High-entropy materials for electrocatalytic oxygen reduction reaction

Abstract

High-entropy materials (HEMs) have garnered significant attention in the field of catalysis due to their highly tunable compositions, complex yet advantageous electronic structures, abundant active sites, and exceptional physicochemical properties. Despite these promising attributes, a limited fundamental understanding of their properties continues to hinder their broader application, particularly in the electrocatalytic oxygen reduction reaction (ORR). This review provides a comprehensive overview of recent advancements in HEMs for ORR, beginning with an introduction to diverse classes of high-entropy systems—including high-entropy alloys, intermetallics, ceramics, and emerging single-atom configurations. It then delves into the underlying structure–property correlations within these systems, highlighting how entropy-driven design strategies influence their electrocatalytic behavior. Subsequently, the latest progress in applying various high-entropy systems to the ORR is critically analyzed, with a particular emphasis on elucidating the structure–activity relationships that govern catalytic performance. Finally, we outline key challenges and future directions, offering perspectives on the rational design of next-generation high-entropy materials for electrocatalytic ORR and related applications.

---

Ziheng Liang

Ziheng Liang is pursuing his bachelor's degree in chemistry at Beijing Normal University. His primary research focuses on the preparation, synthesis, and performance study of metal-based electrocatalysts.

Yaping Chen

Yaping Chen received her PhD degree in 2020 from the Institute for Superconducting & Electronic Materials at the University of Wollongong, Australia. She is currently an Associate Professor at Beijing Normal University. Her research interests include the design, synthesis, and mechanism research of high-efficiency electrocatalysts for energy-related applications.

Peng Li

Peng Li received his PhD degree in 2021 from the Institute for Superconducting & Electronic Materials at the University of Wollongong, Australia. Currently, Dr Li is a Research Fellow at RMIT University, focusing on the rational design and development of efficient materials and structures, as well as the fundamental understanding of electrochemical processes including water-splitting reactions and carbon capture, utilization, and storage (CCUS).

---

1. Introduction

The dual global challenges of energy scarcity and environmental degradation underscore the urgent need for clean, efficient, and sustainable energy technologies.^1,2 Among emerging solutions, hydrogen fuel cells—particularly proton exchange membrane fuel cells (PEMFCs) and anion exchange membrane fuel cells (AEMFCs)—offer high energy conversion efficiency and zero emissions, positioning them as a pivotal technology for the renewable energy future.^3,4 However, their widespread deployment is hindered by the sluggish kinetics of the oxygen reduction reaction (ORR) at the cathode, which involves a complex four-electron transfer pathway and necessitates highly active and durable electrocatalysts.⁵ Currently, Pt nanoparticles supported on carbon (Pt/C) remain the benchmark ORR catalysts. Yet, they suffer from severe limitations, including high cost, insufficient long-term stability, and performance degradation due to Ostwald ripening of Pt and corrosion of carbon supports under operational conditions.^5–9 Overcoming these challenges requires the development of alternative ORR catalysts that combine high activity, durability, and cost-effectiveness, while offering insight into the structure–activity relationship to guide rational catalyst design, particularly for practical applications in fuel cells and metal–air batteries.

Among numerous candidates, high-entropy materials (HEMs)—comprising high-entropy alloys (HEAs), oxides (HEOs), intermetallics (HEIs), carbon (HECs), and other emerging compounds—have garnered growing interest as a next-generation catalyst platform.¹⁰ By incorporating five or more principal elements in near-equimolar ratios, HEMs exhibit high configurational entropy, leading to unique physicochemical properties such as lattice distortion, sluggish diffusion, and synergistic multi-element interactions.^11–13 These characteristics enable a broad distribution of active sites and tunable electronic structures conducive to enhanced ORR activity. HEOs, for instance, promote oxygen vacancy formation to boost catalytic reactivity, while HEIs offer ordered structures that facilitate precise modulation of catalytic behavior—combining the advantages of both entropy-driven randomness and structural control inherited from traditional intermetallic compounds.^14,15 Beyond these, novel high-entropy systems including fluorides, nitrides, sulfides, phosphides, and inorganic–organic hybrids further expand the design space for ORR catalysis. Elements such as N, P, and S, with extended orbitals and improved electronic coupling with transition metal d-orbitals, offer additional opportunities for electronic tuning and performance optimization.^16–18 Despite promising advances, existing reviews often overlook the distinctive role of high-entropy effects in modulating ORR mechanisms, the structure–property–activity relationships, and the practical challenges associated with scalability and long-term durability (Fig. 1).

Fig. 1 High-entropy material systems for the electrocatalytic oxygen reduction reaction (ORR).

This review aims to bridge these gaps by systematically summarizing recent progress in HEM-based ORR catalysts. We highlight the design principles, entropy-driven phenomena, and electronic modulation strategies that underpin enhanced ORR activity. Case studies of HEAs, HEOs, HEIs, HECs, and novel high-entropy systems are presented to illustrate the structure–activity correlations and catalytic mechanisms. Finally, we discuss key challenges and future opportunities toward the rational design and scalable implementation of HEMs for sustainable energy conversion applications.

2. Fundamental understanding of high-entropy materials

The concept of high-entropy materials (HEMs) originates from the groundbreaking proposal of high-entropy alloys (HEAs) in 2004, where the preparation of single-phase solid solution alloys was achieved by incorporating five or more principal elements in near-equimolar ratios.^19,20 This paradigm marked a departure from conventional alloy design, which typically relies on one or two dominant elements. Instead, the HEA approach leverages compositional complexity to introduce a new stabilizing mechanism: configurational entropy.¹⁷ As this principle evolved, it gave rise to the broader class of high-entropy materials, encompassing systems that may be metallic, ceramic, polymeric, or even composite in nature, unified by the inclusion of multiple principal elements and the entropic stabilization of their microstructures.^16,21 High-entropy materials are now widely recognized as multi-principal component systems, typically composed of four to six or more elements.²² Unlike conventional materials, where stability is often dictated by enthalpic interactions, the phase stability in HEMs is governed primarily by entropic contributions.^23,24 These materials tend to form disordered solid solutions or single-phase structures under the right thermodynamic conditions. Key representatives of the HEM family include high-entropy oxides (HEOs), high-entropy ceramics (HECs), high-entropy intermetallics (HEIs), high-entropy polymers (HEPs), and other advanced composite systems, all of which benefit from configurational complexity.^22,25–28 The thermodynamic basis for the formation of high-entropy phases can be described using the Gibbs free energy equation (eqn (1)).²⁹

ΔG = ΔH − T × ΔS (1)

Here, ΔG is the change in Gibbs free energy, ΔH is the change in enthalpy, T is the absolute temperature, and ΔS is the configurational entropy of mixing. In multicomponent systems, particularly those approximating ideal solutions, the entropic term becomes dominant. The configurational entropy can be expressed as eqn (2).^17,29

ΔS = −R × ∑(x_i)ln(x_i) (2)

where x_i denotes the mole fraction of each component and R is the universal gas constant. In idealized equimolar mixtures, this expression simplifies. For instance, in a five-component equimolar system (eqn (3)).¹⁷

ΔS = R × ln 5 = 1.61R (3)

This entropic contribution increases with the number of elements. For two-, three-, four-, and six-component equimolar systems, the maximum theoretical entropy values are 0.69R, 1.10R, 1.39R, and 1.79R, respectively. One should note that the prepared solutions usually include ideal, regular, and sub-regular solutions according to their structural symmetry (Fig. 2a and b). For ideal solutions, atoms are distributed randomly and can occupy any position within the unit cell structure. Meanwhile, a random distribution of atoms is also considered in regular solutions. While for sub-regular solutions, atoms are not randomly distributed, which leads to a tendency toward phase separation when ΔH > 0 or chemical short-range ordering (SRO) when ΔH < 0. Literature analysis reveals that a slightly lower ratio of ideal (4%) and regular (11%) solutions can be found among 1176 binary liquid metallic systems. As shown in Fig. 2c, systems with ΔS_mix ≥ 1.61R are classified as high-entropy; those between 0.69R and 1.61R are medium-entropy systems; and those below 0.69R are considered low-entropy systems.³⁰ These thresholds provide a quantitative framework for evaluating the entropic influence on phase formation, guiding the design of materials with desired structural complexity and stability.

Fig. 2 The correlations between Gibbs free energy change, entropy change, and enthalpy change as a function of composition fraction for (a) regular solution and (b) sub-regular solution. Reproduced from ref. 23. Copyright 2017 Elsevier. (c) Effect of an equimolar number of mixing elements on the mixing entropy. Reproduced from ref. 30. Copyright 2024 American Chemical Society. (d) The four core effects of HEMs driven by entropy. Reproduced from ref. 22. Copyright 2021 Royal Society of Chemistry. (e) High entropy materials for oxygen reduction reaction (ORR). (f) Oxygen reduction reaction pathways in acidic media. Reproduced from ref. 35. Copyright 2023 Elsevier.

The stabilization and functionality of high-entropy materials (HEMs) arise not merely from compositional diversity but also from four fundamental characteristics first proposed by Yeh and colleagues in the context of high-entropy alloys (HEAs): the high-entropy effect, severe lattice distortion, sluggish diffusion, and the cocktail effect, as shown in Fig. 2d.^29,31 These attributes align closely with foundational aspects of materials science—thermodynamics, structure, kinetics, and functionality—and collectively underpin the unique physicochemical properties observed in HEMs.^17,32 As mentioned, the high-entropy effect refers to the stabilization of disordered solid solution phases over intermetallic or ordered phases, due to the maximization of configurational entropy in the Gibbs free energy expression (ΔG = ΔH − TΔS). In systems containing five or more principal elements with near-equimolar ratios, the entropy term (−TΔS) can dominate the enthalpy of mixing (ΔH), rendering the formation of a homogeneous, single-phase matrix thermodynamically favorable—even when significant atomic-scale heterogeneity exists. Lattice distortion is a direct result of incorporating atoms with diverse atomic radii, valence states, and electronegativities into a shared crystalline lattice.^31,33 This structural heterogeneity induces local strain fields and disrupts long-range order, which not only enhances mechanical robustness and defect tolerance but also modulates electronic structures critical for catalytic performance. In electrocatalysis, such local distortions can shift the adsorption energies of key intermediates, influencing the activity and selectivity of ORR pathways. Sluggish diffusion, another hallmark of HEMs, stems from the complex, heterogeneous atomic environment. Varying bonding strengths and potential energy landscapes increase the activation barriers for atom migration, thereby suppressing bulk diffusion kinetics.^10,24 This trait enhances structural stability at high temperatures or during electrochemical cycling, delaying sintering, coarsening, or surface reconstruction—factors essential for long-term electrocatalyst durability. The cocktail effect represents the emergence of synergistic, non-linear properties that cannot be predicted from the behavior of individual elements alone.^29,34 In catalytic systems, this can manifest as enhanced activity, corrosion resistance, or unique electronic and magnetic properties. The interplay between multiple constituent elements allows for fine-tuning of surface adsorption energies and stabilization of active sites, which are critical in optimizing ORR efficiency.

In terms of the oxygen reduction reaction (ORR), the four electron- and two electron-transfer pathways are described in Fig. 2e and f and have been reported in numerous classical studies.³⁶ The 4e⁻ ORR pathway typically involves sequential adsorption and reduction of O₂via intermediate species such as OOH*, O*, and OH*, with each step requiring precise control over adsorption energies to minimize overpotentials. The 4e⁻ ORR pathway is preferred in fuel cells and metal–air batteries due to its high energy efficiency.

However, even the most active ORR catalysts—such as Pt and Pd—suffer from nonideal scaling relationships between these intermediates.³⁷ This leads to a high intrinsic overpotential, representing a significant barrier to efficient fuel cell operation. In contrast the 2e⁻ ORR pathway reduces O₂ to hydrogen peroxide (H₂O₂), which is less desirable in fuel cells due to lower efficiency and the potential for peroxide-induced degradation of membrane materials. Interestingly, the 2e⁻ ORR pathway offers a valuable solution to synthesize industrial products (H₂O₂) via a renewable coupled electrochemical process. From this viewpoint, it is also important to develop functional catalysts to promote the 2e⁻ ORR pathway with high performance and low overall cost. The detailed reaction mechanisms and reaction pathways for 4e⁻ ORR vs. 2e⁻ ORR pathways are presented in Fig. 2f and Table 1. HEMs offer a promising strategy to address this bottleneck. The tunable surface electronic structures and adsorption energies afforded by compositional complexity allow for the fine manipulation of intermediate binding energies, potentially breaking the unfavorable linear scaling relationships. Moreover, HEMs can be designed to minimize the use of platinum-group metals while maintaining or surpassing the catalytic performance of conventional Pt-based systems.²⁴ By exploiting entropy-driven stabilization, lattice distortion effects, and electronic tunability, HEMs serve as a transformative platform in electrocatalysis—particularly for the ORR, where catalytic activity, selectivity, and durability must be balanced. Their compositional flexibility and inherent disorder are no longer viewed as challenges, but as deliberate design features that unlock new regimes of performance beyond conventional material limits.

Table 1 ORR reaction pathways under acidic and alkaline reaction conditions

Reaction pathways	Working electrolyte conditions	Fundamental reaction step	E ^θ (V vs. SHE)
4e^− pathway	Acidic condition (pH = 0)	O2 + 4H^+ + 4e^− → 2H2O	1.229
	Alkaline condition (pH = 14)	O2 + 2H2O + 4e^− → 4OH^−	0.401
2e^− pathway	Acidic condition (pH = 0)	O2 + 2H^+ + 2e^− → H2O2	0.695
	Alkaline condition (pH = 14)	O2 + H2O + 2e^− → HO2^− + OH^−	−0.076

---

3. High-entropy alloys (HEAs)

High-entropy alloys (HEAs), the earliest and most extensively studied subclass of high-entropy materials, have garnered significant attention as electrocatalysts due to their unique compositional complexity and tunable physicochemical properties. Defined by the presence of five or more principal elements in near-equimolar ratios (typically 5–35 atomic% each) and a configurational entropy exceeding 1.61R, HEAs promote multi-site catalysis via synergistic interactions among diverse elemental constituents.³⁸ These alloys exemplify the four core characteristics of high-entropy systems—high-entropy stabilization, severe lattice distortion, sluggish diffusion, and the cocktail effect—making them a foundational platform for designing next-generation oxygen reduction reaction (ORR) catalysts.^10,42–44 The development of high-performance ORR electrocatalysts is crucial for the large-scale deployment of energy devices such as PEMFCs, AEMFCs, and metal–air batteries. An ongoing challenge is to reduce dependence on precious metals like Pt, Pd, and Ru while maintaining—or ideally surpassing—the activity, stability, and durability benchmarks of commercial catalysts.⁴⁵ Although Pt-based catalysts remain the current standard, their high cost, scarcity, and performance degradation over time demand innovative solutions. In this context, HEAs—particularly those containing reduced amounts of Pt (e.g., 20–30 at%)—have shown remarkable promise by delivering superior ORR performance and durability.⁴⁶ For instance, Chen et al. reported a nanoporous PtIrRuCuOs HEA fabricated via chemical dealloying of mechanically alloyed precursors, yielding a three-dimensional bicontinuous ligament–channel architecture (Fig. 3a and b).³⁹ This structure exhibited mass activity and specific activity enhancements of 1.8 and 3.8 times, respectively, over commercial Pt/C, alongside exceptional long-term stability. The inclusion of 3d transition metals (e.g., Cu and Ru) introduced strong ligand and strain effects, weakening the O–O bond and facilitating its cleavage—thus enhancing the kinetics of the 4-electron ORR pathway. Similarly, a nanoporous AlNiCuPtPdAu HEA, designed with precisely tuned composition and ligament morphology, demonstrated 10-fold higher mass activity than Pt/C and retained 92.5% of its initial activity after 100 000 electrochemical cycles, compared to just 35% retention for Pt/C under identical conditions.⁴⁷ These results highlight how the high-entropy design framework enables long-lasting active sites and inhibits catalyst degradation via atomic rearrangement or coarsening. Beyond Pt-rich systems, HEAs have also enabled the development of noble-metal-free catalysts with impressive ORR performance. Jin et al. synthesized a spinel-type high-entropy catalyst, AlNiCoRuMo, via a dealloying-assisted route that formed a hybrid HEA/high-entropy oxide (HEO) nanowire structure (Fig. 3c and d).⁴⁰ This design significantly reduced the Ru content (from ∼1/3 to ∼1/5) and facilitated the modulation of electronic structure across a broad compositional space. The resulting catalyst outperformed commercial Pt/C in terms of limiting current density and half-wave potential, with negligible performance decay after 20 000 electrochemical cycles—attesting to its high durability under prolonged operation. Additionally, Schuhmann et al. explored CrMnFeCoNi HEA as a non-noble metal ORR catalyst, synthesized via combinatorial co-sputtering into an ionic liquid (Fig. 3e).^41,48 This quinary HEA, composed of inexpensive transition metals, exhibited higher activity and lower overpotential than commercial Pt/C, highlighting the potential of HEAs as cost-effective alternatives for ORR catalysis. These advancements underscore the potential of HEAs to address the challenges of ORR catalysis, offering a pathway to reduce precious metal reliance while achieving high performance and durability.

Fig. 3 (a) TEM images showing the nanoporous microstructure of the nanoporous PtRuCuOsIr alloy fabricated by the dealloying method. (b) HRTEM images of the nanoporous PtRuCuOsIr alloy. Reproduced from ref. 39. Copyright 2015 Elsevier. (c and d) SEM images of the dealloyed AlNiCoRuMo nanowires. Reproduced from ref. 40. Copyright 2020 American Chemical Society. (e) Strategy for evaluation of intrinsic activity of multinary alloy NPs. After synthesis of NPs by means of combinatorial co-sputtering into an ionic liquid, potential-assisted immobilization at an etched carbon nanoelectrode utilizing nanoimpacts allows extraction of NPs, which are then transferred to a suitable electrolyte solution for electrocatalytic activity measurements. Reproduced from ref. 41. Copyright 2018 WILEY-VCH GmbH.

Understanding the surface atomic distribution and coordination structures of HEAs is challenging due to their complex local coordination environment.^49,50 To address the issue, researchers often employ data-driven models or theoretical calculations to predict properties and guide future studies. For instance, Lu et al. developed a neural network model based on density functional theory (DFT) calculations to simultaneously account for ligand and coordination effects, enabling accurate prediction of the *OH adsorption energy on IrPdPtRhRu HEA surfaces with 12 distinct coordination structures (Fig. 4a and b).⁵¹ This model quantitatively unifies the ligand effect, arising from electron perturbations of nearby metals, and the coordination effect, stemming from variations in local environments such as atomic vacancies and surface sections, providing a robust framework for understanding HEA surface adsorption properties (Fig. 4c). Similarly, Rosssmeisl et al. used DFT to evaluate the adsorption energy of *OH and *O intermediates at randomly selected binding sites on the IrPdPtRhRu HEA surface.⁵² They constructed a comprehensive adsorption energy dataset and established model functions to predict catalytic activity and optimize HEA compositions. Their predictions indicate that IrPdPtRhRu exhibits a lower overpotential than Pt(111), highlighting its potential as a highly efficient ORR electrocatalyst. Although computational methods for high-entropy alloys (HEAs) have advanced rapidly, there is a growing need to integrate computational models with experimental data to unravel the complex behavior of HEAs and accelerate the design of advanced catalysts. This combined approach holds significant potential for driving innovation and overcoming the limitations of traditional methods. While computational methods for HEAs have advanced rapidly, experimental progress in developing HEAs for the oxygen reduction reaction (ORR) has lagged. This gap is primarily due to the lack of efficient synthesis and measurement tools, the enormous number of different types of HEAs, and the trial-and-error nature of traditional experimental approaches.^17,51 Hence, integrating high-throughput synthesis, theoretical predictions, and rapid screening pipelines is crucial for accelerating and potentially automating the discovery of high-performance HEA catalysts.¹⁷ Hu et al. developed a high-throughput synthesis method for multimetallic nanoclusters (MMNCs) by modifying the carbothermal shock technique (Fig. 4d).⁵³ In this approach, metal salt precursors with desired compositions are dispersed onto a carbon carrier and patterned on a copper plate. Rapid radiative heating enabled the one-pot synthesis of up to 88 MMNC samples, ranging from single metal to quintuple- and octal-metal samples (Fig. 4e). The results showed that PtPdRhNi and PtPdFeCoNi exhibited significantly higher ORR activity than the commercial Pt/C. In another impressive work, Ludwig et al. proposed a closed-loop strategy combining DFT calculations, machine learning, combinatorial synthesis, and high-throughput measurement to discover advanced electrocatalysts.⁵⁴ Using a machine learning model trained on a DFT dataset of *OH and *O binding energies, they predicted the composition-dependent ORR activity of AgIrPdPtRu HEA. The most promising HEA compositions identified by the model were synthesized as thin films and evaluated using a scanning droplet pool. The experimental results were then fed back to refine the theoretical model, enhancing predictive accuracy through iterative optimization.

Fig. 4 (a) Input features, their examples (green, blue, and red indicate ligand, coordination, and nearest-neighbor descriptors, respectively), and neural network layout, including the dense layers depicted in the inset. (b) One of the 12 structure where labeled metal atoms are the active site and its nearest neighbours. (c) Parity plot and performance metrics following random data shuffling and a 50%/50% training–testing data split. Dotted lines indicate ±0.15 eV deviation. Reproduced from ref. 51. Copyright 2020 Elsevier. (d) Schematic illustration of the combinatorial and high-throughput synthesis of uniform multimetallic nanoclusters. (e) The scanning droplet cell setup and patterned samples on the copper substrate. CE, counter electrode; RE, reference electrode; WE, working electrode. Reproduced from ref. 55. Copyright 2020 PNAS.

4. High-entropy intermetallics (HEIs)

Compared with HEAs, HEIs uniquely combine the structural benefits of ordered intermetallics and the compositional versatility of HEAs. Structurally, HEIs exhibit long-range chemical ordering due to the presence of sublattice frameworks, a feature absent in single-phase solid-solution HEAs.^15,27,56 This ordered sublattice architecture not only enhances atomic diffusion resistance but also improves thermodynamic and mechanical stability, addressing a critical limitation of conventional HEAs.^27,57–60

HEAs often suffer from stability degradation during catalytic reaction due to the leaching of non-precious metals. In contrast, HEIs demonstrate superior catalytic stability and activity, making them promising candidates for ORR electrocatalysis.^61,62 HEIs are typically modeled after binary intermetallic systems (e.g., AB-type or AB₃-type), where random substitution of primary lattice sites with multiple elements preserves structural integrity while introducing high-entropy effects.^15,60,63,64 Among these, L1₀-type (AB-type) and L1₂-type (AB₃-type) structures are the most widely studied, with L1₀-type HEIs exhibiting significantly higher ORR activity than their L1₂-type counterparts.⁶⁵ While traditional Pt–transition metal alloys demonstrate substantial ORR activity, their face-centered cubic (fcc-type) solid solution structure fundamentally constrains the reactivity of transition metals under ORR conditions. L1₀-type HEIs overcome these structural limitations through a dual transformation process: from fcc-type solid solution structures to face-centered tetragonal (fct-type) intermetallic compound arrangements, and from disordered to ordered atomic configurations.⁶⁶ This transformation, achieved under specific high-temperature conditions and at precise elemental ratios, significantly reduces interatomic bond lengths.^63,67–69 The shortened bonds enhance orbital overlap between constituent atoms, facilitating more efficient electron transfer pathways critical for ORR kinetics and stronger atomic bonding vital for improving catalyst stability during electrocatalysis.^55,63,67,68 Huang et al. synthesized highly ordered L1₀-type Pt₄FeCoCuNi nanoparticles (NPs) and demonstrated exceptional ORR performance.⁶³ The highly ordered variant achieved a half-wave potential (E_1/2) of 0.943 V (Fig. 5a) and a mass activity of 3.78 A mg_Pt⁻¹ at 0.9 V vs. RHE, outperforming partially ordered and disordered samples. Stability tests (30 000 cycles) revealed minimal degradation, with only a 7 mV negative shift in E_1/2. This stability arises from the ordered sublattice suppressing atomic rearrangement and modulating the electronic structure, which lowers the d-band center relative to pure Pt and weakens intermediate adsorption. Additionally, Xia et al. developed L1₀-type PtFeCoNiCuZn HEIs (PFCNCZ-HEI), where ligand effects drove electron transfer from non-precious metals to Pt, downshifting the Pt d-band center and optimizing intermediate adsorption.⁶⁷ After 10 000 accelerated durability test (ADT) cycles, PFCNCZ-HEI exhibited negligible E_1/2 decay of 1 mV, highlighting its robust stability. Xia et al. further engineered L1₀-type PtIrFeCoCu HEIs with highly active (001) facets.⁶⁸ The HEIs with (001) surfaces exhibited the largest d-band center downshift, resulting in ultrahigh intrinsic activity (Fig. 5b). Remarkably, after 60 000 ADT cycles, the E_1/2 loss was limited to 9 mV, far surpassing that of commercial Pt/C catalysts. Beyond L1₀-type structures, L1₂-type high-entropy intermetallics have also demonstrated exceptional catalytic performance for ORR, although research in this domain remains limited with only two studies reported to date. Luo et al. developed L1₂-type PdFeCoNiCu intermetallics that exhibited an impressive E_1/2 of 0.90 V, surpassing the performance of several comparative catalysts (Fig. 5c).⁵⁵ Their investigations revealed a critical electronic structure modification in ordered HEIs (OHEAs), where the 3d orbital of Co shifts downward toward the Fermi level. This electronic reconfiguration facilitates electron transfer during the ORR process, resulting in superior catalytic activity compared to their relatively disordered high-entropy alloy (DHEA) counterparts (Fig. 5d and e). Wang et al. synthesized L1₂-type Pt(FeCoNiCuZn)₃ that simultaneously achieved high activity and exceptional stability.⁷⁰ Its remarkable electrochemical stability was demonstrated after 30 000 cycles of ADT, with the ORR polarization curve showing minimal changes, a negligible negative shift in E_1/2 of only 2 mV, and a mere 2.9% decrease in mass activity. TEM analysis confirmed that the HEI nanoparticles maintained uniform distribution on the support material even after extensive potential cycling, validating their structural integrity under operating conditions (Fig. 5f). The practical viability of these materials was further demonstrated in high-temperature PEMFC testing, where L1₂-type Pt(FeCoNiCuZn)₃/C cathodes sustained stable operation for approximately 150 h, highlighting the significant potential of L1₂-type high-entropy intermetallics for practical ORR applications.

Fig. 5 (a) Polarization curves comparing highly-ordered HEI NPs(Pt₄FeCoCuNi) to other catalysts. Reproduced from ref. 63. Copyright 2023 Wiley-VCH GmbH. (b) Electronic density of states of the d-band for the surface Pt atoms on different crystal faces. Reproduced from ref. 68. Copyright 2023 American Chemical Society. (c) LSV curves of OHEA-mNC, OHEA-nNC, DHEA-mNC (OHEA refers to the HEI in this article; mNC denotes a mesoporous carbon framework and nNC denotes a non-mesoporous carbon framework) and Pt/C for the ORR. (d) The PDOSs of clean DHEA-mNC. (e)The PDOSs for the oxygen adsorption of DHEA-mNC. Reproduced from ref. 55. Copyright 2022 Wiley-VCH GmbH. (f) TEM image of Pt(FeCoNiCuZn)₃/C. Reproduced from ref. 70. Copyright 2024 Royal Society of Chemistry.

5. High-entropy oxides (HEOs)

HEOs exhibit exceptional potential in catalysis due to their unique structural and electronic properties. Beyond the intrinsic advantages of high-entropy stabilization, abundant active sites, and tunable composition/electronic structure, HEOs are further characterized by their rich diversity of metal cations, metal–oxygen (M–O) bonding configurations, and oxygen vacancies, which collectively contribute to their distinct catalytic performance.^22,71–75 The synergistic charge compensation and valence balance among multivalent metal cations enable precise optimization of the e_g orbital filling to approach unity. This tuning facilitates balanced adsorption/desorption kinetics of oxygen intermediates, thereby enhancing catalytic activity.^76,77 For example, Wang et al. demonstrated this principle by synthesizing a perovskite-type high-entropy oxide, La(Cr_0.2Mn_0.2Fe_0.2Co_0.2Ni_0.2)O₃ (LCMFCNO), via the spray pyrolysis method enabling rapid synthesis of materials with tailored properties.⁷⁸ The resulting LCMFCNO catalyst exhibited superior oxygen reduction reaction (ORR) activity and stability compared to binary and quaternary metal oxides. This enhancement arises from two key factors: (1) the wrinkled surface morphology and abundant crystal defects in LCMFCNO provide a large electrochemically active surface area for the ORR (Fig. 6a), and (2) the valence state equilibrium among transition metal cations optimizes the e_g orbital filling to near unity (Fig. 6b). This optimization strengthens the covalency of transition metal–oxygen (TM–O) bonds, which promotes efficient O species (O₂/OH⁻) exchange and balances the kinetic competition between intermediate reaction steps (e.g., steps 1 and 4 in the ORR pathway) (Fig. 6c). What's more, Qiu's team developed an eight-component nanoporous AlNiCoRuMoCrFeTi HEO as a highly efficient electrocatalyst for the alkaline ORR.⁷⁹ By incorporating stabilizing elements such as Fe, Cr, and Ti, the diffusion of Ru and Ni atoms during dealloying was effectively suppressed. This restriction promotes the preferential oxidation of Ru and Ni, facilitating the formation of a stable HEO structure (Fig. 6d). Furthermore, theoretical calculations further revealed that the synergistic interplay between Fe and Cr modulates the electronic structure of Co and Ru, optimizing their e_g orbital occupancy to approach unity (Fig. 6e). As a result, AlNiCoRuMoCrFeTi HEO demonstrated excellent catalytic performance with mass activity (MA) higher than that of commercial Pt/C and RuO₂, and the half-wave potential of AlNiCoRuMoCrFeTi HEO decayed by only 7 mV, far below that of RuO₂ (48 mV) after 10 000 cycles CV. The structural and catalytic stability of HEOs is intrinsically supported by the abundance of high-binding-energy M–O bonds within their lattice frameworks.⁸¹ Moreover, the dynamic modulation of M–O bond characteristics, including vibrational modes, bond stretching, and structural distortions, enables simultaneous enhancement of catalytic activity.⁸² An example is demonstrated by Zhang et al., who engineered a high-entropy spinel oxide, (Cr_1/5Mn_1/5Ni_1/5Fe_1/5Co_1/5)₃O₄ (CMNFCO) as an efficient electrocatalyst for the ORR.⁸⁰ The Raman spectrum indicates that the M–O bond at octahedral sites progressively vibrates and CMNFCO undergoes surface reconstruction in the electrolyte, thereby facilitating ORR activity (Fig. 6f). In another inspiring work, Hou et al. synthesized a B-site (composed of transition metal ions) modified K₂NiF₄-structured high-entropy oxide, La_1.2Sr_0.8Mn_0.2Fe_0.2Co_0.2Ni_0.2Cu_0.2O_4+δ (LSMFCNC), which exhibited higher ORR catalytic activity than the traditional K₂NiF₄-structured catalyst La_1.2Sr_0.8NiO_4+δ (LSNO).⁸³ XRD indicates that LSMFCNC undergoes lattice expansion owing to the introduction of Fe⁴⁺, Mn⁴⁺ and Cu⁺ for B-site modulation compared to LSNO (Fig. 7a), which increases the M–O bond length and makes it easier to break, thereby forming more oxygen vacancies (Fig. 7b). The enriched oxygen vacancy concentration facilitated enhanced oxygen adsorption and dissociation on the LSMFCNC surface, while electrochemical impedance spectroscopy (EIS) confirmed accelerated oxygen ion and proton transport. These combined effects optimized the interfacial reaction kinetics between O²⁻ and H⁺ at the LSMFCNC/BZCY interface, significantly boosting catalytic efficiency (Fig. 7c). HEOs have also emerged as a highly promising class of multifunctional substrates for enhancing the performance of electrocatalysts. Their unique structural and electronic properties, including diverse M–O bonding configurations, mixed metal cations, and abundant oxygen vacancies, enable precise modulation of interfacial interactions between the substrate and loaded catalytic species (e.g., single atoms, clusters, or nanoparticles).⁸⁶ These interactions, governed by bonding effects and electron transfer mechanisms, significantly improve the catalytic activity and stability of hybrid electrocatalyst systems.^80,83,87,88 For example, Qiu et al. synthesized (AlCoFeMoCr)₃O₄/Pt electrocatalysts via a dealloying–etching method, achieving exceptional ORR performance.⁸⁴ First-principles calculations exhibit that the (AlCoFeMoCr)₃O₄ substrate regulated the Gibbs free energy of the ORR process on the loaded Pt, changing the rate-determining step and reducing the activation energy of the rate-determining step from 0.63 eV to 0.38 eV, compared with pure Pt (Fig. 7d). As a result, the E_1/2 of (AlCoFeMoCr)₃O₄/Pt is positively shifted by 30 mV compared with Pt/C, while maintaining robust stability over 15 000 CV cycles. In addition to the four-electron oxygen reduction process, HEO also exhibits fascinating catalytic potential in two-electron oxygen reduction to produce H₂O₂ based on the above-mentioned structural properties. For example, Kang et al. synthesized a high entropy perovskite oxide ceramic Pb(NiWMnNbZrTi)_1/6O₃ as an effective two-electron ORR electrocatalyst with selectivity beyond 91% for the production of H₂O₂.⁸⁵ The intensity of metal–O bonds at the B-site of Pb(NiWMnNbZrTi)_1/6O₃ increased gradually with rising applied potential, as displayed by in situ Raman spectroscopy, facilitating the 2e⁻ ORR to produce H₂O₂ (Fig. 7e). Transient photovoltage measurement (TPV) and continuous wavelet transformation (CWT) proved that electron accumulation and sluggish electron transfer induced by high entropy on the Pb(NiWMnNbZrTi)_1/6O₃ surface compared with Pb(ZrTi)_1/2O₃ (Fig. 7f and g), suppressed the 4e⁻ ORR process (faster electron transfer needed) and facilitated the 2e⁻ ORR process. Another example was demonstrated by Zhao et al.⁸⁹ They prepared a high-entropy spinel oxide, (Fe_0.2Zn_0.2Co_0.2Ni_0.2Cu_0.2)Fe₂O₄, achieving high selectivity of up to 85.78% and comparable durability during the i–t test for the 2e⁻ ORR. The enhanced catalytic performance was attributed to more oxygen vacancies in (Fe_0.2Zn_0.2Co_0.2Ni_0.2Cu_0.2)Fe₂O₄, boosting adsorption and desorption behavior of oxygen species and mass transportation. Despite these advances, fundamental challenges remain in understanding the dynamic structural evolution of HEOs during catalytic processes. The complex composition of HEOs introduces variables such as bond reconfiguration, vacancy distribution, cation valence states, and coordination environments of active metal sites, all of which can dynamically change under reaction conditions.

Fig. 6 (a) High-resolution XPS spectrum of O 1s. (b) Relative valence state portion of each transition metal and e_g occupancy. (c) The illustration of the catalytic process. Reproduced from ref. 78. Copyright 2022 Wiley-VCH GmbH. (d) Schematic description of the formation of FCC alloy and spinel HEO. (e) The PDOS of Co and Ru. Reproduced from ref. 79. Copyright 2019 Nature. (f) In situ Raman spectra of CMNFCO at different potentials for the ORR. Reproduced from ref. 80. Copyright 2023 Elsevier.

Fig. 7 (a) Partial XRD pattern of LSMFCNC and LSNO. (b) Lattice schematic of LSMCFNC and LSNO. (c) Schematic diagram of interface reaction for LSMNFCNC and LSNO. Reproduced from ref. 83. Copyright 2024 Elsevier. (d) Atomic structure of the composite model and corresponding free energy diagrams for the ORR processes on Pt and the prepared composite. Reproduced from ref. 84. Copyright 2021 Royal Society of Chemistry. (e) In situ Raman spectra of Pb(NiWMnNbZrTi)_1/6O₃ at different potentials for 2e⁻ pathways of the ORR. (f) TPV curves of Pb(NiWMnNbZrTi)_1/6O₃ and Pb(ZrTi)_1/2O₃. (g) CWT curves of Pb(NiWMnNbZrTi)_1/6O₃ and Pb(ZrTi)_1/2O₃. Reproduced from ref. 85. Copyright 2022 WILEY-VCH GmbH.

6. Other novel high-entropy systems

Building on the pioneering work since 2004, the field of high-entropy materials has rapidly evolved, extending beyond metallic systems to encompass diverse classes such as high-entropy phosphides (HEPs),⁹³ fluorides (HEFs),⁹⁴ carbides (HECs),⁹⁵ metal–organic frameworks (HE-MOFs),⁹⁶ and sulfides (HESs).⁹⁷ While HEAs, HEOs, and HEIs have been extensively studied, emerging systems like HEFs (first reported in 2017) and HEPs (synthesized in 2020) remain relatively underexplored, particularly in ORR electrocatalysis.^17,46 These novel systems, however, offer distinctive advantages for ORR applications. By replacing oxygen with anions such as phosphorus, fluorine, or sulfur, these systems enable enhanced orbital extension and improved energetic alignment with the d-orbitals of transition metals, creating opportunities for precise electronic structure tuning.⁹⁸ Furthermore, the incorporation of high-entropy effects into framework structures like MOFs enables hierarchical porosity and tunable active sites that can significantly enhance mass transport and catalytic performance.⁹⁹ He et al. synthesized FeCoNiPdWP HEP nanoparticles using a low-temperature colloidal method and evaluated their performance as bifunctional electrocatalysts for both the OER and ORR.⁹⁰ The FeCoNiPdWP catalyst demonstrated excellent ORR activity with a half-wave potential comparable to that of Pd/C but with significantly higher current density when normalized to Pd content, positioning it among state-of-the-art ORR catalysts (Fig. 8a).^100–114 Critically, FeCoNiPdWP exhibited outstanding stability, maintaining performance after 5000 cycles with minimal degradation. The enhanced catalytic activity can be attributed to the strong d–d orbital coupling between different elements, particularly Pd, Co, and W, which optimizes electron transfer pathways during the ORR (Fig. 8b–d). Additionally, electronic modulation of Pd by neighbouring metal elements effectively reduces the energy barrier for oxygen reduction, demonstrating how compositional complexity in HEPs can be leveraged to tune catalytic performance at the electronic level. In the field of HEFs, researchers developed a fluorine-incorporated high-valent high-entropy layered double hydroxide, FeCoNi₂F₄(OH)₄, through multi-ion co-precipitation.⁹¹ In this structure, fluoride ions are firmly embedded within individual hydroxide layers, stabilized by the entropy effect (Fig. 8e). The resulting stable octahedral M–O(F)₆ coordination geometry (where M represents Fe, Co, and Ni ions) effectively suppresses structural reorganization during catalytic reaction, endowing the material with exceptional long-term stability. This approach illustrates how anion engineering in high-entropy systems can be strategically employed to enhance the durability of ORR catalysts by tailoring local coordination environments.

Fig. 8 (a) Half-wave potential comparison between metal phosphides and Pd/C catalysts prepared in this work and recently reported state-of-the-art ORR catalysts. (b–d) DFT calculations for ORR. Site-dependent PDOSs of Pd, Co, and W sites in the FeCoNiPdWPOH, a reconstructed HEP considering the experimental results. Reproduced from ref. 90. Copyright 2024 Royal Society of Chemistry. (e) XPS peaks of FiCoNi₂F₄(OH)₄ and electrochemically evolved FiCoNi₂F₄(OH)₄ (evo-FiCoNi₂F₄(OH)₄). Reproduced from ref. 91. Copyright 2018 WILEY-VCH GmbH. (f) SEM image and (g) TEM image of MOF-74. Reproduced from ref. 92. Copyright 2024 American Chemical Society.

Metal–organic frameworks (MOFs) represent another promising platform for incorporating high-entropy effects into ORR catalysts. By judiciously selecting metal nodes and organic linkers, MOFs inherently align with the compositional complexity of high-entropy materials.^13,115,116 A breakthrough by Hu et al. demonstrated the first template-free synthesis of hollow porous high-entropy MOF-74 structures via a single-step hydrothermal method, incorporating six distinct transition metals.⁹² The optimized nanocomposite, comprising metal particles (Mn, Fe, Co, and Cu), metal alloys (Cu_0.18Ni_0.91 and Co_0.52Cu_0.48), and metal oxides (ZnO), exhibited superior ORR performance attributed to the synergistic effect of the multi-component high-entropy feature and the hollow nanostructure (Fig. 8f and g). The hierarchical arrangement of active sites, combined with the high surface area and porous architecture, facilitated efficient mass transport and maximized catalytic site accessibility. Similarly, Liang et al. developed an MOF-assisted thermal reduction method to synthesize single-phase PtPdNiCoV, PtPdNiCoZn, and PtPdNiCoFe nanoparticles.¹¹⁷ These catalysts featured surface-enriched metal–nitrogen bonds and undercoordinated metal sites, which synergistically modulated electronic structures to enhance ORR activity. Remarkably, they exhibited approximately an order of magnitude higher mass activity compared to commercial Pt/C for the ORR, while maintaining exceptional stability with less than 20% activity loss after 50 000 cycles. These advancements highlight the expanding versatility of high-entropy design beyond conventional alloys and oxides. By incorporating diverse anions (P, F, and S) or hybridizing with MOFs, researchers are unlocking new frontiers in ORR catalyst engineering, precisely tuning electronic properties, stabilizing coordination environments, and mitigating degradation mechanisms.

Beyond the aforementioned high entropy sulfides/fluorides/carbides, a range of innovative systems have recently emerged, offering transformative potential in electrocatalysis for the oxygen reduction reaction. Among them, high-entropy single-atom (HESA) catalysts represent a significant advancement. These materials build upon the conceptual foundation of high-entropy materials by incorporating multiple single metal atoms into a particular support matrix, thereby combining the high configurational entropy of multi-metal systems with the maximal atomic dispersion of single-atom catalysts.¹¹⁸ A pioneering contribution in this domain was made by Tang and colleagues, who developed a HESA catalyst comprising five different metal atoms—Fe, Mn, Co, Ni, and Cu—each atomically dispersed within a nitrogen-doped carbon framework.¹¹⁹ The design rationale centers around the deliberate reduction of local symmetry within the graphitic carbon matrix, thereby modulating the π-electron distribution to optimize the adsorption energies of critical intermediates involved in the ORR. As shown schematically (Fig. 9a), the pristine graphitic carbon structure, characterized by high D_6h symmetry, undergoes progressive symmetry breaking upon nitrogen doping (yielding D_3h in C₃N₄), and further symmetry reduction to D_2h with single-metal incorporation. The introduction of dual-metal sites drives the system to an even lower C_2v symmetry. Intriguingly, a direct correlation has been observed between decreased structural symmetry and enhanced catalytic activity, suggesting that maximizing entropy while minimizing symmetry could synergistically enhance ORR/OER performance. The HESA catalyst was synthesized via a solid-state pyrolysis route (Fig. 9b), yielding a carbon-based composite with a broad shoulder peak between 2θ of 20°–30°, indicative of amorphous carbon, and no detectable crystalline phases associated with metal or metal oxides. X-ray absorption spectroscopy (XAS) confirmed the atomic dispersion of the metal species, revealing distinct coordination environments for Mn, Fe, Co, Ni, and Cu, each coordinated via M–N₄ motifs within the carbon matrix. When tested as an ORR electrocatalyst, the HESA catalyst displayed a remarkable onset potential of 0.96 V and a half-wave potential (E_1/2) of 0.87 V, surpassing those of benchmark 20% Pt/C (E_1/2 ≈ 0.84 V) and Fe-based single-atom catalysts (E_1/2 = 0.84 V). The catalyst also exhibited exceptional durability, with only a 5 mV drop in E_1/2 after 1000 cycles—three times more stable than commercial Pt/C catalysts, which degraded by 17 mV under identical conditions. To further validate practical utility, the HESA catalyst was integrated into a gas diffusion electrode and employed as a cathode in zinc–air batteries. The device achieved a specific capacity of 779 mA h g⁻¹_Zn, outperforming the Pt/C-based system (765 mA h g⁻¹_Zn). Moreover, the peak power density reached an impressive 207 mW cm⁻², underscoring the outstanding ORR catalytic efficiency and potential for real-world energy applications. In parallel with metal-based HESA catalysts, attention has also been directed toward nonmetal-doped high-entropy carbon frameworks, which may further expand the design space of entropy-driven catalysis. Inspired by the atomic-level “cocktail effect” and electronic synergy, Wen and co-authors proposed a high-entropy nanocarbon (HENC) system incorporating five nonmetallic dopants—B, N, F, P, and S—each introduced in equimolar ratios via an in situ polymerization approach (Fig. 9e).¹²⁰ This strategy represents an evolution beyond traditional single- or dual-doped carbon materials such as N-doped or B, N co-doped carbon, long studied for their electrocatalytic behavior. However, the incorporation of multiple heteroatoms opens new possibilities for tuning active sites, potentially disrupting well-known scaling relationships that limit catalytic performance across multiple reactions including the ORR, OER, HER, HOR, and CO₂ reduction.

Fig. 9 (a and b) Design rationale of high entropy single atom catalysts and their structure in a carbon matrix. (c) Polarization and power density curves of Fe-SA, HESA and Pt/C (20%). Reproduced from ref. 119. Copyright 2023 Springer Nature. (d) illustration of high-entropy carbon materials with the incorporation of multi nonmetal elements. (e and f) B, F, P, S, and N co-doped high entropy carbon materials and the polarization curve and power density. Reproduced from ref. 120. Copyright 2025 John Wiley and Sons.

The effects of each dopant were systematically elucidated. Nitrogen doping enhances the intrinsic activity of the carbon matrix by modifying the electronic density of states. Boron and fluorine increase the surface polarity and adsorption strength, while phosphorus induces structural distortion and layer exfoliation, enriching site diversity and contributing to configurational entropy. Sulfur modulates the electron cloud distribution, further diversifying the active sites. The synergy among these elements creates a heterogeneous electronic landscape that promotes facile oxygen activation and electron transfer. Electrochemical testing revealed that the HENC material exhibited a half-wave potential of 0.851 V for the ORR, with remarkable stability sustained over 15 000 cycles—far exceeding that of Pt/C under the same conditions. When employed in a zinc–air battery, the HENC-based device achieved a peak power density of 604 mW cm⁻², surpassing the 542 mW cm⁻² achieved by its Pt/C counterpart (Fig. 9f), clearly establishing the competitive advantage of high-entropy carbon frameworks in energy device integration. Together, these findings reinforce the transformative potential of high-entropy approaches—whether through metal or nonmetal integration at the atomic level—in advancing the next generation of ORR electrocatalysts. The strategic combination of multiple elements not only enables a rich diversity of active sites and enhanced adsorption behaviours but also opens the door to rational catalyst design governed by symmetry, electronic structure, and entropy principles. The detailed high entropy material systems and their applications in ORR are compared in Table 2.

Table 2 State-of-the-art high entropy materials for the electrocatalytic oxygen reduction reaction

Materials	Category and structure	Synthetic route	E 1/2 (V)	Stability	Overall performances
FeNiCuCoPt HEA on carbon	HEAs, face-centered cubic (fcc)	Theory-guided high throughput screening and pulse high-temperature preparation	0.892	∼10 mV decay after 10 000 cycles of LSV	A mass activity of 1.32 A mgPt^−1 at 0.85 V in 0.1 M HClO4 system^134
PtFeCoNiMn on mesoporous carbon	HEAs, fcc	Shock-heating and shock-cooling preparation	0.88	Negligible potential decay after 30 000 cycles	An ECSA of 39.49 m^2 g^−1. A mass and specific activities of 1.12 A mgPt^−1 and 2.84 mA cmPt^−2 in 0.1 M HClO4 system^135
PdCuMoNiCo HEA on hybrid carbon	HEAs, fcc	One-pot solvothermal method	0.86	∼84.7% of the current density retention after 20 h stability test	An ECSA of 142 m^2 gPd^−1 and a mass activity of 0.882 A mgPd^−1 at 0.8 V in 0.1 M HClO4 system^136
AlCuNiPtMn HEA on carbon	HEAs, fcc	Melt-spinning method	0.945	Slightly increased diffusion limited current after 30 000 CV cycles	A mass activity of 3.466 A mgPt^−1 at 0.9V^137
(Hf, Zr, La, V, Ce, Ti, Nd, Gd, Y, and Pd)O2−x HEO nanoparticles on carbon	HEOs, single fluorite phase	Electrical Joule heating (short thermal treatment)	0.85	∼86% of the current density retention after 100 h operation	A mass activity of 0.49 A mgPd^−1 at 0.85 V^138
(AlCoFeCrMo)3O4/Pt HEO composite	HEOs, spinel structure	Alloying and chemically de-alloy treatment	∼0.88	Negligible activity decay after 15 000 cycles	A mass activity of 0.81 A mgPt^−1 in 0.1 M KOH system^84
S-doped La0.8Sr0.2(CrMnFeCoNi)O3 HEOs	HEOs, single phase	Simple co-precipitation followed by thermal annealing	0.73	Not available	A lower peroxide yield (8%) and electrons transferred of 3.87 in 0.1 M KOH system^139
(Hf, Zr, La, V, Ce, Ti, Nd, Gd, Y, Pd)O2−x HEO	HEOs, single fluorite phase	Far-equilibrium synthesis enabled by electrical Joule heating	0.85	∼86% of the initial current density after 100 h of operation	A mass activity of 0.49 A mgPd^−1 at 0.85 V^140
PtFeCoNiCuZn HEI nanoparticles	HEIs, face-centered tetragonal (fct) ordered intermetallic	High temperature annealing and H2 reaction treatment	0.906	∼94.1% of the initial mass activity after 10 000 cycles	A mass activity of 2.403 A mgPt^−1 at 0.90 V. A power density of 1.4 W cm^−2 and a high mass normalized rated power of 45 W mgPt^−1^67
PtZnFeCoNiCr HEI on carbon	HEIs, face-centered tetragonal (fct) ordered intermetallic	High temperature annealing and H2 reaction treatment	0.948	A 7 mV decay in half wave potential after 20 000 cycles	A mass activity of 4.12 A mgPt^−1 at 0.90 V. A peak power density of 1.9 W cm^−2 and a mass activity of up to 3.0 A mgPt^−1 at 0.9 V^141
Pt(FeCoNiCuZn)3 HEI nanoparticles on carbon	HEIs, fcc	Impregnation followed by high temperature annealing and H2 reaction treatment	0.922	A negligible decay of the half-wave potential after 30 000 cycles	A mass and specific activity of 0.70 A mgPt^−1 and 1.34 mA cmPt^−2 at 0.90 V in 0.1 M HClO4 system^70
Fe, Mn, Co, Ni and Cu HE single atom catalyst	HE single atom, amorphous	High temperature pyrolysis	0.87	5 mV negative shift after 1000 cycles	A maximum power density of 207 mW cm^−2 and stable operation at 2 mA cm^−2 for 200 h in a Zn–air battery^119
B, F, P, S, and N co-doped high entropy engineered nanocarbon	HE carbon, amorphous	High temperature pyrolysis	0.851	11 mV negative shift after 15 000 cycles	A maximum power density of 604 mW cm^−2 and stable operation at 20 mA cm^−2 for 400 h in a Zn–air battery^120
FeCoNiPdWP HE phosphide	HE phosphide, Pd15P2 rhombohedral phase	Heating-up colloidal synthesis method	0.81	Slight degradation in current density after 5000 cycles	A maximum power density of 123 mW cm^−2 and stable operation at 8 mA cm^−2 for over 700 h in a Zn–air battery^90

---

7. Conclusions and outlooks

This review has systematically summarized recent advances in the design and application of high-entropy materials (HEMs) for the oxygen reduction reaction (ORR), with a particular focus on subclass-specific strategies encompassing high-entropy alloys (HEAs), oxides (HEOs), intermetallics (HEIs), carbon, and other novel systems. The inherent advantages of HEMs—including high configurational entropy, lattice distortion, sluggish diffusion, and the cocktail effect—offer a powerful framework for engineering multifunctional electrocatalysts with enhanced activity, selectivity, and long-term durability. Despite the remarkable catalytic performance achieved to date, several critical challenges remain that must be addressed to unlock the full potential of HEMs in both fundamental research and large-scale energy conversion technologies.

Synthetic control and scalability

Current synthesis approaches, such as mechanical alloying,¹²¹ pyrolysis,¹²² sol–gel processes,¹²³ and electrodeposition,¹²⁴ not only offer unique benefits but also present substantial limitations. These include non-uniform surface coverage in electrodeposited films, contamination from mechanical alloying, and low carrier efficiency in high-throughput pyrolysis systems. Achieving precise compositional control, nanoscale homogeneity, and morphology-specific architectures—particularly those with large electrochemically active surface areas—is critical for enhancing catalytic performance.¹¹ Future efforts should prioritize scalable and environmentally benign synthesis methods capable of producing HEMs with high yield and tunable structures under mild conditions. Techniques enabling rapid prototyping of compositionally diverse materials libraries will also be key to accelerating development.

Advanced characterization

Despite considerable progress, the multiscale structural complexity of HEMs remains a significant obstacle to fully understanding their catalytic behavior.¹⁰ Conventional techniques often fall short in resolving local strain distributions, electronic heterogeneity, and surface coordination environments.¹⁷ The development of in situ and operando spectroscopy/microscopy tools, such as ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), time- and space-based scanning transmission electron microscopy (STEM), and synchrotron-based X-ray absorption spectroscopy (XAS), is essential for capturing real-time structural and electronic transformations during the ORR.⁹⁸ When integrated with computational simulations, these tools can reveal key structure–function relationships and guide rational catalyst design.

Entropic contributions: an underexplored dimension

While entropy is increasingly acknowledged as a pivotal factor in stabilizing multi-component systems, most HEM studies remain centered on enthalpic considerations—such as metal–support interactions or charge-transfer mechanisms within binary or ternary oxide frameworks.¹⁷ In contrast, configurational entropy, stemming from the statistical mixing of multiple elements, has received comparatively limited attention. This oversight largely stems from the difficulty of independently tuning entropy while decoupling it from other structural variables. However, a deeper exploration of entropy-driven effects—such as disorder-induced electronic delocalization, surface reconstruction, or enhanced defect tolerance—may reveal novel catalytic pathways inaccessible via enthalpic control alone.^11,16 Future directions should aim to establish quantitative metrics for entropy contributions, enabling the deliberate modulation of configurational entropy as a design parameter in catalyst optimization.

Accelerated discovery via high-throughput and machine learning frameworks

The enormous compositional space inherent to HEMs makes traditional trial-and-error exploration inefficient and often impractical.¹⁸ High-throughput experimental platforms, incorporating automated synthesis, rapid screening, and AI-assisted data analysis, have emerged as a transformative approach for mapping composition–property landscapes.¹²⁵ These platforms enable the systematic generation of large, high-quality datasets that can be mined for performance trends, outlier behavior, and compositionally sensitive descriptors.¹²⁶ On the theoretical front, although density functional theory (DFT) remains indispensable for predicting reaction energetics and electronic structures, its application to multicomponent HEMs is computationally prohibitive due to their configurational complexity.^127–131 Machine learning (ML) offers a scalable alternative: ML models trained on DFT-derived data sets can capture structure–property relationships across high-dimensional composition spaces, enabling rapid screening of candidate materials. The integration of active learning strategies—which iteratively refine model predictions through feedback loops between simulations and experiments—will further enhance predictive power and accelerate discovery.^132,133

In summary, high-entropy materials represent a paradigm shift in electrocatalyst design, where compositional disorder and structural complexity are leveraged as assets rather than liabilities. To fully harness their potential in the ORR and broader energy conversion applications, concerted efforts are needed to (i) refine synthesis protocols for precise control and scalability; (ii) advance characterization methods to provide real-time insight into dynamic catalytic phenomena; (iii) deepen our understanding of entropic stabilization mechanisms; and (iv) establish AI-guided design pipelines for accelerated discovery. Through interdisciplinary collaboration across materials chemistry, physics, and data science, HEMs are poised to become cornerstone materials in the next generation of sustainable energy technologies.

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.

Author contributions

Z. H. Liang and Y. Y. Yang contributed to the conceptualization and drafting of the manuscript. Y. P. Chen and P. Li provided supervision, critical feedback, and revisions. All authors contributed to the review and editing of the final manuscript and approved its submission.

Conflicts of interest

The authors declare that they have no conflict of interest.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant No. 52401260). The authors would like to acknowledge the financial support from the Australian Research Council (ARC) through a DECRA Fellowship (DE230101068).

References

1. J. M. Andújar and F. Segura, Renewable Sustainable Energy Rev., 2009, 13, 2309.
2. Z. Yang, H. Nie, X. Chen, X. Chen and S. Huang, J. Power Sources, 2013, 236, 238.
3. P. P. Edwards, V. L. Kuznetsov, W. I. F. David and N. P. Brandon, Energy Policy, 2008, 36, 4356.
4. L. Fan, Z. Tu and S. H. Chan, Energy Rep., 2021, 7, 8421.
5. X. Ge, A. Sumboja, D. Wuu, T. An, B. Li, F. W. T. Goh, T. S. A. Hor, Y. Zong and Z. Liu, ACS Catal., 2015, 5, 4643.
6. S. Yi, X. Song, Y. Shen, R. Xu, Y. Zhao and P. Chen, J. Alloys Compd., 2024, 1002, 175258.
7. Y. Zeng, X. Wang, Y. Hu, W. Qi, Z. Wang, M. Xiao, C. Liu, W. Xing and J. Zhu, Carbon Neutralization, 2024, 3(4), 710–736.
8. Q. Li, R. Cao, J. Cho and G. Wu, Adv. Energy Mater., 2014, 4, 1301415.
9. A. Kulkarni, S. Siahrostami, A. Patel and J. K. Nørskov, Chem. Rev., 2018, 118, 2302.
10. C. Deng, T. Wang, P. Wu, W. Zhu and S. Dai, Nano Energy, 2024, 120, 109153.
11. J. Wu, H. Wang, N. Liu, B. Jia and J. Zheng, Small, 2024, 20, 2403162.
12. Y. Li, Q. Li, H. Wang, L. Zhang, D. P. Wilkinson and J. Zhang, Electrochem. Energy Rev., 2019, 2, 518.
13. M. Cui, B. Xu, Y. Ding, W. Sun, H. Liu and S. Dou, Energy Environ. Sci., 2024, 17, 4010.
14. A. Sarkar, Q. Wang, A. Schiele, M. R. Chellali, S. S. Bhattacharya, D. Wang, T. Brezesinski, H. Hahn, L. Velasco and B. Breitung, Adv. Mater., 2019, 31, 1806236.
15. H. Wang, Q.-F. He and Y. Yang, Rare Met., 2022, 41, 1989.
16. S. Schweidler, M. Botros, F. Strauss, Q. Wang, Y. Ma, L. Velasco, G. Cadilha Marques, A. Sarkar, C. Kübel, H. Hahn, J. Aghassi-Hagmann, T. Brezesinski and B. Breitung, Nat. Rev. Mater., 2024, 9, 266.
17. Y. Sun and S. Dai, Sci. Adv., 2021, 7(20), eabg1600.
18. W. Huo, S. Wang, F. J. Dominguez-Gutierrez, K. Ren, Ł. Kurpaska, F. Fang, S. Papanikolaou, H. S. Kim and J. Jiang, Mater. Res. Lett., 2023, 11, 713.
19. J.-W. Yeh, S.-K. Chen, S.-J. Lin, J.-Y. Gan, T.-S. Chin, T.-T. Shun, C.-H. Tsau and S.-Y. Chang, Adv. Eng. Mater., 2004, 6, 299.
20. B. Cantor, I. T. H. Chang, P. Knight and A. J. B. Vincent, Mater. Sci. Eng., A, 2004, 375–377, 213.
21. E. P. George, D. Raabe and R. O. Ritchie, Nat. Rev. Mater., 2019, 4, 515.
22. W.-L. Hsu, C.-W. Tsai, A.-C. Yeh and J.-W. Yeh, Nat. Rev. Chem., 2024, 8, 471.
23. D. B. Miracle and O. N. Senkov, Acta Mater., 2017, 122, 448.
24. Y. Xin, S. Li, Y. Qian, W. Zhu, H. Yuan, P. Jiang, R. Guo and L. Wang, ACS Catal., 2020, 10, 11280.
25. Y. Wang and Y. Wang, Nano Energy, 2022, 104, 107958.
26. W. Chen, A. Hilhorst, G. Bokas, S. Gorsse, P. J. Jacques and G. Hautier, Nat. Commun., 2023, 14, 2856.
27. Q. Zhang, M. Song, G. Luo, T. Shen, H. Hu and D. Wang, Chem. Mater., 2024, 36, 10967.
28. C. Oses, C. Toher and S. Curtarolo, Nat. Rev. Mater., 2020, 5, 295.
29. W.-L. Hsu, C.-W. Tsai, A.-C. Yeh and J.-W. Yeh, Nat. Rev. Chem., 2024, 8, 471.
30. L. Sun, K. Wen, G. Li, X. Zhang, X. Zeng, B. Johannessen and S. Zhang, ACS Mater. Au, 2024, 4, 547.
31. R. K. Nutor, Q. Cao, X. Wang, D. Zhang, Y. Fang, Y. Zhang and J.-Z. Jiang, Adv. Eng. Mater., 2020, 22, 2000466.
32. X. Yan, Y. Zhou and S. Wang, Adv. Funct. Mater., 2025, 35, 2413115.
33. H. Song, F. Tian, Q.-M. Hu, L. Vitos, Y. Wang, J. Shen and N. Chen, Phys. Rev. Mater., 2017, 1, 023404.
34. B. Wang, Y. Yao, X. Yu, C. Wang, C. Wu and Z. Zou, J. Mater. Chem. A, 2021, 9, 19410.
35. L. Yan, P. Li, Q. Zhu, A. Kumar, K. Sun, S. Tian and X. Sun, Chem, 2023, 9, 280.
36. Y. Yang, P. Li, X. Zheng, W. Sun, S. X. Dou, T. Ma and H. Pan, Chem. Soc. Rev., 2022, 51, 9620.
37. Z. W. Seh, J. Kibsgaard, C. F. Dickens, I. Chorkendorff, J. K. Nørskov and T. F. Jaramillo, Science, 2017, 355, eaad4998.
38. Y. Chen, Y. Yang, Z. Liang, Z. Tao, Q. Ni and W. Sun, J. Mater. Chem. A, 2025, 13, 1659–1668.
39. X. Chen, C. Si, Y. Gao, J. Frenzel, J. Sun, G. Eggeler and Z. Zhang, J. Power Sources, 2015, 273, 324.
40. Z. Jin, J. Lyu, Y.-L. Zhao, H. Li, X. Lin, G. Xie, X. Liu, J.-J. Kai and H.-J. Qiu, ACS Mater. Lett., 2020, 2, 1698.
41. T. Löffler, H. Meyer, A. Savan, P. Wilde, A. Garzón Manjón, Y. Chen, E. Ventosa, C. Scheu, A. Ludwig and W. Schuhmann, Adv. Energy Mater., 2018, 8, 1802269.
42. G. Gao, Y. Yu, G. Zhu, B. Sun, R. He, A. Cabot and Z. Sun, J. Energy Chem., 2024, 99, 335.
43. E. J. Pickering and N. G. Jones, Int. Mater. Rev., 2016, 61, 183.
44. X. Han, G. Wu, S. Zhao, J. Guo, M. Yan, X. Hong and D. Wang, Matter, 2023, 6, 1717.
45. M. Liu, X. Xiao, Q. Li, L. Luo, M. Ding, B. Zhang, Y. Li, J. Zou and B. Jiang, J. Colloid Interface Sci., 2022, 607, 791.
46. J. S. Shaikh, M. Rittiruam, T. Saelee, V. Márquez, N. S. Shaikh, P. Khajondetchairit, S. Pathan, P. Kanjanaboos, T. Taniike, M. K. Nazeeruddin, P. Praserthdam and S. Praserthdam, Mater. Sci. Eng., R, 2024, 160, 100813.
47. H.-J. Qiu, G. Fang, Y. Wen, P. Liu, G. Xie, X. Liu and S. Sun, J. Mater. Chem. A, 2019, 7, 6499.
48. A. G. Manjón, T. Löffler, M. Meischein, H. Meyer, J. Lim, V. Strotkötter, W. Schuhmann, A. Ludwig and C. Scheu, Nanoscale, 2020, 12, 23570.
49. Y. Men, D. Wu, Y. Hu, L. Li, P. Li, S. Jia, J. Wang, G. Cheng, S. Chen and W. Luo, Angew. Chem., Int. Ed., 2023, 62, e202217976.
50. X. Wang, W. Guo and Y. Fu, J. Mater. Chem. A, 2021, 9, 663.
51. Z. Lu, Z. W. Chen and C. V. Singh, Matter, 2020, 3, 1318.
52. T. A. A. Batchelor, J. K. Pedersen, S. H. Winther, I. E. Castelli, K. W. Jacobsen and J. Rossmeisl, Joule, 2019, 3(3), 834–845.
53. Y. Yao, Z. Huang, T. Li, H. Wang, Y. Liu, H. S. Stein, Y. Mao, J. Gao, M. Jiao, Q. Dong, J. Dai, P. Xie, H. Xie, S. D. Lacey, I. Takeuchi, J. M. Gregoire, R. Jiang, C. Wang, A. D. Taylor, R. Shahbazian-Yassar and L. Hu, Proc. Natl. Acad. Sci. U. S. A., 2020, 117, 6316.
54. J. K. Pedersen, C. M. Clausen, O. A. Krysiak, B. Xiao, T. A. A. Batchelor, T. Löffler, V. A. Mints, L. Banko, M. Arenz, A. Savan, W. Schuhmann, A. Ludwig and J. Rossmeisl, Angew. Chem., Int. Ed., 2021, 133, 24346.
55. G. Zhu, Y. Jiang, H. Yang, H. Wang, Y. Fang, L. Wang, M. Xie, P. Qiu and W. Luo, Adv. Mater., 2022, 34, 2110128.
56. M.-H. Tsai, Entropy, 2016, 18, 252.
57. A. M. Russell, Adv. Eng. Mater., 2003, 5, 629.
58. M. Cui, C. Yang, S. Hwang, M. Yang, S. Overa, Q. Dong, Y. Yao, A. H. Brozena, D. A. Cullen, M. Chi, T. F. Blum, D. Morris, Z. Finfrock, X. Wang, P. Zhang, V. G. Goncharov, X. Guo, J. Luo, Y. Mo, F. Jiao and L. Hu, Sci. Adv., 2022, 8, eabm4322.
59. Y. Yao, Q. Dong, A. Brozena, J. Luo, J. Miao, M. Chi, C. Wang, I. G. Kevrekidis, Z. J. Ren, J. Greeley, G. Wang, A. Anapolsky and L. Hu, Science, 2022, 376, eabn3103.
60. Y. Nakaya and S. Furukawa, Chem. Rev., 2023, 123, 5859.
61. E. Zhu, M. Wu, H. Xu, B. Peng, Z. Liu, Y. Huang and Y. Li, Adv. Funct. Mater., 2022, 32, 2203883.
62. Y. Nakaya and S. Furukawa, Chem. Sci., 2024, 15, 12644.
63. Y. Wang, N. Gong, H. Liu, W. Ma, K. Hippalgaonkar, Z. Liu and Y. Huang, Adv. Mater., 2023, 35, 2302067.
64. J. Ma, F. Xing, Y. Nakaya, K. Shimizu and S. Furukawa, Angew. Chem., 2022, 134, e202200889.
65. C.-L. Yang, L.-N. Wang, P. Yin, J. Liu, M.-X. Chen, Q.-Q. Yan, Z.-S. Wang, S.-L. Xu, S.-Q. Chu, C. Cui, H. Ju, J. Zhu, Y. Lin, J. Shui and H.-W. Liang, Science, 2021, 374, 459.
66. J. Liu, C. Lee, Y. Hu, Z. Liang, R. Ji, X. Y. D. Soo, Q. Zhu and Q. Yan, SmartMat, 2023, 4, e1210.
67. T. Chen, C. Qiu, X. Zhang, H. Wang, J. Song, K. Zhang, T. Yang, Y. Zuo, Y. Yang, C. Gao, W. Xiao, Z. Jiang, Y. Wang, Y. Xiang and D. Xia, J. Am. Chem. Soc., 2024, 146, 1174.
68. G. Feng, F. Ning, Y. Pan, T. Chen, J. Song, Y. Wang, R. Zou, D. Su and D. Xia, J. Am. Chem. Soc., 2023, 145, 11140.
69. J. Li, S. Sharma, X. Liu, Y.-T. Pan, J. S. Spendelow, M. Chi, Y. Jia, P. Zhang, D. A. Cullen, Z. Xi, H. Lin, Z. Yin, B. Shen, M. Muzzio, C. Yu, Y. S. Kim, A. A. Peterson, K. L. More, H. Zhu and S. Sun, Joule, 2019, 3, 124.
70. Q. Zhang, T. Shen, M. Song, S. Wang, J. Zhang, X. Huang, S. Lu and D. Wang, J. Energy Chem., 2023, 86, 158.
71. J. Chen, W. Liu, J. Liu, X. Zhang, M. Yuan, Y. Zhao, J. Yan, M. Hou, J. Yan, M. Kunz, N. Tamura, H. Zhang and Z. Yin, J. Phys. Chem. C, 2019, 123, 17735.
72. M. Fu, X. Ma, K. Zhao, X. Li and D. Su, iScience, 2021, 24, 102177.
73. D. Wang, Z. Liu, S. Du, Y. Zhang, H. Li, Z. Xiao, W. Chen, R. Chen, Y. Wang, Y. Zou and S. Wang, J. Mater. Chem. A, 2019, 7, 24211.
74. K. Gu, D. Wang, C. Xie, T. Wang, G. Huang, Y. Liu, Y. Zou, L. Tao and S. Wang, Angew. Chem., Int. Ed., 2021, 60, 20253.
75. A. Chatterjee, D. Ganguly, R. Sundara and S. S. Bhattacharya, ChemistrySelect, 2024, 9, e202401836.
76. J. Zou, L. Tang, W. He and X. Zhang, ACS Nano, 2024, 18(51), 34492–34530.
77. Y. Pan, J.-X. Liu, T.-Z. Tu, W. Wang and G.-J. Zhang, Chem. Eng. J., 2023, 451, 138659.
78. W. Li, Z. Zhao, J. Zhao, Y. Wang and X. Wang, J. Mater. Sci. Technol., 2024, 194, 236.
79. Z. Jin, J. Lyu, K. Hu, Z. Chen, G. Xie, X. Liu, X. Lin and H. Qiu, Small, 2022, 18, 2107207.
80. Y. Du, F. Yang, J. Li, M. Jiang, J. Yin, Q. Hu and J. Zhang, J. Alloys Compd., 2024, 1004, 175923.
81. X. Wang, X. J. Gao, L. Qin, C. Wang, L. Song, Y.-N. Zhou, G. Zhu, W. Cao, S. Lin, L. Zhou, K. Wang, H. Zhang, Z. Jin, P. Wang, X. Gao and H. Wei, Nat. Commun., 2019, 10, 704.
82. G. M. Tomboc, X. Zhang, S. Choi, D. Kim, L. Y. S. Lee and K. Lee, Adv. Funct. Mater., 2022, 32, 2205142.
83. J. Gong and J. Hou, J. Mater. Sci. Technol., 2024, 186, 158.
84. Z. Jin, J. Lyu, Y.-L. Zhao, H. Li, Z. Chen, X. Lin, G. Xie, X. Liu, J.-J. Kai and H.-J. Qiu, Chem. Mater., 2021, 33, 1771.
85. Z. Chen, J. Wu, Z. Chen, H. Yang, K. Zou, X. Zhao, R. Liang, X. Dong, P. W. Menezes and Z. Kang, Angew. Chem., Int. Ed., 2022, 61, e202200086.
86. Z.-Y. Liu, Y. Liu, Y. Xu, H. Zhang, Z. Shao, Z. Wang and H. Chen, Green Energy Environ., 2023, 8, 1341.
87. N. Wang, C. Tang, L. Du, R. Zhu, L. Xing, Z. Song, B. Yuan, L. Zhao, Y. Aoki and S. Ye, Adv. Energy Mater., 2022, 12, 2201882.
88. B. L. Musicó, D. Gilbert, T. Z. Ward, K. Page, E. George, J. Yan, D. Mandrus and V. Keppens, APL Mater., 2020, 8, 040912.
89. L. Danyang, S. Liping, L. Qiang, X. Tian, H. Lihua and Z. Hui, J. Alloys Compd., 2022, 913, 165148.
90. R. He, S. Wang, L. Yang, S. Horta, Y. Ding, C. Di, X. Zhang, Y. Xu, M. Ibáñez, Y. Zhou, S. Mebs, H. Dau, J. N. Hausmann, W. Huo, P. W. Menezes and A. Cabot, Energy Environ. Sci., 2024, 17, 7193.
91. B. Li, J. Zhong, H. Wang, J. Gu, F. Lyu, S. Chen, H. Wu, L. Li, C. Zhi, J. Lu and Y. Y. Li, Angew. Chem., Int. Ed., 2024, 63, e202410978.
92. J. Hu, L. Cao, Z. Wang, J. Liu, J. Zhang, Y. Cao, Z. Lu and H. Cheng, Compos. Commun., 2021, 27, 100866.
93. D. Lai, Q. Kang, F. Gao and Q. Lu, J. Mater. Chem. A, 2021, 9, 17913.
94. X. Li, Z. Qiang, G. Han, S. Guan, Y. Zhao, S. Lou and Y. Zhu, Nano-Micro Lett., 2024, 16, 55.
95. C. M. Rost, T. Borman, M. D. Hossain, M. Lim, K. F. Quiambao-Tomko, J. A. Tomko, D. W. Brenner, J.-P. Maria and P. E. Hopkins, Acta Mater., 2020, 196, 231.
96. W. Tanmathusorachai, S. Aulia, M. Rinawati, L.-Y. Chang, C.-Y. Chang, W.-H. Huang, M.-H. Lin, W.-N. Su, B. Yuliarto and M.-H. Yeh, ACS Appl. Mater. Interfaces, 2024, 16, 62022.
97. R.-Z. Zhang, F. Gucci, H. Zhu, K. Chen and M. J. Reece, Inorg. Chem., 2018, 57, 13027.
98. L. Han, S. Zhu, Z. Rao, C. Scheu, D. Ponge, A. Ludwig, H. Zhang, O. Gutfleisch, H. Hahn, Z. Li and D. Raabe, Nat. Rev. Mater., 2024, 9, 846–865.
99. J. Xing, Y. Liu, G. Mathew, Q. He, J. Aghassi-Hagmann, S. Schweidler and B. Breitung, Adv. Sci., 2024, 2411175.
100. C.-X. Zhao, J.-N. Liu, J. Wang, C. Wang, X. Guo, X.-Y. Li, X. Chen, L. Song, B.-Q. Li and Q. Zhang, Sci. Adv., 2022, 8, eabn5091.
101. X. Yang, F. Wang, Z. Jing, M. Chen, B. Wang, L. Wang, G. Qu, Y. Kong and L. Xu, Small, 2023, 19, 2301985.
102. Y. Zhou, Y. Yu, D. Ma, A. C. Foucher, L. Xiong, J. Zhang, E. A. Stach, Q. Yue and Y. Kang, ACS Catal., 2021, 11, 74.
103. L. Yang, X. Zhang, L. Yu, J. Hou, Z. Zhou and R. Lv, Adv. Mater., 2022, 34, 2105410.
104. J.-T. Ren, Y.-S. Wang, L. Chen, L.-J. Gao, W.-W. Tian and Z.-Y. Yuan, Chem. Eng. J., 2020, 389, 124408.
105. Y. Lin, L. Yang, Y. Zhang, H. Jiang, Z. Xiao, C. Wu, G. Zhang, J. Jiang and L. Song, Adv. Energy Mater., 2018, 8, 1703623.
106. C. Yao, J. Li, Z. Zhang, C. Gou, Z. Zhang, G. Pan and J. Zhang, Small, 2022, 18, 2108094.
107. D. Ren, J. Ying, M. Xiao, Y. Deng, J. Ou, J. Zhu, G. Liu, Y. Pei, S. Li, A. M. Jauhar, H. Jin, S. Wang, D. Su, A. Yu and Z. Chen, Adv. Funct. Mater., 2020, 30, 1908167.
108. Q. Shi, Q. Liu, Y. Ma, Z. Fang, Z. Liang, G. Shao, B. Tang, W. Yang, L. Qin and X. Fang, Adv. Energy Mater., 2020, 10, 1903854.
109. H. Liu, J. Guan, S. Yang, Y. Yu, R. Shao, Z. Zhang, M. Dou, F. Wang and Q. Xu, Adv. Mater., 2020, 32, 2003649.
110. T. Wang, X. Cao, H. Qin, L. Shang, S. Zheng, F. Fang and L. Jiao, Angew. Chem., 2021, 133, 21407.
111. Y. Li, Y. Liu, Q. Qian, G. Wang and G. Zhang, Energy Storage Mater., 2020, 28, 27.
112. R. He, T. Lu, N. Xu, G. Liu, Y. Zhang and J. Qiao, Chem. Eng. J., 2023, 461, 141843.
113. Q. P. Ngo, T. T. Nguyen, M. Singh, R. Balaji, N. H. Kim and J. H. Lee, Appl. Catal., B, 2023, 331, 122674.
114. J. Li, Y. Kang, Z. Lei and P. Liu, Appl. Catal., B, 2023, 321, 122029.
115. Y. Xu, Q. Li, H. Xue and H. Pang, Coord. Chem. Rev., 2018, 376, 292.
116. L. S. Xie, G. Skorupskii and M. Dincă, Chem. Rev., 2020, 120, 8536.
117. J. Liang, Y. Ma, Y. Li, W. Zhang, H. Hu, J. Su, Z. Yao, W. Gao, W. Shang, T. Deng and J. Wu, Nano Lett., 2024, 24, 7293.
118. A. B. Haruna, E. U. Onoh and K. I. Ozoemena, Curr. Opin. Electrochem., 2023, 39, 101264.
119. X. Lei, Q. Tang, Y. Zheng, P. Kidkhunthod, X. Zhou, B. Ji and Y. Tang, Nat. Sustain., 2023, 6, 816.
120. H. Wang, K. Chen, Z. Lu, S. Lin, Y. Yuan, X. Liu, Y. Zhang, J. Chen and Z. Wen, Angew. Chem., Int. Ed., 2025, e202501290.
121. W. Xu, H. Chen, K. Jie, Z. Yang, T. Li and S. Dai, Angew. Chem., Int. Ed., 2019, 58, 5018.
122. H. Xu, Z. Zhang, J. Liu, C.-L. Do-Thanh, H. Chen, S. Xu, Q. Lin, Y. Jiao, J. Wang, Y. Wang, Y. Chen and S. Dai, Nat. Commun., 2020, 11, 3908.
123. B. Niu, F. Zhang, H. Ping, N. Li, J. Zhou, L. Lei, J. Xie, J. Zhang, W. Wang and Z. Fu, Sci. Rep., 2017, 7, 3421.
124. C. C. L. McCrory, S. Jung, I. M. Ferrer, S. M. Chatman, J. C. Peters and T. F. Jaramillo, J. Am. Chem. Soc., 2015, 137, 4347.
125. N. Vervoort, K. Goossens, M. Baeten and Q. Chen, Anal. Sci. Adv., 2021, 2, 109.
126. Y. Ma, Y. Ma, Q. Wang, S. Schweidler, M. Botros, T. Fu, H. Hahn, T. Brezesinski and B. Breitung, Energy Environ. Sci., 2021, 14, 2883.
127. V. Sorkin, S. Chen, T. L. Tan, Z. G. Yu, M. Man and Y. W. Zhang, J. Alloys Compd., 2021, 882, 160776.
128. E.-W. Huang, W.-J. Lee, S. S. Singh, P. Kumar, C.-Y. Lee, T.-N. Lam, H.-H. Chin, B.-H. Lin and P. K. Liaw, Mater. Sci. Eng., R, 2022, 147, 100645.
129. Y. Yao, Z. Li, Y. Dou, T. Jiang, J. Zou, S. Y. Lim, P. Norby, E. Stamate, J. O. Jensen and W. Zhang, Dalton Trans., 2023, 52, 4142.
130. R. Zerdoumi, A. Ludwig and W. Schuhmann, Curr. Opin. Electrochem., 2024, 48, 101590.
131. M. C. Gao, D. B. Miracle, D. Maurice, X. Yan, Y. Zhang and J. A. Hawk, J. Mater. Res., 2018, 33, 3138.
132. Q. Wang, L. Velasco, B. Breitung and V. Presser, Adv. Energy Mater., 2021, 11, 2102355.
133. Y. Wang, J. Mi and Z.-S. Wu, Chem Catal., 2022, 2, 1624.
134. Y. Pan, X. Shan, F. Cai, H. Gao, J. Xu and M. Zhou, Angew. Chem., Int. Ed., 2024, 63, e202407116.
135. K. Wang, R. Chen, H. Yang, Y. Chen, H. Jia, Y. He, S. Song and Y. Wang, Adv. Funct. Mater., 2024, 34, 2310683.
136. X. Zuo, R. Yan, L. Zhao, Y. Long, L. Shi, Q. Cheng, D. Liu and C. Hu, J. Mater. Chem. A, 2022, 10, 14857.
137. S. Li, X. Tang, H. Jia, H. Li, G. Xie, X. Liu, X. Lin and H.-J. Qiu, J. Catal., 2020, 383, 164.
138. J. Liao, W. Ding, S. Tao, Y. Nie, W. Li, G. Wu, S. Chen, L. Li and Z. Wei, Chin. J. Catal., 2016, 37, 1142.
139. T. X. Nguyen, C.-H. Lee, J.-H. Sun, C.-K. Peng, W.-H. Chu, H. Pourzolfaghar, Y.-R. Lin, M. Ghufron, Y.-Y. Li, Y.-H. Chang, Y.-G. Lin, H.-Y. T. Chen, S.-W. Tseng, C.-Y. Su and J.-M. Ting, Chem. Eng. J., 2025, 511, 161731.
140. T. Li, Y. Yao, B. H. Ko, Z. Huang, Q. Dong, J. Gao, W. Chen, J. Li, S. Li, X. Wang, R. Shahbazian-Yassar, F. Jiao and L. Hu, Adv. Funct. Mater., 2021, 31, 2010561.
141. T. Chen, X. Zhang, H. Wang, C. Yuan, Y. Zuo, C. Gao, W. Xiao, Y. Yu, J. Cai, T. Luo, Y. Xiang and D. Xia, Nat. Commun., 2025, 16, 3308.

---