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.

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.

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.

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.

Air-stable plasmonic bubbles as a versatile three-dimensional surface-enhanced Raman scattering platform for bi-directional gas sensing.

Harnessing large hotspot volumes is key for enhanced gas-phase surface-enhanced Raman scattering (SERS) sensing. Herein, we introduce versatile, air-stable 3D 'Plasmonic bubbles' with bi-directional sensing capabilities. Our Plasmonic bubbles are robust, afford strong and homogenous SERS signals, and can swiftly detect both encapsulated and surrounding 4-methylbenzenethiol vapors.

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.

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.

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.

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.

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.

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.

ZIF-Induced d-Band Modification in a Bimetallic Nanocatalyst: Achieving Over 44 % Efficiency in the Ambient Nitrogen Reduction Reaction.

The electrochemical nitrogen reduction reaction (NRR) offers a sustainable solution towards ammonia production but suffers poor reaction performance owing to preferential catalyst-H formation and the consequential hydrogen evolution reaction (HER). Now, the Pt/Au electrocatalyst d-band structure is electronically modified using zeolitic imidazole framework (ZIF) to achieve a Faradaic efficiency (FE) of >44 % with high ammonia yield rate of >161 µg mgcat -1 h-1 under ambient conditions. The strategy lowers electrocatalyst d-band position to weaken H adsorption and concurrently creates electron-deficient sites to kinetically drive NRR by promoting catalyst-N2 interaction. The ZIF coating on the electrocatalyst doubles as a hydrophobic layer to suppress HER, further improving FE by >44-fold compared to without ZIF (ca. 1 %). The Pt/Au-NZIF interaction is key to enable strong N2 adsorption over H atom.

Multiplex Surface-Enhanced Raman Scattering Identification and Quantification of Urine Metabolites in Patient Samples within 30 min.

Successful translation of laboratory-based surface-enhanced Raman scattering (SERS) platforms to clinical applications requires multiplex and ultratrace detection of small biomarker molecules from a complex biofluid. However, these biomarker molecules generally exhibit low Raman scattering cross sections and do not possess specific affinity to plasmonic nanoparticle surfaces, significantly increasing the challenge of detecting them at low concentrations. Herein, we demonstrate a "confine-and-capture" approach for multiplex detection of two families of urine metabolites correlated with miscarriage risks, 5ß-pregnane-3α,20α-diol-3α-glucuronide and tetrahydrocortisone. To enhance SERS signals by 1012-fold, we use specific nanoscale surface chemistry for targeted metabolite capture from a complex urine matrix prior to confining them on a superhydrophobic SERS platform. We then apply chemometrics, including principal component analysis and partial least-squares regression, to convert molecular fingerprint information into quantifiable readouts. The whole screening procedure requires only 30 min, including urine pretreatment, sample drying on the SERS platform, SERS measurements, and chemometric analyses. These readouts correlate well with the pregnancy outcomes in a case-control study of 40 patients presenting threatened miscarriage symptoms.

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.