Mapping Molecular Adsorption Configurations with <5 nm Spatial Resolution through Ambient Tip-Enhanced Raman Imaging.

We interrogate para-mercaptobenzoic acid (MBA) molecules chemisorbed onto plasmonic silver nanocubes through tip-enhanced Raman (TER) spectral nanoimaging. Through a detailed examination of the spectra, aided by correlation analysis and density functional theory calculations, we find that MBA chemisorbs onto the plasmonic particles with at least two distinct configurations: S- and CO2-bound. High spatial resolution TER mapping allows us to distinguish between the distinct adsorption geometries with a pixel-limited (<5 nm) spatial resolution under ambient laboratory conditions.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing.

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Effects of Size and Structural Defects on the Vibrational Properties of Lanthanum Hexaboride Nanocrystals.

Lanthanum hexaboride (LaB6) is notable for its thermionic emission and mechanical strength and is being explored for its potential applications in IR-absorbing photovoltaic cells and thermally insulating window coatings. Previous studies have not investigated how the properties of LaB6 change on the nanoscale. Despite interest in the tunable plasmonic properties of nanocrystalline LaB6, studies have been limited due to challenges in the synthesis of phase-pure, size-controlled, high-purity nanocrystals without high temperatures or pressures. Here, we report, for the first time, the ability to control particle size and boron content through reaction temperature and heating ramp rate, which allows the effects of size and defects on the vibrational modes of the nanocrystals to be studied independently. Understanding these effects is important to develop methods to fully control the properties of nanocrystalline LaB6, such as IR absorbance. In contrast to previous studies on stoichiometric LaB6 nanocrystals, we report here that boron content and lanthanum vacancies have a greater influence on their vibrational properties than their particle size.