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Updated: Nov 27, 2025

Preparation of Silver-Palladium Alloyed Nanoparticles for Plasmonic Catalysis under Visible-Light Illumination

Preparation of Silver-Palladium Alloyed Nanoparticles for Plasmonic Catalysis under Visible-Light Illumination

Published on: August 18, 2020

First-Principles Insights into Plasmon-Induced Catalysis.

John Mark P Martirez¹, Junwei Lucas Bao², Emily A Carter^1,2,3

¹Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California 90095, USA;

Annual Review of Physical Chemistry

|December 3, 2020

Summary

This summary is machine-generated.

Quantum mechanical modeling aids understanding of metal nanoparticles' localized surface plasmon resonance (LSPR) for enhanced photocatalysis. This approach guides efforts to improve reaction kinetics using plasmonic excitations.

Keywords:

correlated wavefunction methods density functional theory embedding methods heterogeneous catalysis photocatalysis plasmonic catalysis

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Last Updated: Nov 27, 2025

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Published on: March 2, 2016

Area of Science:

Materials Science
Physical Chemistry
Computational Chemistry

Background:

Metal nanoparticles (NPs) exhibit enhanced optical properties via localized surface plasmon resonance (LSPR).
LSPR amplifies electric fields, boosting light absorption and generating hot carriers in coupled materials.
Plasmonic effects mediated by NPs are crucial for advancing photocatalysis.

Purpose of the Study:

To review the role of quantum mechanical modeling in understanding plasmonic excitations for photocatalysis.
To guide the application of quantum mechanics in improving heterogeneously catalyzed reactions.
To assess computational methods for capturing plasmonic effects in NPs.

Main Methods:

Utilizing first-principles quantum mechanics techniques.
Applying ground-state methods like density functional theory (DFT).
Employing excited-state theories, including multireference correlated wavefunction methods.

Main Results:

Quantum mechanics provides fundamental insights into LSPR phenomena.
Modeling helps elucidate how plasmonic excitations influence catalytic reaction kinetics.
Different quantum methods offer varying degrees of accuracy for plasmonic effects.

Conclusions:

Quantum mechanical modeling is essential for advancing plasmon-enhanced photocatalysis.
Understanding the interplay between plasmonics and catalysis requires sophisticated computational approaches.
Accurate modeling guides the design of NP-based catalysts for improved performance.