Machine Learning-Aided Design of Gold Core-Shell Nanocatalysts toward Enhanced and Selective Photooxygenation.

We demonstrate the use of the machine learning (ML) tools to rapidly and accurately predict the electric field as a guide for designing core-shell Au-silica nanoparticles to enhance 1O2 sensitization and selectivity of organic synthesis. Based on the feature importance analysis, obtained from a deep neural network algorithm, we found a general and linear dependent descriptor (θ ∝ aD0.25t-1, where a, D, and t are the shape constant, size of metal nanoparticles, and distance from the metal surface) for the electric field around the core-shell plasmonic nanoparticle. Directed by the new descriptor, we synthesized gold-silica nanoparticles and validated their plasmonic intensity using scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) mapping. The nanoparticles with θ = 0.40 demonstrate an ∼3-fold increase in the reaction rate of photooxygenation of anthracene and 4% increase in the selectivity of photooxygenation of dihydroartemisinic acid (DHAA), a long-standing goal in organic synthesis. In addition, the combination of ML and experimental investigations shows the synergetic effect of plasmonic enhancement and fluorescence quenching, leading to enhancement for 1O2 generation. Our results from time-dependent density functional theory (TD-DFT) calculations suggest that the presence of an electric field can favor intersystem crossing (ISC) of methylene blue to enhance 1O2 generation. The strategy reported here provides a data-driven catalyst preparation method that can significantly reduce experimental cost while paving the way for designing photocatalysts for organic drug synthesis.

Boosting in-plane anisotropy by periodic phase engineering in two-dimensional VO2 single crystals.

In-plane anisotropy (IPA) due to asymmetry in lattice structures provides an additional parameter for the precise tuning of characteristic polarization-dependent properties in two-dimensional (2D) materials, but the narrow range within which such method can modulate properties hinders significant development of related devices. Herein we present a novel periodic phase engineering strategy that can remarkably enhance the intrinsic IPA obtainable from minor variations in asymmetric structures. By introducing alternant monoclinic and rutile phases in 2D VO2 single crystals through the regulation of interfacial thermal strain, the IPA in electrical conductivity can be reversibly modulated in a range spanning two orders of magnitude, reaching an unprecedented IPA of 113. Such an intriguing local phase engineering in 2D materials can be well depicted and predicted by a theoretical model consisting of phase transformation, thermal expansion, and friction force at the interface, creating a framework applicable to other 2D materials. Ultimately, the considerable adjustability and reversibility of the presented strategy provide opportunities for future polarization-dependent photoelectric and optoelectronic devices.