Photonic and plasmonic effects in inverse opal films with Au nanoparticles

Author(s):  
Sergey Klimonsky ◽  
Alexander Baranchikov ◽  
V.N. Lad ◽  
Elena Eremina ◽  
Alexey Garshev ◽  
...  
2019 ◽  
Vol 123 (39) ◽  
pp. 24234-24242 ◽  
Author(s):  
Matthias Linke ◽  
Marvin Hille ◽  
Michael Lackner ◽  
Ludmilla Schumacher ◽  
Sebastian Schlücker ◽  
...  

2016 ◽  
Vol 100 (3) ◽  
pp. 988-997 ◽  
Author(s):  
Bo Shao ◽  
Zhengwen Yang ◽  
Jun Li ◽  
Jianzhi Yang ◽  
Yida Wang ◽  
...  

RSC Advances ◽  
2014 ◽  
Vol 4 (98) ◽  
pp. 55658-55665 ◽  
Author(s):  
N. Chander ◽  
P. S. Chandrasekhar ◽  
V. K. Komarala

Solution synthesized perovskite CsSnI3 works well as a solid-state electrolyte in DSCs and Au nanoparticles enhance device photocurrent by plasmonic effects.


Solar RRL ◽  
2018 ◽  
Vol 2 (5) ◽  
pp. 1800028 ◽  
Author(s):  
Qingduan Li ◽  
Shuangshuang Chen ◽  
Jianwei Yang ◽  
Jizhao Zou ◽  
Weiguang Xie ◽  
...  

PIERS Online ◽  
2008 ◽  
Vol 4 (6) ◽  
pp. 625-630 ◽  
Author(s):  
Yu-Yang Feng ◽  
Morten Willatzen
Keyword(s):  

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.


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