Abstract

Dye-sensitized solar cells (DSSC) have recently gained significant attention in a number of markets and are recognized as a better option for energy generation than conventional ones, providing clean, sustainable, and renewable green energy. Several studies have focused on photoanode optimization using machine learning. Photoanode produces performance results in the form of Voc and Isc, which are then used as model training and validation data. The highest predictive results were obtained for A3+A2 (TiO2 18NR-T and TiO2 R/SP). The TiO2 layers combinations were prepared according to the optimization training performed was carried out with an efficiency of 2.630%.

Details

Title
Optimization of photoanode on dye-sensitized solar cell structure using k-nearest neighbor method
Author
Paramitha, T 1 ; Supriyanto, A 2 ; Marcus, S 2 ; Purwanto, A 1 ; Widiyandari, H 2 ; Aliwarga, H K 3 ; Kisdina, R H 4 ; Nisa, S S 4 ; Subekti, N Y S 4 ; Kisdina, R T 4 

1  Department of Chemical Engineering, Faculty of Engineering, Universitas Sebelas Maret , Jl. Ir. Sutami 36A, Kentingan, Surakarta 57126 , Indonesia; PT Lectro , Surakarta , Indonesia 
2  Department of Physics, Faculty of MIPA, Universitas Sebelas Maret , Jl. Ir. Sutami 36A, Kentingan, Surakarta 57126 , Indonesia; PT Lectro , Surakarta , Indonesia 
3  UMG Idealab , Jl. Tangkas Baru no.2, Setiabudi, Jakarta Selatan 12930 , Indonesia 
4  PT Lectro , Surakarta , Indonesia 
First page
012005
Publication year
2023
Publication date
Aug 2023
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1088/1742-6596/2556/1/012005
ProQuest document ID
2857137067
Copyright
Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.