Harnessing Cooperative Multivalency in Thioguanine for SERS Differentiation of Polyfunctional Analytes Differing by Single Functional Group.

Small-molecule receptors are increasingly employed to probe various functional groups for (bio)chemical analysis. However, differentiation of polyfunctional analogs sharing multiple functional groups remains challenging for conventional mono- and bidentate receptors because their insufficient number of binding sites limits interactions with the least reactive yet property-determining functional group. Herein, we introduce 6-thioguanine (TG) as a supramolecular receptor for unique tridentate receptor-analyte complexation,achieving ≥ 95% identification accuracy among 16 polyfunctional analogs across three scenarios: glycerol derivatives, disubstituted propanes, and vicinal diols. Crucially, we demonstrate distinct spectral changes induced by the tridentate interaction between TG's three anchoring points and all the analyte's functional groups, even the least reactive ones. Notably, H-bond networks formed in the TG-analyte complexes demonstrate additive effect in binding strength originating from good bond linearity, cooperativity, and resonance, thus strengthens complexation events and amplifies the differences in spectral changes induced among analytes. It also enhances spectral consistency by selectively form a sole configuration that is stronger than the respective analyte-analyte interaction. Finally, we achieve 95.4% accuracy for multiplex identification of a mixture consisting of multiple polyfunctional analogs. We envisage that extension to other multidentate non-covalent interactions enables the development of interference-free small molecule-based sensors for various (bio)chemical analysis applications.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back.

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.

Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with <10% errors. First, we utilize 4-mercaptophenylboronic acid to selectively capture the epimers at molecular sites of isomerism to form epimer-specific SERS fingerprints. Corroborating with in-silico experiments, we establish five spectral features, each corresponding to a structural characteristic: (1) presence/absence of epimers, (2) monosaccharide/cerebroside, (3) saturated/unsaturated cerebroside, (4) glucosyl/galactosyl, and (5) GlcCer or GalCer's carbon chain lengths. Leveraging these insights, we create a fully generalizable framework to identify and quantify cerebrosides at concentrations between 10-4 to 10-10 M and achieve multiplex quantification of binary mixtures containing biomarkers GlcCer24:1, and GalCer24:1 using their untrained spectra in the models.

Surface-Enhanced Raman Scattering-Based Surface Chemotaxonomy: Combining Bacteria Extracellular Matrices and Machine Learning for Rapid and Universal Species Identification.

Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research.

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.

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.

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring.

Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X.

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Inducing Ring Complexation for Efficient Capture and Detection of Small Gaseous Molecules Using SERS for Environmental Surveillance.

Gas-phase surface-enhanced Raman scattering (SERS) remains challenging due to poor analyte affinity to SERS substrates. The reported use of capturing probes suffers from concurrent inconsistent signals and long response time due to the formation of multiple potential probe-analyte interaction orientations. Here, we demonstrate the use of multiple non-covalent interactions for ring complexation to boost the affinity of small gas molecules, SO2 and NO2 , to our SERS platform, achieving rapid capture and multiplex detection down to 100 ppm. Experimental and in-silico studies affirm stable ring complex formation, and kinetic investigations reveal a 4-fold faster response time compared to probes without stable ring complexation capability. By synergizing spectral concatenation and support vector machine regression, we achieve 91.7 % accuracy for multiplex quantification of SO2 and NO2 in excess CO2 , mimicking real-life exhausts. Our platform shows immense potential for on-site exhaust and air quality surveillance.

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.

Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min.

Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.

Enantiospecific Molecular Fingerprinting Using Potential-Modulated Surface-Enhanced Raman Scattering to Achieve Label-Free Chiral Differentiation.

Chiral differentiation is critical in diverse fields ranging from pharmaceutics to chiral synthesis. While surface-enhanced Raman scattering (SERS) offers molecule-specific vibrational information with high detection sensitivity, current strategies rely on indirect detection using additional selectors and cannot exploit SERS' key advantages for univocal and generic chiral differentiation. Here, we achieve direct, label-free SERS sensing of biologically important enantiomers by synergizing asymmetric nanoporous gold (NPG) nanoparticles with electrochemical-SERS to generate enantiospecific molecular fingerprints. Experimental and in silico studies reveal that chiral recognition is two pronged. First, the numerous surface atomic defects in NPG provide the necessary localized asymmetric environment to induce enantiospecific molecular adsorptions and interaction affinities. Concurrently, the applied potential drives and orients the enantiomers close to the NPG surface for maximal analyte-surface interactions. Notably, our strategy is versatile and can be readily extended to detect various enantiomers. Furthermore, we can achieve multiplex quantification of enantiomeric ratios with excellent predictive performance. Our combinatorial approach thus offers an important paradigm shift from current approaches to achieve label-free chiral SERS sensing of various enantiomers.

In Situ Differentiation of Multiplex Noncovalent Interactions Using SERS and Chemometrics.

Probing changes of noncovalent interactions is crucial to study the binding efficiencies and strengths of (bio)molecular complexes. While surface-enhanced Raman scattering (SERS) offers unique molecular fingerprints to examine such interactions in situ, current platforms are only able to recognize hydrogen bonds because of their reliance on manual spectral identification. Here, we differentiate multiple intermolecular interactions between two interacting species by synergizing plasmonic liquid marble-based SERS platforms, chemometrics, and density functional theory. We demonstrate that characteristic 3-mercaptobenzoic acid (probe) Raman signals have distinct peak shifts upon hydrogen bonding and ionic interactions with tert-butylamine, a model interacting species. Notably, we further quantify the contributions from each noncovalent interaction coexisting in different proportions. As a proof-of-concept, we detect and categorize biologically important nucleotide bases based on molecule-specific interactions. This will potentially be useful to study how subtle changes in biomolecular interactions affect their structural and binding properties.

Two-Photon-Assisted Polymerization and Reduction: Emerging Formulations and Applications.

Two-photon lithography (TPL) is an emerging approach to fabricate complex multifunctional micro/nanostructures. This is because TPL can easily develop various 2D and 3D structures on a variety of surfaces, and there has been a rapidly expanding pool of processable photoresists to create different materials. However, challenges in developing two-photon processable photoresists currently impede progress in TPL. In this review, we critically discuss the importance of photoresist formulation in TPL. We begin by evaluating the commercial photoresists to design micro/nanostructures for promising applications in anti-counterfeiting, superomniphobicity, and micromachines with movable parts. Next, we discuss emerging hydrogel/organogel photoresists, focusing on customizing photoresist formulations to fabricate reconfigurable structures that can respond to changes in local pH, solvent, and temperature. We also review the development of metal salt-based photoresists for direct metal writing, whereby various formulations have been developed to enable applications in online sensing, catalysis, and electronics. Finally, we provide a critical outlook and highlight various outstanding challenges in formulating processable photoresists for TPL.

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.

Plasmonic-induced overgrowth of amorphous molybdenum sulfide on nanoporous gold: An ambient synthesis method of hybrid nanoparticles with enhanced electrocatalytic activity.

Hybrid materials of earth abundant transition metal dichalcogenides and noble metal nanoparticles, such as molybdenum sulfide (MoSx) and gold nanoparticles, exhibit synergistic effects that can enhance electrocatalytic reactions. However, most current hybrid MoSx-gold synthesis requires an energy intensive heat source of >500 °C or chemical plating to achieve deposition of MoSx on the gold surface. Herein, we demonstrate the direct overgrowth of MoSx over colloidal nanoporous gold (NPG), conducted feasibly under ambient conditions, to form hybrid particles with enhanced electrocatalytic performance toward hydrogen evolution reaction. Our strategy exploits the localized surface plasmon resonance-mediated photothermal heating of NPG to achieve >230 °C surface temperature, which induces the decomposition of the (NH4)2MoS4 precursor and direct overgrowth of MoSx over NPG. By tuning the concentration ratio between the precursor and NPG, the amount of MoSx particles deposited can be systematically controlled from 0.5% to 2% of the Mo/(Au + Mo) ratio. Importantly, we find that the hybrid particles exhibit higher bridging and an apical S to terminal S atomic ratio than pure molybdenum sulfide, which gives rise to their enhanced electrocatalytic performance for hydrogen evolution reaction. We demonstrate that hybrid MoSx-NPG exhibits >30 mV lower onset potential and a 1.7-fold lower Tafel slope as compared to pure MoSx. Our methodology provides an energy- and cost-efficient synthesis pathway, which can be extended to the synthesis of various functional hybrid structures with unique properties for catalysis and sensing applications.

2H → 1T Phase Change in Direct Synthesis of WS2 Nanosheets via Solution-Based Electrochemical Exfoliation and Their Catalytic Properties.

Metallic 1T-WS2 has various interesting properties such as increased density of catalytically active sites on both the basal planes and edges as well as metallic conductivity which allows it to be used in applications such as biosensing and energy devices. Hence, it is highly beneficial to develop a simple, efficient, and low-cost synthesis method of 1T-WS2 nanosheets from commercially available bulk 2H-WS2. In this study, we reported WS2 nanosheets synthesized directly from bulk WS2 via solution-based electrochemical exfoliation with bipolar electrodes and investigated their electrocatalytic performances toward hydrogen evolution and oxygen reduction reactions. We successfully synthesized WS2 nanosheets of regular hexagonal symmetry with a 2H → 1T phase transition. This represents a novel method of producing 1T-WS2 nanosheets from bulk 2H-WS2 without compromising on its electrocatalytic properties.

A study of the effect of sonication time on the catalytic performance of layered WS2 from various sources.

WS2 is a transition metal dichalcogenide (TMD) with many potential applications from catalysis to sensing, and is of interest both in its bulk and monolayer forms. There is discrepancy in the literature on the reported electrocatalytic effect of layered WS2. In this study, we examine two issues: the influence of the WS2 source and the effect of a common agitation technique via ultrasonication on the observed electrocatalysis. Bulk WS2 from five different chemical providers demonstrated different HER electrocatalytic performances. Changes to the duration of sonication result in different HER electrocatalytic performances across all WS2 materials. This may affect the efficiency of subsequent modifications from which these TMD materials serve as precursor materials. On the other hand, while WS2 materials from different suppliers showed varying HET performances, changes in sonication time have no significant effect on their HET performances. Both the WS2 source and the duration of sonication have different implications for the electrochemical performance of bulk WS2 and thus represent important variables to consider in research involving WS2.