To unlock the full potential of PSCs,machine learning(ML)was implemented in this research to predict the optimal combination of mesoporous-titanium dioxide(mp-TiO_(2))and weight percentage(wt%)of phenyl-C_(61)-butyric...To unlock the full potential of PSCs,machine learning(ML)was implemented in this research to predict the optimal combination of mesoporous-titanium dioxide(mp-TiO_(2))and weight percentage(wt%)of phenyl-C_(61)-butyric acid methyl ester(PCBM),along with the current density(J_(sc)),open-circuit voltage(V_(oc)),fill factor(ff),and energy conversion efficiency(ECE).Then,the combination that yielded the highest predicted ECE was selected as a reference to fabricate PCBM-PSCs with nanopatterned TiO_(2)layer.Subsequently,the PCBM-PSCs with nanopatterned TiO_(2)layers were fabricated and characterized to further understand the effects of nanopatterning depth and wt%of PCBM on PSCs.Experimentally,the highest ECE of 17.338%is achieved at 127 nm nanopatterning depth and 0.10 wt%of PCBM,where the J_(sc),V_(oc),and ff are 22.877 mA cm^(-2),0.963 V,and 0.787,respectively.The measured J_(sc),V_(oc),ff,and ECE values show consistencies with the ML prediction.Hence,these findings not only revealed the potential of ML to be used as a preliminary investigation to navigate the research of PSCs but also highlighted that nanopatterning depth has a significant impact on J_(sc),and the incorporation of PCBM on perovskite layer influenced the V_(oc)and ff,which further boosted the performance of PSCs.展开更多
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),which received financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(No.20204010600470)the Korea Evaluation Institute of Industrial Technology(KEIT)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.20018608)Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2022R1I1A1A01064236)
文摘To unlock the full potential of PSCs,machine learning(ML)was implemented in this research to predict the optimal combination of mesoporous-titanium dioxide(mp-TiO_(2))and weight percentage(wt%)of phenyl-C_(61)-butyric acid methyl ester(PCBM),along with the current density(J_(sc)),open-circuit voltage(V_(oc)),fill factor(ff),and energy conversion efficiency(ECE).Then,the combination that yielded the highest predicted ECE was selected as a reference to fabricate PCBM-PSCs with nanopatterned TiO_(2)layer.Subsequently,the PCBM-PSCs with nanopatterned TiO_(2)layers were fabricated and characterized to further understand the effects of nanopatterning depth and wt%of PCBM on PSCs.Experimentally,the highest ECE of 17.338%is achieved at 127 nm nanopatterning depth and 0.10 wt%of PCBM,where the J_(sc),V_(oc),and ff are 22.877 mA cm^(-2),0.963 V,and 0.787,respectively.The measured J_(sc),V_(oc),ff,and ECE values show consistencies with the ML prediction.Hence,these findings not only revealed the potential of ML to be used as a preliminary investigation to navigate the research of PSCs but also highlighted that nanopatterning depth has a significant impact on J_(sc),and the incorporation of PCBM on perovskite layer influenced the V_(oc)and ff,which further boosted the performance of PSCs.
