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.

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.

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.

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.

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.

Tracking Airborne Molecules from Afar: Three-Dimensional Metal-Organic Framework-Surface-Enhanced Raman Scattering Platform for Stand-Off and Real-Time Atmospheric Monitoring.

Stand-off Raman spectroscopy combines the advantages of both Raman spectroscopy and remote detection to retrieve molecular vibrational fingerprints of chemicals at inaccessible sites. However, it is currently restricted to the detection of pure solids and liquids and not widely applicable for dispersed molecules in air. Herein, we realize real-time stand-off SERS spectroscopy for remote and multiplex detection of atmospheric airborne species by integrating a long-range optic system with a 3D analyte-sorbing metal-organic framework (MOF)-integrated SERS platform. Formed via the self-assembly of Ag@MOF core-shell nanoparticles, our 3D plasmonic architecture exhibits micrometer thick SERS hotspot to allow active sorption and rapid detection of aerosols, gas, and volatile organic compounds down to parts-per-billion levels, notably at a distance up to 10 m apart. The platform is highly sensitive to changes in atmospheric content, as demonstrated in the temporal monitoring of gaseous CO2 in several cycles. Importantly, we demonstrate the remote and multiplex quantification of polycyclic aromatic hydrocarbon mixtures in real time under outdoor daylight. By overcoming core challenges in current remote Raman spectroscopy, our strategy creates an opportunity in the long-distance and sensitive monitoring of air/gaseous environment at the molecular level, which is especially important in environmental conservation, disaster prevention, and homeland defense.

Concentrating Immiscible Molecules at Solid@MOF Interfacial Nanocavities to Drive an Inert Gas-Liquid Reaction at Ambient Conditions.

Gas-liquid reactions form the basis of our everyday lives, yet they still suffer poor reaction efficiency and are difficult to monitor in situ, especially at ambient conditions. Now, an inert gas-liquid reaction between aniline and CO2 is driven at 1 atm and 298 K by selectively concentrating these immiscible reactants at the interface between metal-organic framework and solid nanoparticles (solid@MOF). Real-time reaction SERS monitoring and simulations affirm the formation of phenylcarbamic acid, which was previously undetectable because they are unstable for post-reaction treatments. The solid@MOF ensemble gives rise to a more than 28-fold improvement to reaction efficiency as compared to ZIF-only and solid-only platforms, emphasizing that the interfacial nanocavities in solid@MOF are the key to enhance the gas-liquid reaction. Our strategy can be integrated with other functional materials, thus opening up new opportunities for ambient-operated gas-liquid applications.

Shape-dependent thermo-plasmonic effect of nanoporous gold at the nanoscale for ultrasensitive heat-mediated remote actuation.

Nanoporous gold (NPG) promises efficient light-to-heat transformation, yet suffers limited photothermal conversion efficiency owing to the difficulty in controlling its morphology for the direct modulation of thermo-plasmonic properties. Herein, we showcase a series of shape-controlled NPG nanoparticles with distinct bowl- (NPG-B), tube- (NPG-T) and plate-like (NPG-P) structures for quantitative temperature regulation up to 140 °C in <1 s using laser irradiation. Notably, NPG-B exhibits the highest photothermal efficiency of 68%, which is >12 and 39 percentage points better than those of other NPG shapes (NPG-T, 56%; NPG-P, 49%) and Au nanoparticles (29%), respectively. We attribute NPG-B's superior photothermal performance to its >13% enhanced light absorption cross-section compared to other Au nanostructures. We further realize an ultrasensitive heat-mediated light-to-mechanical "kill switch" by integrating NPG-B with a heat-responsive shape-memory polymer (SMP/NPG-B). This SMP/NPG-B hybrid is analogous to a photo-triggered mechanical arm, and can be activated swiftly in <4 s simply by remote laser irradiation. Achieving remotely-activated "kill switch" is critical in case of emergencies such as gas leaks, where physical access is usually prohibited or dangerous. Our work offers valuable insights into the structural design of NPG for optimal light-to-heat conversion, and creates opportunities to formulate next-generation smart materials for on-demand and multi-directional responsiveness.

Morphological Growth and Theoretical Understanding of Gold and Other Noble Metal Nanoplates.

For the last decades, the chemical reduction of Au3+ to Au0 has been widely employed to produce various gold nanostructures. In comparison with the fast reduction, the slow reduction is systematically investigated in this research to provide more insights to reveal intermediary process and further disclose the underlying mechanism for growing gold nanostructures by using a series of simple ligands with aldehyde groups as weak reducing agents. The different binding energies of ligands to Aun+ (n=3, 1 and 0) exhibit variable binding affinities in starting, intermediate, and final gold species. For example, formic acid has much stronger binding affinity to Au+ than Au3+ , and thus Au+ intermediate is able to be stabilized/captured during slow reduction of Au3+ . Upon the disproportionation of Au+ to Au0 and Au3+ , formic acid has much stronger binding affinity to the newly formed Au0 than other ligands for the controlled formation of gold nanostructures. Meanwhile, the adsorption of ligands causes substantially decreased surface energies on different gold planes. There are much higher energies on {110} planes compared to the other two {111} and {100} planes with certain ratios in these energies, leading to morphological growth of gold nanosheets. In this paper, we experimentally demonstrate anisotropic growth of gold nanosheets by using various ligands with weak reducing and appropriate coordination capabilities, and further provide insights to understand their morphological growth mechanism behind. This synthetic strategy is successfully extended to prepare silver, palladium, and platinum nanoplates.

Favoring the unfavored: Selective electrochemical nitrogen fixation using a reticular chemistry approach.

Electrochemical nitrogen-to-ammonia fixation is emerging as a sustainable strategy to tackle the hydrogen- and energy-intensive operations by Haber-Bosch process for ammonia production. However, current electrochemical nitrogen reduction reaction (NRR) progress is impeded by overwhelming competition from the hydrogen evolution reaction (HER) across all traditional NRR catalysts and the requirement for elevated temperature/pressure. We achieve both excellent NRR selectivity (~90%) and a significant boost to Faradic efficiency by 10 percentage points even at ambient operations by coating a superhydrophobic metal-organic framework (MOF) layer over the NRR electrocatalyst. Our reticular chemistry approach exploits MOF's water-repelling and molecular-concentrating effects to overcome HER-imposed bottlenecks, uncovering the unprecedented electrochemical features of NRR critical for future theoretical studies. By favoring the originally unfavored NRR, we envisage our electrocatalytic design as a starting point for high-performance nitrogen-to-ammonia electroconversion directly from water vapor-abundant air to address increasing global demand of ammonia in (bio)chemical and energy industries.

Plasmonic Hotspots in Air: An Omnidirectional Three-Dimensional Platform for Stand-Off In-Air SERS Sensing of Airborne Species.

Molecular-level airborne sensing is critical for early prevention of disasters, diseases, and terrorism. Currently, most 2D surface-enhanced Raman spectroscopy (SERS) substrates used for air sensing have only one functional surface and exhibit poor SERS-active depth. "Aerosolized plasmonic colloidosomes" (APCs) are introduced as airborne plasmonic hotspots for direct in-air SERS measurements. APCs function as a macroscale 3D and omnidirectional plasmonic cloud that receives laser irradiation and emits signals in all directions. Importantly, it brings about an effective plasmonic hotspot in a length scale of approximately 2.3 cm, which affords 100-fold higher tolerance to laser misalignment along the z-axis compared with 2D SERS substrates. APCs exhibit an extraordinary omnidirectional property and demonstrate consistent SERS performance that is independent of the laser and analyte introductory pathway. Furthermore, the first in-air SERS detection is demonstrated in stand-off conditions at a distance of 200 cm, highlighting the applicability of 3D omnidirectional plasmonic clouds for remote airborne sensing in threatening or inaccessible areas.

Aluminum nanostructures with strong visible-range SERS activity for versatile micropatterning of molecular security labels.

The application of aluminum (Al)-based nanostructures for visible-range plasmonics, especially for surface-enhanced Raman scattering (SERS), currently suffers from inconsistent local electromagnetic field distributions and/or inhomogeneous distribution of probe molecules. Herein, we lithographically fabricate structurally uniform Al nanostructures which enable homogeneous adsorption of various probe molecules. Individual Al nanostructures exhibit strong local electromagnetic field enhancements, in turn leading to intense SERS activity. The average SERS enhancement factor (EF) for individual nanostructures exceeds 104 for non-resonant probe molecules in the visible spectrum. These Al nanostructures also retain more than 70% of their original SERS intensities after one-month storage, displaying superb stability under ambient conditions. We further achieve tunable polarization-dependent SERS responses using anisotropic Al nanostructures, facilitating the design of sophisticated SERS-based security labels. Our micron-sized security label comprises two-tier security features, including a machine-readable hybrid quick-response (QR) code overlaid with a set of ciphertexts. Our work demonstrates the versatility of Al-based structures in low-cost modern chemical nano-analytics and forgery protection.

Direct Metal Writing and Precise Positioning of Gold Nanoparticles within Microfluidic Channels for SERS Sensing of Gaseous Analytes.

We demonstrate a one-step precise direct metal writing of well-defined and densely packed gold nanoparticle (AuNP) patterns with tunable physical and optical properties. We achieve this by using two-photon lithography on a Au precursor comprising poly(vinylpyrrolidone) (PVP) and ethylene glycol (EG), where EG promotes higher reduction rates of Au(III) salt via polyol reduction. Hence, clusters of monodisperse AuNP are generated along raster scanning of the laser, forming high-particle-density, well-defined structures. By varying the PVP concentration, we tune the AuNP size from 27.3 to 65.0 nm and the density from 172 to 965 particles/µm2, corresponding to a surface roughness of 12.9 to 67.1 nm, which is important for surface-based applications such as surface-enhanced Raman scattering (SERS). We find that the microstructures exhibit an SERS enhancement factor of >105 and demonstrate remote writing of well-defined Au microstructures within a microfluidic channel for the SERS detection of gaseous molecules. We showcase in situ SERS monitoring of gaseous 4-methylbenzenethiol and real-time detection of multiple small gaseous species with no specific affinity to Au. This one-step, laser-induced fabrication of AuNP microstructures ignites a plethora of possibilities to position desired patterns directly onto or within most surfaces for the future creation of multifunctional lab-on-a-chip devices.

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.

Polyurethane acrylates as effective substrates for sustained in vitro culture of human myotubes.

Muscular disease has debilitating effects with severe damage leading to death. Our knowledge of muscle biology, disease and treatment is largely derived from non-human cell models, even though non-human cells are known to differ from human cells in their biochemical responses. Attempts to develop highly sought after in vitro human cell models have been plagued by early cell delamination and difficulties in achieving human myotube culture in vitro. In this work, we developed polyurethane acrylate (PUA) materials to support long-term in vitro culture of human skeletal muscle tissue. Using a constant base with modulated crosslink density we were able to vary the material modulus while keeping surface chemistry and roughness constant. While previous studies have focused on materials that mimic soft muscle tissue with stiffness ca. 12kPa, we investigated materials with tendon-like surface moduli in the higher 150MPa to 2.4GPa range, which has remained unexplored. We found that PUA of an optimal modulus within this range can support human myoblast proliferation, terminal differentiation and sustenance beyond 35days, without use of any extracellular protein coating. Results show that PUA materials can serve as effective substrates for successful development of human skeletal muscle cell models and are suitable for long-term in vitro studies. STATEMENT OF SIGNIFICANCE: We developed polyurethane acrylates (PUA) to modulate the human skeletal muscle cell growth and maturation in vitro by controlling surface chemistry, morphology and tuning material's stiffness. PUA was able to maintain muscle cell viability for over a month without any detectable signs of material degradation. The best performing PUA prevented premature cell detachment from the substrate which often hampered long-term muscle cell studies. It also supported muscle cell maturation up to the late stages of differentiation. The significance of these findings lies in the possibility to advance studies on muscle cell biology, disease and therapy by using human muscle cells instead of relying on the widely used animal-based in vitro models.