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Phillip Christopher

Publications and source records attributed to Phillip Christopher.

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Floquet-Plasmon Enhanced Charge Transfer at Catalytic Interfaces

Plasmonic excitation of metallic nanostructures can generate energetic carriers capable of transferring charge into nearby adsorbed molecules, providing a possible pathway for driving chemical transformations. In many theoretical descriptions of this process, charge transfer is determined by the equilibrium electronic structure of the molecule and substrate together with the time-dependent carrier distribution produced during plasmon decay. However, the intense transient electric fields associated with localized surface plasmons can also dynamically perturb the molecular electronic structure itself on ultrafast timescales. In this work, we investigate how these time-dependent fields modify the molecular spectral function and influence charge injection at catalytic interfaces. Using a model of CO2 adsorbed on Au(111), we compute the real-time molecular Green's function within a correlated frontier-orbital active space under plasmon-like driving. We find that the driving field rapidly produces transient Floquet-type replica bands in the molecular density of states, opening additional resonant pathways for hot-carrier injection that are absent without external driving or in time-local descriptions. Coupling the evolving molecular spectrum to a time-dependent hot-electron distribution described within a two-temperature Sommerfeld framework, we predict large enhancements in quasiparticle spectral overlap that governs injection during plasmon dephasing. These results suggest that dynamically generated non-equilibrium spectral structure may play an important role in plasmon-assisted catalysis and provide a framework for studying driven catalytic interfaces beyond static electronic structure descriptions.

physics.chem-ph

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationships and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology was applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalysts. The model's performance in detecting and segmenting NPs was validated across diverse heterogeneous catalyst systems, including various metals (Cu, Ru, Pt, and PtCo), supports (silica ($\text{SiO}_2$), $γ$-alumina ($γ$-$\text{Al}_2\text{O}_3$), and carbon black), and particle diameter size distributions with means and standard deviations of 2.9 $\pm$ 1.1 nm, 1.6 $\pm$ 0.2 nm, 9.7 $\pm$ 4.6 nm, and 4 $\pm$ 1.0 nm. Additionally, the proposed machine learning (ML) approach successfully detects and segments overlapping NPs anchored on non-uniform catalytic support materials, providing critical insights into their spatial arrangements and interactions. Our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

cond-mat.mtrl-sci