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.

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.

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.

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.