Hybrid-biotaxonomy-like machine learning enables an anticipated surface plasmon resonance of Au/Ag nanoparticles assembled on ZnO nanorods-Reference-Cited by-同舟云学术

Hybrid-biotaxonomy-like machine learning enables an anticipated surface plasmon resonance of Au/Ag nanoparticles assembled on ZnO nanorods

Published:2023 Issue:21 Volume:11 Page:11187-11201
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Liao Yu-Kai¹,Lai Yi-Sheng¹²,Pan Fei³^ORCID,Su Yen-Hsun¹

Affiliation:

1. Department of Materials Science and Engineering, National Cheng Kung University, University Road No. 1, 70101 Tainan, Taiwan

2. Department of Mechanical Engineering, National Chung Cheng University, No. 168, Sec. 1, University Rd., Minhsiung, 62102 Chiayi, Taiwan

3. Department of Health Science and Technology, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland

Abstract

A genetic algorithm neural network model based on machine learning has been established for precise prediction of plasmonic resonance by considering the fabrication parameters of heterostructural noble metal nanoparticles.

Funder

National Cheng Kung University

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TA/D3TA00324H

Reference78 articles.

1. Stimuli-Responsive Reversible Transport of Nanoparticles Across Water/Oil Interfaces

2. Surface plasmon enhanced band edge luminescence of ZnO nanorods by capping Au nanoparticles

3. Au–ZnO: A tunable localized surface plasmonic nanocomposite

4. Synthesis of ZnO/Au and ZnO/Ag nanoparticles and their photocatalytic application using UV and visible light

5. Controllable synthesis of ZnO nanoflowers and their morphology-dependent photocatalytic activities

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