Single plasmonic nanostructures for biomedical diagnosis

Xingyi Ma and Sang Jun Sim *
Department of Chemical and Biological Engineering, Korea University, Seoul 02841, Korea. E-mail:

Received 9th February 2020 , Accepted 4th May 2020

First published on 20th May 2020

The field of single nanoparticle plasmonics has grown enormously. There is no doubt that a wide diversity of the nanoplasmonic techniques and nanostructures represents a tremendous opportunity for fundamental biomedical studies as well as sensing and imaging applications. Single nanoparticle plasmonic biosensors are efficient in label-free single-molecule detection, as well as in monitoring real-time binding events of even several biomolecules. In the present review, we have discussed the prominent advantages and advances in single particle characterization and synthesis as well as new insight into and information on biomedical diagnosis uniquely obtained using single particle approaches. The approaches include the fundamental studies of nanoplasmonic behavior, two typical methods based on refractive index change and characteristic light intensity change, exciting innovations of synthetic strategies for new plasmonic nanostructures, and practical applications using single particle sensing, imaging, and tracking. The basic sphere and rod nanostructures are the focus of extensive investigations in biomedicine, while they can be programmed into algorithmic assemblies for novel plasmonic diagnosis. Design of single nanoparticles for the detection of single biomolecules will have far-reaching consequences in biomedical diagnosis.

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Xingyi Ma

Dr Xingyi Ma graduated with Outstanding Honour from Harbin Institute of Technology (HIT) in Biological Technology and was selected by CSC of China in 2009 to study in Korea. He then completed MEng and PhD degrees in Chemical & Biological Engineering at Sungkyunkwan University (SKKU) and Korea University in 2011 and 2015, respectively. He was promoted as a Research Professor at the Institute of Convergence Chemical Engineering Systems of Korea University to develop techniques in plasmonic nanoparticle-based precision biomedicine, in which field he has been a Taishan Scholar of Industrial Leading Talent. His research focuses on synthesis with design of novel nanostructures and systems for LSPR sensing and imaging, and fundamental investigations into biomolecular interactions for drug discovery, drug delivery and biomedical diagnostics.

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Sang Jun Sim

Dr Sang Jun Sim received his PhD in Chemical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 1994 and conducted his postdoctoral research at Massachusetts Institute of Technology (MIT) from 1994 to 1996. He was appointed as an associated professor of Chemical Engineering in Sungkyunkwan University in 2002, and invited as a tenured professor of Chemical and Biological Engineering in Korea University in 2011. His research interest is on photosynthetic CO2 conversion using microalgae, bio-chip development and nanoplasmonic sensors for disease diagnosis. He authored over 200 papers, with a H-index of 49 and citation >8500 (recorded by Google scholar).

1. Introduction

The emerging field of nanoplasmonics has vastly impacted the development of biomedical diagnostic tools.1–4 Bio-sensing and imaging based on surface plasmon resonance (SPR) with thin gold films have been successfully commercialized in biomedical and pharmaceutical research and development (R&D).5 Surface plasmon resonance involves the interaction of light with gold. When light interacts with certain metals, such as gold (Au) and silver (Ag), a collective oscillation of conduction band electrons occurs at the metal–dielectric interface. These oscillations provide an elegant way of coupling and confining electromagnetic radiation.6,7 When the frequencies of the incident light and the free electrons in the metals are matched, the SPR extinction occurs to the maximum, leading to unique scattering and absorption spectra,8 as well as excellent catalytic abilities.9,10

In optics, the plasmonic resonance properties and extinction spectra of plasmonic nanoparticles (NPs) are affected by their morphologies and the physico-chemical environment.11 The extinction cross section (σext) of spherical gold nanoparticles (AuNPs) with radius R embedded in a medium with dielectric function (εm) at a wavelength λ can be calculated by Mie theory.12,13 Mie theory describes the extinction spectra of a given NP based on an analytical solution to Maxwell's equations with spherical boundary conditions. It describes a relationship for the extinction cross section (σext = σabs + σsca), that is, the summation of the absorption cross section and the scattering cross section of the metal NPs. When R is much smaller than the electromagnetic radiation wavelength (Rλ), the Mie solution is represented by,

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c is the speed of light, ω is the radiation frequency, V is the volume of the single sphere and dielectric constant ε(ω, R′) = ε1(ω, R′) + iε2(ω, R′).14 R′ = Rr is the free electron oscillation volume contributed to surface plasmon absorption; and r is the thickness of the shell where electrons cannot oscillate due to NP surface modification.15 Subsequently, the extinction efficiency Qext = σextR2 can be calculated.13 Furthermore, the calculated value can be fitted with a spline-fit to enable the calculation of Qext over a continuous range of λ, which can be related to the measured absorption (Abs) through the number of particles per unit volume (N) and the path length of the spectrometer (l0) as below.16,17
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In order to understand the factors that affect the large increase in absorption and scattering when LSPR occurs, the modified long wavelength approximation is used to extend Mie theory to more complex shapes:18

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where N is the electron density and χ accounts for the shape of the NP. The factor χ models the NP as an ellipse and is proportional to a/b where a and b are the minor and major axis respectively of the ellipse. Therefore, the metal and its surrounding material and structure determining dielectric properties play an important role in determining the optical properties.19 Minor changes in the molecules on the interface lead to remarkable shifts in the optical signals. Contrarily, the intense electromagnetic fields associated with plasmonic nanostructures can be exploited to enhance the fluorescence and Raman scattering intensities. The sub-wavelength area of a highly localized and extraordinarily amplified electromagnetic field is called the “hot-spot”. In addition, the electromagnetic fields generate thermal energy that can be utilized for the fabrication of nanoscale lasers,20 targeted cancer therapy,21 or used as non-blinking imaging markers and reporters.22,23

Nanoscale tools present interesting physicochemical properties that enable the possibility of acquiring richer information than conventional methods.24 Single-entity sensing methods using NPs, nanopores, nanopipettes, nanoimpacts, and even nanomachines have attracted vast attention in electrochemistry, analytical chemistry, and biophysics fields.25–33 The detection systems at the single-entity level underpin knowledge of the heterogeneity and stochastics in the behavior of molecules and cells at high spatial and temporal resolution.34 These systems provide a promising confined space in the nanoscale that enables advances in electrochemical, optical, and mass spectrometric measurements. For example, nanopores have emerged as a promising label-free platform for DNA sequencing and diagnostics.35 The nanopore concept was first demonstrated with the protein pore α-hemolysin,36 after which various types of solid-state nanopores made of different materials have been under extensive development.37,38 A nanopore sensor contains an electrolytic solution and is operated by applying a constant electric field across detection chambers. The readout is electric current with individual dips corresponding to a charged molecule translocation, usually called an event. The molecule can be electrophoretically driven through the nanoscale hole, and thereby its length, shape, charge and composition can be interpreted according to characteristic changes in the density of the electric current. The platform can output optical signals associated with a nanometer-sized plasmonic hot-spot.39,40 The electric field providing the driving force can be even replaced by a plasmonic field, by which the reactivity between a molecule and the plasmonic nanopore surface can be regulated in discrete steps.41 The implementation of plasmonic nanopores detects single biomolecules by monitoring the backscattered light intensity from the plasmonic nanoantennas in a label-free manner without the need for electrolyte,42 and thus significantly outperforms conventional solid-state nanopores.3 Likewise, the single-entity sensing methods based on plasmonic NPs have gained great interest due to their intrinsically small size and localized sensing volume/area, which is requisite for the parallel readout.43 In contrast, most of the other sensing techniques using bulk solutions or planar surfaces show a limited performance to localize and separate the sensing elements, and are limited by slow molecular diffusion, stochastic binding, and frequent dissociation of complexed biomolecules with resulting disequilibrium of reactions, causing fluctuation in signals with a low signal-to-noise (S/N) ratio.44 The S/N ratio is an important factor in single NP-based sensing and spherical NPs; with R as an example, the scattering power is inversely proportional to the sixth power of R. Typically, NPs with R < 10 nm would not generate a sufficient S/N ratio in a reasonable time scale (seconds to minutes), from a conventional charge coupled device (CCD) camera and halogen lamp illumination.43 Plasmonics integrated with scattering spectroscopy has contributed to advances in single-particle investigations by allowing light to be focused in plasmonic hot-spots.45 Dark-field (DF) microscopy is one of the current optical techniques with a high S/N ratio and has provided solutions to enhance the detection sensitivity and spatial resolution, thus enabling easy monitoring of the scattering spectra of single plasmonic NPs.46 In DF imaging, the single NPs are illuminated with a high angle by annular incidence or near-field excitation,47 and each NP can be recognized as a bright spot with the naked eye in the field of view. The light scattered by the NPs is collected using an objective lens for imaging, which is then guided to the spectrometer to obtain an optical spectrum (Fig. 1). In principle, a single-particle sensor could also be used as a tiny probe to report local biological information on a nanometer resolution in living cells or tissues, and arrays of these single particle sensors could realize multiplexing with high throughput.43,48 To avoid artifacts generated by focus or NP drift, the stability of the instrument is essential.

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Fig. 1 (a) Schematic illustration of the experimental setup and optical path diagram for measurements. The 633 nm laser-line from a He–Ne laser is used as an excitation light source for LSPR and SERS. Note that the light source is changeable, e.g. to another 647 nm Kr–Fe laser. One end of an OF is open for light entrance and the other end is prepared for sensing area. The objective lens is used to collimate the laser lights into the OF. Only the scattered light by the sample is collected for imaging and spectrometry since the directly transmitted light from high angle annular illumination is excluded by the low collection angle of the objective lens. The light enters the spectrometer through the entrance slit, passing by the collimating mirror, and then the light is collimated and directed to the diffraction grating. After separation, the diffraction beam from the grating is focused to the exit slit. The spectra are acquired by the detection of scattering signals sent through the pH and delivered into the polychromator and CCD camera. (b) Illustration of a single NP sensing platform using different nanostructures. (c) Illustration of optical signals. For LSPR, the signals show a wavelength shift of the plasmonic nanoparticle upon molecular binding. For SERS, the enhanced electromagnetic field at the nanoparticle surface excites vibration modes of molecules and the scattered light shows a slight energy shift (Stokes shift) from the incident light. The inset shows DF microscopy of single NPs. Reprinted with permission from ref. 57 and 258.

One advantage of single-particle measurements to note is the possibility of distinguishing innate differences in size and shape that occur unavoidable during NP production.49 According to the Mie theory, the structural factors, including shape and size, affect the SPR properties of NPs, providing delicate tunability as well as characterization challenges.50 However, even optimally synthesized products contain certain degrees of shape inhomogeneity which inhibit obtaining quantitative information from bulk measurements.51 Indeed, the single particle analysis has proved that particles from the same batch of fabrication can have different shapes and shape-dependent sensitivities. Advances in techniques and instruments have greatly increased their robustness, reliability, and popularity, thus producing a voluminous body of literature on plasmonics studied at the single particle level.47 The NP dynamics are best unraveled in one particle in a time-manner, in the same way biological processes can be better understood via single biomolecule studies. The studies on single biomolecules can be uniquely conducted by single particle-based approaches. In the present review, we have described recent achievements and applications of single plasmonic biosensors categorized by the used techniques. Furthermore, we have highlighted the synthetic methods to control the NP structures for single NP biosensing. The current limitations, as well as future possibilities, for biomedical diagnosis with the novel and practical techniques and nanostructures have also been discussed.

2. Single nanoparticle characterization

The Scientific American has identified plasmonic sensing as one of the top 10 emerging technologies of 2018. Almost every single new plasmonic or photonic structure would be explored to test its sensing ability.52 Typical single particle plasmonic sensing utilizes either the resonance shifts generated from the local change in refractive index upon molecular adsorption (refractive index sensing) or characteristic light radiation boosted by the plasmonic resonance-induced field enhancement (for instance, surface-enhanced Raman scattering, SERS).

2.1 Refractive index sensing

The signal of resonance-shift-based localized SPR (LSPR) biosensors relies on the change of refractive index (RI) induced from molecular adsorption on the NP surface (Fig. 2a). McFarland et al. have reported the adsorption of <60[thin space (1/6-em)]000 molecules of 1-hexadecanethiol on single silver NPs with an LSPR shift of 40 nm, considering the surface coverage of the molecules on a single particle.53 Real-time biomolecular detection with a single particle was first reported in 2003, and the study included the use of AuNPs functionalized with biotinylated bovine serum albumin to selectively detect a few hundred streptavidin molecules.54 Two parameters are used to characterize and compare the performance of optical RI sensors: RI sensitivity (SRI) and figure of merit (FoM), given by SRI = Δλresn and FoM = SRI/FWHM, respectively, where Δλres is spectral interrogations, Δn is RI variations, and FWHM is the full width at half maximum. SRI defines the ratio of the change in sensor output (for instance, resonance angle, intensity, or resonant wavelength), while the FoM normalizes the SRI to the width of the resonance curve that describes how precisely the resonance minimum can be measured. Generally, higher SRI and larger FoM are desirable.52 Regarding SRI, the SPR sensing methods are superior to LSPR because of the longer decay length of 200–300 nm of SPR compared to that of 5–15 nm of LSPR. For example, a Au nanodisk array exhibited a RI sensitivity of 178 nm RIU−1 with an FoM of 2 RIU−1, whereas the SPR on the Au film showed a much higher RI sensitivity of 3300 nm RIU−1 and an FoM of 54 RIU−1 under the same experimental conditions.55 In spite of its lower SRI, the LSPR sensor can be miniaturized and is particularly suitable for surface RI sensing and biomolecule detection.52,55 The resonance shift by single molecule binding is hardly detectable with bulk systems in a label-free manner, due to a minor increase in refractive index caused by a single molecule compared to the sensing volume. Possibilities to raise the resonance shift to a detectable scale (0.1 nm) have been theoretically predicted for LSPR using different structures, including crescent-shaped particles, or plasmonic particles arranged in the Wheatstone bridge configuration.43 Experimental extrapolation also suggests that single-molecular sensitivity is available when the molecule sticks in the gap between optically coupled particles or pointed edges, where the field is greatly enhanced by hot-spots.56 Therefore, the analyte molecules should be brought selectively to the hot-spots by local functionalization to efficiently receive a signal from a single molecule.
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Fig. 2 (a) Left: Resonance shift of the plasmonic nanoparticle upon molecular binding. Reprinted with permission from ref. 43. Right: A 3D plot of various representative sensing structures in the map of bulk RI sensitivity SRI (x-axis), FoM (y-axis), and working wavelength (z-axis). The sensors under the same category are drawn using the same color in order to illustrate the general differences between each category. Reprinted with permission from ref. 32. (b) Left: Representative resonant Rayleigh light scattering spectra of single AuNPs in solvents of different RI, fitted to the Lorentzian algorithm. LSPR λres values are shifted to the longer wavelengths after exposing to the solutions with various RI. Right: Linear fits to the plots of λres shift versus surrounding RI of the Au NS, oval-shaped Au nanospheres (oval-NS) and NR with different sizes. Reprinted with permission from ref. 57. (c) Illustrations of the NP models (upper; dimensional unit, nm) and plasmon resonance electric field patterns (below; unit, V m−1) generated by numerical simulations. The NPs with nanobridges shown in the TEM image with the scale bar, 20[thin space (1/6-em)]nm. Reprinted with permission from ref. 44.

Single NR sensors have shown more resonance shifts upon molecular adsorption than spherical ones by analyzing the measured SRI of NPs with various shapes, sizes, and compositions. A linear correlation between the SRI and the aspect ratio (AR) can be fitted in the form of SRI = kAR + b (Fig. 2b), and an optimal AR of 3.5 has been found by comparing the optical properties of differently shaped NPs in glycerol solutions.57 Recently, NPs with a gap or a bridged structure showed better performance than the NRs, and these will be the optimal structures of high electromagnetic field concentration provided by the nanogap and nanobridge structures (Fig. 2c). In particular, the nanobridge structure showed two-fold higher sensitivity as compared to the NR, making it an ultrasensitive candidate material for biosensing.44 Nevertheless, most of the RI sensors based on metallic NPs possess RI sensitivities lower than 1000 nm RIU−1, with FoMs below 10 RIU−1. The highest SRI and FoM have been reported to be 1816 nm RIU−1 23 RIU−1, respectively.52,58,59 It has also been found that the SRI depends linearly on the initial plasmonic resonance wavelength λ0LSPR. In contrast, no apparent relationships have been observed between λ0LSPR and FoM.60

Plasmonic metal NPs with sharp tips, such as nanobipyramids, nanostars and nanotriangles, possess higher SRI than spherical metal NPs because the nano-tips provide natural focusing of electromagnetic field.61–63 This is a phenomenon similar to the fact that NRs typically exhibit a higher local electromagnetic field than nanospheres (NSs). Compared to NRs, the structures of nanobipyramids, nanostars and nanotriangles offer sharper tips that can support optical hot spots. For example, the SRI and FoM of Au nanobipyramids (longitudinal plasmon wavelength 645–1100 nm) are 150–540 nm RIU−1 and 1.7–6.44 RIU−1 respectively.52,64–69 The conditions employed in the etching and growth processes have offered valuable insights into the growth pathways to generate unique structures originating from the bipyramids.70,71 However, the structural control of the sharp tips often suffers from the loss of monodispersity that is frequently associated with various morphologies of NPs, especially for the control of the tip numbers and positions of nanostars.72,73

2.2 SERS

The Raman scattering phenomenon was discovered in 1928 by Sir Chandrasekhara Venkata Raman, while the initial observation of the SERS effect originated from the remarkable enhancement of Raman spectra of pyridine on the surface of a roughened silver electrode in 1974. However, the researchers did not realize that the roughened metal surface gives rise to the enhanced signals until 1977.74–76 It is believed that both a long-range electromagnetic (EM) effect and a short-range chemical effect are simultaneously operative for this phenomenon (Fig. 3a).77 The EM mechanism arises from the optical excitation of SPR in small metal particles, which leads to a significant increase in the electromagnetic field strength at the particle surface by LSPR. The Raman scattering from molecules can be enhanced dramatically, with theoretical enhancement factors (EFs) even up to 1012 in the hot-spots.78–80 In the chemical mechanism, molecules adsorbed at certain sites on the surface (such as atomic clusters, terraces, and steps) are coupled electronically with the surface (i.e. charge transfer), leading to an enhancement effect similar to resonance-Raman scattering.81 The chemical enhancement mechanism is molecule-specific and generally shows lower EF (in the order of 103–105) than the EM enhancement (over 108). The SERS signal rapidly fades away as the target molecule moves away from the surface, probably due to the drop of charge transfer efficiency and the evanescent field decay. The SERS intensity of a molecule can be described as PN·σSERS·|E0|2 |Eloc/E0|4, where N is the number of Stokes-active scatters (number of molecules within the laser focal volume), σSERS is the scattering cross-section (chemical enhancement factor), |E0|2 is the incident laser power, and Eloc and E0 are the amplitudes of the enhanced and incident electric field, respectively. The equation shows that the SERS intensity is proportional to the fourth power of the field EF (|Eloc/E0|); therefore, a field enhancement, greatly dependent on LSPR, is highly desirable for effectively collecting SERS spectra even from single particles (Fig. 3b). The clusters of plasmonic particles or sharp nanostructures are often used for this purpose, because hot-spots, where the field is strongly amplified, are created at the gaps between NPs or at the tips with high curvature.82
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Fig. 3 (a) Illustration of SERS. An enhanced optical field at the nanoparticle surface excites the vibrational modes of molecules and the scattered light shows a slight energy shift (Stokes shift) from the incident light. The vibrational modes of atomic bonds have specific energies; therefore, the SERS signal shows a finger print of the adsorbed molecule. The two-step enhancements are considered as the electromagnetic enhancement mechanism in SERS. Reprinted with permission from ref. 43 and 259. (b) Artist's view of a plasmonic gap formed by NPs hosting a set of organic molecules. A picocavity localizes the electromagnetic field at the atomic scale, thus producing sub-nanometer resolution and close-to-strong optomechanical coupling. Theoretical methods to address this extreme interaction regime in surface-enhanced Raman scattering are outlined on the sides. Reprinted with permission from ref. 260. (c) SERS-active nanostructures of NSs, NRs, nanobars, nanodome arrays, nanoclusters, and nanoholes from A to F, respectively. Reprinted with permission from ref. 261.

Superior to RI sensing, SERS provides distinct spectral characteristics that originate from the vibration mode of the adsorbed molecules.83 In other words, SERS has a further advantage of producing a signal that directly shows fingerprints of certain molecules without the need for selective functionalization. Therefore, single-molecule SERS can, in principle, monitor the conformational changes within a single molecule.84,85 However, the evidence of single molecular detection has not been completely clarified, because the SERS signal is generated from various factors and has large fluctuations. Moreover, artificial signal reporters and high-energy lasers focusing on NPs for Raman sensing hinder the original probe–target interactions. To prove single molecule detection in various fields, such as molecular imaging, biomedical diagnosis, and biological trace monitoring, different methods, including the employment of two distinct probes and the frequency domain approach, have been proposed.56,86–91 However, every application introduces appropriate SERS-active nanostructures which can bring dramatic enhancement in the Raman signals of target molecules (Fig. 3c).43,92,93 Nanogap is the most commonly used form of SERS that can be synthesized as a core–shell structure, or be fabricated by programming NPs into a close assembly.

For gap-based nanostructures, built-in SERS hot-spots are generated in the metallic core–shell structures made up of a dielectric layer of DNA, SiO2, and some small organic molecules, such as 1,4-benzenedithiol and 4-methylbenzenethiol (Fig. 4a–c).74,94–97 LSPR between the core and shell increases as the gap decreases, resulting in high SERS.98 However, when the gap reaches a sub-nanometer level, the SERS effect decreases due to a reduction in the electromagnetic enhancement by the quantum mechanical effects.99 SERS can be tuned by changing both the gap size and the shell thickness (Fig. 4d). The thickness of the shell should match well with the core size to achieve optimal Raman enhancement. In a previous study, a uniform Au core of 15 nm and a nanogap of 1.2 nm were used, and the SERS performance was observed to be increased with an increase in shell thickness from 41 nm to 61 nm, however, it was observed to be decreased in a threshold size of 61 nm.100 With increasing shell thickness, the coupling of internal and external energies is stronger, resulting in an increase in the SERS intensity of antibonding plasmons. However, the energy dissipates when the plasmonic resonance of the shell increases compared to that of the core, resulting in a decrease in SERS intensity. To further improve SERS intensity, a new strategy is to develop nanostructures that have more than one gap, thus obtaining more hot-spots and a larger gap area for Raman reporters.101,102 However, too many gap-layers reduce the light penetration, and thus the contribution of the multi-gap to SERS also exhibits a threshold. The gap between metals can also be fabricated with inorganic materials, such as silica.103 Silica easily forms an ultrathin shell on Au and Ag surfaces and the silica shell can be further functionalized with Raman molecules. The nanogaps can be precisely controlled by the shell thickness of silica (even <1.5 nm) for improved SERS performance.104–109

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Fig. 4 (a) DNA on gold NPS facilitates the formation of nanogap particles with ultrasmall (∼1 nm) interior gap and tunable surface roughness in a highly controllable manner. Particle surface roughness can be associated with and enhance the electromagnetic field inside the interior gap, and stronger nanogap-enhanced Raman scattering signals can be generated from particles by increasing particle surface roughness. (b) TEM images showing the formation processes of the core–shell nanostructures in the middle stage (1 to 3) and final stage (4 to 6) under the controllable number of nucleation sites from low to high, respectively. The uniform and hollow gap can be precisely loaded with a quantifiable amount of Raman dyes. Reprinted with permission from ref. 262. (c) Schematic illustration of the synthesis of core–shell NPs on the basis of self-assembly of amphiphilic block copolymers on the Au core surface. The TEM image illustrates an interior nanogap of 1.5 nm. Reprinted with permission from ref. 263. (d) Schematic diagram of a core–shell nanostructure with embedded Raman reporters and the simulated spectra of the enhancement factor for different/constant core diameters (Dc), gap size (G), and shell thickness (Sh). Adapted with permission from ref. 264.

For assembled nanostructures, plasmonic coupling in the gaps among adjacent NPs enable the electromagnetic field to increase drastically, creating sufficient hot-spots for SERS. Assembled plasmonic nanostructures can be classified as NP aggregates, achieved by covalent binding, electrostatic adsorption, and hydrophobic interaction,110–113 and offer a labor- and cost-efficient strategy for the expansion of the library of plasmonic nanostructures with highly tunable, coupled optical properties.114,115 Typical assembled nanostructures include dimers, trimers, multimers, and core-satellites (Fig. 5a).74,116–119 Methods based on salt-induced agglomeration and electrostatic attraction can readily yield assembled nanostructures by changing the surface charge of NPs with ions and surfactants, and then by gradient centrifugation to separate dimers, trimers and so on for further investigations.120–123 To control the interparticle distance, DNA, RNA, and polymers have been employed with dual roles of the NP linkers and gap spacers (Fig. 5b and c).124–128 Each end of the linkers is thiolated to conjugate with the metallic surface; meanwhile the smart molecules, such as DNA, can further guide the NPs to build blocks by programmable hybridization. For example, DNA origami have been widely used as templates to make plasmonic nanostructures (Fig. 5d). The rigidity of the DNA origami block provides a stable and designable scaffold for delivering Raman reporters to the hot-spots located among NPs, while the gap size could not be controlled at an extremely small level (<3 nm). DNA of length shorter than 3 nm (∼10 bases) suffers from the problems of a strong steric hindrance effect and low hybridization efficiency. Small organic molecules, such as dithiols and diamines, also have been used to fabricate NP-assemblies by covalent binding for smaller gaps.129–132 However, the small molecules lead to ineluctable aggregation of NPs in the assembling process. Therefore, polymers, such as polyethylene glycol (PEG) and polystyrene, phospholipids, and peptides that can quench the self-aggregation are introduced to encapsulate the assembly.133–137 Furthermore, Raman reporters can be infused through the coating agents to locate at the hot-spots. The resultant assembly can be viewed as a SERS nanocapsule (Fig. 6). The nanogap junctions in the nanocapsules can generate an enormous electromagnetic field for SERS, by which the Raman signals from reporters embedded in the junction can be amplified over two orders of magnitude higher than those from reporters adsorbed on the surface of NPs.138–140 Core–satellite structures are another special type of assembled NPs in which one metallic core is covered by some small satellite metallic NPs, providing many hot-spots in one particle.74 Similar to other assembled NPs, the general fabrication of core–satellite structures is achieved by electrostatic adsorption, covalent binding, and DNA hybridization.141,142 For example, aptamers specific to target biomolecules and their corresponding complementary DNA are employed to assemble the core–satellite NPs.143 The satellites could be combined with the core by DNA hybridization when a target biomolecule is absent generating high SERS signals. The introduction of targets caused the release of satellites from the core, and thus reduced SERS intensity. Because the change in SERS intensity is directly related to the change in the number of satellites around the core, the core–satellite NPs can be utilized to quantitatively detect biomolecules.74,114

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Fig. 5 (a) Illustration of plasmonic coupling in the gap among adjacent NPs for SERS. Reprinted with permission from ref. 82. SEM images and SERS enhancement factors of gold NPs and their dimers, trimers and tetramers. Reprinted with permission from ref. 265. (b) Outline of the assembly of Raman dye-functionalized oligonucleotide-NP conjugates. A head-to-head NP triplex assembly was initiated by addition of complementary double stranded target DNA, which includes an internal sequence with no complementarity to the NP probes, formed by 0, 5, 10, and 15 base pairs that progressively tune the interparticle spacing with an increase in distances. After an initial dimer formation step, the NP assembly proceeds leading to larger clusters formed at a growth rate controlled by the dsDNA length (the shorter the target sequence, the faster the aggregation rate). (c) Baseline corrected SERS spectra of the dye-labelled NP probes before (black line) and after the addition of a complementary double stranded target. The maximum SERS intensity of the band at 1647 cm−1 for each spectrum versus the number of base pairs forming the non-complementary internal sequence of dsDNA showed a clear dependence of the SERS response with changes in the average interparticle distance. Reprinted with permission from ref. 266. (d) Assembly scheme for 60 nm gold and silver dimers with DNA origami. Reprinted with permission from ref. 267.

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Fig. 6 SERS nanocapsule synthesis. (a) Ag nanoparticles are cross-linked with the bifunctional linker 4-aminobenzenethiol (ABT, blue) or (b) 1,6-hexamethylenediamine (HMD, black), each then coated with a layer of polymer. PEG thiol or streptavidin and bovine serum albumin proteins are then adsorbed (not shown). In b, the SERS tag (red) is infused through the polymer coat. The inset represents SERS from tags in the junction. (c) Structures of 4-mercaptobenzoic acid (MBA, red), 4-aminobenzenethiol (ABT), 5,5′-dithiobis(2-nitrobenzoic acid) (DTNB), and other reporters. (d) Color-coded spectra of SERS nanocapsules with MBA and DTNB prepared as in b and ABT as in a. Reprinted with permission from ref. 268.

3. Single nanoparticle synthesis

Following the commercial success of SPR-based biomedical approaches, researchers have invested great efforts in improving their performance by replacing thin gold films in sensors with engineered “building blocks” of nanoplasmonics, such as nanometer-scale particles, disks, and holes.2,144,145

3.1 Synthetic strategies

The physical and chemical properties of materials are heavily affected by their morphologies at the nanometer scale. Therefore, the synthesis of nanomaterials with precise control over many aspects, including compositions and shapes, has long been a crucial topic in the field of nanoscale science and technology.146–148 Advances in top-down nanofabrication enabled researchers to produce metallic nanostructures with serial processes.149 However, top-down lithography (for instance, electron beam lithography and focused-ion-beam lithography) has limited resolution, particularly for three-dimensional (3D) shapes and nanostructures with diverse chemical compositions.150 To justify the added cost and complexity of nanofabrication, and consider the high requirements of a buffer environment in advanced biomedicine, these engineered nanoplasmonic devices and/or particles should extend beyond the solid substrates, such as glass, and offer designable functionalities that cannot be acquired from conventional top-down nanotechnology. In contrast, adopting the natural principle of bottom-up assembly, the hierarchical organization of well-defined nanoscale building blocks into micro- and macro-scale orders can lead to the formation of material structures with exceptional sophistication, precision, and performance.146 While relatively simple, generally isotropic, and homogeneous nanostructures have been introduced for understanding the fundamental phenomena underlying LSPR of nanostructures, building structures with elevated complexity and hierarchy, and importantly in high throughput, has remained a great challenge in the field.151 In contrast to assembly, the solution-based synthesis of nanocrystals has been most extensively studied and best executed (Fig. 7a).
image file: d0tb00351d-f7.tif
Fig. 7 (a) Completed evolutionary tree of AuNP growth consisting of three branches. The seeds carry three sets of codes guiding the morphological evolution and grow into various shapes. Reprinted with permission from ref. 269. (b) Schematic illustration showing the evolution from metal atoms (in the centre) to five major types of seeds (in the middle ring), and then nanocrystals with diversified shapes (in the outer ring). The symmetry of each species is labelled by its point group. Reprinted with permission from ref. 270. (c) Schematic representation of nanocrystal growth including classical nucleation and growth (regime C), aggregative nucleation and growth (regime A), and Ostwald ripening (regime OR). The three regimes may overlap temporally to varying extents. Nanocrystal nucleation, growth, and Ostwald ripening steps are color-coded in the sigmoidal (S-shaped) kinetic profile as blue, red, and black, respectively. The smallest yellow circles represent atoms, ions, molecules, or small clusters in regime C, and primary nanocrystals in regime A. Reprinted with permission from ref. 156. (d) Optimization of synthesis methods is required for NPs through careful tuning of various parameters for biomedical applications. Reprinted with permission from ref. 182 and 183.

Synthesis-with-design of shape-controlled inorganic structures underlies diverse applications in biomedicine.152 Although many reports on a wide variety of synthetic nanostructures exist, the formation of NPs with arbitrarily designed 3D shape and positional surface modification with sub-5 nm resolution has rarely been demonstrated with inorganic materials. The process is kinetically controlled in affecting the growth of the colloidal nanoseeds into larger nanocrystals, where low-energy facets persist while high-energy facets vanish, which has been discussed in detail in a few comprehensive reviews.51,146,151 It has also been found that the energies of the crystallographic facets and thus their relative growth rates can be controlled by specific coating chemicals (surfactants, ligands, or ions), leading to shapes with particular edges and/or ARs.153 A number of moieties have been evaluated as surfactants to control surface energies leading to the synthesis of nanocrystals with well-defined shapes. However, reliable dynamic growth models are usually limited to highly symmetrical shapes of identical surface facets with the lowest energy (for instance, nano-spheres, cubes, plates, and rods; Fig. 7b), and it is difficult to predict the irregular shapes or control final particle dimensions.154,155

The current state of nanocrystal synthesis is similar to that of organic synthesis a century ago.156 The resultant nanostructures were yielded by a trial-and-error procedure followed by optimization of synthetic conditions to provide narrow size distributions. However, this process is more of an empirical art than science. For example, synthesis of a nanosphere with one conical branch cannot be achieved by the chemical-coating methods. In our view, the following four reasons make the synthesis-with-design of colloidal NPs to be invoked without evidence, and until recently, little frontier experimental works have been completed.73,157 First, the transient nature of metallic atoms is uncontrollable in solution. Furthermore, no two nanoseeds are identical in crystallographic facets although both of them appear to be spherical. Third, no surfactant can bind to a particular crystal facet with perfect specificity to alter its surface energy, which is essential to the shape-controlled synthesis of nanocrystals. It is believed that one can generate structures with a similar level of intricacy, precision, and superior functions as seen in organic macromolecules, if one harvests the power of exquisite recognition properties towards an inorganic crystal facet.146 To date, the selection of functional coating groups for targeted crystal facets can be conducted by theoretical simulations, while a general way to identify material-specific organic molecules is still experimentally lacking due to a complexity in the vastly diverse atomic arrangements on different crystal facets. Finally, a set of reliable growth mechanisms to intently guide nanocrystal synthesis is yet to be developed.156 Studies on the formation mechanism of gold crystals date back to 1951.158 However, the modern era for mechanistic studies of nanocrystal growth appears to have begun just from 1994.159 Banfield and co-workers have elucidated an oriented-attachment mechanism of nanocrystal growth in 1998, which is an aggregative process.160 Most experimental studies focusing on nanocrystal growth and size control have followed the LaMer mechanism,161–163 Ostwald ripening,164–167 or their combination (Fig. 7c).168–173 Technologies, such as in situ transmission electron microscopy (TEM), have provided solutions to directly observe the aggregation and coalescence of nanocrystals in real time.174–177 Therefore, the mechanism-based synthetic control of nanocrystals is supposed to reach.

We have discussed here regarding the promising directions for synthesizing active plasmonic nanostructures for biomedical diagnosis. The nanostructures should meet certain requirements. The structures can form highly intense areas of field enhancement with high sensitivity. Thereafter, the functional units of the structures can be designed with phenomenal light manipulating abilities. Finally, the structures can be synthesized in solution in a uniform and scalable manner with mechanism-based guidelines for replicated production. Consequently, the sphere and rod nanostructures were the focus of extensive investigations owing to their tips and tricks to satisfy the above requirements of biomedical applications (Fig. 7d).

3.2 Nanospheres

Methods for enhancing the uniformity of the nanostructures can be applied to remove sharp corners,178 high-energy facets,179 and roughness.180 Accordingly, highly ultrasmooth and spherical monocrystalline Au nanospheres (NSs) were prepared by a cyclic growth process followed by etching (Fig. 8).70 Octahedral AuNPs were firstly synthesized by slow reduction of chloroauric acid in ethylene glycol where the unwashed chloroauric acid subsequently oxidized the Au atoms with the lowest coordination number.181 In this way, the sharp corners were removed. This etching process was carried out for 20 h to avoid instabilities and roughness in the size and shape of the nanospheres. The process is scalable up to the particle size of 200 nm or more. Scanning electron microscopy (SEM) images have indicated that the slow chemical etching step selectively removes the edges and vertices, and effectively makes the surface tension isotropic, so that NSs are favored under quasi-static conditions. Based on the Maxwell equation, these ultra-smooth and highly monocrystalline NSs were further utilized to directly measure the extinction coefficients in their subsequent work. Narrow size distributions are essential to control not only the spectral properties, but also the morphology and yield of certain clustered assembly schemes.
image file: d0tb00351d-f8.tif
Fig. 8 Growth followed by etching produces uniform, monocrystalline NSs. (a) Schematic diagram of iterated etching and regrowth of AuNPs to produce large NSs. (b) Distribution of circularity and diameter of NSs (blue) and citrate-stabilized AuNPs (red). Scale bars are 100 nm. (c) SEM images of the NSs. Reprinted with permission from ref. 70.

3.3 Nanorods

Gold nanorods (AuNRs) have been the first successful example of anisotropic plasmonic nanostructures synthesized in solution with a practical guide.182 One of the most interesting features of AuNRs is their well-defined shape anisotropy, which results in the longitudinal SPR which is aspect-ratio (AR) dependent and broadly tunable. The resonant excitation of consistent plasmon oscillations in the metallic nanoantennas of AuNRs provides promising opportunities for manipulating electromagnetic fields beyond the diffraction limit and for adjusting light–matter interactions at the nanoscale. Considerable research efforts have been invested for developing synthetic methods that provide tight control over particle size distribution and extended tunability in the ARs simultaneously. The most commonly used method for the synthesis of AuNRs is seed-mediated growth in the presence of the hexadecyltrimethylammonium bromide (CTAB) surfactant and Ag (Fig. 9). The fundamental roles of CTAB and Ag have drawn intense interest and debate within the field of nanoscience, and even contradictory explanations exist in the literature.183,184 Many groups have unequivocally demonstrated that a high concentration of bromide is essential in controlling the morphology of AuNRs, with both the most popular Ag-assisted seeded growth method and the Ag-free seeded growth using citrate-stabilized seeds. However, it is a recent finding that neither bromide as a surfactant counterion nor a high concentration of bromide ions in the growth solution is required for AuNR formation. Subsequently, monodisperse AuNRs have been synthesized using a bromide-free surfactant mixture composed of alkyltrimethylammonium chloride and sodium oleate, where AuNRs having a previously unprecedented octagonal prismatic structure, predominantly enclosed by high index {310} crystal planes, were acquired even with an iodide concentration up to 100 μM in the growth solution.183 Although key aspects of the AuNR growth mechanism remain poorly understood, these findings should have profound implications for a complete understanding of the mechanism behind seeded growth of anisotropic metal nanocrystals.
image file: d0tb00351d-f9.tif
Fig. 9 Schematic of proposed anisotropic growth pathways in the synthesis of AuNRs. Reprinted with permission from ref. 271.

3.4 Building blocks and others

Individual NSs and NRs can be further programmed into dimers, trimers, and block-particles for single nanoplasmonics.185 An important basis is that both plasmonic materials and oligonucleotides are easy to modify with the thiol-group and the resulting DNA–thiol-NPs can be rationally assembled through DNA hybridization (Fig. 10).186,187 This strategy can be traced back to the innovation by Nadrian Seeman in the 1980s, who was the first to use DNA as a chemical material for the construction of branched junctions and topological structures having relative flexibility.188–190 In 1996, novel DNA–AuNP composites were simultaneously exploited by Mirkin and Alivisatos, which provided a massive impact on the macroscale self-assembly of AuNP complexes.191,192 When the AuNPs are arranged into well-defined configurations, they exhibit distinctive and substantial optical properties different from either the bulk or single particles.193,194 Most importantly, 2D and 3D DNA origami tiles were designed by Rothemund and Douglas, successively from 2006 to 2009,193,194 and the frontier works for the development of the structural DNA nanotechnology have been used as a versatile and robust platform for precisely constructing a variety of sophisticated NP-patterns.195 The history of DNA nanotechnology, their applications in NP self-assembly, and how the varieties of gold nanostructures are related to their unique optical properties have been discussed in great detail.190,196–200 For instance, a long scaffold strand (single-stranded DNA (ssDNA) from the M13 bacteriophage genome, ∼7429 nucleotides long) was folded into prescribed structures with the aid of a pool of ∼200 short staple strands by sequence-specific hybridization and the formation of multiple crossovers. The spatial addressability of DNA nanostructures derived from sequence specificity is especially well-suited for the arrangement of NPs.190 For example, Bidault et al. demonstrated the synthesis of well-defined AuNP dimers and trimers with nanometer spacing obtained by hybridizing mono-conjugated DNA–particle building blocks, where two types of DNA–NP conjugates were developed to entail the coupling of oligonucleotides to colloidal gold particles through terminal thiol groups.201
image file: d0tb00351d-f10.tif
Fig. 10 DNA-based colloidal assemblies. (a) Schematics of NP assembly with DNA motifs. (b) 2D arrays of AuNPs assembled by lithographically confined DNA origami. Scale bar: 500 nm. (c) Scheme of three designed clusters assembled on correspondingly encoded vertices of octahedral DNA frames. (d) Self-assembly of methane-like NP molecules. (e) Engineering of lattice parameters and crystallographic symmetry of NP superlattices through particle size and DNA spacer length. Representative examples: face-cantered-cubic (fcc) superlattices. From left to right, each panel shows a model unit cell (not to scale), 1D and 2D (inset) X-ray diffraction (SAXS) patterns, and a TEM image of resin-embedded superlattices. Scale bar: 50 nm. Reprinted with permission from ref. 272.

Apart from NSs, NRs, and their building blocks, nanocubes (NCs) have been the heavily studied nanostructures with high potential in biomedicine. However, an ultraprecise synthesis with length, width and height over a large number has not been well established.202 A new strategy for precisely controlling the growth rate of different facets and AuNC-specific flocculation has paved the ways to ultrahigh yield synthesis of AuNCs with a controllable size (ranging from 17 to 78 nm) that offered single-particle-level spectral controllability and reproducibility over a large number of particles.203 Apart from the solid NPs, LSPR in hollow NPs has also been studied for sensing applications with improved RI sensitivity, which was attributed to the coupling between the interior and exterior plasmonic fields of the hollow NPs over solid NPs.

4. Single nanoparticle biosensors

The current research on single particle sensing has already shown possibilities for a wide variety of applications in monitoring small molecules, chemical reactions, and the RI of the local surrounding environment that mostly take advantage of their distinctively small sensing volume. Single NPs can also serve as non-bleaching labels or biomarkers for bioimaging.43 Signals obtained from single NPs could offer more accurate local biological information on a nanometer scale in living cells or in biological tissues, eliminating an averaging effect in bulk systems and reaching countable numbers of molecules in the sensing scale.

4.1 Biosensing

In principle, the single NP-based sensing platform can readout all kinds of biomolecular interactions between nucleic acids and proteins on the NP surface. Particularly, protein molecules are large enough to be readout by the nanoscaled platform. The binding of even one single target protein to the NP-probe, or the bending of DNA by an enzyme can change the local RI of the system, resulting in signal variations in real time. The design of NP-probes that selectively capture the targets is essential in the sensor fabrication.

Based on the design of the DNA probes on the NPs, microRNA (miRNA) complementary to the probes was analyzed at the picomolar level of sensitivity. With a similar concept, a eukaryotic ribonucleoprotein enzyme, telomerase (diameter ∼12.5 nm), which functions as a telomere terminal transferase by adding the telomeric repeats (TTAGGG)n onto the 3′-end of chromosomes using its intrinsic RNA as a template, can be captured by the complementary DNA probes.204 Telomerase is overexpressed in >85% of all known human tumors, but not detected in normal cells.205–207 The single NP sensor can detect telomerase from as few as 10 HeLa cells. Predesigned NP-probes can also recognize single-point mutations in target DNA facilitated by the DNA mismatch repair (MMR) protein MutS. MutS (diameter ∼10 nm) is responsible for repairing errors in DNA replication by binding in wrapped DNA grooves. The conformational alteration in the grooves results from even a single base mismatch, and biochemically results in the trap of MutS on the NPs. Different bases resulted in different conformational changes in DNA, generating distinguishable DNA-MutS binding affinities that could be recognized only by computation before the implementation of the single-NP approach (Fig. 11). Based on the particle-sensing, an atlas of MutS binding affinities to DNA at single-base resolution has been developed, which can reveal changes in gene regulation in the stage of disease, describing the relationship between the type of point mutation and repair efficiency of the MMR system, and predict gene mutation-induced cancers. Importantly, the sensing process was conducted within 15 min, without prior knowledge of the target DNA sequence, and using samples of several tens of microliters in volume. The DNA hybridization can be used to design the sensor system as a molecular ruler that reports conformational changes and intra-NP distances according to the plasmonic coupling effect (Fig. 12).208 The plasmon rulers continuously monitor separations of NPs up to 70 nm for >3000 s. A similar strategy based on the molecular ruler was applied to gain fast (almost in real time), sensitive, and simple monitoring of the molecular interactions between nucleic acids and promoters.209 The sensor analyzed the promoter activity through the direct assay of interactions between a promoter and RNA polymerase, rather than measuring endpoint RNA or protein levels. In a precise design, the distance between the protein molecule and the NP along with DNA can be measured by the single-NP sensor, where the change in plasmon resonance wavelength of individual Au-DNA conjugates depends on the length of the DNA and can be measured with sub-nanometer axial resolution. An average wavelength shift of approximately 1.24 nm per DNA base pair is observed.210 This system allows a label-free, quantitative, and real-time measurement of nuclease activity, and also serves as a new platform for DNA footprinting, which can accurately detect and map the specific binding of a protein to DNA (Fig. 12a). Functionalized AuNPs were also exploited to study the conformational change of guanine-rich DNA.211 The spectral signals showed a continuous red-shift with the conformational change of the DNA into the G-quadruplex upon associating with K+ and hemin. The change formed DNAzyme and further catalyzed the oxidation of benzidine in the presence of hydrogen peroxide (H2O2). Therefore, this technique also holds promise as a potential nanosensing approach for the detection of H2O2.211 The single NP-based platform could report a full story of epigenetics, including DNA structure bending, steric competition under interaction of epigenetic proteins with transcription factors, and epigenetics-mediated suppression of transcription,212 compared to the methylcytosine-based techniques only. In fact, the probe-design of the single NP system can be easily and broadly translated to decipher the key interactions between nucleic acids and proteins during the development and progress of disease (Fig. 12c and d).

image file: d0tb00351d-f11.tif
Fig. 11 Fabrication of a single NP biosensor of high sensitivity and fidelity. (a) Schematic illustration of single NP sensing for identifying single point DNA mutations. λmax shifts upon binding of DNA and MutS after each step of molecular binding (1, 2, and 3). Insets: Real-time images of a single NP obtained with a charge-coupled device camera. (b) Identifiable detection of eight single point mutations and a homoduplex. The order of relative activity of MutS towards the mutations was determined as GT > GG > +C > AA > TC > −C > AC > GA. (c) Replotting of rate constants of interactions by sensing a countable number of binding events between MutS and eight different mutations. The atlas of protein binding affinities to DNA can be used in clinical applications by input of sample results on the atlas through the comparison. Reprinted with permission from ref. 44.

image file: d0tb00351d-f12.tif
Fig. 12 (a) Schematic of a nanoplasmonic molecular ruler for measuring nuclease activity and DNA footprinting with typical scattering spectra. The calibration curve showed plasmon resonance peak wavelengths of the Au–DNA nanoconjugates after cleavage reactions with four endonuclease enzymes, and as a function of the number of base pairs remaining attached to the AuNP after the cleavage. Reprinted with permission from ref. 210. Other typical applications using plasmon rulers shown in (b) monitoring of DNA hybridization events by the NP dimer model, (c) detecting MMP3 proteins secreted from live cells and (d) probing of drug response by selective detection of caspase-3 in live cells. Reprinted with permission from ref. 273.

Other types of bimolecular interactions, such as protein–protein interactions, can also be investigated through a similar approach (Fig. 13a).213 Combined with the plasmon-enhanced enzyme-linked immunosorbent assay (ELISA), a single-NP can be designed to detect single enzymes.214 By the analysis of binding affinities between proteins and target molecules, the single-NP strategy is quite suitable to be applied in drug discovery (Fig. 13b). Moreover, an ultrasensitive sensor detecting adenosine triphosphate (ATP) was fabricated by modulation of the AuNP surface catalytic activity through the interaction between ATP and an aptamer.9,215 Based on ApoE4-inducing Aβ42 self-assemblies on AuNPs through their surface charge interaction, the biomimetic platform can detect real-time Aβ aggregation processes which can be used to directly investigate Alzheimer's disease. The single NP-based sensors can be further developed for simultaneous on-chip analysis of multiple molecular interactions.

image file: d0tb00351d-f13.tif
Fig. 13 (a) Setup of label-free biomolecular absorption nanospectroscopic sensing of biomolecular interactions. Plasmon resonance energy transfers from a single nanoplasmonic particle to adsorbed biomolecules, which creates quantized quenching dips within the Rayleigh scattering spectrum of the probe. The quenching dips allow quantitative and long-term dynamic imaging of the target molecule without the drawbacks of photobleaching and blinking inherent to fluorescent markers, which cannot provide chemical fingerprints. Reprinted with permission from ref. 274. (b) A label-free sensor for anticancer drug-discovery by tracking of STAT3 signalling, including phosphorylation and dimerization. In the screening of anticancer candidates, the system worked well in the presence of STA-21 which inhibits STAT3 dimerization. Reprinted with permission from ref. 275. (c) Schematic illustration of the aptasensor. The specific aptamer-ATP binding induced conformational change could modulate the surface-dependent self-catalytic growth of AuNPs, which enabled the detection of ATP with ultra-sensitivity and selectivity. Reprinted with permission from ref. 276.

The catalytic activity of plasmonic materials expands their applications in biomolecular sensors. AuNPs can serve as the catalyst in the oxidization of glucose, generating H2O2 in the presence of O2 (Fig. 13c). Then, the AuNPs can grow in size in the presence of the Au precursor (HAuCl4) during the oxidization of the resultant H2O2. The catalytic ability is surface-dependent so that the absorption of molecules on the NPs will confine the catalysis. Therefore, the NPs can be used in the double-jointed role of a biosensor. For instance, a sensing strategy has been developed for detecting DNA and miRNA based on the marked difference in the adsorption efficiencies of single- and double-stranded nucleic acids on the NP surface.216 With the hybridization between probes and targets, the single-stranded nucleic acids were pushed off from the NP surface into solution, leaving the NP enlarged with glucose and HAuCl4, and thus leading to the scattering spectral changes in real time.

4.2 Bioimaging and particle tracking

Owing to the benefits of plasmonic sensing at the single-nanoparticle level, cell imaging can be used to map the biomolecular distribution in cells.9,43 Plasmonic NPs larger than 30 nm in size exhibit strong scattering at their resonant wavelength that can be easily recognized by DF microscopy and have a range of applications in drug delivery, cancer cell diagnostics, metabolic mechanisms, and therapeutics. Although the signal selectivity to recognize scattering objects from background is not as good as that of the fluorescent molecules or quantum dots, single NPs neither bleach nor blink because their light radiation originates from free electrons, and they show strong chemical stability in the physiological environment.217 For in vivo imaging, the signals have to be normalized against the heterogeneous signal from the cellular background, dependent on the particular cell line, cellular growth stage, and physiological environment; therefore, the major differences between in vitro and in vivo measurements are in the complexity of instrumentation to exclude the strong spectral background and analysis algorithm to maximize the S/N ratio. In thick tissues, where strong light scattering and autofluorescence become a major problem, the technologies such as two-photon imaging (light radiation of double the energy as excitation) can be exploited with an excellent S/N ratio.218–220 Notably, it is important to remove surface-bound NPs and distinguish internalized NPs from bound ones.221 The incubation period of plasmonic NPs in living cells depends on their morphology and surface modifications. The surface fouling of non-modified NPs was a ubiquitous and problematic phenomenon as it can significantly reduce the dispersibility and the transport efficiency, and enhance the toxicity of NPs in cells.9 PEG-derived polymers are recognized as powerful anti-fouling materials, and have been applied in a range of biosensors.222

Transport of NPs through the cell membrane has been studied by probing the spectral change due to accumulation of the NPs. In another way of measuring the diffusion of single NPs, molecular transport in a living zebrafish embryo has been monitored directly.223 Plasmonic coupling of NPs on a live cell membrane has enabled visualization of the complex formation of fibronectin and integrins.224,225 An elegant plasmonic NP network in structure-based approach has been conducted to generate a plasmon-coupled dimer capable of detecting single mRNA mutants both in vitro and in vivo (Fig. 14a).226 DNA probes targeting specific sequences of BRCA1 mRNA are conjugated to 40 nm AuNPs. Hybridization of these probes to a single target mRNA resulted in the formation of nanoparticle dimers with narrow inter-particle distances, producing a spectral peak shift and distinguishable color change due to strong plasmonic coupling. The changes in spectral color and intensity indicated the identification of one copy of the target mRNA. Distinct spectral peak shifts and intensity changes were observed over variation of inter-particle distances up to approximately 30 nm. Fundamentally, the dimer strategy enabled detection of variants in mRNA splice through the analysis of spectral peak shifts that rely on inter-particle distances between probes, by which the number of dimers in the colorimetric images compared to the results of spectral quantification could be calculated by static light scattering and Mie theory. The hyperspectral imaging mode that captured the complete spectrum at each spot in the image was suitable to distinguish the presence or absence of monomers and dimers inside a cell. By mapping the characteristic NP peaks onto the cellular images through hyperspectral imaging with scanning, the number of each mRNA splice variant was quantified and tracked. Accordingly, this approach could detect three BRCA1 splice variants having extremely low expression levels in different breast cancer cell lines under physiological conditions, paving the way for quantitative genetic profiling on single cells in the long-term. The plasmonic imaging technique provides a quantitative platform for the examination of many important biological problems in single cells at the single copy resolution, including studies of biomolecular interactions, trafficking, and their kinetics.

image file: d0tb00351d-f14.tif
Fig. 14 (a) Quantitative imaging of single mRNA splice variants in living cells. Reprinted with permission from ref. 226. (b) Circulating tumor cell identification by functionalized Ag–Au NRs with multicolored, super-enhanced SERS and photothermal resonances. Reprinted with permission from ref. 277. (c) Intracellular uptake, transport, processing, and excretion of NPs. NPs are internalized by receptor-mediated endocytosis and trapped in endosomes. These endosomes fuse with acidic organelles and lysosomes for processing. Finally, they are transported to the cell periphery for excretion. Reprinted with permission from ref. 278.

The single NP imaging and tracking methods are powerful, since the scattering spectrum of each tracked particle that provides local biological information can be simultaneously measured (Fig. 14b). Benefiting from the catalytic function of plasmonic materials again, the NPs could help in tracking the entire process of autophagy by introducing the concept of “relay probe” and enabled long-term detection in living cells.221 In the system, Au@Ag core–shell NRs conjugated on the HeLa cell membrane were etched by superoxide radicals (O2˙), which are the main regulators of autophagy. The silver shell was etched into Ag+ ions, and thus induced changes in scattering spectra and color of the NRs. Interestingly, rod-shaped NPs in solution can be rotated in exceptionally high speed through optical torques dominated by plasmonic resonant scattering of circularly polarized laser light,227 and subsequently, polarization-dependent imaging with the single NP resolution can be carried out for the detection of a biomolecular configuration,228 such as cell membrane organization and intracellular transportation.229–231 Combined with photothermal imaging, the NPs can provide information regarding the shear strength of biological structures with high temporal resolution.232 Combined with two-photon imaging, plasmonic NPs can provide 3D structural information about biological tissues by particle trapping.233–235 Finally, studies on the behavior of NPs in cells have important implications in cancer cell diagnostics.236 Interactions between NPs and blood proteins, uptake and clearance of NPs by the reticuloendothelial system (RES), permeation into solid tumors, and optimization of targeting for diagnosis of cancer are the main barriers in clinical applications (Fig. 14c). There are two approaches by which NPs can target tumor cells: passive or active targeting. Passive targeting utilizes only the osmotic pressure to accumulate NPs in tumor tissues to enable enhanced imaging, while active targeting uses NPs coupled with tumor-specific targeting agents, such as epidermal growth factor receptor monoclonal antibodies.237 Using the single NP-imaging technology, tumors with a size of few millimeters can be detected in vivo, which is of great significance for early-stage diagnosis.

5. Outlook and conclusions

Plasmonics integrated with new spectroscopy techniques has an impact on advances in single-particle investigations by allowing light to be focused in plasmonic hot-spots. Despite meaningful advances, such technologies have critical challenges that it is difficult to synthesize predesigned plasmonic nanostructures in solution. A minor difference in the nanostructure generates significant difference in plasmonic signals. However, a biomedical sensor requires repeatable results of high sensitivity and reliability.238 Many recent advances from chemists and materials scientists to achieve these demanding levels of intricacy in nanostructures can be generalized to the strategy of synthesis-with-design; that is, the use of a nanoscale template with logically designed dimensions, morphologies, phases, and/or super-crystal symmetries, which serves to govern the growth or assembly of nanostructures.151 The foundational understanding of organic–plasmonic material interactions is essential to the further progress and the eventual success of NP-design. Among the templates, the smart molecule DNA has been adequately exploited in the inorganic material world and incorporated into 2D arrays and 2D origami structures, leading to capture systems and a nanomechanical assembly line (Fig. 15a). The ssDNA with diverse base sequences has also been exploited to synthesize NPs with different morphologies.239 Even a single dsDNA can be used to direct non-DNA species. Recently, researchers have demonstrated that single dsDNA enables synthesis-with-direction to synthesize plasmonic nanostructures comprising pre-designable nanobranches, nanobridges or nanogaps in solution (Fig. 15b).44,73 The feature-rich nanostructures grew along with the dsDNA backbone from one end to another, achieving the structural controllability of sub-5 nm, and yielding nanostars of defined branch numbers, nanobridges of 3 nm in length, nanogaps of only 0.44 nm in size, asymmetric topologies, including structure resembled with pushpin-, star- and biconcave disk, as well as more elaborate jellyfish- and flower-like structures. Contrary to metallization of polyploid structures mediated by DNA or DNA origami, the products are individual crystalline particles in colloidal form. DNA-template metallization using cationic metal ions generates either sequential necklaces or aggregative granular objects with a poorly controlled structural precision. The pH condition was proved to be important for the synthesis-with-direction, in which the dsDNA was surrounded by an inner shell with a high concentration of NH3OH+ (r = ∼8 Å) and a rather distant outer space with abundant AuCl4 (r = 25–50[thin space (1/6-em)]Å). The concentration of AuCl4 ions around DNA is higher near the nanoseed, and the gradient of AuCl4 availability in the direction along the dsDNA reflects the surrounding electrostatic gradient for directing the nanostructure formation at pH 5. The dsDNA contour leads a direction-specific synthesis of colloidal nanomaterials, contrary to the conventional seed-coating agents, which are selected rather empirically than logically, to produce symmetric nanostructures by adjusting energy difference of selected crystallographic facets of nanoseeds. Controlling the metallic crystallization process in desired directions using DNA molecules will be a key to unlock the designable features of the bottom-up synthesis of colloidal nanometals. The research on plasmonic NPs is still in the beginning step, and many problems need to be settled with urgency, including reducing the biological toxicity while improving the sensitivity and biological stability.240 The potential application of plasmonic NPs in bio-sensing and -imaging is very prominent, and it will certainly play an important role in precision medicine in the near future.241
image file: d0tb00351d-f15.tif
Fig. 15 Synthesis of predesigned plasmonic nanostructures. (a) Casting nanostructures with DNA origami. Reprinted with permission from ref. 157. (b) Gold nanocrystals with DNA-directed morphologies. The growth of AuNPs was directed by dsDNA anchored onto seeds through reaction between two oppositely charged species of reaction sources (AuCl4 and NH3OH+) in the presence of a mildly acidic electrolyte at pH 5, producing individual nanocrystals. The process of the “directional crystallization” makes this approach fundamentally distinct from the conventional approaches of DNA metallization, where negatively charged DNA or DNA origami is patterned on a solid substrate, followed by isotropically interacting with positively charged metal precursors, producing bulge structures. Scale bars are 10 nm for all TEM images of nanostars, and 100 nm for high-angle annular dark-field scanning TEM images of disk-like, jellyfish-like and flower-like AuNPs. Reprinted with permission from ref. 73.

Another challenge is the detection of an optical signal from individual nanoparticles. The application of color information of plasmonic NPs needs to satisfy the requirements of semi-quantitative and quantitative detection. Digital RGB values of plasmonic NPs having three basic colors, R (red), G (green), and B (blue), should be optimized to detect the scattering light before and after the binding of single molecules by computing for avoiding human recognition errors.242 A manually operated DF microscope connected with a spectrometer is not suitable for practical high-throughput detection. Single NP-sensing requires high throughput because observing multiple individual NPs not only eliminates the averaging effect of bulk systems, but also prevents unexpected events in single NP detection. Therefore, the establishment of an automated system with rapid parallel readout and a bias-modified algorithm is required.243,244 The solution might be the intelligent readout integrated circuit system capable of local operation in pixel-definition “on-a-chip”, in which an array of photodiodes is implemented,245 white light of low energy is used for signal generation, and possibly, an auto-readout circuit is integrated to eliminate the need for raw data transmission, conversion, and processing externally.246,247 Moreover, the implementation of the single NP platform is dependent on microfluidic systems. On the ten-nanometer scale, detecting a few molecules dispersed in a solution needs to encounter single NPs and their hot-spots.44 Under ideal conditions, where every biomolecule flowing through the detection channel must travel through the plasmonic field of the positioned single particle, the efficiency of sensing can be greatly improved. Such nanofluidic devices can be produced by advanced nanofabrication techniques.248 Finally, single plasmonic biosensing platforms are equipped with a relatively large-scale microscope, spectrometer and accessories, and thus they cannot be applied in resource-limited settings. The fabrication of a portable, cost-saving, and easy-to-operate single NP-based platform is still underway to construct analytical biosensors for point-of-care (POC) applications and mobile health.249–252

Although applications such as plasmonic sensing have been extensively studied, the development of single particle-based biosensors is still at an early stage. Therefore, great efforts need to be invested in device fabrication. We hope that the potential of the major members of active plasmonic structures, in tunable RI properties and SERS, will be exploited in practical applications by integrating photonic and electronic platforms. The availability of the photonic integration technologies is of key importance to enable highly generic medical uses with electronic (digital) display devices. Electrochromism is one of the most promising solutions whereby material's optical property changes in response to electric charge forms the basis for operation of a number of display devices.253 Plasmonic nanostructures with tunable gaps and/or bridges are expected to be ideal electrochromic smart windows for biomedical devices,254 which can confer stimulus-responsive function, versatile colors, user-friendly operation and inherent fast switching without hysteresis.255 To date, the combination among active plasmonic nanostructures and counter-electrode materials has been a challenge. As the plasmonic electrochromic technology matures, full device integration of the existing photonic and electronic platforms will become an important gateway to commercialization.256,257

In the present review, we have shared our views on promising directions for single NP characterization and summarized how its exquisite sensitivity to nanostructures can be harnessed to detect a countable number of molecular binding events. We then presented an in-depth description of the synthetic mechanisms of NPs, which forms a basis for active plasmon control. This review moves towards the practical design of the single NP-based biosensors with new characterization techniques and synthetic methods, which could advance the fields as diverse as POC diagnostics, food safety screening, and drug discovery.

Conflicts of interest

There are no conflicts to declare.


This study was supported by the National Research Foundation (NRF) grant (No. NRF-2019R1A2C3009821) of the National Research Foundation (NRF) of the Ministry of Science, ICT, and Future Planning of Korea.


  1. M. Soler, C. S. Huertas and L. M. Lechuga, Expert Rev. Mol. Diagn., 2019, 19, 71–81 CrossRef CAS PubMed.
  2. S. H. Oh and H. Altug, Nat. Commun., 2018, 9, 5263 CrossRef CAS PubMed.
  3. J. D. Spitzberg, A. Zrehen, X. F. van Kooten and A. Meller, Adv. Mater., 2019, 31, e1900422 CrossRef PubMed.
  4. A. R. Ferhan, J. A. Jackman, J. H. Park, N. J. Cho and D. H. Kim, Adv. Drug Delivery Rev., 2018, 125, 48–77 CrossRef CAS PubMed.
  5. J. A. Jackman, A. Rahim Ferhan and N. J. Cho, Chem. Soc. Rev., 2017, 46, 3615–3660 RSC.
  6. M. Sriram, K. Zong, S. R. Vivekchand and J. J. Gooding, Sensors, 2015, 15, 25774–25792 CrossRef CAS PubMed.
  7. R. Chikkaraddy, B. de Nijs, F. Benz, S. J. Barrow, O. A. Scherman, E. Rosta, A. Demetriadou, P. Fox, O. Hess and J. J. Baumberg, Nature, 2016, 535, 127–130 CrossRef CAS PubMed.
  8. Q. Zhang, T. Hernandez, K. W. Smith, S. A. Hosseini Jebeli, A. X. Dai, L. Warning, R. Baiyasi, L. A. McCarthy, H. Guo, D. H. Chen, J. A. Dionne, C. F. Landes and S. Link, Science, 2019, 365, 1475–1478 CrossRef CAS PubMed.
  9. T. Xie, C. Jing and Y. T. Long, Analyst, 2017, 142, 409–420 RSC.
  10. L. Zhou, D. F. Swearer, C. Zhang, H. Robatjazi, H. Zhao, L. Henderson, L. Dong, P. Christopher, E. A. Carter, P. Nordlander and N. J. Halas, Science, 2018, 362, 69–72 CrossRef CAS PubMed.
  11. E. Cortes, Science, 2018, 362, 28–29 CrossRef CAS PubMed.
  12. U. Kreibig and M. Vollmer, Optical Properties of Metal Clusters, Springer, Berlin, 1995 Search PubMed.
  13. C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles, Wiley-Interscience, New York, 1983 Search PubMed.
  14. X. Ma and S. J. Sim, Analyst, 2013, 138, 1794–1802 RSC.
  15. M. A. Garcia, J. de la Venta, P. Crespo, J. LLopis, S. Penades, A. Fernandez and A. Hernando, Phys. Rev. B: Condens. Matter Mater. Phys., 2005, 72, 241403(R) CrossRef.
  16. K. L. Kelly, E. Coronado, L. L. Zhao and G. C. Schatz, J. Phys. Chem. B, 2003, 107, 668–677 CrossRef CAS.
  17. V. Amendola and M. Meneghetti, J. Phys. Chem. C, 2009, 113, 4277–4285 CrossRef CAS.
  18. W. H. Yang, G. C. Schatz and R. P. Vanduyne, J. Chem. Phys., 1995, 103, 869–875 CrossRef CAS.
  19. A. I. Kuznetsov, A. E. Miroshnichenko, M. L. Brongersma, Y. S. Kivshar and B. Luk'yanchuk, Science, 2016, 354, aag2472 CrossRef PubMed.
  20. M. A. Noginov, G. Zhu, A. M. Belgrave, R. Bakker, V. M. Shalaev, E. E. Narimanov, S. Stout, E. Herz, T. Suteewong and U. Wiesner, Nature, 2009, 460, 1110–1112 CrossRef CAS PubMed.
  21. X. Huang, I. H. El-Sayed, W. Qian and M. A. El-Sayed, J. Am. Chem. Soc., 2006, 128, 2115–2120 CrossRef CAS PubMed.
  22. K. S. Lee and M. A. El-Sayed, J. Phys. Chem. B, 2006, 110, 19220–19225 CrossRef CAS PubMed.
  23. K. A. Willets and R. P. Van Duyne, Annu. Rev. Phys. Chem., 2007, 58, 267–297 CrossRef CAS PubMed.
  24. M. Neves and D. Martin-Yerga, Biosensors, 2018, 8, 100 CrossRef PubMed.
  25. W. E. Moerner, Y. Shechtman and Q. Wang, Faraday Discuss., 2015, 184, 9–36 RSC.
  26. S. Nandi and D. Das, ACS Sens., 2016, 1, 81–87 CrossRef CAS.
  27. Y. Y. Bai, Z. Wu, C. M. Xu, L. Zhang, J. Feng, D. W. Pang and Z. L. Zhang, Anal. Chem., 2020, 92, 853–858 CrossRef CAS PubMed.
  28. R. J. Yu, Y. L. Ying, R. Gao and Y. T. Long, Angew. Chem., 2019, 58, 3706–3714 CrossRef CAS PubMed.
  29. H. S. Toh and R. G. Compton, ChemistryOpen, 2015, 4, 261–263 CrossRef CAS PubMed.
  30. T. J. Stockmann, L. Angele, V. Brasiliense, C. Combellas and F. Kanoufi, Angew. Chem., 2017, 56, 13493–13497 CrossRef CAS PubMed.
  31. S. D. Zhang, M. Z. Li, B. Su and Y. Shao, Annu. Rev. Anal. Chem., 2018, 11, 265–286 CrossRef PubMed.
  32. L. Wang and Q. Li, Chem. Soc. Rev., 2018, 47, 1044–1097 RSC.
  33. H. Inaba and T. Ueno, Biophys. Rev., 2018, 10, 641–658 CrossRef CAS PubMed.
  34. T. H. T. Nguyen, J. Lee, H. Y. Kim, K. M. Nam and B. K. Kim, Biosens. Bioelectron., 2020, 151, 111999 CrossRef CAS PubMed.
  35. R. Gao, Y. Lin, Y. L. Ying, Y. X. Hu, S. W. Xu, L. Q. Ruan, R. J. Yu, Y. J. Li, H. W. Li, L. F. Cui and Y. T. Long, Nat. Protoc., 2019, 14, 2015–2035 CrossRef CAS PubMed.
  36. J. J. Kasianowicz, E. Brandin, D. Branton and D. W. Deamer, Proc. Natl. Acad. Sci. U. S. A., 1996, 93, 13770–13773 CrossRef CAS PubMed.
  37. T. Albrecht, Annu. Rev. Anal. Chem., 2019, 12, 371–387 CrossRef PubMed.
  38. K. Lee, K. B. Park, H. J. Kim, J. S. Yu, H. Chae, H. M. Kim and K. B. Kim, Adv. Mater., 2018, 30, e1704680 CrossRef PubMed.
  39. X. Shi, D. V. Verschueren and C. Dekker, Nano Lett., 2018, 18, 8003–8010 CrossRef CAS PubMed.
  40. D. V. Verschueren, S. Pud, X. Shi, L. D. Angelis, L. Kuipers and C. Dekker, ACS Nano, 2019, 13, 61–70 CrossRef CAS PubMed.
  41. M. Belkin, S. H. Chao, M. P. Jonsson, C. Dekker and A. Aksimentiev, ACS Nano, 2015, 9, 10598–10611 CrossRef CAS PubMed.
  42. D. Garoli, H. Yamazaki, N. Maccaferri and M. Wanunu, Nano Lett., 2019, 19, 7553–7562 CrossRef CAS PubMed.
  43. T. Sannomiya and J. Voros, Trends Biotechnol., 2011, 29, 343–351 CrossRef CAS PubMed.
  44. X. Ma, S. Song, S. Kim, M. S. Kwon, H. Lee, W. Park and S. J. Sim, Nat. Commun., 2019, 10, 836 CrossRef PubMed.
  45. E. Kazuma, J. Jung, H. Ueba, M. Trenary and Y. Kim, Science, 2018, 360, 521–526 CrossRef CAS PubMed.
  46. N. Liu, M. Hentschel, T. Weiss, A. P. Alivisatos and H. Giessen, Science, 2011, 332, 1407–1410 CrossRef CAS PubMed.
  47. E. Ringe, B. Sharma, A. I. Henry, L. D. Marks and R. P. Van Duyne, Phys. Chem. Chem. Phys., 2013, 15, 4110–4129 RSC.
  48. A. Portela, E. C. Pelaez, O. Calvo-Lozano, M. C. Estevez and L. M. Lechuga, Methods Mol. Biol., 2019, 2027, 115–140 CrossRef CAS PubMed.
  49. D. Alcaraz Iranzo, S. Nanot, E. J. C. Dias, I. Epstein, C. Peng, D. K. Efetov, M. B. Lundeberg, R. Parret, J. Osmond, J. Y. Hong, J. Kong, D. R. Englund, N. M. R. Peres and F. H. L. Koppens, Science, 2018, 360, 291–295 CrossRef CAS PubMed.
  50. J. Lin, J. P. Mueller, Q. Wang, G. Yuan, N. Antoniou, X. C. Yuan and F. Capasso, Science, 2013, 340, 331–334 CrossRef CAS PubMed.
  51. Y. Xia, Y. Xiong, B. Lim and S. E. Skrabalak, Angew. Chem., Int. Ed., 2009, 48, 60–103 CrossRef CAS PubMed.
  52. Y. Xu, P. Bai, X. Zhou, Y. Akimov, C. E. Png, L.-K. Ang, W. Knoll and L. Wu, Adv. Opt. Mater., 2019, 7, 1801433 CrossRef.
  53. A. D. McFarland and R. P. Van Duyne, Nano Lett., 2003, 3, 1057–1062 CrossRef CAS.
  54. G. Raschke, S. Kowarik, T. Franzl, C. Sönnichsen, T. A. Klar, J. Feldmann, A. Nichtl and K. Kürzinger, Nano Lett., 2003, 3, 935–938 CrossRef CAS.
  55. M. Svedendahl, S. Chen, A. Dmitriev and M. Käll, Nano Lett., 2009, 9, 4428–4433 CrossRef CAS PubMed.
  56. Y. Zhang, Y. Gu, J. He, B. D. Thackray and J. Ye, Nat. Commun., 2019, 10, 3905 CrossRef PubMed.
  57. P. L. Truong, X. Ma and S. J. Sim, Nanoscale, 2014, 6, 2307–2315 RSC.
  58. X. Liu, L. Li, Y. Yang, Y. Yin and C. Gao, Nanoscale, 2014, 6, 4513–4516 RSC.
  59. W. Kubo and S. Fujikawa, Nano Lett., 2011, 11, 8–15 CrossRef CAS PubMed.
  60. A. U. Khan, S. Zhao and G. Liu, J. Phys. Chem. C, 2016, 120, 19353–19364 CrossRef CAS.
  61. W. Wang, P. Yu, Z. Zhong, X. Tong, T. Liu, Y. Li, E. Ashalley, H. Chen, J. Wu and Z. Wang, Nanotechnology, 2018, 29, 355402 CrossRef PubMed.
  62. S. M. Mousavi, M. Zarei, S. A. Hashemi, S. Ramakrishna, W. H. Chiang, C. W. Lai and A. Gholami, Drug Metab. Rev., 2020, 1–20,  DOI:10.1080/03602532.2020.1734021.
  63. C. Kuttner, M. Mayer, M. Dulle, A. Moscoso, J. M. Lopez-Romero, S. Forster, A. Fery, J. Perez-Juste and R. Contreras-Caceres, ACS Appl. Mater. Interfaces, 2018, 10, 11152–11163 CrossRef CAS PubMed.
  64. H. Chen, X. Kou, Z. Yang, W. Ni and J. Wang, Langmuir, 2008, 24, 5233 CrossRef CAS PubMed.
  65. Q. Li, X. Zhuo, S. Li, Q. Ruan, Q.-H. Xu and J. Wang, Adv. Opt. Mater., 2015, 3, 801 CrossRef CAS.
  66. J. Lee, W. Hasan and T. W. Odom, J. Phys. Chem. C, 2009, 113, 2205 CrossRef CAS PubMed.
  67. C. Fang, G. Zhao, Y. Xiao, J. Zhao, Z. Zhang and B. Geng, Sci. Rep., 2016, 6, 36706 CrossRef CAS PubMed.
  68. C. M. Sweeney, C. L. Stender, C. L. Nehl, W. Hasan, K. L. Shuford and T. W. Odom, Small, 2011, 7, 2032 CrossRef CAS PubMed.
  69. L. H. Guo, J. A. Jackman, H.-H. Yang, P. Chen, N.-J. Cho and D.-H. Kim, Nano Today, 2015, 10, 213–239 CrossRef CAS.
  70. Y. J. Lee, N. B. Schade, L. Sun, J. A. Fan, D. R. Bae, M. M. Mariscal, G. Lee, F. Capasso, S. Sacanna, V. N. Manoharan and G. R. Yi, ACS Nano, 2013, 7, 11064–11070 CrossRef CAS PubMed.
  71. J. H. Lee, K. J. Gibson, G. Chen and Y. Weizmann, Nat. Commun., 2015, 6, 7571 CrossRef CAS PubMed.
  72. T. Vo-Dinh, Y. Liu, B. M. Crawford, H. N. Wang, H. Yuan, J. K. Register and C. G. Khoury, J. Immunol. Sci., 2018, 2, 1–8 CrossRef.
  73. X. Ma, J. Huh, W. Park, L. P. Lee, Y. J. Kwon and S. J. Sim, Nat. Commun., 2016, 7, 12873 CrossRef CAS PubMed.
  74. M. Li, Y. Qiu, C. Fan, K. Cui, Y. Zhang and Z. Xiao, Acta Pharm. Sin. B, 2018, 8, 381–389 CrossRef PubMed.
  75. M. Fleischmann, P. J. Hendra and A. J. McQuillan, Chem. Phys. Lett., 1974, 26, 163–166 CrossRef CAS.
  76. D. L. Jeanmaire and R. P. V. Duyne, J. Electroanal. Chem. Interfacial Electrochem., 1977, 84, 1–20 CrossRef CAS.
  77. P. Strobbia, T. Sadler, R. A. Odion and T. Vo-Dinh, Anal. Chem., 2019, 91, 3319–3326 CrossRef CAS PubMed.
  78. D. K. Lim, K. S. Jeon, J. H. Hwang, H. Kim, S. Kwon, Y. D. Suh and J. M. Nam, Nat. Nanotechnol., 2011, 6, 452–460 CrossRef CAS PubMed.
  79. D. K. Lim, K. S. Jeon, H. M. Kim, J. M. Nam and Y. D. Suh, Nat. Mater., 2010, 9, 60–67 CrossRef CAS PubMed.
  80. S. S. Sinha, S. Jones, A. Pramanik and P. C. Ray, Acc. Chem. Res., 2016, 49, 2725–2735 CrossRef CAS PubMed.
  81. X. M. Qian and S. M. Nie, Chem. Soc. Rev., 2008, 37, 912–920 RSC.
  82. S. F. Tan, L. Wu, J. K. Yang, P. Bai, M. Bosman and C. A. Nijhuis, Science, 2014, 343, 1496–1499 CrossRef CAS PubMed.
  83. R. Zhang, Y. Zhang, Z. C. Dong, S. Jiang, C. Zhang, L. G. Chen, L. Zhang, Y. Liao, J. Aizpurua, Y. Luo, J. L. Yang and J. G. Hou, Nature, 2013, 498, 82–86 CrossRef CAS PubMed.
  84. F. Benz, M. K. Schmidt, A. Dreismann, R. Chikkaraddy, Y. Zhang, A. Demetriadou, C. Carnegie, H. Ohadi, B. de Nijs, R. Esteban, J. Aizpurua and J. J. Baumberg, Science, 2016, 354, 726–729 CrossRef CAS PubMed.
  85. C. Ciraci, R. T. Hill, J. J. Mock, Y. Urzhumov, A. I. Fernandez-Dominguez, S. A. Maier, J. B. Pendry, A. Chilkoti and D. R. Smith, Science, 2012, 337, 1072–1074 CrossRef CAS PubMed.
  86. K. Zhang, Y. Liu, Y. Wang, J. Zhao and B. Liu, Chem. Sci., 2019, 10, 1741–1745 RSC.
  87. Y. Zeng, J. Q. Ren, A. G. Shen and J. M. Hu, J. Am. Chem. Soc., 2018, 140, 10649–10652 CrossRef CAS PubMed.
  88. Y. Wu, N. Choi, H. Chen, H. Dang, L. Chen and J. Choo, Anal. Chem., 2020, 92, 2628–2634 CrossRef CAS PubMed.
  89. Z. Yan, S. Dutta, Z. Liu, X. Yu, N. Mesgarzadeh, F. Ji, G. Bitan and Y. H. Xie, ACS Sens., 2019, 4, 488–497 CrossRef CAS PubMed.
  90. T. Koker, N. Tang, C. Tian, W. Zhang, X. Wang, R. Martel and F. Pinaud, Nat. Commun., 2018, 9, 607 CrossRef PubMed.
  91. A. Kowalczyk, J. Krajczewski, A. Kowalik, J. L. Weyher, I. Dziecielewski, M. Chlopek, S. Gozdz, A. M. Nowicka and A. Kudelski, Biosens. Bioelectron., 2019, 132, 326–332 CrossRef CAS PubMed.
  92. E. C. Le Ru, M. Meyer and P. G. Etchegoin, J. Phys. Chem. B, 2006, 110, 1944–1948 CrossRef CAS PubMed.
  93. H. H. Shin, G. J. Yeon, H. K. Choi, S. M. Park, K. S. Lee and Z. H. Kim, Nano Lett., 2018, 18, 262–271 CrossRef CAS PubMed.
  94. S. Fateixa, H. I. Nogueira and T. Trindade, Phys. Chem. Chem. Phys., 2015, 17, 21046–21071 RSC.
  95. D. Wu, Y. Chen, S. Hou, W. Fang and H. Duan, ChemBioChem, 2019, 20, 2432–2441 CrossRef CAS PubMed.
  96. L. A. Wu, W. E. Li, D. Z. Lin and Y. F. Chen, Sci. Rep., 2017, 7, 13066 CrossRef PubMed.
  97. M. G. Cha, H. M. Kim, Y. L. Kang, M. Lee, H. Kang, J. Kim, X. H. Pham, T. H. Kim, E. Hahm, Y. S. Lee, D. H. Jeong and B. H. Jun, PLoS One, 2017, 12, e0178651 CrossRef PubMed.
  98. A. Lagarkov, I. Budashov, V. Chistyaev, A. Ezhov, A. Fedyanin, A. Ivanov, I. Kurochkin, S. Kosolobov, A. Latyshev, D. Nasimov, I. Ryzhikov, M. Shcherbakov, A. Vaskin and A. K. Sarychev, Opt. Express, 2016, 24, 7133–7150 CrossRef CAS PubMed.
  99. L. Lin, M. Zapata, M. Xiong, Z. Liu, S. Wang, H. Xu, A. G. Borisov, H. Gu, P. Nordlander, J. Aizpurua and J. Ye, Nano Lett., 2015, 15, 6419–6428 CrossRef CAS PubMed.
  100. C. Hu, J. Shen, J. Yan, J. Zhong, W. Qin, R. Liu, A. Aldalbahi, X. Zuo, S. Song, C. Fan and D. He, Nanoscale, 2016, 8, 2090–2096 RSC.
  101. L. Lin, H. Gu and J. Ye, Chem. Commun., 2015, 51, 17740–17743 RSC.
  102. W. Xie, P. Qiu and C. Mao, J. Mater. Chem., 2011, 21, 5190–5202 RSC.
  103. J. Xu, L. Zhang, H. Gong, J. Homola and Q. Yu, Small, 2011, 7, 371–376 CrossRef CAS PubMed.
  104. M. Wang, A. Hoff, J. E. Doebler, S. R. Emory and Y. Bao, Langmuir, 2019, 35, 16886–16892 CrossRef CAS PubMed.
  105. W. Li, C. Ma, L. Zhang, B. Chen, L. Chen and H. Zeng, Nanomaterials, 2019, 9, 251 CrossRef CAS PubMed.
  106. A. H. Mo, P. B. Landon, K. S. Gomez, H. Kang, J. Lee, C. Zhang, W. Janetanakit, V. Sant, T. Lu, D. A. Colburn, S. Akkiraju, S. Dossou, Y. Cao, K. F. Lee, S. Varghese, G. Glinsky and R. Lal, Nanoscale, 2016, 8, 11840–11850 RSC.
  107. Y. Wang, C. Zhou, W. Wang, D. Xu, F. Zeng, C. Zhan, J. Gu, M. Li, W. Zhao, J. Zhang, J. Guo, H. Feng and X. Ma, Angew. Chem., 2018, 57, 13110–13113 CrossRef CAS PubMed.
  108. H. Kang, S. Jeong, A. Jo, H. Chang, J. K. Yang, C. Jeong, S. Kyeong, Y. W. Lee, A. Samanta, K. K. Maiti, M. G. Cha, T. K. Kim, S. Lee, B. H. Jun, Y. T. Chang, J. Chung, H. Y. Lee, D. H. Jeong and Y. S. Lee, Adv. Healthcare Mater., 2018, 7, 1700870 CrossRef PubMed.
  109. N. Zohar and G. Haran, Langmuir, 2014, 30, 7919–7927 CrossRef CAS PubMed.
  110. M. Nguyen, I. Kherbouche, M. Braik, A. Belkhir, L. Boubekeur-Lecaque, J. Aubard, C. Mangeney and N. Felidj, ACS Omega, 2019, 4, 1144–1150 CrossRef CAS PubMed.
  111. L. Velleman, L. Scarabelli, D. Sikdar, A. A. Kornyshev, L. M. Liz-Marzan and J. B. Edel, Faraday Discuss., 2017, 205, 67–83 RSC.
  112. Y. Wang, H. Wang, Y. Wang, Y. Shen, S. Xu and W. Xu, ACS Appl. Mater. Interfaces, 2016, 8, 15623–15629 CrossRef CAS PubMed.
  113. A. B. Serrano-Montes, D. Jimenez de Aberasturi, J. Langer, J. J. Giner-Casares, L. Scarabelli, A. Herrero and L. M. Liz-Marzan, Langmuir, 2015, 31, 9205–9213 CrossRef CAS PubMed.
  114. A. Klinkova, R. M. Choueiri and E. Kumacheva, Chem. Soc. Rev., 2014, 43, 3976–3991 RSC.
  115. S. Y. Ding, E. M. You, Z. Q. Tian and M. Moskovits, Chem. Soc. Rev., 2017, 46, 4042–4076 RSC.
  116. W. Leng and P. J. Vikesland, Langmuir, 2014, 30, 8342–8349 CrossRef CAS PubMed.
  117. G. Zito, G. Rusciano and A. Sasso, J. Chem. Phys., 2016, 145, 054708 CrossRef PubMed.
  118. H. Lee, G. H. Kim, J. H. Lee, N. H. Kim, J. M. Nam and Y. D. Suh, Nano Lett., 2015, 15, 4628–4636 CrossRef CAS PubMed.
  119. X. Cao, Z. Wang, L. Bi, C. Bi and Q. Du, Nanoscale, 2020, 12, 1513–1527 RSC.
  120. G. Chen, Y. Wang, M. Yang, J. Xu, S. J. Goh, M. Pan and H. Chen, J. Am. Chem. Soc., 2010, 132, 3644–3645 CrossRef CAS PubMed.
  121. P. Makam, R. Shilpa, A. E. Kandjani, S. R. Periasamy, Y. M. Sabri, C. Madhu, S. K. Bhargava and T. Govindaraju, Biosens. Bioelectron., 2018, 100, 556–564 CrossRef CAS PubMed.
  122. B. Pinter, L. Broeckaert, J. Turek, A. Ruzicka and F. De Proft, Chemistry, 2014, 20, 734–744 CrossRef CAS PubMed.
  123. J. H. Yoon, F. Selbach, L. Langolf and S. Schlucker, Small, 2018, 14, 1702754 CrossRef PubMed.
  124. P. Kuhler, E. M. Roller, R. Schreiber, T. Liedl, T. Lohmuller and J. Feldmann, Nano Lett., 2014, 14, 2914–2919 CrossRef PubMed.
  125. L. Xu, Y. Gao, H. Kuang, L. M. Liz-Marzan and C. Xu, Angew. Chem., 2018, 57, 10544–10548 CrossRef CAS PubMed.
  126. P. Dey, T. A. Tabish, S. Mosca, F. Palombo, P. Matousek and N. Stone, Small, 2020, e1906780,  DOI:10.1002/smll.201906780.
  127. S. Ye, M. Wang, Z. Wang, N. Zhang and X. Luo, Chem. Commun., 2018, 54, 7786–7789 RSC.
  128. S. A. Belhout, F. R. Baptista, S. J. Devereux, A. W. Parker, A. D. Ward and S. J. Quinn, Nanoscale, 2019, 11, 19884–19894 RSC.
  129. Y. Wang, A. B. Serrano, K. Sentosun, S. Bals and L. M. Liz-Marzan, Small, 2015, 11, 4314–4320 CrossRef CAS PubMed.
  130. X. Feng, F. Ruan, R. Hong, J. Ye, J. Hu, G. Hu and Z. Yang, Langmuir, 2011, 27, 2204–2210 CrossRef CAS PubMed.
  131. R. M. Jarvis, H. E. Johnson, E. Olembe, A. Panneerselvam, M. A. Malik, M. Afzaal, P. O'Brien and R. Goodacre, Analyst, 2008, 133, 1449–1452 RSC.
  132. X. Huang, Y. Liu, J. Barr, J. Song, Z. He, Y. Wang, Z. Nie, Y. Xiong and X. Chen, Nanoscale, 2018, 10, 13202–13211 RSC.
  133. F. Biscaglia, S. Rajendran, P. Conflitti, C. Benna, R. Sommaggio, L. Litti, S. Mocellin, G. Bocchinfuso, A. Rosato, A. Palleschi, D. Nitti, M. Gobbo and M. Meneghetti, Adv. Healthcare Mater., 2017, 6, 1700596 CrossRef PubMed.
  134. M. Bhamidipati and L. Fabris, Bioconjugate Chem., 2017, 28, 449–460 CrossRef CAS PubMed.
  135. I. Blakey, Z. Merican and K. J. Thurecht, Langmuir, 2013, 29, 8266–8274 CrossRef CAS PubMed.
  136. C. T. Ertsgaard, N. J. Wittenberg, D. J. Klemme, A. Barik, W. C. Shih and S. H. Oh, Nano Lett., 2018, 18, 5946–5953 CrossRef CAS PubMed.
  137. V. Zivanovic, Z. Kochovski, C. Arenz, Y. Lu and J. Kneipp, J. Phys. Chem. Lett., 2018, 9, 6767–6772 CrossRef CAS PubMed.
  138. Y. Huang, Y. Chen, X. Xue, Y. Zhai, L. Wang and Z. Zhang, Opt. Lett., 2018, 43, 2332–2335 CrossRef PubMed.
  139. S. J. Barrow, S. M. Collins, D. Rossouw, A. M. Funston, G. A. Botton, P. A. Midgley and P. Mulvaney, ACS Nano, 2016, 10, 8552–8563 CrossRef CAS PubMed.
  140. J. R. Matthews, C. R. Shirazinejad, G. A. Isakson, S. M. Demers and J. H. Hafner, Nano Lett., 2017, 17, 2172–2177 CrossRef CAS PubMed.
  141. C. Kuttner, R. P. M. Holler, M. Quintanilla, M. J. Schnepf, M. Dulle, A. Fery and L. M. Liz-Marzan, Nanoscale, 2019, 11, 17655–17663 RSC.
  142. P. Dey, K. J. Thurecht, P. M. Fredericks and I. Blakey, Appl. Spectrosc., 2019, 73, 1428–1435 CrossRef CAS PubMed.
  143. W. Ma, H. Yin, L. Xu, X. Wu, H. Kuang, L. Wang and C. Xu, Chem. Commun., 2014, 50, 9737–9740 RSC.
  144. J. Hengster and T. Uphues, Opt. Express, 2017, 25, 11347–11358 CrossRef CAS PubMed.
  145. C. Vidal, D. Sivun, J. Ziegler, D. Wang, P. Schaaf, C. Hrelescu and T. A. Klar, Nano Lett., 2018, 18, 1269–1273 CrossRef CAS PubMed.
  146. C. Y. Chiu, L. Ruan and Y. Huang, Chem. Soc. Rev., 2013, 42, 2512–2527 RSC.
  147. P. Dandekar, Z. B. Kuvadia and M. F. Doherty, Annu. Rev. Mater. Res., 2013, 43, 359–386 CrossRef CAS.
  148. J. Ustarroz, J. A. Hammons, T. Altantzis, A. Hubin, S. Bals and H. Terryn, J. Am. Chem. Soc., 2013, 135, 11550–11561 CrossRef CAS PubMed.
  149. I. Zoric, M. Zach, B. Kasemo and C. Langhammer, ACS Nano, 2011, 5, 2535–2546 CrossRef CAS PubMed.
  150. P. D. Howes, R. Chandrawati and M. M. Stevens, Science, 2014, 346, 1247390 CrossRef PubMed.
  151. M. R. Jones, K. D. Osberg, R. J. Macfarlane, M. R. Langille and C. A. Mirkin, Chem. Rev., 2011, 111, 3736–3827 CrossRef CAS PubMed.
  152. C. Cao, X. Li, J. Lee and S. J. Sim, Biosens. Bioelectron., 2009, 24, 1292–1297 CrossRef CAS PubMed.
  153. A. Casu, E. Cabrini, A. Dona, A. Falqui, Y. Diaz-Fernandez, C. Milanese, A. Taglietti and P. Pallavicini, Chem. – Eur. J., 2012, 18, 9381–9390 CrossRef CAS PubMed.
  154. L. Ruan, H. Ramezani-Dakhel, C. Y. Chiu, E. Zhu, Y. Li, H. Heinz and Y. Huang, Nano Lett., 2013, 13, 840–846 CrossRef CAS PubMed.
  155. C. R. Bealing, W. J. Baumgardner, J. J. Choi, T. Hanrath and R. G. Hennig, ACS Nano, 2012, 6, 2118–2127 CrossRef CAS PubMed.
  156. F. D. Wang, V. N. Richards, S. P. Shields and W. E. Buhro, Chem. Mater., 2014, 26, 5–21 CrossRef CAS.
  157. W. Sun, E. Boulais, Y. Hakobyan, W. L. Wang, A. Guan, M. Bathe and P. Yin, Science, 2014, 346, 1258361 CrossRef PubMed.
  158. J. Turkevich, P. C. Stevenson and J. Hillier, J. Discuss. Faraday Soc., 1951, 11, 55–75 RSC.
  159. M. K. Chow and C. F. Zukoski, J. Colloid Interface Sci., 1994, 165, 97–109 CrossRef CAS.
  160. R. L. Penn and J. F. Banfield, Science, 1998, 281, 969–971 CrossRef CAS PubMed.
  161. J. S. Owen, E. M. Chan, H. Liu and A. P. Alivisatos, J. Am. Chem. Soc., 2010, 132, 18206–18213 CrossRef CAS PubMed.
  162. D. T. Robb and V. Privman, Langmuir, 2008, 24, 26–35 CrossRef CAS PubMed.
  163. J. Y. Rempel, M. G. Bawendi and K. F. Jensen, J. Am. Chem. Soc., 2009, 131, 4479–4489 CrossRef CAS PubMed.
  164. L. Meli and P. F. Green, ACS Nano, 2008, 2, 1305–1312 CrossRef CAS PubMed.
  165. Y. Chen, E. Johnson and X. Peng, J. Am. Chem. Soc., 2007, 129, 10937–10947 CrossRef CAS PubMed.
  166. X. Ji, X. Song, J. Li, Y. Bai, W. Yang and X. Peng, J. Am. Chem. Soc., 2007, 129, 13939–13948 CrossRef CAS PubMed.
  167. J. Ohyama, K. Teramura, Y. Higuchi, T. Shishido, Y. Hitomi, K. Kato, H. Tanida, T. Uruga and T. Tanaka, ChemPhysChem, 2011, 12, 127–131 CrossRef CAS PubMed.
  168. J. Li, Z. Wang and F. L. Deepak, ACS Nano, 2017, 11, 5590–5597 CrossRef CAS PubMed.
  169. C. M. Li, I. M. Robertson, M. L. Jenkins, J. L. Hutchison and R. C. Doole, Micron, 2005, 36, 9–15 CrossRef CAS PubMed.
  170. S. Abe, R. K. Capek, B. De Geyter and Z. Hens, ACS Nano, 2012, 6, 42–53 CrossRef CAS PubMed.
  171. S. G. Kwon, Y. Piao, J. Park, S. Angappane, Y. Jo, N. M. Hwang, J. G. Park and T. Hyeon, J. Am. Chem. Soc., 2007, 129, 12571–12584 CrossRef CAS PubMed.
  172. M. D. Clark, S. K. Kumar, J. S. Owen and E. M. Chan, Nano Lett., 2011, 11, 1976–1980 CrossRef CAS PubMed.
  173. S. G. Kwon and T. Hyeon, Acc. Chem. Res., 2008, 41, 1696–1709 CrossRef CAS PubMed.
  174. J. Chai, X. Liao, L. R. Giam and C. A. Mirkin, J. Am. Chem. Soc., 2012, 134, 158–161 CrossRef CAS PubMed.
  175. L. R. Parent, D. B. Robinson, T. J. Woehl, W. D. Ristenpart, J. E. Evans, N. D. Browning and I. Arslan, ACS Nano, 2012, 6, 3589–3596 CrossRef CAS PubMed.
  176. H. G. Liao, L. Cui, S. Whitelam and H. Zheng, Science, 2012, 336, 1011–1014 CrossRef CAS PubMed.
  177. J. M. Yuk, J. Park, P. Ercius, K. Kim, D. J. Hellebusch, M. F. Crommie, J. Y. Lee, A. Zettl and A. P. Alivisatos, Science, 2012, 336, 61–64 CrossRef CAS PubMed.
  178. Y. Wang, D. Wan, S. Xie, X. Xia, C. Z. Huang and Y. Xia, ACS Nano, 2013, 7, 4586–4594 CrossRef CAS PubMed.
  179. I. Zoric, E. M. Larsson, B. Kasemo and C. Langhammer, Adv. Mater., 2010, 22, 4628–4633 CrossRef CAS PubMed.
  180. H. Chang, Y. Y. Lee, H. E. Lee, H. Y. Ahn, E. Ko, K. T. Nam and D. H. Jeong, Phys. Chem. Chem. Phys., 2019, 21, 9044–9051 RSC.
  181. S. Inasawa, M. Sugiyama and Y. Yamaguchi, J. Phys. Chem. B, 2005, 109, 3104–3111 CrossRef CAS PubMed.
  182. L. Scarabelli, A. Sanchez-Iglesias, J. Perez-Juste and L. M. Liz-Marzan, J. Phys. Chem. Lett., 2015, 6, 4270–4279 CrossRef CAS PubMed.
  183. X. Ye, Y. Gao, J. Chen, D. C. Reifsnyder, C. Zheng and C. B. Murray, Nano Lett., 2013, 13, 2163–2171 CrossRef CAS PubMed.
  184. S. E. Lohse and C. J. Murphy, Chem. Mater., 2013, 25, 1250–1261 CrossRef CAS.
  185. H. E. Lee, H. Y. Ahn, J. Mun, Y. Y. Lee, M. Kim, N. H. Cho, K. Chang, W. S. Kim, J. Rho and K. T. Nam, Nature, 2018, 556, 360–365 CrossRef CAS PubMed.
  186. Y. Nagaoka, O. Chen, Z. Wang and Y. C. Cao, J. Am. Chem. Soc., 2012, 134, 2868–2871 CrossRef CAS PubMed.
  187. Q. Y. Lin, J. A. Mason, Z. Li, W. Zhou, M. N. O'Brien, K. A. Brown, M. R. Jones, S. Butun, B. Lee, V. P. Dravid, K. Aydin and C. A. Mirkin, Science, 2018, 359, 669–672 CrossRef CAS PubMed.
  188. N. C. Seeman, Nature, 2003, 421, 427–431 CrossRef PubMed.
  189. H. Yan, X. Zhang, Z. Shen and N. C. Seeman, Nature, 2002, 415, 62–65 CrossRef CAS PubMed.
  190. J. Ou, H. Tan, X. Chen and Z. Chen, Nanomaterials, 2018, 8, 994 CrossRef PubMed.
  191. C. A. Mirkin, R. L. Letsinger, R. C. Mucic and J. J. Storhoff, Nature, 1996, 382, 607–609 CrossRef CAS PubMed.
  192. A. P. Alivisatos, K. P. Johnsson, X. Peng, T. E. Wilson, C. J. Loweth, M. P. Bruchez, Jr. and P. G. Schultz, Nature, 1996, 382, 609–611 CrossRef CAS PubMed.
  193. P. W. Rothemund, Nature, 2006, 440, 297–302 CrossRef CAS PubMed.
  194. S. M. Douglas, H. Dietz, T. Liedl, B. Hogberg, F. Graf and W. M. Shih, Nature, 2009, 459, 414–418 CrossRef CAS PubMed.
  195. Y. Huang, M. K. Nguyen, A. K. Natarajan, V. H. Nguyen and A. Kuzyk, ACS Appl. Mater. Interfaces, 2018, 10, 44221–44225 CrossRef CAS PubMed.
  196. F. Zhang, J. Nangreave, Y. Liu and H. Yan, J. Am. Chem. Soc., 2014, 136, 11198–11211 CrossRef CAS PubMed.
  197. M. R. Jones, N. C. Seeman and C. A. Mirkin, Science, 2015, 347, 1260901 CrossRef PubMed.
  198. L. H. Tan, Y. Yue, N. S. R. Satyavolu, A. S. Ali, Z. D. Wang, Y. Q. Wu and Y. Lu, J. Am. Chem. Soc., 2015, 137, 14456–14464 CrossRef CAS PubMed.
  199. G. Zhang, S. P. Surwade, F. Zhou and H. Liu, Chem. Soc. Rev., 2013, 42, 2488–2496 RSC.
  200. L. H. Tan, H. Xing and Y. Lu, Acc. Chem. Res., 2014, 47, 1881–1890 CrossRef CAS PubMed.
  201. S. Bidault, F. J. Abajo and A. Polman, J. Am. Chem. Soc., 2008, 130, 2750–2751 CrossRef CAS PubMed.
  202. M. J. Lagos, A. Trugler, U. Hohenester and P. E. Batson, Nature, 2017, 543, 529–532 CrossRef CAS PubMed.
  203. J. E. Park, Y. Lee and J. M. Nam, Nano Lett., 2018, 18, 6475–6482 CrossRef CAS PubMed.
  204. X. Ma, P. L. Truong, N. H. Anh and S. J. Sim, Biosens. Bioelectron., 2015, 67, 59–65 CrossRef CAS PubMed.
  205. Z. Yi, H. B. Wang, K. Chen, Q. Gao, H. Tang, R. Q. Yu and X. Chu, Biosens. Bioelectron., 2014, 53, 310–315 CrossRef CAS PubMed.
  206. L. F. Xu, S. Li and B. A. Stohr, Annu. Rev. Pathol.: Mech. Dis., 2013, 8, 49–78 CrossRef CAS PubMed.
  207. K. B. Blagoev, PLoS One, 2009, 4, e4622 CrossRef PubMed.
  208. C. Sonnichsen, B. M. Reinhard, J. Liphardt and A. P. Alivisatos, Nat. Biotechnol., 2005, 23, 741–745 CrossRef PubMed.
  209. M. S. Song, S. P. Choi, J. Lee, Y. J. Kwon and S. J. Sim, Adv. Mater., 2013, 25, 1265–1269 CrossRef CAS PubMed.
  210. G. L. Liu, Y. Yin, S. Kunchakarra, B. Mukherjee, D. Gerion, S. D. Jett, D. G. Bear, J. W. Gray, A. P. Alivisatos, L. P. Lee and F. F. Chen, Nat. Nanotechnol., 2006, 1, 47–52 CrossRef CAS PubMed.
  211. Y. Zhao, Y. K. He, J. Zhang, F. B. Wang, K. Wang and X. H. Xia, Chem. Commun., 2014, 50, 5480–5483 RSC.
  212. A. H. Nguyen, X. Ma and S. J. Sim, Biosens. Bioelectron., 2015, 66, 497–503 CrossRef CAS PubMed.
  213. R. Karlsson and A. Falt, J. Immunol. Methods, 1997, 200, 121–133 CrossRef CAS PubMed.
  214. S. Chen, M. Svedendahl, T. J. Antosiewicz and M. Kall, ACS Nano, 2013, 7, 8824–8832 CrossRef CAS PubMed.
  215. S. M. Taghdisi, N. M. Danesh, M. Ramezani, A. S. Emrani and K. Abnous, ACS Appl. Mater. Interfaces, 2018, 10, 12504–12509 CrossRef CAS PubMed.
  216. X. Zheng, Q. Liu, C. Jing, Y. Li, D. Li, W. Luo, Y. Wen, Y. He, Q. Huang, Y. T. Long and C. Fan, Angew. Chem., 2011, 50, 11994–11998 CrossRef CAS PubMed.
  217. L. Xiao, Y. Qiao, Y. He and E. S. Yeung, Anal. Chem., 2010, 82, 5268–5274 CrossRef CAS PubMed.
  218. A. Horneber, K. Braun, J. Rogalski, P. Leiderer, A. J. Meixner and D. Zhang, Phys. Chem. Chem. Phys., 2015, 17, 21288–21293 RSC.
  219. G. Razinskas, D. Kilbane, P. Melchior, P. Geisler, E. Krauss, S. Mathias, B. Hecht and M. Aeschlimann, Nano Lett., 2016, 16, 6832–6837 CrossRef CAS PubMed.
  220. J. Olesiak-Banska, M. Waszkielewicz, P. Obstarczyk and M. Samoc, Chem. Soc. Rev., 2019, 48, 4087–4117 RSC.
  221. G. B. Braun, T. Friman, H. B. Pang, A. Pallaoro, T. Hurtado de Mendoza, A. M. Willmore, V. R. Kotamraju, A. P. Mann, Z. G. She, K. N. Sugahara, N. O. Reich, T. Teesalu and E. Ruoslahti, Nat. Mater., 2014, 13, 904–911 CrossRef CAS PubMed.
  222. X. Yang, M. Yang, B. Pang, M. Vara and Y. Xia, Chem. Rev., 2015, 115, 10410–10488 CrossRef CAS PubMed.
  223. K. J. Lee, P. D. Nallathamby, L. M. Browning, C. J. Osgood and X. H. Xu, ACS Nano, 2007, 1, 133–143 CrossRef CAS PubMed.
  224. Q. Jin, M. Li, B. Polat, S. K. Paidi, A. Dai, A. Zhang, J. V. Pagaduan, I. Barman and D. H. Gracias, Angew. Chem., 2017, 56, 3822–3826 CrossRef CAS PubMed.
  225. G. Borile, S. Rossi, A. Filippi, E. Gazzola, P. Capaldo, C. Tregnago, M. Pigazzi and F. Romanato, Biophys. Chem., 2019, 254, 106262 CrossRef CAS PubMed.
  226. K. Lee, Y. Cui, L. P. Lee and J. Irudayaraj, Nat. Nanotechnol., 2014, 9, 474–480 CrossRef CAS PubMed.
  227. L. Shao, Z. J. Yang, D. Andren, P. Johansson and M. Kall, ACS Nano, 2015, 9, 12542–12551 CrossRef CAS PubMed.
  228. M. K. Nguyen and A. Kuzyk, ACS Nano, 2019, 13, 13615–13619 CrossRef CAS PubMed.
  229. K. Chen, Y. Gu, W. Sun, D. Bin, G. Wang, X. Fan, T. Xia and N. Fang, Nat. Commun., 2017, 8, 887 CrossRef PubMed.
  230. M. Raab, C. Vietz, F. D. Stefani, G. P. Acuna and P. Tinnefeld, Nat. Commun., 2017, 8, 13966 CrossRef CAS PubMed.
  231. L. Wei, J. Xu, Z. Ye, X. Zhu, M. Zhong, W. Luo, B. Chen, H. Duan, Q. Liu and L. Xiao, Anal. Chem., 2016, 88, 1995–1999 CrossRef CAS PubMed.
  232. W. S. Chang, J. W. Ha, L. S. Slaughter and S. Link, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 2781–2786 CrossRef CAS PubMed.
  233. J. Morales-Dalmau, C. Vilches, V. Sanz, I. de Miguel, V. Rodriguez-Fajardo, P. Berto, M. Martinez-Lozano, O. Casanovas, T. Durduran and R. Quidant, Nanoscale, 2019, 11, 11331–11339 RSC.
  234. Z. Chen, H. Fan, J. Li, S. Tie and S. Lan, Opt. Express, 2017, 25, 15093–15107 CrossRef PubMed.
  235. T. Zhao, K. Yu, L. Li, T. Zhang, Z. Guan, N. Gao, P. Yuan, S. Li, S. Q. Yao, Q. H. Xu and G. Q. Xu, ACS Appl. Mater. Interfaces, 2014, 6, 2700–2708 CrossRef CAS PubMed.
  236. Y. Zhang, M. Li, X. Gao, Y. Chen and T. Liu, J. Hematol. Oncol., 2019, 12, 137 CrossRef PubMed.
  237. J. Peng and X. Liang, Medicine, 2019, 98, e15311 CrossRef CAS PubMed.
  238. S. W. Lee, C. D. Kang, D. H. Yang, J. S. Lee, J. M. Kim, D. J. Ahn and S. J. Sim, Adv. Funct. Mater., 2007, 17, 2038–2044 CrossRef CAS.
  239. T. J. Song, L. H. Tang, L. H. Tan, X. J. Wang, N. S. R. Satyavolu, H. Xing, Z. D. Wang, J. H. Li, H. J. Liang and Y. Lu, Angew. Chem., Int. Ed., 2015, 54, 8114–8118 CrossRef CAS PubMed.
  240. S. Lee, J. Lee, K. Kim, S.-J. Sim, M. B. Gu, J. Yi and J. Lee, Biotechnol. Bioprocess Eng., 2009, 14, 490–495 CrossRef CAS.
  241. D. N. Basov and M. M. Fogler, Science, 2017, 357, 132–133 CrossRef CAS PubMed.
  242. H. Kim, J. U. Lee, S. Song, S. Kim and S. J. Sim, Biosens. Bioelectron., 2018, 101, 96–102 CrossRef CAS PubMed.
  243. Z. Gu, C. Jing, Y. L. Ying, P. He and Y. T. Long, Theranostics, 2015, 5, 188–195 CrossRef PubMed.
  244. J. Zhu, J. He, M. Verano, A. T. Brimmo, A. Glia, M. A. Qasaimeh, P. Chen, J. O. Aleman and W. Chen, Lab Chip, 2018, 18, 3550–3560 RSC.
  245. A. Shakoor, J. Grant, M. Grande and D. R. S. Cumming, Sensors, 2019, 19, 1715 CrossRef PubMed.
  246. R. F. Service, Science, 2018, 362, 1228 CrossRef CAS PubMed.
  247. C. Haffner, A. Joerg, M. Doderer, F. Mayor, D. Chelladurai, Y. Fedoryshyn, C. I. Roman, M. Mazur, M. Burla, H. J. Lezec, V. A. Aksyuk and J. Leuthold, Science, 2019, 366, 860–864 CrossRef CAS PubMed.
  248. X. Li, M. Soler, C. Szydzik, K. Khoshmanesh, J. Schmidt, G. Coukos, A. Mitchell and H. Altug, Small, 2018, 14, e1800698 CrossRef PubMed.
  249. X. Huang, D. Xu, J. Chen, J. Liu, Y. Li, J. Song, X. Ma and J. Guo, Analyst, 2018, 143, 5339–5351 RSC.
  250. J. M. Perkel, Nature, 2017, 545, 119–121 CrossRef CAS PubMed.
  251. S. Zhang and F. Cicoira, Nature, 2018, 561, 466–467 CrossRef CAS PubMed.
  252. Q. Yu, L. Xue, J. Hiblot, R. Griss, S. Fabritz, C. Roux, P. A. Binz, D. Haas, J. G. Okun and K. Johnsson, Science, 2018, 361, 1122–1126 CrossRef CAS PubMed.
  253. E. L. Runnerstrom, A. Llordes, S. D. Lounis and D. J. Milliron, Chem. Commun., 2014, 50, 10555–10572 RSC.
  254. M. Wang, X. Xing, I. F. Perepichka, Y. Shi, D. Zhou, P. Wu and H. Meng, Adv. Energy Mater., 2019, 9, 1900433 CrossRef.
  255. R. C. Evans, A. Ellingworth, C. J. Cashen, C. R. Weinberger and J. B. Sambur, Proc. Natl. Acad. Sci. U. S. A., 2019, 116, 12666–12671 CrossRef CAS PubMed.
  256. Y. Wang, E. L. Runnerstrom and D. J. Milliron, Annu. Rev. Chem. Biomol. Eng., 2016, 7, 283–304 CrossRef CAS PubMed.
  257. N. Jiang, X. Zhuo and J. Wang, Chem. Rev., 2018, 118, 3054–3099 CrossRef CAS PubMed.
  258. R. R. Sathuluri, H. Yoshikawa, E. Shimizu, M. Saito and E. Tamiya, PLoS One, 2011, 6, e22802 CrossRef PubMed.
  259. S.-Y. Ding, J. Yi, J.-F. Li, B. Ren, D.-Y. Wu, R. Panneerselvam and Z.-Q. Tian, Nat. Rev. Mater., 2016, 1, 16021 CrossRef CAS.
  260. J. Langer, D. Jimenez de Aberasturi, J. Aizpurua, R. A. Alvarez-Puebla, B. Auguie, J. J. Baumberg, G. C. Bazan, S. E. J. Bell, A. Boisen, A. G. Brolo, J. Choo, D. Cialla-May, V. Deckert, L. Fabris, K. Faulds, F. J. Garcia de Abajo, R. Goodacre, D. Graham, A. J. Haes, C. L. Haynes, C. Huck, T. Itoh, M. Kall, J. Kneipp, N. A. Kotov, H. Kuang, E. C. Le Ru, H. K. Lee, J. F. Li, X. Y. Ling, S. A. Maier, T. Mayerhofer, M. Moskovits, K. Murakoshi, J. M. Nam, S. Nie, Y. Ozaki, I. Pastoriza-Santos, J. Perez-Juste, J. Popp, A. Pucci, S. Reich, B. Ren, G. C. Schatz, T. Shegai, S. Schlucker, L. L. Tay, K. G. Thomas, Z. Q. Tian, R. P. Van Duyne, T. Vo-Dinh, Y. Wang, K. A. Willets, C. Xu, H. Xu, Y. Xu, Y. S. Yamamoto, B. Zhao and L. M. Liz-Marzan, ACS Nano, 2020, 14, 28–117 CrossRef CAS PubMed.
  261. M. Kahraman, E. R. Mullen, A. Korkmaz and S. Wachsmann-Hogiu, Nanophotonics, 2017, 6, 831–852 CAS.
  262. J. H. Lee, J. W. Oh, S. H. Nam, Y. S. Cha, G. H. Kim, W. K. Rhim, N. H. Kim, J. Kim, S. W. Han, Y. D. Suh and J. M. Nam, Small, 2016, 12, 4726–4734 CrossRef CAS PubMed.
  263. J. Song, B. Duan, C. Wang, J. Zhou, L. Pu, Z. Fang, P. Wang, T. T. Lim and H. Duan, J. Am. Chem. Soc., 2014, 136, 6838–6841 CrossRef CAS PubMed.
  264. N. G. Khlebtsov, L. Lin, B. N. Khlebtsov and J. Ye, Theranostics, 2020, 10, 2067–2094 CrossRef PubMed.
  265. N. Pazos-Perez, C. S. Wagner, J. M. Romo-Herrera, L. M. Liz-Marzan, F. J. Garcia de Abajo, A. Wittemann, A. Fery and R. A. Alvarez-Puebla, Angew. Chem., 2012, 51, 12688–12693 CrossRef CAS PubMed.
  266. L. Guerrini, F. McKenzie, A. W. Wark, K. Faulds and D. Graham, Chem. Sci., 2012, 3, 2262–2269 RSC.
  267. C. Heck, Y. Kanehira, J. Kneipp and I. Bald, Molecules, 2019, 24, 2324 CrossRef CAS PubMed.
  268. G. B. Braun, S. J. Lee, T. Laurence, N. Fera, L. Fabris, G. C. Bazan, M. Moskovits and N. O. Reich, J. Phys. Chem. C, 2009, 113, 13622–13629 CrossRef CAS.
  269. K. Sohn, F. Kim, K. C. Pradel, J. Wu, Y. Peng, F. Zhou and J. Huang, ACS Nano, 2009, 3, 2191–2198 CrossRef CAS PubMed.
  270. K. D. Gilroy, H. C. Peng, X. Yang, A. Ruditskiy and Y. Xia, Chem. Commun., 2017, 53, 4530–4541 RSC.
  271. L. M. Moreau, M. R. Jones, E. W. Roth, J. Wu, S. Kewalramani, M. N. O'Brien, B. R. Chen, C. A. Mirkin and M. J. Bedzyk, Nanoscale, 2019, 11, 11744–11754 RSC.
  272. Y. Hu and C. M. Niemeyer, Adv. Mater., 2019, 31, e1806294 CrossRef PubMed.
  273. W. Chen, H. Hu, W. Jiang, Y. Xu, S. Zhang and H. Xu, Chin. Phys. B, 2018, 27, 107403 CrossRef.
  274. G. L. Liu, Y. T. Long, Y. Choi, T. Kang and L. P. Lee, Nat. Methods, 2007, 4, 1015–1017 CrossRef CAS PubMed.
  275. S. Song, A. H. Nguyen, J. U. Lee, M. Cha and S. J. Sim, Analyst, 2016, 141, 2493–2501 RSC.
  276. Q. Liu, C. Jing, X. Zheng, Z. Gu, D. Li, D. W. Li, Q. Huang, Y. T. Long and C. Fan, Chem. Commun., 2012, 48, 9574–9576 RSC.
  277. Z. A. Nima, M. Mahmood, Y. Xu, T. Mustafa, F. Watanabe, D. A. Nedosekin, M. A. Juratli, T. Fahmi, E. I. Galanzha, J. P. Nolan, A. G. Basnakian, V. P. Zharov and A. S. Biris, Sci. Rep., 2014, 4, 4752 CrossRef CAS PubMed.
  278. D. B. Chithrani, Mol. Membr. Biol., 2010, 27, 299–311 CrossRef CAS PubMed.

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