their applications in biomedicine.⁶⁴ As a comparison, piezoelectric polymers possess modulus in the range of a few GPa, and demonstrate excellent biocompatibility. Applications of such materials range from implantable sensors to wound healing applications.^65,66

To suit the requirement of wearable devices, scientists have come up with several strategies to balance and improve the mechanical and piezoelectric properties of piezoelectric materials. The first strategy is integration: mixing materials with high piezoelectric constant and appropriate mechanical performance. For example, combining piezoelectric ceramics like PZT with passive elastomers such as PDMS or epoxy can yield two-phase composites. The designation of these composites always follows an “x–y” pattern, in which the “x” represents the connectivity of piezoelectric materials, and the “y” refers to this value of elastomer. For example, several PZT rods embedded in a 3D matrix of epoxy can form a typical structure of 1–3 composite. The integration can result in better properties than single phase materials. The 1–3 composite, composed of PZT and elastomers, surpasses rigid normal piezoelectric materials in terms of both electromechanical coupling coefficients and flexibility. Therefore, it can be fabricated into ultrasound devices that work stably on complex surfaces like skin, making them useful in the development of wearable electronics.^67–69 Another strategy focused on the structural design of piezoelectric materials. Micro/nanoelectromechanical system (MEMS/NEMS) techniques and 3D-printing techniques allow researchers to process piezoelectric materials into a variety of micro/nanostructures, such as nanowires, needles and ultrathin films.^14,70–73 These structures increase the response of the materials to external mechanical stimuli. Besides, the ultra-thin structure also transforms the rigid and air-proof materials into flexible and breathable ones. These features greatly improve the wearability of the device, and support the development of many wearable nanogenerators and actuators.^74–78 The combination of these strategies leads to an interesting type of material: piezoelectric fibre. In these cases, piezoelectric polymers, such as PVDF and P(VDF-TrFE), are combined with nanoscale piezoelectric materials and fabricated into fibres through techniques like melting spinning or thermal drawing. The fibre can be woven into fabrics with outstanding piezoelectricity and wearability, making it a great candidate for wearable self-powered devices.^79–81 Recently, a series of piezoelectric fibres combined with Mxenes, a class of novel two-dimensional transition metal carbides/nitrides materials, have been reported. By minimizing the charge loss and inducing polarization locking, the introduction of MXenes greatly improved the performance of piezoelectric fibres, especially in high-frequency applications like accelerometers, which indicates their promising future in wearable devices.^82–84 In Table 1, we summarize the piezoelectric performance, mechanical properties and fabrication methods of several typical piezoelectric materials. To sum up, both strategies facilitate the emergence of piezoelectric materials with extraordinary piezoelectric and mechanical performances, which contribute to the development of state-of-the-art wearable electronic devices.⁸⁵

Table 1 Piezoelectric and mechanical properties of several typical piezoelectric materials and their fabrication methods

Type	Typical material	Filler or dopant	d 33 (piezoelectric constant, pC N^−1)	k 33 (electromechanical coupling coefficient)	ε r (dielectric constant)	Elastic modulus (GPa)	Common fabrication method	Ref.
Ceramics	AlN	N/A	5.5	0.1–0.3	1.2–20.7	338	Sputtering/CVD	86,87
Ceramics	PZT-5H	N/A	1800	0.87	10 000	50–60	Solid state crystal growth	88,89
Ceramics	BaTiO3-xAlN	AlN	305	N/A	N/A	N/A	Solid state reaction sintering	90
Ceramics	ZnO nanosheets	Y-doped	420	N/A	N/A	N/A	Wet chemical co-precipitation	91
Polymers	PVDF	N/A	20	0.2	25	2–4	Electrospinning, spin coating	92–94
Polymers	PVDF-TrFE	N/A	20–25	0.27	12–25	3–4	Solvent casting	92,93
Polymers	P(VDF-TrFE-CFE-FA)	N/A	>1100	0.71	50–60	0.224	Copolimerization	95,96
Composite	MXene/PVDF-TrFE	MXene	38–45	N/A	34	13–14	Electrospinning + MXene doping	92,97
Composite	0–3 PZT/silicone resin	PZT particles	10–50	N/A	3.3	2.5	Vacuum mixing	98
Composite	1–3 PZT/epoxy	Epoxy	500–700	0.75	1300	54	Dice-and-fill, freeze-casting	99

---

2.2 Fundamental principles of magnetic activation

As Table 2 shows, magnetic materials with mechanically active properties usually involve hard magnetic materials (i.e., materials with large coercivity M_r and retentivity H_c),¹⁰⁰ soft magnetic materials (i.e., materials with low remanence and low coercivity)¹⁰¹ and superparamagnetic materials (i.e., materials with no remanence in the absence of an external magnetic field).¹⁰² Each type offers unique properties that make it useful in wearable electronics systems.

Table 2 Magnetic properties of several typical magnetic materials and their fabrication methods

Type	Typical material	Magnetic properties	Response under magnetic field	Fabrication method	Ref.
Hard-magnetic	Nd2Fe14B, BaFe12O19, SmCo5	Large coercivity Mr and retentivity Hc	Bending and twisting	Molding, casting, 3D printing, laser heating, micromolding and assembly	103–106
Soft-magnetic	Iron–nickel and iron–silicon alloys	Low coercivity Mr and retentivity Hc	Magnetostriction	Magnetic field-assisted molding, soft lithography	107,108
Superparamagnetic	Fe3O4 nanoparticles	No coercivity Mr and retentivity Hc	Heat	Ultraviolet light photolithography, thermal curing, laser/mechanical cutting	109–112

---

For example, neodymium–iron–boron (Nd₂Fe₁₄B), a representative hard magnetic material widely used in wearable magnetic actuators, exhibits high remanence and coercivity, enabling it to retain magnetization long after the external magnetic field is removed.¹¹³ When exposed to a spatially uniform magnetic field, these hard magnetic materials experience magnetic torque that can realign their remanent magnetization direction with the applied field, generating mechanical deformation including bending and twisting. In contrast, soft-magnetic materials (e.g. iron–nickel and iron–silicon alloys) magnetized rapidly under an external magnetic field but lose magnetization almost immediately when the field is removed. Under a uniform magnetic field, soft magnetic materials generally produce less mechanical torque but can induce elongation or contraction along the field direction, which is often referred to as “magnetostriction”.¹¹⁴ Superparamagnetic materials, such as Fe₃O₄ nanoparticles, display two unique properties: (1) zero hysteresis, allowing them to lose magnetization immediately when the external field is removed, and (2) exceptionally high magnetic susceptibility, enabling a strong response even under weak external fields. In addition, their great biocompatibility and “switchable” magnetic behavior make them ideal for biomedical applications. A representative example is temporary magnetic guidance during drug delivery followed by harmless dissolution in the body.¹⁰⁹

Among the above three types of magnetic materials, hard and soft magnetic materials are of particular interest for constructing mechanically active systems. Common hard magnetic materials involve alnico, barium hexaferrite (BaFe₁₂ O₁₉), samarium–cobalt (SmCo₅ or Sm₂Co₁₈) and neodymium–iron–boron, whose remanent can exceed 1000 kA m⁻¹. Due to their high remanence, hard magnetic materials play an important role in wearable sensing and actuation. However, biocompatibility is the core challenge of these conventional hard magnetic materials in biomedical applications. One potential hard magnetic material that could solve this problem is iron–platinum (FePt), which exhibits relatively high remanent and low cytotoxicity.¹¹⁵ A recent study has demonstrated the potential of iron–platinum nanomagnets in transfecting DNA plasmids into cancer cells. Soft magnetic materials, such as iron and nickel- or silicon-based alloys of iron, can also be used in wearable electronics. For example, when embedded into soft polymer matrices as particles, they can cause the polymer to shrink in strong magnetic fields, acting as an artificial muscle.

To fully realize the potential of magnetic materials in wearable electronics, it is essential to devise new strategies for enhancing their mechanical output, functional diversity, and controllability. One effective approach is to incorporate magnetic particles into soft elastomers and exploit the inherent coupling between the magnetic domains and elastic matrices. These magnetic composites (e.g., silicone gel mixed with NdFeB particles) have demonstrated advantages of various wearable sensors such as muscle physiotherapy monitoring and voice recognition. The second strategy is to expand the functionality of magnetic materials by spatially programming their magnetization direction. For example, wrapping a magnetic film on a cylinder and applying a strong magnetic field along the radial direction can create a film with single-wavelength harmonic magnetization after unwrapping. This configuration facilitates diverse modes of locomotion when controlled with programmed magnetic fields. For instance, a periodic magnetic field is applied to the film sequentially adapting its tilting angle and curvature, thereby enabling forward movement in a desired direction.¹¹⁶ The third strategy is spatiotemporal programming of the actuating magnetic field. Current approaches primarily include changing the position and/or posture of a permanent magnet and adjusting the input currents of multiple orthogonally arranged coils. Among these approaches, using permanent magnets is particularly useful in medical applications due to their low power consumption and small temperature increase. A representative example is the use of a permanent magnet for manipulating small magnetic objects.¹¹⁷ Electromagnets (i.e. coils with currents) complement permanent magnets by offering enhanced spatiotemporal control of the magnetic field. Current-driven coils enable rapid polarity switching and superposition of multi-axis fields, facilitating complex actuation sequences unachievable with static magnets. Orthogonal (such as Helmholtz coil) or nonorthogonal flux interaction patterns (Abbott et al.) represent two important designs for electromagnetic actuation.

In summary, magnetic actuation offers unique advantages for mechanically active wearables, enabling remote, wireless, and programmable deformation through material–field interactions. By enhancing material properties and field configurations, magnetic materials have the potential for advancing the bridge between biological and electronic functionalities.

2.3 Fundamental principles of elastomeric activation

Unlike piezoelectric materials that deform under electric input or magnetic materials that exhibit shape deformation (via torque-driven rotation) and spatial movement (via force-induced translation) under magnetic fields, elastomeric materials usually cannot deform under external stimuli other than force. However, their low modulus and high stretchability enable large deformation, which is advantageous for high displacement.¹¹⁸ This can lead to substantial sensations as well, such as replicating the experience of joint torque. To precisely control the deformation of elastomeric materials, pneumatic and hydraulic systems have been employed, providing programmable mechanical actuation.^119–121 Although the basic behavior of the fluid can be complex, the working mechanism of elastomer-based pneumatic or hydraulic systems is relatively straightforward, as wearable applications mainly rely on macro-scale responses. Taking advantage of the high transmissibility of pressure change, especially in liquids due to their incompressibility, these systems are suitable for constructing actuators and pressure sensors, capable of delivering conformal mechanical interactions at bio-interfaces.¹²²

Elastomer-based pneumatic/hydraulic systems have been widely employed in applications ranging from industrial machines to biomedical electronics. The structure of pneumatic and hydraulic systems generally follows a similar design. It generally consists of three components: a lower stiffened substrate, a cavity that serves as a fluid chamber and a thin elastic membrane that responds to fluid-induced pressure changes.¹²³ By optimizing the unit's dimensional parameters and geometric arrangement, such systems can generate mechanical stimuli of a broad range, from strong forces to subtle sensations, which enables versatile interactions with the human body.

The nature of its polymer crosslinking primarily determines the mechanical properties of elastomers. Chemically crosslinked elastomers, represented by silicones including PDMS and Ecoflex, generally form a three-dimensional network through strong covalent bonds, featuring them with outstanding mechanical strength, thermal stability and chemical inertness.¹²⁴ In contrast, physically crosslinked elastomers, such as styrene-butadiene terpolymers (SBS), heavily rely on non-covalent interactions like hydrogen bonds to form dynamic network structures. Although these materials offer high flexibility and responsiveness, their networks are susceptible to structural dissociation under pressure fluctuations, temperature changes or solvent exposure.¹²⁵ Hybrid crosslinked elastomers (i.e. polyurethane) combine covalent and non-covalent bonds to balance stiffness and flexibility to overcome this trade-off. In wearable sensor applications, chemically crosslinked and hybrid crosslinked elastomers are preferred for their superior resistance to environmental fatigue and long-term durability.^126–129

To improve the mechanical performance and clinical practicality of these pneumatic/hydraulic elastomeric systems, researchers have dedicated substantial efforts to overcome challenges of durability in biological environments and the reliance on wired connections. One notable strategy is to improve the elastomeric material itself. By advancing the synthetic techniques, new elastomers with superior biocompatibility and enhanced stretchability have been developed, such as polycaprolactone-based polyurethane (b-DCPU) and thermoplastic polyurethane (TPU).^130,131 Another promising direction is functionalizing the fluid by incorporating different ions or organic solvents. Such functionalized fluids can actively generate mechanical responses such as motion or inflation when triggered by external stimuli, including electric or thermal fields. This approach makes it possible to eliminate bulky air or liquid pumps, enabling pneumatic/hydraulic elastomeric systems to become more portable, versatile, and better suited for integration into wearable electronic devices.

Beyond material selection and fluid functionalization, the fabrication method is also critical in realizing functional and reliable elastomeric actuators for wearable applications. The chosen manufacturing approach directly influences the device architecture, achievable complexity, and performance characteristics. Recognizing the importance of this aspect, Table 3 provides a summary of several typical elastomeric materials employed in pneumatic and hydraulic wearable systems.

Table 3 Mechanical properties of several typical elastomeric materials and their fabrication methods

Actuation approach	Typical elastomeric material	Response time (ms)	Young's modulus	Fabrication method	Ref.
Pneumatic	Silicone Ecoflex series	<500	0.7–2.4 MPa	3D printing and molding for airbag	62,132
Pneumatic	Polydimethylsiloxane	<500	27–169 kPa	3D printing and molding for airbag	62,132
Pneumatic	Thermoplastic polyurethane	<120	1.8–30 GPa	Screen printing and thermal bonding	133,134
Pneumatic	Silicone dragon skin series	N/A	0.15–0.52 MPa	3D printing and molding	135
Pneumatic	Thermoplastic elastomer (styrene–ethylene–butylene–styrene, SEBS)	N/A	1 MPa	Extrusion based on fused deposition modeling	136
Pneumatic	Liquid crystal elastomer	Almost 2000	N/A	Extrusion based on direct ink writing and ultraviolet light crosslinking	137
Hydraulic	Silicone rubber tube	N/A	1.586–1.648 MPa	Assembly and weaving	138
Hydraulic	Photopolymer resin	<31	N/A	3D printing and Stereo lithography apparatus for microchannel	139
Hydraulic	Thermoplastic polyurethane	Almost 200	1.8–30 GPa	TPU filament winding and thermal bonding	63

---

In summary, Tables 1–3 detail the properties of piezoelectric, magnetic, and elastomeric materials with a particular emphasis on cataloguing their associated fabrication methods. A closer examination of these tables reveals that the choice of fabrication method is confined by both the intrinsic properties of the materials themselves and the specific requirements of the intended device architecture. For instance, for piezoelectric materials (Table 1), the fabrication of thin-film piezoelectric devices uses materials like AlN or ZnO. These materials typically necessitate vacuum-based deposition techniques such as sputtering or chemical vapor deposition (CVD), which are chosen due to the material's need for controlled crystalline growth and thin-film morphology. It confines their application to substrates compatible with such processes and often limits the ease of creating complex, non-planar bulk structures.

For magnetic systems (Table 2), a key fabrication advantage lies in the ability to integrate magnetic materials, often as powders, into elastomeric matrices. This composite approach makes them highly compatible with versatile fabrication techniques such as molding and 3D printing, which enables the creation of complex, flexible magnetic actuators and sensors. However, this strategy has its confinements. It is a critical challenge to achieve a high and uniform loading of magnetic particles for sufficient magnetic responses without compromising the mechanical integrity or processability of the elastomeric host.

In the case of elastomeric actuators (Table 3), the elastomer's liquid precursor state and curing requirements like PDMS naturally lend themselves to molding or 3D printing for creating complex channels or airbags. Moreover, elastomers like TPU are more suitable for film-based thermal bonding or specialized extrusion-based techniques such as filament winding, especially when specific mechanical properties or continuous fiber forms are desired. Thus, factors such as material processability and architectural demands critically narrow the selection of viable fabrication strategies for each class of active material, underscoring a crucial interplay that must be considered in device design.

3. Wearable electronics based on piezoelectric materials

Piezoelectric materials have been widely used as actuators in robotic haptic sensing,¹⁴⁰ energy harvesters in MEMS¹⁴¹ and filters in signal processing.¹⁴² In wearable electronics, they are always used to generate surface acoustic waves (SAWs) and ultrasound waves. These waves have a wide range of usage in the field of biomedicine. The following two sub-sections discuss the working mechanism, advances in materials, and biomedical applications of wearable devices based on SAWs and ultrasound waves.

3.1 Wearable electronics based on SAWs

SAWs propagate along the surface of elastic media with energy concentrated near the interface.¹⁴³ Leveraging the piezoelectric effect, these waves are generated through radio-frequency (RF, usually in the range of 10 MHz–10 GHz)^58,144,145 excitation of interdigital electrodes patterned on piezoelectric films. The resonant frequency, determined by the excitation parameters and film-specific acoustic velocity, is sensitive to many surface perturbations including but not limited to strain, temperature variations, and mass loading.^146–148 An important advancement in this area is the development of interdigital transducers (IDTs) that allow for precise SAW modulation and facilitate widespread adoption in telecommunications, precision sensing, and biomedical technologies.¹⁴⁹

Conventional SAW devices comprise three critical components: a piezoelectric film, interdigital electrodes, and a substrate. These systems exhibit intrinsic advantages such as sub-millisecond response times, passive wireless operation, and compatibility with miniaturized architectures—attributes synergistically aligned with the requirements of wearable electronics.^150,151 However, conventional SAW platforms predominantly employ rigid inorganic piezoelectric (e.g., quartz, lithium niobate) or silicon-based substrates, fundamentally limiting conformal integration with soft biological tissues. Recent innovations have addressed this constraint through strategic hybridization of materials: low-modulus polymeric substrates (e.g., polyvinyl alcohol [PVA], polyester [PET], polyimide [PI]) combined with ultrathin piezoelectric coatings (ZnO, AlN, GaN) to achieve unprecedented mechanical compliance while maintaining strong electromechanical coupling coefficients (k² > 2%).^58,152,153 Such advancements in materials, coupled with sophisticated thin-film deposition techniques, has yielded SAW devices with piezoelectric nanomembranes down to sub-100 nm in thickness.¹⁵⁴ This ultra-thin feature enhances the adaptability of the SAW sensors to complex epidermal interfaces and facilitates applications in wearable physiological monitoring.

3.1.1 Wearable temperature sensors based on SAWs. The monitoring of temperature is always important in the evaluation of body condition. As temperature is a determinant of acoustic wave velocity. SAW devices could be used as temperature sensors. The temperature coefficient of frequency (TCF), which represent the sensitivity of SAW devices to the temperature, is described as in the following equation:¹⁵⁵where Δf is the variation of resonance frequency with temperature change (ΔT) and f₀ is the initial resonance frequency at 25 °C. Typically, the frequency decreases linearly as the temperature increases. However, traditional SAW devices are not suitable for wearable temperature sensors due to their high stiffness and low TCF.

Recent progress in the design of flexible piezoelectric materials plays an important role in the realization of wearable temperature sensors based on SAWs. In the last few years, researchers have found ways to successfully deposit a thin layer of piezoelectric materials on flexible polymer substrates, which enables wearable applications. Moreover, the higher thermal expansion coefficient of these polymer substrates can enhance the sensitivity of the SAW temperature sensor.^156,157 In the past few years, many wearable sensors have used the SAW mechanism for temperature detection.^152,158 Lamanna et al. reported the first wearable temperature sensor based on SAWs (Fig. 2C).¹⁵⁶ In this research, they chose aluminium nitride (AlN) and polyethylene naphthalate (PEN) as the piezoelectric material and flexible substrate respectively. Both materials feature chemical stability, thermal stability and biocompatibility.

Fig. 2 Wearable sensors based on surface acoustic waves (SAWs). (A) Chip-less wireless e-skin based on SAW devices made of GaN freestanding membranes. Top left: Structural illustration of the chip-less e-skin with excellent breathability and stretchability. Top middle: Architecture of a SAW transducer based on GaN integrated with sodium-selective polymeric films for remote ion concentration monitoring. Top right: Photograph and microscopy image of the SAW device attached to the skin. Bottom left: Transmission electron microscope (TEM) image and diffraction images of the freestanding 200-nm-thick GaN nanomembrane. Bottom middle: Scanning electron microscope (SEM) images of the GaN device attached to skin replica samples, showing the GaN film's excellent flexibility. Bottom right: Frequency modulation characteristics of a SAW device demonstrating concentration-dependent resonant peak shifts during Na⁺ ion detection. Reproduced with permission.⁵⁸ Copyright 2022, American Association for the Advancement of Science. (B) Ultrathin glass-based flexible SAW humidity sensor with ZnO nanowires and graphene quantum dots. Left: Illustration of the experimental setup of the flexible SAW device for breathing detection on the wrist. Right: Resonant frequency changes in the flexible SAW humidity sensor under a cyclic breathing test. Reprinted with permission.⁷¹ Copyright 2020, American Chemical Society. (C) Skin-attached PEN-based SAW temperature sensor. Left: Photograph of a flexible temperature sensor attached to the wrist. Top right: Illustration of a flexible SAW device with material and structural details. Bottom right: Infrared image with the evidence of the uniform heat conduction through a PEN/AlN-based patch from the skin to the SAW device. Reproduced with permission.¹⁵⁶ Copyright 2019, Wiley.

To be compatible with standard integrated circuit (IC) technology, the process adopts reactive sputtering techniques to grow high orientation AlN film on the substrate. The TCF of the flexible SAW device is ≈810 and ≈67 ppm °C⁻¹ for the Rayleigh and Lamb wave, respectively. Likewise, TCF for the rigid device is ≈59 and ≈40 ppm °C⁻¹ for the two modes. This result demonstrates the great potential of flexible SAW sensors to provide high-resolution temperature sensing for wearable clinical applications.

3.1.2 Wearable humidity sensor based on SAWs. Another parameter that can be measured using the SAW device is humidity. Humidity is crucial not only for human health (e.g. respiratory) but also for human–machine interfaces (e.g. contactless control).^159–162 Flexible SAW devices can monitor the moisture level wirelessly, as the mass distribution on the piezoelectric film will change if the humidity varies, thus shifting the resonant frequency. The key part of SAW-based humidity sensors in material science is to select a material that can absorb water molecules. To achieve this purpose, many flexible humidity sensors exploit ZnO, a hydrophilic piezoelectric material, to absorb water molecules to its surface.¹⁶³ These devices realize a sensitivity of 3.5 kHz/%RH (relative humidity).¹⁶⁴

Incorporating nanostructures into piezoelectric materials can further enhance the performance of SAW-based humidity sensors, due primarily to the large surface area enabled by the nanostructures. As shown in Fig. 2B, Wu et al. found that the 3D nanowire structure of ZnO can bond more water molecules compared with standard 2D films.⁷¹ The integration of graphene quantum dots (GQD) further improved the absorption capacity by providing more hydrophilic functional groups and by facilitating more hydroxyl groups to be formed at the active sites. As a result, an ultrahigh humidity sensitivity of 40.16 kHz/% RH was obtained with excellent stability and repeatability. The sensor also functions well in wearable applications for humidity sensing and human breathing detection. Similar SAW humidity sensors based on nanostructures including 3D tripodal networks,¹⁶⁵ flower-like structures¹⁶⁶ and nanorods¹⁶⁷ have been reported recently. These structures enhance the performance of wearable humidity sensors in multiple aspects (e.g. response time < 1 s, sensitive range from 0.5% RH to 85% RH), demonstrating great potential in personal healthcare, human–machine interfaces and other emerging fields related to wearable electronics.

3.1.3 Wearable multimodal sensors based on SAWs. With the rapid advancement of technology and the growing demand for personalized medicine, multi-modal sensors that combine several sensing functions have gained popularity.^168,169 Electronic skin (e-skin) based health monitoring platforms are among the most promising options.^170–172 However, conventional e-skin relies on rigid substrates and other elements to achieve wireless communication, thereby degrading its comfort level as wearable electronics due to the disturbance of skin function over time due to sweat impermeability and occlusion. Moreover, the chips also consume substantial power and spread extra heat, which further limits the application of e-skin in wearable situations.^173,174

The advancements of piezoelectric materials provide new solutions for wearable multi-modal sensors. For example, some piezoelectric materials (e.g. ZnO, MoS₂,BaTiO₃) feature optoelectrical properties for optical sensing;^175–178 integration of specific functional materials that can selectively bind with other biomolecules enable biochemical sensing.^179,180 Besides, advanced material processing technologies, represented by remote epitaxy, provide opportunities for fabricating ultra-thin piezoelectric films that can attach to skin conformably while showing sufficient electromechanical coupling coefficients simultaneously. Such properties lay the foundation for creating e-skin based on SAWs.¹⁸¹ A representative work by Kim et al. reported a set of chip-less, wireless e-skin based on ultrathin GaN SAW sensors. Here, GaN has both optoelectronic and piezoelectric properties, and can serve as the core element for ultraviolet (UV) sensing. Procedures such as remote epitaxy and transfer printing integrate a 200-nm-thick GaN membrane on a patch of polydimethylsiloxane (PDMS; ∼20 μm) with perforations that allow removal of sweat and skin by-products, thereby improving the softness, skin-conformability, and long-term wearability. Images from transmission electron microscopy (TEM) and scanning electron microscopy (SEM) UV light sensing and strain sensing (Fig. 2A). To monitor the biochemical signals on the skin, the team coated the surface of a GaN SAW device with ion-selective membranes (ISMs) that can trap specific ions, which resulted in changes of the viscosity and mass of ISM. Thus, the ion concentration in surrounding liquid can be wirelessly detected by analyzing the resonant shift of SAWs. To sum up, this e-skin platform successfully integrates the sensing of strain, UV light and ion concentration, indicating the multimodal sensing capability of SAW devices in wireless health monitoring.⁵⁸ To sum up, this e-skin platform successfully integrates the sensing of strain, UV light and ion concentration, indicating the capability of SAW devices in achieving multifunctional wireless health monitoring.

3.2 Wearable electronics based on ultrasound

Ultrasound transducers exemplify the quintessential application of mechanically active piezoelectric systems, leveraging inherent electromechanical reciprocity to enable dynamic energy conversion between electrical and mechanical domains. Piezoelectric transduction mechanisms allow these devices to convert electrical stimuli into mechanical waves capable of propagating through media, with reflected wave analysis providing critical information through signal intensity and spectral signature decoding. This operational principle underpins their widespread implementation in medical diagnostics, where real-time imaging of cardiovascular networks, cerebral structures, and metabolic interfaces relies fundamentally on piezoelectric material responsiveness. Technological evolution in two domains drives progress: adaptive transducer architectures now optimize acoustic impedance matching with biological tissues, while machine learning algorithms enhance image reconstruction from raw signal datasets. Continuous refinement in both hardware and computational processing rapidly expands diagnostic capabilities, advancing toward unobtrusive diagnostic platforms for wearable health monitoring.

The emergence of mechanically adaptive ultrasound systems addresses critical limitations in conventional diagnostic paradigms, where rigid transducer architectures inherently restrict continuous physiological monitoring required for precision medicine. Bulk instrumentation and specialized signal processing requirements in traditional ultrasound setups compromise their capacity for sustained biophysical tracking, a key demand in modern healthcare frameworks. Wearable piezoelectric probes, through active material tuning of mechanical impedance profiles, theoretically resolve these constraints but necessitate carefully engineered material solutions. Current development strategies bifurcate along integration methodologies: while sharing common piezoelectric compositions and transduction mechanisms, divergent fabrication protocols produce either epidermal-compliant arrays or semi-implantable micronode systems. These approaches derive functional differentiation from distinct interfacial engineering philosophies—surface-mount designs maximize epidermal conformity through modulus gradient transitions, whereas embedded configurations prioritize electromechanical signal fidelity via contact impedance minimization. Besides acoustic performance, wearability and biocompatibility are also important considerations when evaluating these wearable designs. An index to quantify them is the continuous imaging duration, defined as the longest time that a device can be adhered to or worn on the skin without discomfort or potential negative effects to related deep tissues or organs. Therefore, it not only reveals the comfortability of the skin–machine interface but also indicates the biocompatibility of epidermal and deep tissue of the wearable device.¹³

3.2.1 Flexible and stretchable ultrasound patch. The inherent mechanical rigidity of conventional ultrasound probes fundamentally constrains their adaptability to biological surfaces, where nonplanar topology and dynamic tissue movement necessitate active interfacing strategies.¹⁸² A traditional approach is to add liquid couplers between the rigid probe and tissue, but usually suffers from acoustic impedance mismatch that can cause over 60% signal attenuation, particularly at high-frequency detection bands above 5 MHz.¹⁸³ To address this challenge, Hu et al. introduced an approach by developing flexible and stretchable ultrasound patches in the form of wearable electronics. The material used in this system is anisotropic 1–3 piezoelectric composites comprising PZT pillars in epoxy matrices. Such 1–3 piezoelectric composites can achieve directional wave confinement, thereby enhancing the longitudinal wave amplitude by selectively dampening transverse vibrations while maintaining kt values exceeding 0.65. Additionally, the design exploits Ag-epoxy composite as backing layers to provide viscoelastic damping to suppress parasitic oscillations, improving axial resolution beyond 200 μm. To obtain flexibility and stretchability, the system adopts a ‘bridge-island’ design,¹⁸⁴ where all the rigid transducers are located at the islands and stretchable serpentines serve as the bridge for interconnection. This hard–soft heterogeneous integration allows 28% uniaxial strain tolerance while preserving piezoelectric responsiveness across concave, convex, and mobile anatomical surfaces. Thanks to recent developments in planar printing techniques, systems in the bridge-island structure can be fabricated with relative low-cost and large scalability, which enables their practicability.¹⁸⁵ Such mechanically active systems establish new operational paradigms for wearable ultrasound, with imaging fidelity comparable to benchtop systems under 15% cyclic compression.¹⁸⁶

Such design strategies in materials and structures allow for effective development of wearable ultrasound patches and have been widely used in diverse medical scenarios. Typical applications include continuous monitoring of important organs, such as the heart and brain.^187,188 Zhou et al. reported the first conformal ultrasound patch for accurate and continuous monitoring of cerebral blood flow in 3D. The flexible and stretchable design of the patch provides comfortable contact with the skin surface for accurate sensation. However, the transcranial sensing is more challenging, due to the signal attenuation and phase aberration caused by the skull as well as the expansive area that requires 3D imaging. Therefore, the team set the central frequency of the 1–3 composite to a relatively low value (i.e. 2 MHz), thereby reducing the attenuation and phase aberration. Besides, adding a copper mesh shielding layer to the 5-layer electrode can further improve the signal-to-noise ratio (SNR) to 5 dB. To increase the imaging area, the system exploits a large array with 16 by 16 piezoelectric elements to extend the acoustic window to ∼60 mm × 60 mm at a depth of 50 mm. Such a large area allows for simultaneous imaging of multiple cerebral arteries. In vivo testing indicates that the patch can record blood flow at selected locations continuously for 4 h with high accuracy (Fig. 3C). During the 4 h imaging, no discomfort was reported. Besides, the thermal effect of this patch is below the threshold for bioeffects (thermal index < 1.5). Therefore, the patch is both biocompatible to the skin surface and deep tissue. A potential application of this wearable ultrasound patch is to evaluate blood flow spectra during different interventions.¹⁸⁹ Apart from imaging organs, this strategy also allows for monitoring of important physical and biochemical parameters within the body, such as blood pressure,¹⁹⁰ Young's modulus of deep tissue⁵⁹ and concentration of biomarkers,¹⁹¹ all of which can provide rich information related to human health.

Fig. 3 Wearable sensors based on ultrasound. (A) Illustration of thin and stretchable devices physically attached to the skin. Reproduced with permission.¹³ Copyright 2022, American Association for the Advancement of Science. (B) Soft photoacoustic patch for 3D hemoglobin and core temperature imaging. Left: Schematics of the device structure and the working principle of the soft photoacoustic patch. Right: Demonstration of 13 slices of dual-mode images detected by the photoacoustic patch. The photoacoustic images of the internal jugular vein are superimposed on ultrasound B-mode images. Reproduced under the terms of the Creative Commons CC BY 4.0 license.⁸ (C) Ultrasound patch for Transcranial Doppler (TCD). Left: The working configuration and structure of the patch for TCD. Middle: Optical images of the patch bending on a cylindrical surface. Right: Mean blood flow velocities of target arteries recorded during handgrip and Valsalva maneuvers. Reproduced with permission.¹⁸⁹ Copyright 2024, Springer Nature. (D) Illustration of a thin and rigid ultrasound probe robustly adhered to the skin via a couplant. Reproduced with permission.¹³ Copyright 2022, American Association for the Advancement of Science. (E) Conformable ultrasound bladder patch (cUSB-Patch). Left: Illustration of the cUSB-patch on the lower abdomen for bladder imaging. Right: An optical image showing the cross-section of the array featuring diced La/Sm-PMN-PT ceramic elements embedded in epoxy, along with a backing layer and two matching layers. Reproduced with permission.¹⁹² Copyright 2023, Springer Nature. (F) Bioadhesive ultrasound (BAUS) probe. Left: The structural illustration of the BAUS probe. Middle: Photograph of the BAUS device adhered to the skin over 48 hours. Right: Colour-flow imaging of the carotid artery by the BAUS device. Reproduced with permission.¹³ Copyright 2022, American Association for the Advancement of Science.

The integration of other flexible elements to the wearable ultrasound patch can provide multi-modal sensing capabilities. This feature leads to more comprehensive and precise evaluation of physiological conditions. Recently, Zhao et al. reported a wearable structural-functional sensing patch (WSFP) that enables synchronous analysis of muscle structure and function. A soft and stretchable electrode array is incorporated in the patch for electrophysiological monitoring of the moving muscle. The multi-modal design results in superior accuracy stability and wearability (continuous imaging durations up to 72 hours) in dynamic action recognition and disease assessment compared with single-modal methods.¹⁹¹ In another example, Gao et al. demonstrated photoacoustic patches for 3D mapping of hemoglobin in deep tissues by integrating an array of high-power VCSEL diodes in the flexible ultrasound patch. When illuminated by the laser beam generated by the vertical-cavity surface emitting laser (VCSEL) array, hemoglobin will vibrate and emit acoustic waves. The piezoelectric transducers receive the acoustic waves for generating the spatial distribution of the wave emitters, thus building up the 3D mapping of the hemoglobin in the deep tissue. Besides, this device can monitor the core temperature of the tissue based on the linear relationship between the amplitude of photoacoustic waves and the temperature. Both of these monitoring techniques can achieve high accuracy high spatial resolution in real time (Fig. 3B).⁸

Apart from monitoring, wearable ultrasound patches can provide many other functions as well. For example, Yu et al. developed a conformable ultrasound patch (cUSP) that enhances transdermal transport of niacinamide by inducing intermediate frequency sonophoresis in the fluid coupling medium between the patch and the skin.¹⁹³ Gao et al. used the ultrasound patch with a single transducer as a human–machine interface to control a virtual object and a robotic arm.¹⁹⁴ These cases, together with the capability of wearable imaging, implicate the promising future for wearable ultrasound patches in areas of both basic research and clinical practice.

3.2.2 Bio-adhesive ultrasound. Although stretchable ultrasound patches have advantages of high wearability, flexibility and versatility, they still suffer from limitations such as low image resolution, relatively short image duration (in the range of a few hours, defined as the longest time the device can be adhered to the skin), and instability in imaging.¹⁹⁵ Specifically, the stretchable substrate is incompatible with the backing and matching layers, which causes low resolution, and the positions of rigid elements might change unpredictably when the patch deforms at the skin. An alternative means is to directly adhere a rigid, high-density array of ultrasound transducers to the skin surface. However, typical materials of adhesion, such as hydrogels and elastomers,¹⁹⁶ are either too easy to get dehydrated or too damping for deep organ imaging. A strategy to tackle this challenge is to use hydrogel–elastomer hybrid materials with soft, tough, anti-dehydrating, and bio-adhesive properties. In the last decade, researchers have reported a variety of methods to fabricate soft bio-adhesive materials that can be exploited as human–machine interfaces in the lab effectively.^197–199 In this case, rigid and thin ultrasound elements with high density can firmly adhere to the skin with excellent acoustic impedance matching (Fig. 3d). Theoretically, by effectively transmitting acoustic waves and insulating the probe from skin deformation, this layer can enable a stable element position under dynamic body motions, thereby ensuring high reliability of the probe in long-term applications. This strategy can achieve a continuous imaging duration of more than 48 hours, which proves its great wearability and biocompatibility.^13,200,201

Wang et al. reported the first bioadhesive ultrasound (BAUS) device. The couplant layer consists of 3 parts: a hydrogel layer made of chitosan–polyacrylamide interpenetrating polymer networks (10 wt%) and water (90 wt%), a thin elastomer membrane of polyurethane encapsulating the hydrogel to prevent dehydration and provide dry interface, and a bioadhesive layer synthesized by copolymerizing poly(ethylene glycol) diacrylate, 2-ethylhexyl acrylate, acrylic acid, and acrylic acid–NHS ester coating the hydrogel-elastomer hybrid. The device can withstand high pulling forces, and maintain robust adhesion on the skin, achieving 48 hours of continuous monitoring with only 1 in 15 subjects reporting slight itchiness, indicating its high comfortability next to the skin surface. The high-density element array enables the extraordinary acoustic performance of the probe, with improved axial resolution from 5.775 mm (at central frequency of 2 MHz) (80) to 0.77, 0.225, and 0.1924 mm at central frequencies of 3-, 7-, and 10-MHz respectively. The BAUS device is also capable of performing 48 h continuous imaging for different human organs. The obtained image was clear enough for researchers to observe valuable results such as blood flow rate of the carotid artery before and after exercise.¹³

Despite its late emergence, very exciting progress has been made with the bio-adhesive strategy. For example, Lu et al. reported wearable bio-adhesive ultrasound elastography (BAUS-E) capable of continuously measuring liver stiffness over a 48-hour period. Although the device design and material choice are almost the same as the example shown in Fig. 3F, its working mechanism is distinctive from typical ultrasound imaging. The thin, high density transducer array generates acoustic radiation force impulse (ARFI) as an excitation source to produce shear waves, rather than longitudinal waves, to measure the stiffness of liver, providing a non-invasive way to monitor the moduli of internal organs.²⁰⁰ In another example, Zhang et al. developed a conformable phased-array ultrasound patch for bladder volume monitoring. This device does not involve a specific bio-adhesive layer. Instead, it exploits a thick layer (thickness > 2 mm) of biocompatible silicone rubber (Eco-flex 00-30, Smooth-On) to adhere the device to the skin. Similarly, the high-density array of ultrasound transducers offers high resolution imaging. Here, five array blocks of Sm/La-doped PMN-PT ceramic, a material with high piezoelectric properties, are connected by flexible cables and integrated in a large patch to cover the imaging area. The rare-earth-doped ceramics greatly improve the performance of wearable ultrasound transducers due to its ultra-high longitudinal piezoelectric coefficient and electromechanical coupling coefficients (d₃₃ = 1000 pC N⁻¹, k₃₃ = 0.77), compared with traditional PZT-5H ceramic used in previous devices (d₃₃ < 700 pC N⁻¹, k₃₃ < 0.6). The blocks are positioned in horizontal and vertical planes, acquiring 2D images in each direction. These 2D slices are combined to produce a high-quality 3D image of bladder volume.¹⁹²

As a summary, mechanically active wearable electronics based on piezoelectric materials mainly include SAW and ultrasound devices. Although both types of devices leverage the piezoelectric effect to generate ultrasound waves, they differ in their wave propagation directions, leading to distinct applications. SAW devices can detect detailed information on the surface they propagate through making them suitable for e-skin and human–machine interfaces. In contrast, ultrasound devices generate waves that travel through the human body, which is especially beneficial for evaluating deep tissue properties. These devices are easy to fabricate in the lab and perform well in tests. However, it remains difficult to manufacture them on a large scale, as the intricate microstructures of the device require high stability in the complex fabrication procedure. The high cost of production is another noteworthy issue to be addressed.^198,202 Future developments should involve enhancing material flexibility and piezoelectricity, in order to construct wearable devices with sufficient wearability and sensing performance that can be broadly utilized in our daily life. Besides, piezoelectric fibers have emerged as a fascinating solution for self-powered wearable electronics in recent years.^80,203 Their integration with existing wearable piezoelectric devices is also a pivotal direction for advancement in this field.

4. Wearable electronics based on magnetic materials

Unlike piezoelectric materials that deform under electricity, magnetic materials change their shapes in response to magnetic fields. Since magnetic fields can be programmed in three dimensions and can penetrate biological tissue without attenuation, they are particularly useful to expand the modalities of sensing and actuation. Key research frontiers include design principles, biological compliance, sensing modes and actuation mechanisms. The following sections provide an overview of recent advancements in these areas: Section 4.1 explores developments in sensing, while Section 4.2 focuses on actuation.

4.1 Wearable sensors based on magnetic materials

The utilization of magnetic materials in sensing applications entails the conversion of targeted signals (e.g., force, vibration) into voltage through coils based on Faraday's law^204–211 or resistance via magnetoresistive sensors.^212–216 Mechanically active magnetic materials can generate alternate magnetic fields at certain frequencies to eliminate the interferences such as geomagnetic field, or generate programmable forces to interact with biological tissues for probing biomechanics.²¹⁷

4.1.1 Magneto-mechanical resonators. One promising approach to enable new sensing capabilities is to induce the vibration of magnets and measure the dynamics of the vibration.^218–222 Analyzing such dynamic signals has the following advantages. First, the alternate magnetic field contains information in both time domain and frequency domain. The frequency, damping, amplitude and other features can all be used for sensing. Second, a high frequency magnetic field can be captured by coils with high sensitivity, whereas a constant magnetic field, such as the geomagnetic field induces no voltage in the coil. This feature eliminates the influence of geomagnetic fields.

Based on the vibration of two interacting magnets, Gleich et al. designed a set of resonant magneto-mechanical sensors with dimensions below 1 mm³.⁶⁰Fig. 4A shows the basic component of the magneto-mechanical resonator (MMR). The key component of MMR is the two spherical NdFeB magnets. One is suspended from a thin filament and the other is fixed to the cylindrical housing. When an external coil applies a strong but short oscillatory magnetic field to the MMR, the suspended spherical magnet will vibrate and generate an oscillatory magnetic field, thereby inducing voltages in an array of external coils. The voltage signals can then reflect a variety of parameters. For example, the amplitude of the induced voltages in the time domain from 16 coils can indicate the position and orientation information (i.e., 6 degrees of freedom (DoF)) of the MMR. Decoding signals in the frequency domain enables additional sensing capabilities for pressure and temperature. Specifically, a change of pressure or temperature alters the distance between two spherical magnets, resulting in a change of the restoring torque given by the magnets. Thus, it leads to variation of resonant frequency determined by restoring torque. The MMR is small enough and can serve as a wearable device even for a flying bee, with capabilities in tracking the position and measuring the surrounding pressure and temperature. The MMR can provide sensing functions appropriately in an unshielded environment since the alternate signal generated by the MMR can be intrinsically separated from the constant geomagnetic field.

Fig. 4 Soft magnetic sensing system for wearable applications. (A) MMR system and its wearable application. Left: Photograph of the MMR compared to a coin (1 Euro Cent, diameter 16.25 mm). Middle: Photographs of a honeybee equipped with an MMR marker and a planar 4 × 4 coil array for signal detection. Right: Demonstration of the sensing capability of the MMR-based tracking system by reconstructing the flying bee's 3D flight position and attitude. Reproduced with permission.⁶⁰ Copyright 2023, American Association for the Advancement of Science. (B) Electromechanical interaction sensing system. Left: Deconstructed-view schematic illustration of the system. Top right: Scheme for attaching the device onto the skin surface. Bottom right: Photograph of the device on the biceps. Reproduced with permission.²²³ Copyright 2021, Springer Nature. (C) Bio-adhesive metal detector array (BioMDA) for deep tissue implant. Left: Schematic illustration of the working principle of the BioMDA. The sensor array is attached to the skin above the cervical area to detect relative position changes during neck movements. Right: The layered schematic depicts BioMDA's components and the robust covalent bonding facilitated by the bio-adhesive with silicone encapsulation and the skin. Reproduced under the terms of the Creative Commons CC BY 4.0 license.²²⁴

Sensors based on this mechanism only require magnets and a supporting structure responsive to targeted parameters, thereby usually exhibiting small dimensions. This kind of feature allows this type of sensor to serve as not only wearables but also implants in many narrow regions inside the body. For example, Wan et al. developed a magnetic implant with a dimension of 6 by 6 by 2.8 mm.²²⁵ The device can be placed inside the brain of a rat to measure intracranial pressure and viscosity of cerebrospinal fluid by analyzing the frequency and damping of the vibration. Additionally, surface modifications on the surface of the magnet enable selective absorption of biomolecules. Here, arrays of vertically aligned carbon nanotubes on the magnet enlarge the surface area, and a protein (concanavalin-A) selectively binds with glucose. As the vibration frequency decreases with the total mass, measuring the vibration frequency can reflect the absorbed glucose and its corresponding concentration. This type of device is capable of detecting a diverse set of physical, chemical and biological parameters, including viscosity of fluid, concentration of ions and concentration of bacteria or biomarkers etc. by analyzing the vibration of magnetic materials and by a series of surface modifications.^226–233

4.1.2 Magnetic materials that interact with the skin. The magneto-mechanical resonators exploit the vibration of the magnetic materials for sensing. Such vibrations can also be applied to biological tissues (e.g. skin in wearable applications). Unlike conventional piezoelectric materials that vibrate at small amplitudes (usually in the range of several nanometres to tens of micrometres),^234–237 magnetic materials attached to some flexible frames can provide larger displacement. Song et al. leveraged this feature to build a wearable sensor for monitoring the modulus of deep tissues (Fig. 4B).²²³ In this wearable sensor, a flexible neodymium magnet serves as a Lorentz force generator. The magnet vibrates under the control of customized copper coils (50 μm in diameter for copper wire, 240 turns) to produce frequency-tunable mechanical stress waves penetrating into biological tissues. The stress wave induces strain changes on the skin, which can be captured by the conformal strain gauges located beside the magnetic actuator. By calibrating the relationship between the modulus of the tissue and the strain change on the surface, the wearable system consisted of a magnetic actuator and an array of strain gauges is capable of evaluating tissue modulus. Here, a larger magnet (diameter: 8 mm) generates stronger stress waves that can reach deep tissue at 8.2 mm, whereas a small magnet (diameter: 1.5 mm) produces weak stress waves only for superficial tissue at a depth of 1.6 mm. Thus, through deploying magnet arrays of graded size (∅1.5–8 mm), the wearable system can measure tissue modulus across epidermal, dermal and muscle layers. The system is curvature-adaptive, and maintains high stability on a 4-cm-radius surface. The property allows the system to locate at almost any position of the human body and monitor modulus variation skin diseases such as psoriasis and lesion.

4.1.3 Magnetic materials that interact with tissue implants. Wearable electronics with mechanically active magnetic materials can not only directly interact with the skin for probing biomechanics but also wirelessly interact with some tissue implants that contain magnetic materials. Fig. 4C shows an example of analysing the positions of implanted screws for surgical aftercare non-invasively. Here, Li et al. integrated 16 neodymium magnets (4 by 4) and a coil into a wearable bio-adhesive metal detector array (BioMDA).²²⁴ Because the material of medical screws contains stainless-steel that can be magnetized by an external magnet array, the screws and magnet array generate a variation of relative position when patients carry out their daily activities of spine. Thus, the coil receives the dynamic magnetic field and transforms it into an amplified voltage signal. By establishing the electromagnetic–kinematic decoupling model, BioMDA achieves 0.5 mm horizontal resolution when tracking cervical screws at depths up to 20 mm, which is promising for portable postoperative monitoring of metal implants (i.e., entire orthopaedics). Compared to conventional clinical imaging (such as Computed Tomography)²³⁸ or emerging wearable methods (such as ultrasound and electromagnetic coupling),^239–242 the approach based on magnetic materials used here eliminates high doses of radiation and thermal risks of implants caused by high-frequency wave absorption or eddy current.^243,244

4.2 Wearable haptic interfaces based on magnetic materials

Human skin involves a rich set of mechanoreceptors such as Merkel cells, Meissner corpuscles, Ruffini endings, and Pacinian corpuscles.²⁴⁵ Physical interactions to the skin cause the afferent neurons connected to mechanoreceptors to be activated. This mechanism serves as the basis for recognizing and locating objects.²⁴⁶ One of the most exciting recent directions in soft wearable electronics is the development of systems capable of rapidly and programmable engaging these afferents.^21–23,247 Mechanically active magnetic materials are particularly useful in this case, because they can provide programmable force feedback for haptic interfaces. The following subsections discuss three types of wearable haptic feedback actuators based on magnetic materials.^248,249

4.2.1 Magnetic materials that augment and substitute haptic sensory systems. The provision of a tactile image on the skin facilitates the acquisition of experiences that extend beyond those supported by visual and auditory input alone, thereby enabling interaction with computer systems and other machinery, such as augmented reality (AR) and virtual reality (VR) systems. The advent of AR/VR systems has demonstrated the capacity to rapidly and programmably manipulate human perception, thus yielding a wide range of applications in domains such as education and training, social media, gaming and entertainment.^24,25

Nonetheless, the conventional devices engineered for such applications are hindered by their bulky form factor, which compromises the comfort of use, the precision of spatiotemporal resolution, and the extent of coverage. The rigidity of hardwired connections contributes to the complexity of the system and restricts the mobility of the user.^250–252 To overcome these problems, Yu et al. designed a technology for skin-integrated wireless haptic interfaces based on magnetic materials. This technology integrates arrays of small vibrating magnetic actuators with stretchable control electronics, wireless powering schemes, and wireless communication methods. The resulting platform is able to provide real-time, programmable patterns of haptic stimulation at different body locations through touchscreens and other computer interfaces. However, the array has a relatively low density and lacks the capability in programmable control.²²

By integrating individual units or units coordinated wirelessly, Jung et al. demonstrated a wireless magnetic haptic interface for the skin to address the above issues (Fig. 5A).²⁵³ The integration of miniaturized magnets (diameter: 7 mm, thickness: 2 mm) allows for programmable control over vibrotactile stimuli across extended skin areas with high density (spatial resolution: 0.73 actuators per cm²). The value reaches or exceeds the two-point discrimination threshold in almost all large areas of the body.⁶¹ This design leverages flexible inductive circuits to eliminate rigid interconnects that limit traditional haptic devices while maintaining operational stability under mechanical deformation. Thus, the system facilitates the implementation of complex representations to the skin with high spatiotemporal fidelity, supporting a wide range of unusual applications for enhanced haptics, such as navigation using haptic information interfaces and haptic ‘movies’ to accompany music.

Fig. 5 Soft magnetic haptic feedback system for wearable applications. (A) High-density epidermal actuators. Top left: Exploded-view schematic illustration of the actuator array. Top right: Photograph of various haptic interfaces for better fitting of different anatomical structures. Bottom left: Photograph of an application scenario of the haptic interface, which is integrated into an armband, realizing a smart armband capable of GPS navigation. Bottom right: Image of a walking route (0.5 miles) based on GPS-enabled navigation software. Reproduced with permission.⁶¹ Copyright 2022, Springer Nature. (B) Bioelastic-magnetic bistable actuators. Top left: Disassembled view of the bistable transducer, showing the skin as an integral part of the haptic feedback system. Top right: Photograph of the transducer array attached to the shoulder. Bottom: Schematic illustration of the transducer in different vibration modes. Reproduced with permission.²⁵⁴ Copyright 2024, Springer Nature. (C) Multimodal haptic feedback system. Left: A deconstructed view illustration of a device with 16 independently controlled multimodal haptic feedback units and three corresponding stimulation modes. Right upper: Photograph of a palm-form haptic feedback patch. Right bottom: 3D illustration of the wireless control circuits. FPCB, flexible printed circuit board. Reproduced with permission.²⁵⁵ Copyright 2023, Springer Nature. (D) Textile-based wearable haptic interface. Left: Photograph of combining a magnet actuator with textiles via digital embroidery. Right: Photograph of an adaptive magnetic haptic glove. Reproduced under the terms of the Creative Commons CC BY 4.0 license.²⁵⁶

Although the work by Jung et al. can realistically convert visual, auditory, pressure and other forms of information obtained from different sensors into tactile interactions on a large area of skin, it still cannot replace haptic sensory systems due to the lack of the participation of mechanoreceptors with fast (∼5–400 Hz) and slow (<∼5 Hz and stretching) adaptation (RA and SA, respectively).²⁵⁷ These specialized cells coordinate with each other and with the natural mechanical structures distributed throughout the skin to produce the human sense of touch. By means of a biomechanical analysis of the mechanical response curves associated with these receptors, Flavin et al. showed a microelectromechanical haptic device with magnetic material (Fig. 5B).²⁵⁴ By using skin as an elastic energy storage element, the haptic device can achieve a bistable, self-induced deformation mode. This haptic device is based on a specific class of mechanoreceptors that can generate different and programmable sensory responses and provide dynamic and static stimuli in the form of normal or shear forces. By coupling the skin's inherent elasticity with a ferromagnetic core (FeCo cylinder) and hard magnetic armature (neodymium permanent magnet and titanium rod), their system thus forms a bistable mechanism, whereby the energy stored by the compressed epidermis is returned after transitioning to the relaxed state. Due to efficient bioelastic energy harvesting, the system can deliver both static indentation (1.4 N) and dynamic stimuli (50–200 Hz) with low power consumption. In addition, the incorporation of a Kirigami structure allows for generating shear force (14° angular displacement of structure). These unique features make it suitable to substitute a sensory system as a multifunctional haptic interface. This was demonstrated in prosthetic applications such as bidirectional force vectors improved visionless navigation, standing balance and foot-strike alignment accuracy combined with modern smartphones. These innovations validate the unique capacity of magnetic materials to encode complex mechanoreceptor engagement patterns, providing a solid foundation for immersive haptic realism and improving the intuitiveness of its application.

4.2.2 Magnetic materials combined with other systems for multimodal haptic sensory systems. By exploiting the features of hard magnetic and soft magnetic materials, the haptic interface holds great promise for the field of mechanical sensory manipulation, particularly in the context of biomedical applications that demand complex mechanical stimuli. However, in addition to mechanical stimulation, the human skin can also perceive temperature variations and other features.²⁵⁸ In order to selectively activate skin receptors and reproduce tactile information such as fine roughness, macroscopic roughness, slipperiness, force and temperature, Huang et al. presented a skin-integrated multimodal haptic interface that leverages magnetic actuators alongside thermoelectric (TE) pellets and electrotactile (ES) electrodes (Fig. 5C).²⁵⁵ The magnetic actuators in this system generate mechanical vibrations with high precision, targeting Pacinian corpuscles and Ruffini endings for deep touch perception without resonance limitations of conventional actuators, such as piezoelectric actuators,²⁵⁹ linear resonant actuators²⁶⁰ and eccentric rotating mass motors.²⁵³ This is complemented by Ag-based ES stimulation, which activates Meissner's corpuscles and Merkel's discs to reproduce fine roughness and pressure sensations (0–250 Hz). Additionally, BiTe TE pellets modulate skin temperature between 25 °C and 45 °C based on the Seebeck effect. This multimodal approach allows the interface to reproduce complex tactile information, such as sliding friction, macro roughness, and temperature changes, with high fidelity. As a result, the accuracy of recognizing texture improves by up to 45%. The work proves the compatibility of magnetic-materials-based actuation with electrotactile and thermal feedback, and provides an effective solution for multisensory engagement.

4.2.3 Magnetic material that records, transfers and reproduces haptic sensory systems. The previous 2 sub-sections discuss how magnetic materials play a vital role in advancing the platform of physical haptic sensory systems. As the technology of wearable haptic feedback continues to develop, its application can expand to more complex scenarios. Compared to delivering haptic sensory systems in a single person, there is an increasing need to leverage this physical haptic experience to enrich technology-mediated interactions between multiple people and between people and machines, with applications ranging from personalized treatment, to AR/VR, and to assisting human daily activities.^26,27,261 Achieving intuitive transfer of haptic interaction is challenging due to the necessity of scalable, shape-preserving haptic sensing and display systems that can be seamlessly integrated into daily life.²⁸ In order to address the aforementioned challenges, Luo et al. introduced a textile-based wearable interface that seamlessly integrates magnetic actuators with tactile sensors for production and transmission of haptic feedback (Fig. 5D).²⁵⁶ The system employs NdFeB magnets coupled with embroidered copper coils to generate localized vibrations, achieving a spatial resolution of 4 cm² and a temporal response within 4 ms. Digital embroidery allows for textile embedding of these magnets, and therefore, achieves conformal integration with gloves and other wearable substrates. This capability is essential for high-fidelity capture of haptic interaction parameters and their cross-user reproduction. The work achieves an accuracy of 94% in recognizing haptic patterns after transmission, and therefore, extends the application of haptic interfaces to skill transfer, tactile occlusion alleviation, and robot teleoperation. The results offer a scalable and user-centric solution for immersive human–machine interactions.

In summary, magnetic materials broaden the functions of wearable electronics through their unique capabilities in multimodal sensing and haptic feedback. Examples demonstrated in Sections 4.1 and 4.2 reveal the potential of magnetic materials in biomedical monitoring and haptic feedback that require programmable actuation. However, persistent limitations include inadequate energy efficiency in active systems, complexity in obtaining the desired pattern of magnetization, and biocompatibility in long-term usage. To solve these limitations, key directions include improving the energy conversion efficiency, developing manufacturing approaches for complex magnetization patterns, and synthesizing biocompatible or bioresorbable magnetic materials.

5. Wearable electronics based on elastomeric materials

Elastomers actuated by pneumatic or hydraulic means represent a critical component of mechanically active wearable devices. Essentially, both pneumatic and hydraulic systems are fluid-driven. Taking advantage of the continuity of fluids and their high force-to-weight ratio, these systems simplify the complex mechanical structures typically required for actuation. Accordingly, they enable localized deformation of system components under applied pressure or other stimuli, thereby achieving actuation with lightweight modules.

Specifically, the sensing strategy based on elastomers actuated by pneumatic or hydraulic means utilizes the internal pressure in the actuator's chamber or bladder. This internal pressure not only improves the conformal contact between the wearable electronics and the skin surface but also enables the deformation of the actuator itself to serve as a sensing mechanism for pressure-related information.

Compared to rigid materials or other actuation strategies, this approach offers several advantages. First, the inherent deformability of elastomers imparts high mechanical flexibility to the overall system, thereby greatly improving adaptability and expanding the range of potential applications. Second, the soft, low modulus elastomers enable large displacement under small forces, and therefore, can provide highly sensitive interactions.

5.1 Wearable sensors based on elastomeric materials

A representative advancement in material-enabled adaptive sensing emerges from Li et al.'s pneumatically assisted system for continuous blood pressure monitoring (Fig. 6A).⁶² In this work, a thin, soft wristband integrates piezoelectric sensors with an elastomer-encapsulated pneumatic module (PDMS/Ecoflex airbags, micro-pump) to dynamically modulate skin–device interfacial pressure. Unlike rigid pressure sensors that suffer motion artefacts, this system applies controlled backpressure, ensuring a tight, conformal interface between the sensor and the skin. This improved contact reduces signal noise and artefacts, leading to more accurate and reliable detection of physiological signals such as blood pressure.²⁶²

Fig. 6 Wearable pneumatic and hydraulic sensing system. (A) Schematic illustrations and photographs of the wireless pneumatic wristband. Upper: Deconstructed view of the wireless pneumatic wristband. Bottom left: Schematic illustration of the working principle of the wireless wristband worn on the wrist. The airbag can provide backpressure to enhance the mechanical deformation of the sensor array caused by blood propagation. Bottom middle: Photograph of the sensor mounted on fabric, exhibiting its excellent flexibility. Bottom right: Photograph of the sensor mounted on human skin. Reproduced under the terms of the Creative Commons CC BY 4.0 license.⁶² (B) Schematic illustration and photograph of the stretchable filament sensor (SFS). Upper: Axial change of the inner elastic microtube at three states: resting, pressurized, and stretched. Bottom: SFS output signal after finger flexion. Reproduced with permission.²⁶³ Copyright 2023, John Wiley and Sons. (C) Schematic illustrations and a photograph of sensing-actuating integrated skin (SAIS). Left upper: Schematic illustration of the SAIS. Left bottom: Sensing and actuating mechanism diagram of the SAIS. Right upper: Schematic of the SAIS with self-backup function. Right bottom: Wearable application of SAIS mounted on human skin. Reproduced with permission.¹³⁹ Copyright 2023, Elsevier.

Elastomers actuated by pneumatic or hydraulic means are of great significance in wearable sensor systems for enhancing conformal contact with the skin and enabling multifunctional signal transduction. Wang et al. reported a fully printed soft pressure sensor integrated with an adaptive pressure system, which is capable of detecting pulse and blood pressure accurately and reliably. Inspired by the three-finger pressure of pulse diagnosis in traditional Chinese medicine, precise segmented pressure is applied through a wristband-type airbag.¹³⁴ This design can identify the individualized “optimal pulse collection pressure (OPCP)” to give the maximum SNR, thereby allowing the sensor array to capture changes in the regional pulse. Hydraulic actuated sensors are not constrained by structure and can come in various forms to achieve sensing functionality. For instance, James Davies et al. developed a soft filament sensor (SFS) that exploits hydraulic actuation to enhance sensing capabilities, where a pressurized elastomeric microtube constrained by an external helical coil serves as the sensing element (Fig. 6B).²⁶³ Fluid is pumped into the microtube to establish an initial internal pressure, which is sensitive to external mechanical deformation. The sensitivity can also be tuned by adjusting the initial pressure to meet various application requirements.

Building on the foundational advances from the previous studies, where pneumatic and hydraulic actuation were shown to enhance conformal contact and to enable high-sensitivity sensing, the convergence of mechanical perception and bioinspired fluid dynamics is further exemplified by Lei et al.'s sensing-actuating integrated skin (SAIS) with skin-mimetic microchannels (SMCs) (Fig. 6C).¹³⁹ The device is built on a thin, flexible substrate engineered with a network of skin-like microchannels and cavities filled with functional fluids. The fluidic component plays two critical roles. First, it enhances conformal contact between the sensor and the skin by using a hydraulic actuation system to reduce signal noise and improve measurement fidelity, achieving an ultrahigh sensitivity of approximately 1363.902 kPa⁻¹ in the low-pressure range (0–5 kPa). Second, the internal fluid dynamics are actively harnessed for sensing. As external forces deform the device, the resulting changes in internal pressure are directly converted into electrical signals. Moreover, the fluidic actuation enables additional functionalities, such as controlled drug delivery, self-backup sensing through capacitance changes, and even visual alarms via colourimetric responses when specific reagents are introduced. By merging these capabilities, the device detects physiological signals with ultrahigh sensitivity and actively responds to external stimuli, making it a multifunctional platform.

By unifying fluidic actuation mechanics with compliant sensing elements, these studies advance the vision of mechanically active materials as integrated platforms, which is crucial for next-generation wearables requiring force-adaptive responses. These advances highlight the unique ability of fluid-actuated elastomers to combine sensing precision with adaptive material responses.

5.2 Wearable haptic interface based on elastomeric materials

Due to its superior power-to-weight ratio and robustness, elastomeric materials actuated by pneumatic and hydraulic systems are widely used in industrial machinery, robotics and automation.^264–267 These systems use fluid as a transmission medium to convert pressure into mechanical force, causing the actuator to move. To provide excellent haptic feedback, pneumatic/hydraulic actuation exploits changes in fluid volume in a microchannel or cavity to adjust the mechanical properties and shape of the feedback device. This method effectively provides kinesthetic and tactile feedback to simulate real-world touch interactions. As shown in Fig. 7A, the elastomer actuated by the pneumatic system enables high-resolution haptic cues through embedded fluidic circuits.²⁶⁸ The system utilizes TPU-coated fabric cells that expand upon pressurization, generating point forces (0–10 N) perpendicular to the skin surface. In four-direction discrimination tests, users achieved 87% accuracy of recognizing haptic feedback, with top performers reaching 97%, validating the system's efficacy in conveying navigational cues. By replacing gas with liquid, the system can achieve greater output of force and strain according to actual needs. Although elastomers actuated by fluid have excellent performance and large outputs of displacement (up to centimetres), the limitation of bulky supporting hardware (i.e., pumps) and wired connections constrains their practicality in wearable electronics. The following three subsections will discuss the studies that solve these challenges using different technological approaches.^269,270

Fig. 7 Wearable pneumatic and hydraulic haptic feedback systems. (A) Wristband featuring an inflatable cell made from TPU-coated nylon taffeta. Reproduced under the terms of the Creative Commons CC BY 4.0 license.²⁶⁸ (B) Schematic illustrations and photographs of HaPouch. Upper: Structure and working principle of the HaPouch. Bottom: Example of the wearable application of HaPouch. Reproduced under the terms of the Creative Commons CC BY 4.0 license.²⁷¹ (C) Schematic illustration and photographs of fibre pumps for the wearable fluidic system. Upper: Structure of the fibre pump. Middle: Schematic illustration of its working principle and fabrication method. Bottom: Photograph of the fibre pumps sewn into woollen textiles for wearable applications. Reproduced with permission.⁶³ Copyright 2023, The American Association for the Advancement of Science. (D) Schematic illustrations and photographs of hydraulically amplified electrostatic zipping taxels (HAXEL). Upper: HAXEL's structure and actuation mechanism. Bottom left: photograph of a transparent 2 × 4 PopTouch array mounted on an OLED display, with the button layout changing dynamically according to the options presented on the screen and in response to user key presses. Bottom right: The photograph shows a sensorized PopTouch with physical buttons on passive surfaces. The synchronized actuation of multiple buttons creates a slider controller moving in response to finger movements. Reproduced with permission.²⁷² Copyright 2023, John Wiley and Sons. (E) Photographs of a fully-printed stretchable HAXEL array placed on the hand and mounted on a fingertip. Reproduced with permission.²⁷³ Copyright 2023, John Wiley and Sons.

5.2.1 Elastomeric material actuated by phase-change fluids. One potential solution is to introduce a phase-change fluid (i.e. changing from a liquid phase to a gas phase) to replace the traditional gas/liquid (e.g. air and water). The phase-change fluid is usually a liquid of small volume at a relatively low temperature. Upon heating, the liquid quickly evaporates into a gas, causing the volume inside the elastomer to expand rapidly and exert a force outward without the need for a pump. Based on this principle, Uramune et al. introduced HaPouch, a wearable device leveraging low-boiling-point liquid-to-gas phase transitions to generate pressure-driven tactile cues without external pneumatic sources (Fig. 7B).²⁷¹ HaPouch encapsulates hydrofluoroether (Novec 7000), which is a biocompatible liquid with a low boiling point (34 °C), and heats it via a Peltier device to induce controlled vaporization. By integrating pressure sensors to monitor pouch inflation, HaPouch balances spatial resolution and mechanical simplicity, offering tactile feedback with latencies under 6 s in user trials. In addition to Novec 7000, phase-change liquids such as ethanal,^274,275 water,²⁷⁶ FC-72 (3M™ Fluorinert™ Electronic Liquid FC-72)²⁷⁷ and acetone²⁷⁸ are alternative candidates for the phase-change material–elastomer composite (PCMEC) actuators to circumvent the limitations of pumps.

5.2.2 Elastomeric material actuated by flowing dielectric fluids. While PCMEC excels at being lightweight, their reliance on a relatively rigid heater in the phase-change process limits energy density and portability, especially in wearable applications requiring high mechanical power or close contact with the human body. A promising solution emerges from Cacucciolo et al.'s stretchable pump, which consists of an elastomer tube with an embedded compliant electrode and dielectric fluid.²⁷⁹ The embedded electrode allows a high direct current field (6–20 V μm⁻¹) to be applied to the elastomer tube, thereby accelerating the flowing of dielectric liquid inside the tube. Based on the charge-injection electrohydrodynamics (EHD) mechanism, the flexible elastomer tube achieves two-way pumping with relatively high flow rates (48 μl s⁻¹). It can even function under high bending and stretching conditions, demonstrating the potential of miniaturized and portable elastomers driven by fluid for wearable applications. However, the system is limited by relatively low power density, which is key for haptic interfaces. Based on the same EHD mechanism, Smith et al. demonstrated an advanced flexible pump made of elastomeric fiber (Fig. 7C).⁶³ The system enables a high energy output due to optimizing the electrode and dielectric fluid. Specifically, each fiber metre generates a pressure of 100 kPa and flow rates of 916 μl s⁻¹, equivalent to a power density of 15 W kg⁻¹.

By eliminating moving parts, the hydraulic elastomer fiber reduces noise and vibration inherent to conventional mechanical pumps while avoiding rigid tubing constraints. This enables conformal integration into gloves for thermal haptics (2 s cooling response) or fabric muscles capable of 40% strain under load. Thus, this work transcends traditional fluidic architectures, and positions hydraulic-actuated elastomers as versatile platforms for spatially adaptive haptics where both force resolution and thermal fidelity are critical.

5.2.3 Elastomeric material actuated by static dielectric fluids. Another effective alternative solution is dielectric elastomer actuators (DEAs), made of thin elastomer layers and coated by compliant electrodes. Applying a voltage to the electrodes on opposite sides of dielectric layers produces opposite net charges. The Coulombic attraction between them creates a stress, the so-called Maxwell stress, which thins the dielectric. Due to the incompressible property of elastomers, the thinning in the vertical direction caused by the Maxwell stress is compensated by expansion of the elastomer in the horizontal direction. These expansion strains can be large, fast, and reversible and are therefore used to drive actuators in many applications related to soft robots and haptic feedback.^{37,38,280–285} However, the limited dielectric constant of the elastomer and electric breakdown field constrain the output of the DEA force.

The hydraulically amplified electrostatic zipping actuator overcomes the above-mentioned DEA limitations by introducing a dielectric liquid, thereby eliminating the requirements for the stretchability of the electrodes and dielectric material.^37–39,285 These actuators consist of two flexible electrodes separated by a flexible solid dielectric layer and filled with dielectric liquid.^286–289 Applying a voltage in the kV range between the electrodes generates an attractive force between them, which produces a zipping motion that pushes the liquid out of the zipper area and into the unzipped part of the actuator, causing it to bulge and thereby generating mechanical work. The electrodes and dielectric layer in the zipped part only need to be flexible (not stretchy), broadening the range of targeted materials. Unlike the elastomer actuator based on the EHD principle in the previous section, the dielectric liquid in the hydraulically amplified electrostatic zipping actuator does not flow continuously when an electric field is applied. The liquid remains stationary after being squeezed into the cavity. The actuation is based on the switching between zipped and unzipped states. The amplifier only exerts outward force during the state change.

An example of such an actuator is PopTouch, designed by Firouzeh et al., which is a 500 μm-thick system employing hydraulically amplified electrostatic zipping taxels (HAXELs) to create reconfigurable buttons (Fig. 7D).²⁷² Their PopTouch device leverages HAXELs to create on-demand tactile buttons with 1.5 mm out-of-plane displacement, a critical threshold for mimicking mechanical keypress sensations in soft wearables. PopTouch utilizes dielectric liquid confinement within 500 μm-thick layers to achieve a snap-through force profile replicating traditional button “click” feedback (1–4 N snap-through force). Due to the high-frequency vibration (80 Hz) and large displacement, PopTouch Haptic interfaces can be widely used in various scenarios to enhance the tactile experience and improve the interaction between users and machines, especially for button operations that require a click.

Complementing the spatially programmable button arrays enabled by PopTouch's HAXEL architecture, Grasso et al. reduced the dimension scalability of electrostatic-hydraulic coupling into ultrathin (<200 μm), fully printed haptic skins that conform dynamically to biological tissue (Fig. 7E).²⁷³ Unlike discrete tactile buttons optimized for localized force feedback, this work leverages stretchable HAXEL arrays to enable dense cutaneous stimulation across deformable surfaces, such as fingertips, delivering microscale hydraulic displacements up to 200 μm (10–100 μm effective under skin contact) with vibration bandwidth spanning direct current-like quasi-static indentation to 1 kHz oscillation. By integrating all functional layers, including elastomer membranes, stretchable electrodes, and dielectric oil pouches via inkjet printing, the system achieves sub-gram weight (<250 mg per wide 5 mm actuator) and skin-compatible compliance (flexural rigidity <10⁻⁵ N m), validating its seamless integration with dynamic skin deformations. The design of a monolithically sealed channel eliminates fluidic crosstalk in the 2 × 2 arrays, thereby allowing 86% spatial recognition accuracy in human trials. Such advances reveal the versatility of elastomers actuated by dielectric fluid in wearables, where mechanical activity coexists with anatomical compliance.

5.2.4 Elastomeric material actuated by electrorheological fluids. Using electrorheological (ER) fluids is another solution to provide force-based haptic feedback. These fluids can change their viscosity and mechanical strength in response to an applied electric field with low power consumption and fast response time (millisecond range).²⁹⁰ Mazursky et al. demonstrated an actuator based on ER fluid that provides multi-modal hardness modulation and vibrotactile feedback. The structure of this actuator is a cavity formed by an elastomer and an electrode and filled with ER fluid. By precisely controlling the flow of ER fluid using an electric field, the device generates a variety of tactile sensations. A notable demonstration is to make the shape and texture of virtual objects more realistic. Unlike conventional hydraulic actuators which rely on electrostatic mechanisms and involve cavity deformation, this device achieves changes in surface properties by altering the material and can generate forces of several newtons when the voltage is higher than 1 kV.²⁹¹

Overall, elastomeric materials are extremely important for achieving a realistic haptic experience due to their high stretchability and excellent conformability to biological tissue. Since bulky pumps and complex pipe connections can limit their use in wearable applications, many studies try to address this problem from different principles, making elastomer actuator systems more promising for practical use in daily lives. Future improvements in the synthesis process of elastomers will further enhance the robustness and performance of such wearable devices.

6. Conclusions

Despite the tremendous progress in mechanically active materials over the past few decades, the system-level implementation of flexible wearable devices for sensing and actuation remains challenging. This review highlights the technological advancements in multimodal monitoring and haptic interfaces based on mechanically active materials, with a focus on recent progress in piezoelectric, magnetic and elastomeric materials.

As shown in Fig. 8, wearable electronics based on piezoelectric materials enable the sensing on the skin surface and into deep tissue. Specifically, piezoelectric materials can generate (1) SAWs to achieve real-time monitoring of physical parameters at the skin, such as temperature, humidity, and stress/strain and (2) ultrasound waves to measure haemoglobin in blood vessels, image deep organs, and assist drug delivery. Wearable electronics based on magnetic materials, on one hand, have capabilities in measuring the modulus of deep tissue, position of deep tissue implants, and other physical/chemical parameters. On the other hand, magnetic materials play a critical role in haptic feedback systems. They are commonly utilized in constructing actuator arrays for immersive wearable technologies. In contrast, fluid-driven elastomers are specialized in sensing at the skin surface, due to their intrinsic conformability and high-pressure transmissibility of fluids. These systems form a seamless interface with the skin surface, enabling high-fidelity measurements of physiological signals such as blood pressure and pulse. Moreover, elastomers can serve as actuators for haptic feedback applications by providing large deformation and high-intensity outputs, which make them suitable for delivering a wide range of mechanical stimuli. The integration of piezoelectric materials, magnetic materials, and fluid-driven elastomers has transformed wearable electronics from bulky, rigid systems into soft, lightweight, and multifunctional devices.

Fig. 8 Mechanically active materials and their applications. Reproduced under the terms of the Creative Commons CC BY 4.0 license.⁶² Reproduced with permission.¹³⁴ Copyright 2024, John Wiley and Sons. Reproduced with permission.²⁷³ Copyright 2023, John Wiley and Sons.

While each material class offers unique advantages for specific functionalities, as visually summarized in Fig. 8, selecting the optimal material for a wearable application requires careful consideration of their inherent trade-offs. For instance, piezoelectric materials often excel in high-frequency sensing and energy harvesting due to their direct electromechanical coupling. However, their force output as actuators can be limited, and some traditional piezoelectric ceramics may raise biocompatibility concerns or lack mechanical flexibility unless engineered into composites or polymer forms. Magnetic materials, such as tough magnets, can provide substantial force output and enable wireless actuation or deep-tissue sensing, making them suitable for robust haptic feedback or certain implantable device tracking. However, they might require external magnetic field generation, potentially increasing system complexity and energy consumption, and biocompatibility can also be a concern for specific magnetic compositions. Fluid-driven elastomeric systems stand out for their excellent conformability, extensive strain capabilities, and inherent safety due to soft materials. They are well-suited for applications requiring gentle skin contact or large-stroke actuation. Their primary limitations often lie in the potential need for pumps or fluid reservoirs, which might affect the overall form factor and energy efficiency. However, recent advancements in microfluidics and material-integrated pumps are addressing these challenges. Therefore, the choice between these material systems, or indeed their synergistic combination, hinges on a balanced assessment of factors including, but not limited to, required force output, displacement, energy efficiency, and biocompatibility for the target application.

However, there is still much space for improving mechanically active materials in wearable electronics. First, achieving more realistic and refined haptic feedback through the efficient integration of sensing and actuation remains difficult, limiting the immersive quality of virtual interactions. Second, power supply and energy management continue to constrain long-term wearable applications, highlighting the need for improved on-body energy storage and harvesting strategies. Third, ensuring stable, skin-conformal interfaces without compromising biocompatibility is essential for accurate physiological monitoring and reliable haptic feedback, which remains a technical hurdle. Future mechanically active materials have the potential to mimic the properties of human skin. This work extends beyond simply replicating mechanical compliance, calling for a strong synergy with emerging technologies, including material science, engineering and algorithm efforts. Material integration has been proven to be a promising method. For instance, integrating self-healing materials with the piezoelectric, magnetic, or elastomeric systems reviewed herein could enhance wearable devices' operational lifespan and resilience against daily wear and tear.²⁹² Such self-repairing capabilities would be invaluable for maintaining consistent performance in long-term health monitoring or immersive human–machine interfaces. Furthermore, the convergence of mechanically active materials with other advanced material systems, such as biodegradable electronics,²⁹³ stimuli-responsive polymers,²⁹⁴ or advanced soft composites, also exhibits great potential. From an engineering perspective, two key challenges must be addressed: increasing system integration and optimising onboard energy. To boost integration, techniques such as MEMS microfabrication and additive manufacturing of multilayer soft–rigid stacks can co-locate sensors, actuators, and control electronics within millimetre-scale footprints. On the energy front, future mechanically active wearables should tightly integrate high-performance energy harvesters with compact storage and power-management modules, and relevant techniques include micropatterned triboelectric nanogenerators (TENG), thin-film solid-state microbatteries and ultra-low-power power management ICs. Furthermore, algorithm optimisation and the incorporation of artificial intelligence could further elevate the human–machine interaction of devices based on mechanically active materials, enhancing their personalisation capabilities and real-time feedback. By leveraging these approaches, next-generation wearables could become not only intelligent and environmentally sustainable but also capable of adaptive responses far more complex than those achievable today.

Wearable electronics based on these materials could exhibit more sensing modalities and/or enable intelligent adaptation, which is fundamentally an interdisciplinary challenge that offers rich opportunities for collaboration. Progress will heavily rely on the combined expertise of materials scientists, electronics engineers, computer scientists, and biomedical engineers. Insights from robotics engineers, neuroscientists, and even psychologists will also be crucial for designing intuitive haptic feedback and seamless human–computer interaction. The continued development of mechanically active materials, enriched by these interdisciplinary efforts and emerging technologies, creates many opportunities for soft wearable electronics, especially in healthcare, robotics, and human–machine interfaces.²⁹⁵

Conflicts of interest

The authors declare no competing interests.

Data availability

No primary research results, software or code have been included and no new data were generated or analysed as part of this review.

Acknowledgements

Fig. 1, Fig. 8 and TOC are created with https://BioRender.com. This work was supported by the National Key R&D Program of China (No. 2023YFB3208100); the National Natural Science Foundation of China (No. 62471007); the Scientific Research Innovation Capability Support Project for Young Faculty (ZYGXQNJSKYCXNLZCXM-H1); the Basic Research Program of Jiangsu (BM2024001); the State Key Laboratory of Precision Measuring Technology and Instrument (Tianjin University, Pilab2408); and the Asian Young Scientist Fellowship.

References

1. T. R. Ray, J. Choi, A. J. Bandodkar, S. Krishnan, P. Gutruf, L. Tian, R. Ghaffari and J. A. Rogers, Chem. Rev., 2019, 119, 5461–5533.
2. Y. S. Choi, R. T. Yin, A. Pfenniger, J. Koo, R. Avila, K. Benjamin Lee, S. W. Chen, G. Lee, G. Li, Y. Qiao, A. Murillo-Berlioz, A. Kiss, S. Han, S. M. Lee, C. Li, Z. Xie, Y.-Y. Chen, A. Burrell, B. Geist, H. Jeong, J. Kim, H.-J. Yoon, A. Banks, S.-K. Kang, Z. J. Zhang, C. R. Haney, A. V. Sahakian, D. Johnson, T. Efimova, Y. Huang, G. D. Trachiotis, B. P. Knight, R. K. Arora, I. R. Efimov and J. A. Rogers, Nat. Biotechnol., 2021, 39, 1228–1238.
3. T. Nguyen, T. Dinh, H.-P. Phan, T. Anh Pham, V. Thanh Dau, N.-T. Nguyen and D. Viet Dao, Mater. Horiz., 2021, 8, 2123–2150.
4. Y. Shi, Y. Guan, M. Liu, X. Kang, Y. Tian, W. Deng, P. Yu, C. Ning, L. Zhou, R. Fu and G. Tan, ACS Nano, 2024, 18, 3720–3732.
5. Y. Chen, X. Zhang and C. Lu, Chem. Sci., 2024, 15, 16436–16466.
6. T. A. Truong, X. Huang, M. Barton, A. Ashok, A. Al Abed, R. Almasri, M. N. Shivdasanic, R. Reshamwala, J. Ingles, M. T. Thai, C. C. Nguyen, S. Zhao, X. Zhang, Z. Gu, A. Vasanth, S. Peng, T.-K. Nguyen, N. Do, N.-T. Nguyen, H. Zhao and H.-P. Phan, ACS Nano, 2025, 19, 1642–1659.
7. X. Ma, C. Song, F. Zhang, Y. Dai, P. He and X. Zhang, ACS Appl. Mater. Interfaces, 2022, 14, 51291–51300.
8. X. Gao, X. Chen, H. Hu, X. Wang, W. Yue, J. Mu, Z. Lou, R. Zhang, K. Shi, X. Chen, M. Lin, B. Qi, S. Zhou, C. Lu, Y. Gu, X. Yang, H. Ding, Y. Zhu, H. Huang, Y. Ma, M. Li, A. Mishra, J. Wang and S. Xu, Nat. Commun., 2022, 13, 7757.
9. C.-H. Wu, H. J. H. Ma, P. Baessler, R. K. Balanay and T. R. Ray, Sci. Adv., 2023, 9, eadg4272.
10. Y. Tan, K. Yang, B. Wang, H. Li, L. Wang and C. Wang, Nano Res., 2021, 14, 3969–3976.
11. K. Meng, X. Xiao, W. Wei, G. Chen, A. Nashalian, S. Shen, X. Xiao and J. Chen, Adv. Mater., 2022, 34, 2109357.
12. L. Li, L. Zhao, R. Hassan and H. Ren, Machines, 2023, 11, 325.
13. C. Wang, X. Chen, L. Wang, M. Makihata, H.-C. Liu, T. Zhou and X. Zhao, Science, 2022, 377, 517–523.
14. H. Lu, H. Cui, G. Lu, L. Jiang, R. Hensleigh, Y. Zeng, A. Rayes, M. K. Panduranga, M. Acharya, Z. Wang, A. Irimia, F. Wu, G. P. Carman, J. M. Morales, S. Putterman, L. W. Martin, Q. Zhou and X. (Rayne) Zheng, Nat. Commun., 2023, 14, 2418.
15. J. Xing, L. Jiang, C. Zhao, Z. Tan, Q. Xu, J. Wu, Q. Chen, D. Xiao and J. Zhu, J. Materiomics, 2020, 6, 513–522.
16. Z. Lin, S. Duan, M. Liu, C. Dang, S. Qian, L. Zhang, H. Wang, W. Yan and M. Zhu, Adv. Mater., 2024, 36, 2306880.
17. Z. Ji, J. Zhou, Y. Guo, Y. Xia, A. Abkar, D. Liang and Y. Fu, Microsyst. Nanoeng., 2024, 10, 1–12.
18. D. Huang, J. Wang, C. Song and Y. Zhao, Innovation, 2023, 4, 100421.
19. Y. Huang, P. Kr Das and V. R. Bhethanabotla, Sens. Actuators Rep., 2021, 3, 100041.
20. M. Tanter and M. Fink, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2014, 61, 102–119.
21. W. Lin, D. Zhang, W. W. Lee, X. Li, Y. Hong, Q. Pan, R. Zhang, G. Peng, H. Z. Tan, Z. Zhang, L. Wei and Z. Yang, Sci. Adv., 2022, 8, eabp8738.
22. X. Yu, Z. Xie, Y. Yu, J. Lee, A. Vazquez-Guardado, H. Luan, J. Ruban, X. Ning, A. Akhtar, D. Li, B. Ji, Y. Liu, R. Sun, J. Cao, Q. Huo, Y. Zhong, C. Lee, S. Kim, P. Gutruf, C. Zhang, Y. Xue, Q. Guo, A. Chempakasseril, P. Tian, W. Lu, J. Jeong, Y. Yu, J. Cornman, C. Tan, B. Kim, K. Lee, X. Feng, Y. Huang and J. A. Rogers, Nature, 2019, 575, 473–479.
23. Y. H. Jung, J.-Y. Yoo, A. Vázquez-Guardado, J.-H. Kim, J.-T. Kim, H. Luan, M. Park, J. Lim, H.-S. Shin, C.-J. Su, R. Schloen, J. Trueb, R. Avila, J.-K. Chang, D. S. Yang, Y. Park, H. Ryu, H.-J. Yoon, G. Lee, H. Jeong, J. U. Kim, A. Akhtar, J. Cornman, T. Kim, Y. Huang and J. A. Rogers, Nat. Electron., 2022, 5, 374–385.
24. Y. Shi, F. Wang, J. Tian, S. Li, E. Fu, J. Nie, R. Lei, Y. Ding, X. Chen and Z. L. Wang, Sci. Adv., 2021, 7, eabe2943.
25. T.-H. Yang, J. R. Kim, H. Jin, H. Gil, J.-H. Koo and H. J. Kim, Adv. Funct. Mater., 2021, 31, 2008831.
26. Y. Wu, Y. Liu, Y. Zhou, Q. Man, C. Hu, W. Asghar, F. Li, Z. Yu, J. Shang, G. Liu, M. Liao and R.-W. Li, Sci. Rob., 2018, 3, eaat0429.
27. D. Matthews, Nature, 2018, 557, 127–128.
28. Z. Huang, Y. Hao, Y. Li, H. Hu, C. Wang, A. Nomoto, T. Pan, Y. Gu, Y. Chen, T. Zhang, W. Li, Y. Lei, N. Kim, C. Wang, L. Zhang, J. W. Ward, A. Maralani, X. Li, M. F. Durstock, A. Pisano, Y. Lin and S. Xu, Nat. Electron., 2018, 1, 473–480.
29. X. Yu, Z. Xie, Y. Yu, J. Lee, A. Vazquez-Guardado, H. Luan, J. Ruban, X. Ning, A. Akhtar, D. Li, B. Ji, Y. Liu, R. Sun, J. Cao, Q. Huo, Y. Zhong, C. Lee, S. Kim, P. Gutruf, C. Zhang, Y. Xue, Q. Guo, A. Chempakasseril, P. Tian, W. Lu, J. Jeong, Y. Yu, J. Cornman, C. Tan, B. Kim, K. Lee, X. Feng, Y. Huang and J. A. Rogers, Nature, 2019, 575, 473–479.
30. Y. H. Jung, J.-Y. Yoo, A. Vázquez-Guardado, J.-H. Kim, J.-T. Kim, H. Luan, M. Park, J. Lim, H.-S. Shin, C.-J. Su, R. Schloen, J. Trueb, R. Avila, J.-K. Chang, D. S. Yang, Y. Park, H. Ryu, H.-J. Yoon, G. Lee, H. Jeong, J. U. Kim, A. Akhtar, J. Cornman, T. Kim, Y. Huang and J. A. Rogers, Nat. Electron., 2022, 5, 374–385.
31. Y. Liu, C. K. Yiu, Z. Zhao, S. Liu, X. Huang, W. Park, J. Su, J. Zhou, T. H. Wong, K. Yao, L. Zhao, Y. Huang, J. Li, P. Fan, B. Zhang, Y. Dai, Z. Yang, Y. Li and X. Yu, IEEE Internet Things J., 2023, 10, 653–663.
32. D. Li, J. Zhou, K. Yao, S. Liu, J. He, J. Su, Q. Qu, Y. Gao, Z. Song, C. Yiu, C. Sha, Z. Sun, B. Zhang, J. Li, L. Huang, C. Xu, T. Hung Wong, X. Huang, J. Li, R. Ye, L. Wei, Z. Zhang, X. Guo, Y. Dai, Z. Xie and X. Yu, Sci. Adv., 2022, 8, eade2450.
33. Y. Kim and X. Zhao, Chem. Rev., 2022, 122, 5317–5364.
34. L. Cappello, J. T. Meyer, K. C. Galloway, J. D. Peisner, R. Granberry, D. A. Wagner, S. Engelhardt, S. Paganoni and C. J. Walsh, J. NeuroEng. Rehabil., 2018, 15, 59.
35. J. Barreiros, H. Claure, B. Peele, O. Shapira, J. Spjut, D. Luebke, M. Jung and R. Shepherd, IEEE Rob. Autom. Lett., 2019, 4, 277–284.
36. P. Polygerinos, Z. Wang, K. C. Galloway, R. J. Wood and C. J. Walsh, Rob. Auton. Syst., 2015, 73, 135–143.
37. G. Li, X. Chen, F. Zhou, Y. Liang, Y. Xiao, X. Cao, Z. Zhang, M. Zhang, B. Wu, S. Yin, Y. Xu, H. Fan, Z. Chen, W. Song, W. Yang, B. Pan, J. Hou, W. Zou, S. He, X. Yang, G. Mao, Z. Jia, H. Zhou, T. Li, S. Qu, Z. Xu, Z. Huang, Y. Luo, T. Xie, J. Gu, S. Zhu and W. Yang, Nature, 2021, 591, 66–71.
38. Y. Chen, H. Zhao, J. Mao, P. Chirarattananon, E. F. Helbling, N. P. Hyun, D. R. Clarke and R. J. Wood, Nature, 2019, 575, 324–329.
39. E. Acome, S. K. Mitchell, T. Morrissey, M. B. Emmett, C. Benjamin, M. King, M. Radakovitz and C. Keplinger, Science, 2018, 359, 61–65.
40. J. Yin, R. Hinchet, H. Shea and C. Majidi, Adv. Funct. Mater., 2021, 31, 2007428.
41. Z. Nie, J. W. Kwak, M. Han and J. A. Rogers, Adv. Mater., 2024, 36, 2205609.
42. Y. Dong, P.-R. Huang, E. Ghimire, C. A. Lindberg, F. Ram, S. J. Rowan and A. Esser-Kahn, MRS Bull., 2024, 49, 1127–1136.
43. X. Ning, X. Yu, H. Wang, R. Sun, R. E. Corman, H. Li, C. M. Lee, Y. Xue, A. Chempakasseril, Y. Yao, Z. Zhang, H. Luan, Z. Wang, W. Xia, X. Feng, R. H. Ewoldt, Y. Huang, Y. Zhang and J. A. Rogers, Sci. Adv., 2018, 4, eaat8313.
44. Q. Liu, S. Ghodrat, G. Huisman and K. M. B. Jansen, Mater. Des., 2023, 233, 112264.
45. R. Srivastava, S. H. Alsamhi, N. Murray and D. Devine, Sensors, 2022, 22, 6802.
46. N. K. Singh, K. Takashima and S. S. Pandey, Eng. Proc., 2022, 21, 1.
47. M. L. R. Liman, M. T. Islam and M. M. Hossain, Adv. Electron. Mater., 2022, 8, 2100578.
48. Z. Liu, H. Li, B. Shi, Y. Fan, Z. L. Wang and Z. Li, Adv. Funct. Mater., 2019, 29, 1808820.
49. Y. Zou, V. Raveendran and J. Chen, Nano Energy, 2020, 77, 105303.
50. H. Du, G. Li, J. Sun, Y. Zhang, Y. Bai, C. Qian and Y. Liang, Smart Mater. Struct., 2023, 32, 103001.
51. K. K. Shung, J. M. Cannata and Q. F. Zhou, J. Electroceramics, 2007, 19, 141–147.
52. H. Winterfeld, L. Thormählen, H. Lewitz, E. Yarar, T. Birkoben, N. Niethe, N. Preinl, H. Hanssen, E. Quandt and H. Kohlstedt, J. Mater. Sci.: Mater. Electron., 2019, 30, 11493–11498.
53. J. F. Tressler, S. Alkoy and R. E. Newnham, J. Electroceramics, 1998, 2, 257–272.
54. Q. Zhu, T. Wu and N. Wang, Biosensors, 2023, 13, 113.
55. S. Chopra and N. Gravish, Smart Mater. Struct., 2019, 28, 115036.
56. R. De Fazio, V. M. Mastronardi, M. Petruzzi, M. De Vittorio and P. Visconti, Future Internet, 2023, 15, 14.
57. J. Li, H. Jia, J. Zhou, X. Huang, L. Xu, S. Jia, Z. Gao, K. Yao, D. Li, B. Zhang, Y. Liu, Y. Huang, Y. Hu, G. Zhao, Z. Xu, J. Li, C. K. Yiu, Y. Gao, M. Wu, Y. Jiao, Q. Zhang, X. Tai, R. H. Chan, Y. Zhang, X. Ma and X. Yu, Nat. Commun., 2023, 14, 5009.
58. Y. Kim, J. M. Suh, J. Shin, Y. Liu, H. Yeon, K. Qiao, H. S. Kum, C. Kim, H. E. Lee, C. Choi, H. Kim, D. Lee, J. Lee, J.-H. Kang, B.-I. Park, S. Kang, J. Kim, S. Kim, J. A. Perozek, K. Wang, Y. Park, K. Kishen, L. Kong, T. Palacios, J. Park, M.-C. Park, H. Kim, Y. S. Lee, K. Lee, S.-H. Bae, W. Kong, J. Han and J. Kim, Science, 2022, 377, 859–864.
59. H. Hu, Y. Ma, X. Gao, D. Song, M. Li, H. Huang, X. Qian, R. Wu, K. Shi, H. Ding, M. Lin, X. Chen, W. Zhao, B. Qi, S. Zhou, R. Chen, Y. Gu, Y. Chen, Y. Lei, C. Wang, C. Wang, Y. Tong, H. Cui, A. Abdal, Y. Zhu, X. Tian, Z. Chen, C. Lu, X. Yang, J. Mu, Z. Lou, M. Eghtedari, Q. Zhou, A. Oberai and S. Xu, Nat. Biomed. Eng., 2023, 7, 1321–1334.
60. B. Gleich, I. Schmale, T. Nielsen and J. Rahmer, Science, 2023, 380, 966–971.
61. F. Mancini, A. Bauleo, J. Cole, F. Lui, C. A. Porro, P. Haggard and G. D. Iannetti, Ann. Neurol., 2014, 75, 917–924.
62. J. Li, H. Jia, J. Zhou, X. Huang, L. Xu, S. Jia, Z. Gao, K. Yao, D. Li, B. Zhang, Y. Liu, Y. Huang, Y. Hu, G. Zhao, Z. Xu, J. Li, C. K. Yiu, Y. Gao, M. Wu, Y. Jiao, Q. Zhang, X. Tai, R. H. Chan, Y. Zhang, X. Ma and X. Yu, Nat. Commun., 2023, 14, 5009.
63. M. Smith, V. Cacucciolo and H. Shea, Science, 2023, 379, 1327–1332.
64. G. Suo, Y. Yu, Z. Zhang, S. Wang, P. Zhao, J. Li and X. Wang, ACS Appl. Mater. Interfaces, 2016, 8, 34335–34341.
65. T. Li, M. Qu, C. Carlos, L. Gu, F. Jin, T. Yuan, X. Wu, J. Xiao, T. Wang, W. Dong, X. Wang and Z.-Q. Feng, Adv. Mater., 2021, 33, 2006093.
66. H.-F. Guo, Z.-S. Li, S.-W. Dong, W.-J. Chen, L. Deng, Y.-F. Wang and D.-J. Ying, Colloids Surf., B, 2012, 96, 29–36.
67. M. Lin, H. Hu, S. Zhou and S. Xu, Nat. Rev. Mater., 2022, 7, 850–869.
68. D. Liu, Q. Yue, J. Deng, D. Lin, X. Li, W. Di, X. Wang, X. Zhao and H. Luo, Sensors, 2015, 15, 6807–6817.
69. X. Xue, H. Wu, Q. Cai, M. Chen, S. Moon, Z. Huang, T. Kim, C. Peng, W. Feng, N. Sharma and X. Jiang, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2024, 71, 786–810.
70. B. García-Farrera and L. F. Velásquez-García, ACS Appl. Mater. Interfaces, 2019, 11, 29167–29176.
71. J. Wu, C. Yin, J. Zhou, H. Li, Y. Liu, Y. Shen, S. Garner, Y. Fu and H. Duan, ACS Appl. Mater. Interfaces, 2020, 12, 39817–39825.
72. Y. Wu, Y. Ma, H. Zheng and S. Ramakrishna, Mater. Des., 2021, 211, 110164.
73. S. Xu, Z. Yan, K.-I. Jang, W. Huang, H. Fu, J. Kim, Z. Wei, M. Flavin, J. McCracken, R. Wang, A. Badea, Y. Liu, D. Xiao, G. Zhou, J. Lee, H. U. Chung, H. Cheng, W. Ren, A. Banks, X. Li, U. Paik, R. G. Nuzzo, Y. Huang, Y. Zhang and J. A. Rogers, Science, 2015, 347, 154–159.
74. W. Fan, C. Zhang, Y. Liu, S. Wang, K. Dong, Y. Li, F. Wu, J. Liang, C. Wang and Y. Zhang, Nano Res., 2023, 16, 11612–11620.
75. K. K. Das, B. Basu, P. Maiti and A. K. Dubey, Acta Biomater., 2023, 171, 85–113.
76. K. Wang, L. W. Yap, S. Gong, R. Wang, S. J. Wang and W. Cheng, Adv. Funct. Mater., 2021, 31, 2008347.
77. S. Glinsek, M. A. Mahjoub, M. Rupin, T. Schenk, N. Godard, S. Girod, J.-B. Chemin, R. Leturcq, N. Valle, S. Klein, C. Chappaz and E. Defay, Adv. Funct. Mater., 2020, 30, 2003539.
78. D.-S. Choi and S.-Y. Kim, Sensors, 2019, 19, 3490.
79. S. Qian, X. Wang and W. Yan, Front. Optoelectron., 2023, 16, 3.
80. F. Mokhtari, Self-Powered Smart Fabrics for Wearable Technologies, Springer International Publishing, Cham, 2022.
81. Y. Su, W. Li, L. Yuan, C. Chen, H. Pan, G. Xie, G. Conta, S. Ferrier, X. Zhao, G. Chen, H. Tai, Y. Jiang and J. Chen, Nano Energy, 2021, 89, 106321.
82. F. Mokhtari, R. W. Symes, Ž. Simon, B. Dharmasiri, L. C. Henderson, M. W. Joosten and R. J. Varley, J. Mater. Chem. A, 2025, 13, 6482–6492.
83. P. Gajula, J. U. Yoon, I. Woo and J. W. Bae, Small, 2025, 21, 2407001.
84. F. Mokhtari, K. A. S. Usman, J. Zhang, R. Komljenovic, Ž. Simon, B. Dharmasiri, A. Rezk, P. C. Sherrell, L. C. Henderson, R. J. Varley and J. M. Razal, ACS Appl. Mater. Interfaces, 2025, 17, 3214–3228.
85. Z. Nie, J. W. Kwak, M. Han and J. A. Rogers, Adv. Mater., 2024, 36, 2205609.
86. Z. X. Bi, Y. D. Zheng, R. Zhang, S. L. Gu, X. Q. Xiu, L. L. Zhou, B. Shen, D. J. Chen and Y. Shi, J. Mater. Sci.: Mater. Electron., 2004, 15, 317–320.
87. S. T. Haider, M. A. Shah, D.-G. Lee and S. Hur, IEEE Access, 2023, 11, 58779–58795.
88. K. Viswanath Allamraju and S. Korla, Mater. Today: Proc., 2017, 4, 126–132.
89. H. Wang, M. Ma, S. Xia, N. Zhang and Z. Li, J. Mater. Chem. C, 2023, 11, 2664–2671.
90. D. Xu, L. Wang, W. Li, W. Wang, Y. Hou, W. Cao, Y. Feng and W. Fei, Phys. Chem. Chem. Phys., 2014, 16, 13078–13085.
91. N. Sinha, S. Goel, A. J. Joseph, H. Yadav, K. Batra, M. K. Gupta and B. Kumar, Ceram. Int., 2018, 44, 8582–8590.
92. Q. Sun, W. Xia, Y. Liu, P. Ren, X. Tian and T. Hu, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2020, 67, 975–983.
93. P. Kabakov, T. Kim, Z. Cheng, X. Jiang and S. Zhang, Annu. Rev. Mater. Res., 2023, 53, 165–193.
94. Z. Yin, B. Tian, Q. Zhu and C. Duan, Polymers, 2019, 11, 2033.
95. X. Chen, H. Qin, X. Qian, W. Zhu, B. Li, B. Zhang, W. Lu, R. Li, S. Zhang, L. Zhu, F. Domingues Dos Santos, J. Bernholc and Q. M. Zhang, Science, 2022, 375, 1418–1422.
96. W. Zhu, X. Chen and Q. M. Zhang, Polymer, 2023, 285, 126309.
97. F. Mokhtari, K. A. S. Usman, J. Zhang, R. Komljenovic, Ž. Simon, B. Dharmasiri, A. Rezk, P. C. Sherrell, L. C. Henderson, R. J. Varley and J. M. Razal, ACS Appl. Mater. Interfaces, 2025, 17, 3214–3228.
98. S. Jiang, Y. Shen, S. Wang, Y. Zhi and B. Han, Front. Mater., 2022, 9, 958775.
99. R. Guo, C.-A. Wang and A. Yang, J. Eur. Ceram. Soc., 2011, 31, 605–609.
100. J. M. D. Coey, IEEE Trans. Magn., 2011, 47, 4671–4681.
101. B. Sai Ram, A. K. Paul and S. V. Kulkarni, J. Magn. Magn. Mater., 2021, 537, 168210.
102. M. Hu, H.-J. Butt, K. Landfester, M. B. Bannwarth, S. Wooh and H. Thérien-Aubin, ACS Nano, 2019, 13, 3015–3022.
103. J. Zhang, Z. Ren, W. Hu, R. H. Soon, I. C. Yasa, Z. Liu and M. Sitti, Sci. Rob., 2021, 6, eabf0112.
104. X. Wei, M.-L. Jin, H. Yang, X.-X. Wang, Y.-Z. Long and Z. Chen, J. Adv. Ceram., 2022, 11, 665–701.
105. G. Z. Lum, Z. Ye, X. Dong, H. Marvi, O. Erin, W. Hu and M. Sitti, Proc. Natl. Acad. Sci. U. S. A., 2016, 113, E6007–E6015.
106. Y. Alapan, A. C. Karacakol, S. N. Guzelhan, I. Isik and M. Sitti, Sci. Adv., 2020, 6, eabc6414.
107. R. Tan, X. Yang, H. Lu, L. Yang, T. Zhang, J. Miao, Y. Feng and Y. Shen, Matter, 2022, 5, 1277–1295.
108. U. A. Theilade and H. N. Hansen, Int. J. Adv. Manuf. Technol., 2007, 33, 157–166.
109. Y.-L. Liu, D. Chen, P. Shang and D.-C. Yin, J. Controlled Release, 2019, 302, 90–104.
110. H.-W. Huang, M. S. Sakar, A. J. Petruska, S. Pané and B. J. Nelson, Nat. Commun., 2016, 7, 12263.
111. J. Tang and B. Sun, Appl. Phys. Lett., 2022, 120, 244101.
112. J. Zhang, Y. Guo, W. Hu, R. H. Soon, Z. S. Davidson and M. Sitti, Adv. Mater., 2021, 33, 2006191.
113. C. Guo, Z. Fan, Y. Ren, Y. Cheng, Y. Jiang, Q. Zhang, Y. Li, J. Huang and H. Yu, IEEE Sens. J., 2024, 24, 36604–36616.
114. X. Zhao, Y. Zhou, J. Xu, G. Chen, Y. Fang, T. Tat, X. Xiao, Y. Song, S. Li and J. Chen, Nat. Commun., 2021, 12, 6755.
115. B. J. Nelson and S. Pané, Science, 2023, 382, 1120–1122.
116. W. Hu, G. Z. Lum, M. Mastrangeli and M. Sitti, Nature, 2018, 554, 81–85.
117. L. Miao, Y. Song, Z. Ren, C. Xu, J. Wan, H. Wang, H. Guo, Z. Xiang, M. Han and H. Zhang, Adv. Mater., 2021, 33, 2102691.
118. F. Chen, Z. Song, S. Chen, G. Gu and X. Zhu, IEEE Trans. Rob., 2023, 39, 4408–4428.
119. M. Tanaka, Y. Song and T. Nomura, Sci. Rep., 2024, 14, 19175.
120. Z. Xu, J. Liang and Y. Zhou, IEEE Rob. Autom. Lett., 2024, 9, 6632–6639.
121. D. Yu, Z. Wang, G. Chi, Q. Zhang, J. Fu, M. Li, C. Liu, Q. Zhou, Z. Li, D. Chen, Z. Song and Z. He, Nat. Commun., 2024, 15, 1179.
122. S. Das, Y. Kishishita, T. Tsuji, C. Lowell, K. Ogawa and Y. Kurita, IEEE Rob. Autom. Lett., 2018, 3, 2416–2423.
123. N. Gariya, S. Kumar, A. Shaikh, B. Prasad and H. Nautiyal, Sens. Actuators, A, 2024, 372, 115364.
124. G. Zhang, Y. Sun, B. Qian, H. Gao and D. Zuo, Polym. Test., 2020, 90, 106670.
125. M. M. Hassan, T. Takahashi and K. Koyama, Polym. Bull., 2019, 76, 5537–5557.
126. S. Pan, F. Zhang, P. Cai, M. Wang, K. He, Y. Luo, Z. Li, G. Chen, S. Ji, Z. Liu, X. J. Loh and X. Chen, Adv. Funct. Mater., 2020, 30, 1909540.
127. Y. Xu, Z. Ye, G. Zhao, Q. Fei, Z. Chen, J. Li, M. Yang, Y. Ren, B. Berigan, Y. Ling, X. Qian, L. Shi, I. Ozden, J. Xie, W. Gao, P.-Y. Chen and Z. Yan, Nat. Nanotechnol., 2024, 19, 1158–1167.
128. Y. S. Choi, Y.-Y. Hsueh, J. Koo, Q. Yang, R. Avila, B. Hu, Z. Xie, G. Lee, Z. Ning, C. Liu, Y. Xu, Y. J. Lee, W. Zhao, J. Fang, Y. Deng, S. M. Lee, A. Vázquez-Guardado, I. Stepien, Y. Yan, J. W. Song, C. Haney, Y. S. Oh, W. Liu, H.-J. Yoon, A. Banks, M. R. MacEwan, G. A. Ameer, W. Z. Ray, Y. Huang, T. Xie, C. K. Franz, S. Li and J. A. Rogers, Nat. Commun., 2020, 11, 5990.
129. Y. S. Choi, J. Koo, Y. J. Lee, G. Lee, R. Avila, H. Ying, J. Reeder, L. Hambitzer, K. Im, J. Kim, K.-M. Lee, J. Cheng, Y. Huang, S.-K. Kang and J. A. Rogers, Adv. Funct. Mater., 2020, 30, 2000941.
130. Y. S. Choi, Y.-Y. Hsueh, J. Koo, Q. Yang, R. Avila, B. Hu, Z. Xie, G. Lee, Z. Ning, C. Liu, Y. Xu, Y. J. Lee, W. Zhao, J. Fang, Y. Deng, S. M. Lee, A. Vázquez-Guardado, I. Stepien, Y. Yan, J. W. Song, C. Haney, Y. S. Oh, W. Liu, H.-J. Yoon, A. Banks, M. R. MacEwan, G. A. Ameer, W. Z. Ray, Y. Huang, T. Xie, C. K. Franz, S. Li and J. A. Rogers, Nat. Commun., 2020, 11, 5990.
131. J. Koo, M. R. MacEwan, S.-K. Kang, S. M. Won, M. Stephen, P. Gamble, Z. Xie, Y. Yan, Y.-Y. Chen, J. Shin, N. Birenbaum, S. Chung, S. B. Kim, J. Khalifeh, D. V. Harburg, K. Bean, M. Paskett, J. Kim, Z. S. Zohny, S. M. Lee, R. Zhang, K. Luo, B. Ji, A. Banks, H. M. Lee, Y. Huang, W. Z. Ray and J. A. Rogers, Nat. Med., 2018, 24, 1830–1836.
132. O. V. Filippova, A. V. Maksimkin, T. Dayyoub, D. I. Larionov and D. V. Telyshev, Polymers, 2023, 15, 2755.
133. H. Lee, R. Eom and Y. Lee, Adv. Mater. Sci. Eng., 2019, 2019, 5838361.
134. X. Wang, G. Wu, X. Zhang, F. Lv, Z. Yang, X. Nan, Z. Zhang, C. Xue, H. Cheng and L. Gao, Adv. Mater., 2025, 37, 2410312.
135. Z. Wang and S. Hirai, in 2018 IEEE International Conference on Soft Robotics (robosoft), 2018, pp. 382–387.
136. A. Georgopoulou, L. Egloff, B. Vanderborght and F. Clemens, Actuators, 2021, 10, 102.
137. W. Liao and Z. Yang, Mater. Horiz., 2023, 10, 576–584.
138. Y. Gao, H. Ota, E. W. Schaler, K. Chen, A. Zhao, W. Gao, H. M. Fahad, Y. Leng, A. Zheng, F. Xiong, C. Zhang, L.-C. Tai, P. Zhao, R. S. Fearing and A. Javey, Adv. Mater., 2017, 29, 1701985.
139. X. Lei, L.-J. Ma, S.-N. Yu, T.-L. Ren, S.-F. Li, J. Yuan, L. Ye, X. Yu and F. Liu, Chem. Eng. J., 2023, 454, 140548.
140. T.-H. Yang, J. R. Kim, H. Jin, H. Gil, J.-H. Koo and H. J. Kim, Adv. Funct. Mater., 2021, 31, 2008831.
141. N. Sezer and M. Koç, Nano Energy, 2021, 80, 105567.
142. V. Massavie, G. Despesse, S. Carcouet and X. Maynard, Electronics, 2022, 11, 3983.
143. L. Rayleigh, Proc. Lond. Math. Soc., 1885, s1–17, 4–11.
144. P. Zheng, S. Zhang, J. Wu, L. Zhang, H. Yao, X. Fang, Y. Chen, K. Huang and X. Ou, IEEE Trans. Microw. Theory Tech., 2024, 72, 1480–1488.
145. D. Mandal and S. Banerjee, Sensors, 2022, 22, 820.
146. Y. Huang, P. Kr Das and V. R. Bhethanabotla, Sens. Actuators Rep., 2021, 3, 100041.
147. L. Kelly, C. Chen, X. Bao and P. Berini, Sens. Actuators, A, 2022, 338, 113504.
148. Z. Yang, F. Wang, W. Nie, X. Han, X. Guo, X. Shan, X. Lin, H. Dun, Z. Sun, Y. Xie and K. Zhang, Sens. Actuators, A, 2023, 357, 114379.
149. R. M. White and F. W. Voltmer, Appl. Phys. Lett, 1965, 7, 314–316.
150. D. J. Collins, A. Neild and Y. Ai, Lab Chip, 2016, 16, 471–479.
151. L. Lamanna, Adv. Mater. Technol., 2023, 8, 2300362.
152. L. Lamanna, F. Rizzi, V. R. Bhethanabotla and M. De Vittorio, Sens. Actuators, A, 2020, 315, 112268.
153. H. Jin, X. Tao, S. Dong, Y. Qin, L. Yu, J. Luo and M. J. Deen, J. Micromech. Microeng., 2017, 27, 115006.
154. C. Liu, M. Li, N. Wang and Y. Zhu, in 2022 IEEE International Ultrasonics Symposium (IUS), 2022, pp. 1–3.
155. S. Qureshi, M. Hanif, V. Jeoti, G. M. Stojanović and M. T. Khan, Results Eng., 2024, 22, 102323.
156. L. Lamanna, F. Rizzi, F. Guido, L. Algieri, S. Marras, V. M. Mastronardi, A. Qualtieri and M. De Vittorio, Adv. Electron. Mater., 2019, 5, 1900095.
157. H. Jin, J. Zhou, X. He, W. Wang, H. Guo, S. Dong, D. Wang, Y. Xu, J. Geng, J. K. Luo and W. I. Milne, Sci. Rep., 2013, 3, 2140.
158. H. Liu, Z. Zhou and L. Lou, Micromachines, 2024, 15, 562.
159. J. Yang, R. Shi, Z. Lou, R. Chai, K. Jiang and G. Shen, Small, 2019, 15, 1902801.
160. K. C. Xu, Y. Y. Lu and K. Takei, Adv. Mater. Technol., 2019, 4, 1800628.
161. Y. Wang, L. N. Zhang, J. P. Zhou and A. Lu, ACS Appl. Mater. Interfaces, 2020, 12, 7631.
162. Z. Wang, N. Yi, Z. Zheng, J. Zhou, P. Zhou, C. Zheng, H. Chen, G. Shen and M. Weng, Chem. Eng. J., 2024, 497, 154443.
163. M. Guo, P. Diao and S. Cai, Thin Solid Films, 2007, 515, 7162–7166.
164. X. L. He, D. J. Li, J. Zhou, W. B. Wang, W. P. Xuan, S. R. Dong, H. Jin and J. K. Luo, J. Mater. Chem. C, 2013, 1, 6210–6215.
165. X. Tao, H. Jin, M. Mintken, N. Wolff, Y. Wang, R. Tao, Y. Li, H. Torun, J. Xie, J. Luo, J. Zhou, Q. Wu, S. Dong, J. Luo, L. Kienle, R. Adelung, Y. K. Mishra and Y. Q. Fu, ACS Appl. Nano Mater., 2020, 3, 1468.
166. M. Saqib, S. Ali Khan, H. M. Mutee Ur Rehman, Y. Yang, S. Kim, M. M. Rehman and W. Young Kim, Nanomaterials, 2021, 11, 242.
167. C.-H. Liao, T.-C. Huang, Z.-H. Yen, C.-S. Huang, W. Water and S.-H. Lin, in 2023 IEEE 23rd International Conference on Nanotechnology (NANO), 2023, pp. 866–871.
168. T. Dinh, T. Nguyen, H.-P. Phan, N.-T. Nguyen, D. V. Dao and J. Bell, Biosens. Bioelectron., 2020, 166, 112460.
169. Siddharth, A. N. Patel, T.-P. Jung and T. J. Sejnowski, IEEE Trans. Biomed. Eng., 2019, 66, 1137–1147.
170. X. Yu, Z. Xie, Y. Yu, J. Lee, A. Vazquez-Guardado, H. Luan, J. Ruban, X. Ning, A. Akhtar, D. Li, B. Ji, Y. Liu, R. Sun, J. Cao, Q. Huo, Y. Zhong, C. Lee, S. Kim, P. Gutruf, C. Zhang, Y. Xue, Q. Guo, A. Chempakasseril, P. Tian, W. Lu, J. Jeong, Y. Yu, J. Cornman, C. Tan, B. Kim, K. Lee, X. Feng, Y. Huang and J. A. Rogers, Nature, 2019, 575, 473–479.
171. X. Yang, W. Chen, Q. Fan, J. Chen, Y. Chen, F. Lai and H. Liu, Adv. Mater., 2024, 36, 2402542.
172. B. Sun, R. N. McCay, S. Goswami, Y. Xu, C. Zhang, Y. Ling, J. Lin and Z. Yan, Adv. Mater., 2018, 30, 1804327.
173. Y. Lee, J. W. Chung, G. H. Lee, H. Kang, J.-Y. Kim, C. Bae, H. Yoo, S. Jeong, H. Cho, S.-G. Kang, J. Y. Jung, D.-W. Lee, S. Gam, S. G. Hahm, Y. Kuzumoto, S. J. Kim, Z. Bao, Y. Hong, Y. Yun and S. Kim, Sci. Adv., 2021, 7, eabg9180.
174. M. C. Vu, I.-H. Kim, W. K. Choi, C.-S. Lim, M. A. Islam and S.-R. Kim, ACS Appl. Mater. Interfaces, 2020, 12, 26413–26423.
175. Y. Zhang, H. F. Zou, J. F. Peng, Z. H. Duan, M. Ma, X. Xin, W. L. Li and X. J. Zheng, Sens. Actuators, B, 2018, 272, 459.
176. V. Chauhan, S. K. Ghosh, A. Hussain and S. K. Rout, J. Alloys Compd., 2016, 674, 413–424.
177. E. Veys, L. Makower, M. Williamson, L. M. Saure, R. Adelung, F. Schütt, N. M. Pugno and T. J. Marrow, Scr. Mater., 2023, 224, 115153.
178. Q. Liu, Z. Li, J. Li, F. Zhan, D. Zhai, Q. Sun, Z. Xiao, H. Luo and D. Zhang, Nano Energy, 2022, 98, 107267.
179. A. Ks, N. R. Alluri, K. Sharma, N. P. Maria Joseph Raj and S.-J. Kim, Appl. Mater. Today, 2022, 29, 101679.
180. C. Dagdeviren, Y. Shi, P. Joe, R. Ghaffari, G. Balooch, K. Usgaonkar, O. Gur, P. L. Tran, J. R. Crosby, M. Meyer, Y. Su, R. Chad Webb, A. S. Tedesco, M. J. Slepian, Y. Huang and J. A. Rogers, Nat. Mater., 2015, 14, 728–736.
181. B.-I. Park, J. Kim, K. Lu, X. Zhang, S. Lee, J. M. Suh, D.-H. Kim, H. Kim and J. Kim, Nano Lett., 2024, 24, 2939–2952.
182. C. Wang, B. Qi, M. Lin, Z. Zhang, M. Makihata, B. Liu, S. Zhou, Y. Huang, H. Hu, Y. Gu, Y. Chen, Y. Lei, T. Lee, S. Chien, K.-I. Jang, E. B. Kistler and S. Xu, Nat. Biomed. Eng., 2021, 5, 749–758.
183. P. H. Gopalakrishnan and M. R. Panicker, Sens. Actuators, A, 2024, 376, 115588.
184. D.-H. Kim, J. Song, W. M. Choi, H.-S. Kim, R.-H. Kim, Z. Liu, Y. Y. Huang, K.-C. Hwang, Y. Zhang and J. A. Rogers, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 18675–18680.
185. L. Yin, J. Lv and J. Wang, Adv. Mater. Technol., 2020, 5, 2000694.
186. H. Hu, X. Zhu, C. Wang, L. Zhang, X. Li, S. Lee, Z. Huang, R. Chen, Z. Chen, C. Wang, Y. Gu, Y. Chen, Y. Lei, T. Zhang, N. Kim, Y. Guo, Y. Teng, W. Zhou, Y. Li, A. Nomoto, S. Sternini, Q. Zhou, M. Pharr, F. L. di Scalea and S. Xu, Sci. Adv., 2018, 4, eaar3979.
187. H. Hu, H. Huang, M. Li, X. Gao, L. Yin, R. Qi, R. S. Wu, X. Chen, Y. Ma, K. Shi, C. Li, T. M. Maus, B. Huang, C. Lu, M. Lin, S. Zhou, Z. Lou, Y. Gu, Y. Chen, Y. Lei, X. Wang, R. Wang, W. Yue, X. Yang, Y. Bian, J. Mu, G. Park, S. Xiang, S. Cai, P. W. Corey, J. Wang and S. Xu, Nature, 2023, 613, 667–675.
188. J. F. Hou, M. O. G. Nayeem, K. A. Caplan, E. A. Ruesch, A. Caban-Murillo, E. Criado-Hidalgo, S. B. Ornellas, B. Williams, A. A. Pearce, H. E. Dagdeviren, M. Surets, J. A. White, M. G. Shapiro, F. Wang, S. Ramirez and C. Dagdeviren, Nat. Commun., 2024, 15, 4601.
189. S. Zhou, X. Gao, G. Park, X. Yang, B. Qi, M. Lin, H. Huang, Y. Bian, H. Hu, X. Chen, R. S. Wu, B. Liu, W. Yue, C. Lu, R. Wang, P. Bheemreddy, S. Qin, A. Lam, K. A. Wear, M. Andre, E. B. Kistler, D. W. Newell and S. Xu, Nature, 2024, 629, 810–818.
190. S. Min, D. H. Kim, D. J. Joe, B. W. Kim, Y. H. Jung, J. H. Lee, B.-Y. Lee, I. Doh, J. An, Y.-N. Youn, B. Joung, C. D. Yoo, H.-S. Ahn and K. J. Lee, Adv. Mater., 2023, 35, 2301627.
191. A. B. Amado-Rey, A. C. Gonçalves Seabra and T. Stieglitz, IEEE Rev. Biomed. Eng., 2025, 18, 93–112.
192. L. Zhang, C. Marcus, D. Lin, D. Mejorado, S. J. Schoen, T. T. Pierce, V. Kumar, S. V. Fernandez, D. Hunt, Q. Li, I. I. Shuvo, D. Sadat, W. Du, H. Edenbaum, L. Jin, W. Liu, Y. C. Eldar, F. Li, A. P. Chandrakasan, A. E. Samir and C. Dagdeviren, Nat. Electron., 2024, 7, 77–90.
193. C. Yu, A. Shah, N. Amiri, C. Marcus, M. O. G. Nayeem, A. K. Bhayadia, A. Karami and C. Dagdeviren, Adv. Mater., 2023, 35, 2300066.
194. X. Gao, X. Chen, M. Lin, W. Yue, H. Hu, S. Qin, F. Zhang, Z. Lou, L. Yin, H. Huang, S. Zhou, Y. Bian, X. Yang, Y. Zhu, J. Mu, X. Wang, G. Park, C. Lu, R. Wang, R. S. Wu, J. Wang, J. Li and S. Xu, Nat. Electron., 2024, 7, 1035–1046.
195. C. Wang, B. Qi, M. Lin, Z. Zhang, M. Makihata, B. Liu, S. Zhou, Y. Huang, H. Hu, Y. Gu, Y. Chen, Y. Lei, T. Lee, S. Chien, K.-I. Jang, E. B. Kistler and S. Xu, Nat. Biomed. Eng., 2021, 5, 749–758.
196. F. Chasset, A. Soria, P. Moguelet, A. Mathian, Y. Auger, C. Francès and S. Barete, J. Dermatol., 2016, 43, 318–320.
197. H. Yuk, J. Wu and X. Zhao, Nat. Rev. Mater., 2022, 7, 935–952.
198. S. J. Wu and X. Zhao, Chem. Rev., 2023, 123, 14084–14118.
199. X. Pei, J. Wang, Y. Cong and J. Fu, J. Polym. Sci., 2021, 59, 1312–1337.
200. H.-C. Liu, Y. Zeng, C. Gong, X. Chen, P. Kijanka, J. Zhang, Y. Genyk, H. Tchelepi, C. Wang, Q. Zhou and X. Zhao, Sci. Adv., 2024, 10, eadk8426.
201. J. Wu, J. Deng, G. Theocharidis, T. L. Sarrafian, L. G. Griffiths, R. T. Bronson, A. Veves, J. Chen, H. Yuk and X. Zhao, Nature, 2024, 630, 360–367.
202. S. Qureshi, M. Hanif, V. Jeoti, G. M. Stojanović and M. T. Khan, Results Eng., 2024, 22, 102323.
203. B. Mahanty, S. Kumar Ghosh and D.-W. Lee, Chem. Eng. J., 2024, 495, 153481.
204. X. Zhao, Y. Zhou, W. Kwak, A. Li, S. Wang, M. Dallenger, S. Chen, Y. Zhang, A. Lium and J. Chen, Nat. Commun., 2024, 15, 8492.
205. X. Zhao, Y. Zhou, A. Li, J. Xu, S. Karjagi, E. Hahm, L. Rulloda, J. Li, J. Hollister, P. Kavehpour and J. Chen, Nat. Electron., 2024, 7, 924–932.
206. Y. Zhou, X. Zhao, J. Xu, G. Chen, T. Tat, J. Li and J. Chen, Sci. Adv., 2024, 10, eadj8567.
207. Z. Che, X. Wan, J. Xu, C. Duan, T. Zheng and J. Chen, Nat. Commun., 2024, 15, 1873.
208. X. Zhao, Y. Zhou, Y. Song, J. Xu, J. Li, T. Tat, G. Chen, S. Li and J. Chen, Nat. Mater., 2024, 23, 703–710.
209. V. Kashyap, J. Yin, X. Xiao and J. Chen, Nano Res., 2024, 17, 445–461.
210. J. Xu, T. Tat, J. Yin, D. Ngo, X. Zhao, X. Wan, Z. Che, K. Chen, L. Harris and J. Chen, Matter, 2023, 6, 2235–2247.
211. J. Xu, T. Tat, X. Zhao, Y. Zhou, D. Ngo, X. Xiao and J. Chen, Appl. Phys. Rev., 2022, 9, 031404.
212. J. D. Miller, D. Cabarkapa, M. J. Hermes, A. C. Fry and C. J. Berkland, Adv. Mater. Technol., 2022, 7, 2100784.
213. T. Hellebrekers, O. Kroemer and C. Majidi, Adv. Intell. Syst., 2019, 1, 1900025.
214. H. Hu, C. Zhang, C. Pan, H. Dai, H. Sun, Y. Pan, X. Lai, C. Lyu, D. Tang, J. Fu and P. Zhao, ACS Nano, 2022, 16, 19271–19280.
215. Y. Yan, Z. Hu, Z. Yang, W. Yuan, C. Song, J. Pan and Y. Shen, Sci. Rob., 2021, 6, eabc8801.
216. Y. Yan, A. Zermane, J. Pan and A. Kheddar, Nat. Mach. Intell., 2024, 1–12.
217. Z. Nie, J. W. Kwak, M. Han and J. A. Rogers, Adv. Mater., 2024, 36, 2205609.
218. P. G. Stoyanov and C. A. Grimes, Sens. Actuators, A, 2000, 80, 8–14.
219. C. A. Grimes, D. Kouzoudis and C. Mungle, Rev. Sci. Instrum., 2000, 71, 3822–3824.
220. P. Cheng, S. Gao, W. Zhang, T. Wang, A. Jian and S. Sang, Smart Mater. Struct., 2015, 24, 045029.
221. P. Chen, Q. Jiang, S. Horikawa and S. Li, J. Electrochem. Soc., 2017, 164, B247.
222. S. Sang, P. Cheng, W. Zhang, P. Li, J. Hu, G. Li and A. Jian, J. Intell. Mater. Syst. Struct., 2015, 26, 980–987.
223. E. Song, Z. Xie, W. Bai, H. Luan, B. Ji, X. Ning, Y. Xia, J. M. Baek, Y. Lee, R. Avila, H.-Y. Chen, J.-H. Kim, S. Madhvapathy, K. Yao, D. Li, J. Zhou, M. Han, S. M. Won, X. Zhang, D. J. Myers, Y. Mei, X. Guo, S. Xu, J.-K. Chang, X. Yu, Y. Huang and J. A. Rogers, Nat. Biomed. Eng., 2021, 5, 759–771.
224. J. Li, S. Jia, D. Li, L. Chow, Q. Zhang, Y. Yang, X. Bai, Q. Qu, Y. Gao, Z. Li, Z. Li, R. Shi, B. Zhang, Y. Huang, X. Pan, Y. Hu, Z. Gao, J. Zhou, W. Park, X. Huang, H. Chu, Z. Chen, H. Li, P. Wu, G. Zhao, K. Yao, M. Hadzipasic, J. D. Bernstock, G. M. Shankar, K. Nan, X. Yu and G. Traverso, Nat. Commun., 2024, 15, 7800.
225. J. Wan, Z. Nie, J. Xu, Z. Zhang, S. Yao, Z. Xiang, X. Lin, Y. Lu, C. Xu, P. Zhao, Y. Wang, J. Zhang, Y. Wang, S. Zhang, J. Wang, W. Man, M. Zhang and M. Han, Sci. Adv., 2024, 10, eadm9314.
226. S. Atalay, V. S. Kolat, N. Bayri and T. Izgi, J. Supercond. Nov. Magn., 2016, 29, 1551–1556.
227. I. Bravo, A. Arnaiz and A. García-Arribas, IEEE Trans. Magn., 2019, 55, 1–5.
228. X. Guo, S. Sang, A. Jian, S. Gao, Q. Duan, J. Ji, Q. Zhang and W. Zhang, Sens. Actuators, B, 2018, 256, 318–324.
229. R. Liu, X. Guo, J. Wang, J. Guo, Y. Zhang, W. Zhang and S. Sang, J. Mater. Sci., 2019, 54, 9679–9688.
230. C. Xue, C. Yang, T. Xu, J. Zhan and X. Li, Lab Chip, 2015, 15, 2318–2326.
231. W. Shen, R. S. Lakshmanan, L. C. Mathison, V. A. Petrenko and B. A. Chin, Sens. Actuators, B, 2009, 137, 501–506.
232. C. Ruan, K. Zeng, O. K. Varghese and C. A. Grimes, Biosens. Bioelectron., 2004, 20, 585–591.
233. R. Liu, X. Guo, J. Wang, J. Guo, Y. Zhang, W. Zhang and S. Sang, J. Mater. Sci., 2019, 54, 9679–9688.
234. L. Song, S. Glinsek, S. Drnovsek, V. Kovacova, B. Malic and E. Defay, Appl. Phys. Lett., 2022, 121, 212901.
235. D.-S. Choi and S.-Y. Kim, Sensors, 2019, 19, 3490.
236. B. Stadlober, M. Zirkl and M. Irimia-Vladu, Chem. Soc. Rev., 2019, 48, 1787–1825.
237. T. Takeshita, D. Zymelka, Y. Takei, N. Makimoto and T. Kobayashi, Jpn. J. Appl. Phys., 2022, 61, SN1024.
238. H. Arabi and H. Zaidi, Eur. Radiol., 2021, 31, 6384–6396.
239. H. Hu, H. Huang, M. Li, X. Gao, L. Yin, R. Qi, R. S. Wu, X. Chen, Y. Ma, K. Shi, C. Li, T. M. Maus, B. Huang, C. Lu, M. Lin, S. Zhou, Z. Lou, Y. Gu, Y. Chen, Y. Lei, X. Wang, R. Wang, W. Yue, X. Yang, Y. Bian, J. Mu, G. Park, S. Xiang, S. Cai, P. W. Corey, J. Wang and S. Xu, Nature, 2023, 613, 667–675.
240. M. Lin, Z. Zhang, X. Gao, Y. Bian, R. S. Wu, G. Park, Z. Lou, Z. Zhang, X. Xu, X. Chen, A. Kang, X. Yang, W. Yue, L. Yin, C. Wang, B. Qi, S. Zhou, H. Hu, H. Huang, M. Li, Y. Gu, J. Mu, A. Yang, A. Yaghi, Y. Chen, Y. Lei, C. Lu, R. Wang, J. Wang, S. Xiang, E. B. Kistler, N. Vasconcelos and S. Xu, Nat. Biotechnol., 2024, 42, 448–457.
241. K. M. Labus, J. Wolynski, J. Easley, H. L. Stewart, M. Ilic, B. Notaros, T. Zagrocki, C. M. Puttlitz and K. C. McGilvray, Injury, 2023, 54, 111080.
242. J. G. Wolynski, M. M. Ilić, K. M. Labus, B. M. Notaroš, C. M. Puttlitz and K. C. McGilvray, Ann. Transl. Med., 2022, 10, 510.
243. O. Martens, R. Land, M. Min, M. Rist, P. Annus, T. J. Seebeck and A. Pokatilov, in 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018, pp. 1–5.
244. A. Rashad, A. Kaiser, N. Prochnow, I. Schmitz, E. Hoffmann and P. Maurer, Clin. Oral. Implants Res., 2011, 22, 1361–1365.
245. S. Sankar, W.-Y. Cheng, J. Zhang, A. Slepyan, M. M. Iskarous, R. J. Greene, R. DeBrabander, J. Chen, A. Gupta and N. V. Thakor, Sci. Adv., 2025, 11, eadr9300.
246. S. J. Lederman and R. L. Klatzky, Atten. Percept. Psychophys., 2009, 71, 1439–1459.
247. E. Leroy and H. Shea, Adv. Mater. Technol., 2023, 8, 2300242.
248. X. Lin and M. Han, Soft Sci., 2023, 3, 14.
249. S. Patel, Z. Rao, M. Yang and C. Yu, Adv. Funct. Mater., 2025, 2417906.
250. R. W. Lindeman, R. Page, Y. Yanagida and J. L. Sibert, in Proceedings of the ACM symposium on Virtual reality software and technology, Association for Computing Machinery, New York, NY, USA, 2004, pp. 146–149.
251. Teslasuit|Meet our Haptic VR Suit and Glove with Force Feedback, https://teslasuit.io/, (accessed March 18, 2025).
252. Next generation full body haptic suit – bHaptics TactSuit, https://www.bhaptics.com, (accessed March 18, 2025).
253. Y. H. Jung, J.-Y. Yoo, A. Vázquez-Guardado, J.-H. Kim, J.-T. Kim, H. Luan, M. Park, J. Lim, H.-S. Shin, C.-J. Su, R. Schloen, J. Trueb, R. Avila, J.-K. Chang, D. S. Yang, Y. Park, H. Ryu, H.-J. Yoon, G. Lee, H. Jeong, J. U. Kim, A. Akhtar, J. Cornman, T. Kim, Y. Huang and J. A. Rogers, Nat. Electron., 2022, 5, 374–385.
254. M. T. Flavin, K.-H. Ha, Z. Guo, S. Li, J.-T. Kim, T. Saxena, D. Simatos, F. Al-Najjar, Y. Mao, S. Bandapalli, C. Fan, D. Bai, Z. Zhang, Y. Zhang, E. Flavin, K. E. Madsen, Y. Huang, L. Emu, J. Zhao, J.-Y. Yoo, M. Park, J. Shin, A. G. Huang, H.-S. Shin, J. E. Colgate, Y. Huang, Z. Xie, H. Jiang and J. A. Rogers, Nature, 2024, 1–8.
255. Y. Huang, J. Zhou, P. Ke, X. Guo, C. K. Yiu, K. Yao, S. Cai, D. Li, Y. Zhou, J. Li, T. H. Wong, Y. Liu, L. Li, Y. Gao, X. Huang, H. Li, J. Li, B. Zhang, Z. Chen, H. Zheng, X. Yang, H. Gao, Z. Zhao, X. Guo, E. Song, H. Wu, Z. Wang, Z. Xie, K. Zhu and X. Yu, Nat. Electron., 2023, 6, 1020–1031.
256. Y. Luo, C. Liu, Y. J. Lee, J. DelPreto, K. Wu, M. Foshey, D. Rus, T. Palacios, Y. Li, A. Torralba and W. Matusik, Nat. Commun., 2024, 15, 868.
257. A. Handler and D. D. Ginty, Nat. Rev. Neurosci., 2021, 22, 521–537.
258. H. P. Saal, B. P. Delhaye, B. C. Rayhaun and S. J. Bensmaia, Proc. Natl. Acad. Sci. U. S. A., 2017, 114, E5693–E5702.
259. J. Nam, Y. Kim and G. Jang, IEEEASME Trans. Mechatron., 2016, 21, 394–401.
260. D. Pyo, T.-H. Yang, S. Ryu and D.-S. Kwon, Sens. Actuators, A, 2015, 233, 460–471.
261. J. DelPreto, C. Liu, Y. Luo, M. Foshey, Y. Li, A. Torralba, W. Matusik and D. Rus, Adv. Neural Inf. Process. Syst., 2022, 35, 13800–13813.
262. M. Park, Unlocking Potential, https://www.aidentyx.com/post/unlocking-potential-the-effectiveness-of-ai-analytics-in-condition-monitoring, (accessed March 23, 2025).
263. J. Davies, M. T. Thai, T. T. Hoang, C. C. Nguyen, P. T. Phan, H. Phan, N. H. Lovell and T. N. Do, Adv. Mater. Technol., 2023, 8, 2201453.
264. J. H. Low, W. W. Lee, P. M. Khin, N. V. Thakor, S. L. Kukreja, H. L. Ren and C. H. Yeow, IEEE Rob. Autom. Lett., 2017, 2, 880–887.
265. K. Suzumori and A. A. Faudzi, Adv. Robot., 2018, 32, 458–476.
266. S. Kurumaya, H. Nabae, G. Endo and K. Suzumori, Sens. Actuators, A, 2017, 261, 66–74.
267. S. Kurumaya, K. Suzumori, H. Nabae and S. Wakimoto, ROBOMECH J., 2016, 3, 18.
268. B. Jumet, Z. A. Zook, A. Yousaf, A. Rajappan, D. Xu, T. F. Yap, N. Fino, Z. Liu, M. K. O’Malley and D. J. Preston, Device, 2023, 1, 100059.
269. T.-H. Yang, J. R. Kim, H. Jin, H. Gil, J.-H. Koo and H. J. Kim, Adv. Funct. Mater., 2021, 31, 2008831.
270. Y. Gao, K. Yao, S. Jia, Y. Huang, G. Zhao, B. Zhang, Y. Liu and X. Yu, Matter, 2024, 7, 2826–2845.
271. R. Uramune, H. Ishizuka, T. Hiraki, Y. Kawahara, S. Ikeda and O. Oshiro, IEEE Access, 2022, 10, 16830–16842.
272. A. Firouzeh, A. Mizutani, J. Groten, M. Zirkl and H. Shea, Adv. Mater., 2024, 36, 2307636.
273. G. Grasso, S. Rosset and H. Shea, Adv. Funct. Mater., 2023, 33, 2213821.
274. J. Han, W. Jiang, D. Niu, Y. Li, Y. Zhang, B. Lei, H. Liu, Y. Shi, B. Chen, L. Yin, X. Liu, D. Peng and B. Lu, Adv. Intell. Syst., 2019, 1, 1900109.
275. D. J. Kang, S. An, A. L. Yarin and S. Anand, Nanoscale, 2019, 11, 2065–2070.
276. J. Li, M. Sun and Z. Wu, Soft Robot., 2021, 8, 448–461.
277. H. Matsuoka, T. Kanda, S. Wakimoto, K. Suzumori and P. Lambert, Okayama University, Tokyo Institute of Technology, Tokyo, Japan, and Université libre de Bruxelles, Bruxelles, Belgium, Int. J. Autom. Technol., 2016, 10, 517–524.
278. G. Decroly, R. Raffoul, C. Deslypere, P. Leroy, L. Van Hove, A. Delchambre and P. Lambert, Front. Robot. AI, 2021, 8, 672934.
279. V. Cacucciolo, J. Shintake, Y. Kuwajima, S. Maeda, D. Floreano and H. Shea, Nature, 2019, 572, 516–519.
280. E. Hajiesmaili and D. R. Clarke, Mater. Horiz., 2022, 9, 3110–3117.
281. E. Hajiesmaili and D. R. Clarke, Nat. Commun., 2019, 10, 183.
282. X. Ji, X. Liu, V. Cacucciolo, M. Imboden, Y. Civet, A. El Haitami, S. Cantin, Y. Perriard and H. Shea, Sci. Rob., 2019, 4, eaaz6451.
283. X. Ji, X. Liu, V. Cacucciolo, Y. Civet, A. El Haitami, S. Cantin, Y. Perriard and H. Shea, Adv. Funct. Mater., 2021, 31, 2006639.
284. Y. Guo, Y. Luo, R. Plamthottam, S. Pei, C. Wei, Z. Han, J. Fan, M. Possinger, K. Liu, Y. Zhu, Z. Fei, I. Winardi, H. Hong, Y. Zhang, L. Jin and Q. Pei, Sci. Adv., 2024, 10, eadr1765.
285. G. Gu, J. Zou, R. Zhao, X. Zhao and X. Zhu, Sci. Rob., 2018, 3, eaat2874.
286. Y. Chen, H. Wu, X. Wang, P. Qiu, H. Wan, J. Jiao, D. Wang and Z. Zhang, Chem. Eng. J., 2024, 500, 156612.
287. Y. Shao, A. Shagan Shomron, B. Javot, C. Keplinger and K. J. Kuchenbecker, Adv. Mater. Technol., 2025, 2401525.
288. E. Leroy and H. Shea, Adv. Mater. Technol., 2023, 8, 2300242.
289. E. Leroy, R. Hinchet and H. Shea, Adv. Mater., 2020, 32, 2002564.
290. S.-S. Han, S.-B. Choi and C.-C. Cheong, Mech. Mach. Theory, 2000, 35, 1563–1577.
291. A. Mazursky, J.-H. Koo and T.-H. Yang, J. Intell. Mater. Syst. Struct., 2019, 30, 2521–2533.
292. M. D. Hager, P. Greil, C. Leyens, S. van der Zwaag and U. S. Schubert, Adv. Mater., 2010, 22, 5424–5430.
293. J. Zhu, H. Wen, P. Huang, L. Liu and H. Hu, Sustainable Mater. Technol., 2023, 35, e00530.
294. D. Wei, Y. Sun, H. Zhu and Q. Fu, ACS Nano, 2023, 17, 23223–23261.
295. S. Zhang, X. Lin, J. Wan, C. Xu and M. Han, Adv. Mater. Technol., 2024, 9, 2301895.

---

Footnote

† K. W. and W. H. contributed equally to this work.

---