Reduced-Dimensionality Al Nanocrystals: Nanowires, Nanobars, and Nanomoustaches.

Aluminum nanocrystals created by catalyst-driven colloidal synthesis support excellent plasmonic properties, due to their high level of elemental purity, monocrystallinity, and controlled size and shape. Reduction in the rate of nanocrystal growth enables the synthesis of highly anisotropic Al nanowires, nanobars, and singly twinned "nanomoustaches". Electron energy loss spectroscopy was used to study the plasmonic properties of these nanocrystals, spanning the broad energy range needed to map their plasmonic modes. The coupling between these nanocrystals and other plasmonic metal nanostructures, specifically Ag nanocubes and Au films of controlled nanoscale thickness, was investigated. Al nanocrystals show excellent long-term stability under atmospheric conditions, providing a practical alternative to coinage metal-based nanowires in assembled nanoscale devices.

Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures.

Surface-enhanced Raman spectroscopy (SERS) holds exceptional promise as a streamlined chemical detection strategy for biological and environmental contaminants compared with current laboratory methods. Priority pollutants such as polycyclic aromatic hydrocarbons (PAHs), detectable in water and soil worldwide and known to induce multiple adverse health effects upon human exposure, are typically found in multicomponent mixtures. By combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of machine learning (ML), we examine whether individual PAHs can be identified through an analysis of the SERS spectra of multicomponent PAH mixtures. We have developed an unsupervised ML method we call Characteristic Peak Extraction, a dimensionality reduction algorithm that extracts characteristic SERS peaks based on counts of detected peaks of the mixture. By analyzing the SERS spectra of two-component and four-component PAH mixtures where the concentration ratios of the various components vary, this algorithm is able to extract the spectra of each unknown component in the mixture of unknowns, which is then subsequently identified against a SERS spectral library of PAHs. Combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of ML, this effort is a step toward the computational demixing of unknown chemical components occurring in complex multicomponent mixtures.

Al@TiO2 Core-Shell Nanoparticles for Plasmonic Photocatalysis.

Plasmon-induced photocatalysis is a topic of rapidly increasing interest, due to its potential for substantially lowering reaction barriers and temperatures and for increasing the selectivity of chemical reactions. Of particular interest for plasmonic photocatalysis are antenna-reactor nanoparticles and nanostructures, which combine the strong light-coupling of plasmonic nanostructures with reactors that enhance chemical specificity. Here, we introduce Al@TiO2 core-shell nanoparticles, combining earth-abundant Al nanocrystalline cores with TiO2 layers of tunable thickness. We show that these nanoparticles are active photocatalysts for the hot electron-mediated H2 dissociation reaction as well as for hot hole-mediated methanol dehydration. The wavelength dependence of the reaction rates suggests that the photocatalytic mechanism is plasmonic hot carrier generation with subsequent transfer of the hot carriers into the TiO2 layer. The Al@TiO2 antenna-reactor provides an earth-abundant solution for the future design of visible-light-driven plasmonic photocatalysts.