Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra.

Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as-synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time-intensive and tedious. Here, we create a three-dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as-synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.7-7.9 %. We first use plasmonically-driven feature enrichment to extract localized surface plasmon resonance attributes from spectra and establish a lasso regressor (LR) model to predict purity, size, and shape. Leveraging the learned LR, we artificially generate 425,592 augmented extinction spectra to overcome data scarcity and create a comprehensive NP structural space to bidirectionally predict extinction spectra from structural parameters with <4 % error. Our interpretable NP structural space further elucidates the two higher-order combined electric dipole, quadrupole, and magnetic dipole as the critical structural parameter predictors. By incorporating other NP shapes and mixtures' extinction spectra, we anticipate our approach, especially the data augmentation, can create a fully generalizable NP structural space to drive on-demand, autonomous synthesis-characterization platforms.

Incorporating Chaotropic/Kosmotropic Chemistries onto Plasmonic Nanoheater to Boost Steam Generation Beyond its Photothermal Property.

Photothermal steam generation promises decentralized water purification, but current methods suffer from slow water evaporation even at high photothermal efficiency of ≈98%. This drawback arises from the high latent heat of vaporization that is required to overcome the strong and extensive hydrogen bonding network in water for steam generation. Here, light-to-vapor conversion is boosted by incorporating chaotropic/kosmotropic chemistries onto plasmonic nanoheater to manipulate water intermolecular network at the point-of-heating. The chaotropic-plasmonic nanoheater affords rapid light-to-vapor conversion (2.79 kg m-2  h-1  kW-1 ) at ≈83% efficiency, with the steam generation rate up to 6-fold better than kosmotropic platforms or emerging photothermal designs. Notably, the chaotropic-plasmonic nanoheater also lowers the enthalpy of water vaporization by 1.6-fold when compared to bulk water, signifying that a correspondingly higher amount of steam can be generated with the same energy input. Simulation studies unveil chaotropic surface chemistry is crucial to disrupt water hydrogen bonding network and suppress the energy barrier for water evaporation. Using the chaotropic-plasmonic nanoheater, organic-polluted water is purified at ≈100% efficiency, a feat otherwise challenging in conventional treatments. This study offers a unique chemistry approach to boost light-driven steam generation beyond a material photothermal property.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking.

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce "charge and geometry complementarity" between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4-mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof-of-concept study. Our experimental and in silico studies reveal that "charge and geometry complementarity" between MPY's binding pocket and the CS sulfation patterns drives the formation of site-specific, multidentate interactions at the respective CS isomerism sites, which "locks" each CS in its analogue-specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97 % classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast-responding applications across various fields such as biodiagnostics, food and environmental surveillance.

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors.

Integrating machine learning with surface-enhanced Raman scattering (SERS) accelerates the development of practical sensing devices. Such integration, in combination with direct detection or indirect analyte capturing strategies, is key to achieving high predictive accuracies even in complex matrices. However, in-depth understanding of spectral variations arising from specific chemical interactions is essential to prevent model overfit. Herein, we design a machine-learning-driven "SERS taster" to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at parts-per-million levels. Our receptors employ numerous noncovalent interactions to capture chemical functionalities within flavor molecules. By strategically combining all receptor-flavor SERS spectra, we construct comprehensive "SERS superprofiles" for predictive analytics using chemometrics. We elucidate crucial molecular-level interactions in flavor identification and further demonstrate the differentiation of primary, secondary, and tertiary alcohol functionalities. Our SERS taster also achieves perfect accuracies in multiplex flavor quantification in an artificial wine matrix.

Applying a Nanoparticle@MOF Interface To Activate an Unconventional Regioselectivity of an Inert Reaction at Ambient Conditions.

Here we design an interface between a metal nanoparticle (NP) and a metal-organic framework (MOF) to activate an inert CO2 carboxylation reaction and in situ monitor its unconventional regioselectivity at the molecular level. Using a Kolbe-Schmitt reaction as model, our strategy exploits the NP@MOF interface to create a pseudo high-pressure CO2 microenvironment over the phenolic substrate to drive its direct C-H carboxylation at ambient conditions. Conversely, Kolbe-Schmitt reactions usually demand high reaction temperature (>125 °C) and pressure (>80 atm). Notably, we observe an unprecedented CO2 meta-carboxylation of an arene that was previously deemed impossible in traditional Kolbe-Schmitt reactions. While the phenolic substrate in this study is fixed at the NP@MOF interface to facilitate spectroscopic investigations, free reactants could be activated the same way by the local pressurized CO2 microenvironment. These valuable insights create enormous opportunities in diverse applications including synthetic chemistry, gas valorization, and greenhouse gas remediation.

Three-Dimensional Surface-Enhanced Raman Scattering Platforms: Large-Scale Plasmonic Hotspots for New Applications in Sensing, Microreaction, and Data Storage.

Surface-enhanced Raman scattering (SERS) is a molecular-specific spectroscopic technique that provides up to 1010-fold enhancement of signature Raman fingerprints using nanometer-scale 0D to 2D platforms. Over the past decades, 3D SERS platforms with additional plasmonic materials in the z-axis have been fabricated at sub-micrometer to centimeter scale, achieving higher hotspot density in all x, y, and z spatial directions and higher tolerance to laser misalignment. Moreover, the flexibility to construct platforms in arbitrary sizes and 3D shapes creates attractive applications besides traditional SERS sensing. In this Account, we introduce our library of substrate-based and substrate-less 3D plasmonic platforms, with an emphasis on their non-sensing applications as microlaboratories and data storage labels. We aim to provide a scientific synopsis on these high-potential yet currently overlooked applications of SERS and ignite new scientific discoveries and technology development in 3D SERS platforms to tackle real-world issues. One highlight of our substrate-based SERS platforms is multilayered platforms built from micrometer-thick assemblies of plasmonic particles, which can achieve up to 1011 enhancement factor. As an alternative, constructing 3D hotspots on non-plasmonic supports significantly reduces waste of plasmonic materials while allowing high flexibility in structural design. We then introduce our emerging substrate-less plasmonic capsules including liquid marbles and colloidosomes, which we further incorporate the latter within an aerosol to form centimeter-scale SERS-active plasmonic cloud, the world's largest 3D SERS platform to date. We then discuss the various emerging applications arising only from these 3D platforms, in the fields of sensing, microreactions, and data storage. An important novel sensing application is the stand-off detection of airborne analytes that are several meters away, made feasible with aerosolized plasmonic clouds. We also describe plasmonic capsules as excellent miniature lab-in-droplets that can simultaneously provide in situ monitoring at the molecular level during reaction, owing to their ultrasensitive 3D plasmonic shells. We highlight the emergence of 3D SERS-based data storage platforms with 10-100-fold higher storage density than 2D platforms, featuring a new approach in the development of level 3 security (L3S) anti-counterfeiting labels. Ultimately, we recognize that 3D SERS research can only be developed further when its sensing capabilities are concurrently strengthened. With this vision, we foresee the creation of highly applicable 3D SERS platforms that excel in both sensing and non-sensing areas, providing modern solutions in the ongoing Fourth Industrial Revolution.

Designing surface-enhanced Raman scattering (SERS) platforms beyond hotspot engineering: emerging opportunities in analyte manipulations and hybrid materials.

Surface-enhanced Raman scattering (SERS) is a molecule-specific spectroscopic technique with diverse applications in (bio)chemistry, clinical diagnosis and toxin sensing. While hotspot engineering has expedited SERS development, it is still challenging to detect molecules with no specific affinity to plasmonic surfaces. With the aim of improving detection performances, we venture beyond hotspot engineering in this tutorial review and focus on emerging material design strategies to capture and confine analytes near SERS-active surfaces as well as various promising hybrid SERS platforms. We outline five major approaches to enhance SERS performance: (1) enlarging Raman scattering cross-sections of non-resonant molecules via chemical coupling reactions; (2) targeted chemical capturing of analytes through surface-grafted agents to localize them on plasmonic surfaces; (3) physically confining liquid analytes on non-wetting SERS-active surfaces and (4) confining gaseous analytes using porous materials over SERS hotspots; (5) synergizing conventional metal-based SERS platforms with functional materials such as graphene, semiconducting materials, and piezoelectric polymers. These approaches can be integrated with engineered hotspots as a multifaceted strategy to further boost SERS sensitivities that are unachievable using hotspot engineering alone. Finally, we highlight current challenges in this research area and suggest new research directions towards efficient SERS designs critical for real-world applications.

Modulating Orientational Order to Organize Polyhedral Nanoparticles into Plastic Crystals and Uniform Metacrystals.

In nanoparticle self-assembly, the current lack of strategy to modulate orientational order creates challenges in isolating large-area plastic crystals. Here, we achieve two orientationally distinct supercrystals using one nanoparticle shape, including plastic crystals and uniform metacrystals. Our approach integrates multi-faceted Archimedean polyhedra with molecular-level surface polymeric interactions to tune nanoparticle orientational order during self-assembly. Experiments and simulations show that coiled surface polymer chains limit interparticle interactions, creating various geometrical configurations among Archimedean polyhedra to form plastic crystals. In contrast, brush-like polymer chains enable molecular interdigitation between neighboring particles, favoring consistent particle configurations and result in uniform metacrystals. Our strategy enhances supercrystal diversity for polyhedra comprising multiple nondegenerate facets.

Driving CO2 to a Quasi-Condensed Phase at the Interface between a Nanoparticle Surface and a Metal-Organic Framework at 1 bar and 298 K.

We demonstrate a molecular-level observation of driving CO2 molecules into a quasi-condensed phase on the solid surface of metal nanoparticles (NP) under ambient conditions of 1 bar and 298 K. This is achieved via a CO2 accumulation in the interface between a metal-organic framework (MOF) and a metal NP surface formed by coating NPs with a MOF. Using real-time surface-enhanced Raman scattering spectroscopy, a >18-fold enhancement of surface coverage of CO2 is observed at the interface. The high surface concentration leads CO2 molecules to be in close proximity with the probe molecules on the metal surface (4-methylbenzenethiol), and transforms CO2 molecules into a bent conformation without the formation of chemical bonds. Such linear-to-bent transition of CO2 is unprecedented at ambient conditions in the absence of chemical bond formation, and is commonly observed only in pressurized systems (>105 bar). The molecular-level observation of a quasi-condensed phase induced by MOF coating could impact the future design of hybrid materials in diverse applications, including catalytic CO2 conversion and ambient solid-gas operation.

A Chemical Approach To Break the Planar Configuration of Ag Nanocubes into Tunable Two-Dimensional Metasurfaces.

Current plasmonic metasurfaces of nanocubes are limited to planar configurations, restricting the ability to create tailored local electromagnetic fields. Here, we report a new chemical strategy to achieve tunable metasurfaces with nonplanar nanocube orientations, creating novel lattice-dependent field localization patterns. We manipulate the interfacial behaviors of Ag nanocubes by controlling the ratio of hydrophilic/hydrophobic molecules added in a binary thiol mixture during the surface functionalization step. The nanocube orientation at an oil/water interface can consequently be continuously tuned from planar to tilted and standing configurations, leading to the organization of Ag nanocubes into three unique large-area metacrystals, including square close-packed, linear, and hexagonal lattices. In particular, the linear and hexagonal metacrystals are unusual open lattices comprising nonplanar nanocubes, creating unique local electromagnetic field distribution patterns. Large-area "hot hexagons" with significant delocalization of hot spots form in the hexagonal metacrystal. With a lowest packing density of 24%, the hexagonal metacrystal generates nearly 350-fold stronger surface-enhanced Raman scattering as compared to the other denser-packing metacrystals, demonstrating the importance of achieving control over the geometrical and spatial orientation of the nanocubes in the metacrystals.

Assembling substrate-less plasmonic metacrystals at the oil/water interface for multiplex ultratrace analyte detection.

Current substrate-less SERS platforms are limited to uncontrolled aggregation of plasmonic nanoparticles or quasi-crystalline arrays of spherical nanoparticles, with no study on how the lattice structures formed by nanoparticle self-assembly affect their detection capabilities. Here, we organize Ag octahedral building blocks into two large-area plasmonic metacrystals at the oil/water interface, and investigate their in situ SERS sensing capabilities. Amphiphilic octahedra assemble into a hexagonal close-packed metacrystal, while hydrophobic octahedra assemble into an open square metacrystal. The lower packing density square metacrystal gives rise to much stronger SERS enhancement than the denser packing hexagonal metacrystal, arising from the larger areas of plasmonic hotspots within the square metacrystal at the excitation wavelength. We further demonstrate the ability of the square metacrystal to achieve quantitative ultratrace detection of analytes from both the aqueous and organic phases. Detection limits are at the nano-molar levels, with analytical enhancement factors reaching 10(8). In addition, multiplex detection across both phases can be achieved in situ without any loss of signal quantitation.

Shape-shifting 3D protein microstructures with programmable directionality via quantitative nanoscale stiffness modulation.

The ability to shape-shift in response to a stimulus increases an organism's survivability in nature. Similarly, man-made dynamic and responsive "smart" microtechnology is crucial for the advancement of human technology. Here, 10-30 µm shape-changing 3D BSA protein hydrogel microstructures are fabricated with dynamic, quantitative, directional, and angle-resolved bending via two-photon photolithography. The controlled directional responsiveness is achieved by spatially controlling the cross-linking density of BSA at a nanometer lengthscale. Atomic force microscopy measurements of Young's moduli of structures indicate that increasing the laser writing distance at the z-axis from 100-500 nm decreases the modulus of the structure. Hence, through nanoscale modulation of the laser writing z-layer distance at the nanoscale, control over the cross-linking density is possible, allowing for the swelling extent of the microstructures to be quantified and controlled with high precision. This method of segmented moduli is applied within a single microstructure for the design of shape-shifting microstructures that exhibit stimulus-induced chirality, as well as for the fabrication of a free-standing 3D microtrap which is able to open and close in response to a pH change.

Understanding the synthetic pathway of a single-phase quarternary semiconductor using surface-enhanced Raman scattering: a case of wurtzite Cu2ZnSnS4 nanoparticles.

Single-phase Cu2ZnSnS4 (CZTS) is an essential prerequisite toward a high-efficiency thin-film solar cell device. Herein, the selective phase formation of single-phase CZTS nanoparticles by ligand control is reported. Surface-enhanced Raman scattering (SERS) spectroscopy is demonstrated for the first time as a characterization tool for nanoparticles to differentiate the mixed compositional phase (e.g., CZTS, CTS, and ZnS), which cannot be distinguished by X-ray diffraction. Due to the superior selectivity and sensitivity of SERS, the growth mechanism of CZTS nanoparticle formation by hot injection is revealed to involve three growth steps. First, it starts with nucleation of Cu(2-x)S nanoparticles, followed by diffusion of Sn(4+) into Cu(2-x)S nanoparticles to form the Cu3SnS4 (CTS) phase and diffusion of Zn(2+) into CTS nanoparticles to form the CZTS phase. In addition, it is revealed that single-phase CZTS nanoparticles can be obtained via balancing the rate of CTS phase formation and diffusion of Zn(2+) into the CTS phase. We demonstrate that this balance can be achieved by 1 mL of thiol with Cu(OAc)2, Sn(OAc)4, and Zn(acac)2 metal salts to synthesize the CZTS phase without the presence of a detectable binary/ternary phase with SERS.

Hierarchical 3D SERS substrates fabricated by integrating photolithographic microstructures and self-assembly of silver nanoparticles.

Most of the surface-enhanced Raman scattering (SERS) substrates are 2D planar systems, which limits the SERS active area to a single Cartesian plane. Here, we fabricate 3D SERS substrates with the aim to break the traditional 2D SERS active area limitation, and to extend the SERS hotspots into the third dimension along the z-axis. Our 3D SERS substrates are tailored with increased SERS hotspots in the z-direction from tens of nanometers to tens of micrometers, increasing the hotspots in the z-direction by at least an order of magnitude larger than the confocal volume (~1 µm) of most Raman spectrometers. Various hierarchical 3D SERS-active microstructures are fabricated by combining 3D laser photolithography with Langmuir-Blodgett nanoparticle assembly. 3D laser photolithography creates microstructured platforms required to extend the SERS-active area into 3D, and the self-assembly of Ag nanoparticles ensures homogeneous coating of SERS-active Ag nanoparticles over the entire microstructured platforms. Large-area 3D Raman imaging demonstrates that homogeneous signals can be collected throughout the entire 3D SERS substrates. We vary the morphology, height, and inclination angles of the 3D microstructures, where the inclination angle is found to exhibit strong influence on the SERS signals. We also demonstrate a potential application of this hierarchical 3D SERS substrate in information tagging, storage and encryption as SERS micro-barcodes, where multiple micro-barcodes can be created within a single set of microstructures.

Precision synthesis: designing hot spots over hot spots via selective gold deposition on silver octahedra edges.

A major challenge in plasmonic hot spot fabrication is to efficiently increase the hot spot volumes on single metal nanoparticles to generate stronger signals in plasmon-enhanced applications. Here, the synthesis of designer nanoparticles, where plasmonic-active Au nanodots are selectively deposited onto the edge/tip hot spot regions of Ag nanoparticles, is demonstrated using a two-step seed-mediated precision synthesis approach. Such a "hot spots over hot spots" strategy leads to an efficient enhancement of the plasmonic hot spot volumes on single Ag nanoparticles. Through cathodoluminescence hyperspectral imaging of these selective edge gold-deposited Ag octahedron (SEGSO), the increase in the areas and emission intensities of hot spots on Ag octahedra are directly visualized after Au deposition. Single-particle surface-enhanced Raman scattering (SERS) measurements demonstrate 10-fold and 3-fold larger SERS enhancement factors of the SEGSO as compared to pure Ag octahedra and non-selective gold-deposited Ag octahedra (NSEGSO), respectively. The experimental results corroborate well with theoretical simulations, where the local electromagnetic field enhancement of our SEGSO particles is 15-fold and 1.3-fold stronger than pure Ag octahedra and facet-deposited particles, respectively. The growth mechanisms of such designer nanoparticles are also discussed together with a demonstration of the versatility of this synthetic protocol.

Plasmonic liquid marbles: a miniature substrate-less SERS platform for quantitative and multiplex ultratrace molecular detection.

Inspired by aphids, liquid marbles have been studied extensively and have found application as isolated microreactors, as micropumps, and in sensing. However, current liquid-marble-based sensing methodologies are limited to qualitative colorimetry-based detection. Herein we describe the fabrication of a plasmonic liquid marble as a substrate-less analytical platform which, when coupled with ultrasensitive SERS, enables simultaneous multiplex quantification and the identification of ultratrace analytes across separate phases. Our plasmonic liquid marble demonstrates excellent mechanical stability and is suitable for the quantitative examination of ultratrace analytes, with detection limits as low as 0.3 fmol, which corresponds to an analytical enhancement factor of 5×10(8). The results of our simultaneous detection scheme based on plasmonic liquid marbles and an aqueous-solid-organic interface quantitatively tally with those found for the individual detection of methylene blue and coumarin.

Bimetallic platonic Janus nanocrystals.

We demonstrate the creation of Ag-based bimetallic platonic Janus nanostructures by confining galvanic replacement reaction at a nanoscale interface on highly symmetrical nanostructures such as Ag nanocubes and nanooctahedra using reactive microcontact printing (µCP). The extent of galvanic replacement reaction can be controlled kinetically to derive Janus nanostructures with Au nanodots deposited on either one or multiple facets of Ag nanocubes. The selective deposition of Au dots on a single facet of Ag nanocubes breaks the cubic symmetry and brings about unique and anisotropic plasmonic responses. High-resolution cathodoluminescence hyperspectral imaging of single Janus nanocube demonstrates that surface plasmon resonances corresponding to Au and Ag can be excited at different spots on one Janus nanocube. In addition, we demonstrate the fabrication of alternating Janus/non-Janus segments on 2D Ag nanowires by using a line-patterned polydimethylsiloxane (PDMS) stamp for galvanic replacement. Aside from Au, Pt and Pd can also be selectively deposited onto Ag nanocubes. These Janus nanostructures may find important applications in the field of plasmon-enhanced catalysis.

Enhanced optical properties of graphene oxide-Au nanocrystal composites.

A simple strategy based on electrostatic interactions was utilized to assemble Au nanocrystals of various morphologies onto graphene oxide (GO). This method allows deposition of metal nanocrystals of different shapes onto GO. The linear and nonlinear optical properties of GO-Au nanocrystal composites have been examined. The extinction spectra of Au nanocrystals became broadened and red-shifted from the visible to the near IR upon formation of GO-Au nanocrystal composites. A more than 4-fold increase in two-photon excitation emission intensity was observed from the GO-Au nanocrystal composites compared to pure Au nanocrystals. The SERS signals of the composites were found to be strongly dependent on the morphology of Au nanocrystals, with SERS enhancement factors ranging from 9 to 20.

A chemical route to increase hot spots on silver nanowires for surface-enhanced Raman spectroscopy application.

The effective number of surface-enhanced Raman spectroscopy (SERS) active hot spots on plasmonic nanostructures is the most crucial factor in ensuring high sensitivity in SERS sensing platform. Here we demonstrate a chemical etching method to increase the surface roughness of one-dimensional Ag nanowires, targeted at creating more SERS active hot spots along Ag nanowire's longitudinal axis for increased SERS detection sensitivity. Silver nanowires were first synthesized by the conventional polyol method and then subjected to chemical etching by NH(4)OH and H(2)O(2) mixture. The surfaces of silver nanowires were anisotropically etched off to create miniature "beads on a string" features with increased surface roughness while their crystallinity was preserved. Mapping of single-nanowire SERS measurements showed that the chemical etching method has overcome the limitation of conventional one-dimensional Ag nanowires with limited SERS active area at the tips to produce etched Ag nanowires with an increase in Raman hot spots and polarization-independent SERS signals across tens of micrometers length scale.

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution.

Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials' properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles. Using gold nanospheres as our model system, our ML approach achieves the lowest prediction errors of 2.3% and ±1.0 nm for ensemble size and size distribution respectively, which is 3-6 times lower than previously reported ML or Mie approaches. Knowledge elicitation from the plasmonic domain and concomitant translation into featurization allow us to mitigate noise and boost data interpretability. This enables us to overcome challenges arising from size anisotropy and small sample size limitations to achieve highly generalizable ML models. We further showcase inverse prediction capabilities, using size and size distribution as inputs to generate spectra with LSPRs that closely match experimental data. This work illustrates a ML-empowered total nanocharacterization strategy that is rapid (<30 s), versatile, and applicable over a wide size range of 200 nm.