TY - JOUR
T1 - Over 9% Water Splitting Nature Dyes Solar Cells via Artificial Intelligent Selected Combination
AU - Lai, Yi Sheng
AU - Su, Yen Hsun
N1 - Funding Information:
The authors gratefully acknowledge Mr. William Lai for the support in greenhouse setup, high-farmland irrigation planting, and scientific planting in the Office of the President in Taiwan. The authors gratefully acknowledge Mr. S.W. Tseng for the use of the machine equipment belonging to the Core Facility Center of National Cheng Kung University. This work was supported by the Agriculture Bureau and Water Resource Bureau in Tainan, National Chung Kung University, and the Ministry of Science and Technology of Taiwan by grants from project nos. 110-2811-E-006-521, 109-2224-E-006-009, 109-2221-E-006-024-MY3, and 110-2224-E-006-005.
Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/6/20
Y1 - 2022/6/20
N2 - In this work, artificial intelligence is achieved using a genetic algorithm integrated with an artificial neuron network (GANN) to find the optimal combination of natural leaves on ZrO2 architecture anodes for water splitting devices and hydrogen evolution. Around 180 kinds of Aglaonema and 100 kinds of Codiaeum variegatum are evaluated from nine kinds of original Aglaonema and five kinds of Codiaeum variegatum over 10 generations of hybridization, respectively. The types and content of natural chlorophyll and anthocyanin are estimated and recorded by CMOS devices and an absorption spectrum for pixel recording and featured parameter mining for GANN. Optical extinction ZrO2 architecture anodes constructed on ITO substrates, which correspond with a leaf's pigments, demonstrated ultrahigh water splitting performance for hydrogen evolution up to 9.42%. Furthermore, from promising experimental results, the natural pigments extracted from different color parts of Codiaeum variegatum and Aglaonema demonstrated over 85% incident photon to electron conversion efficiency while the pigments correspond with the lowest optical extinction ZrO2 architecture anodes. Ultrahigh current density (157.1
AB - In this work, artificial intelligence is achieved using a genetic algorithm integrated with an artificial neuron network (GANN) to find the optimal combination of natural leaves on ZrO2 architecture anodes for water splitting devices and hydrogen evolution. Around 180 kinds of Aglaonema and 100 kinds of Codiaeum variegatum are evaluated from nine kinds of original Aglaonema and five kinds of Codiaeum variegatum over 10 generations of hybridization, respectively. The types and content of natural chlorophyll and anthocyanin are estimated and recorded by CMOS devices and an absorption spectrum for pixel recording and featured parameter mining for GANN. Optical extinction ZrO2 architecture anodes constructed on ITO substrates, which correspond with a leaf's pigments, demonstrated ultrahigh water splitting performance for hydrogen evolution up to 9.42%. Furthermore, from promising experimental results, the natural pigments extracted from different color parts of Codiaeum variegatum and Aglaonema demonstrated over 85% incident photon to electron conversion efficiency while the pigments correspond with the lowest optical extinction ZrO2 architecture anodes. Ultrahigh current density (157.1
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U2 - 10.1021/acsagscitech.2c00043
DO - 10.1021/acsagscitech.2c00043
M3 - Article
AN - SCOPUS:85132334045
SN - 2692-1952
VL - 2
SP - 615
EP - 624
JO - ACS Agricultural Science and Technology
JF - ACS Agricultural Science and Technology
IS - 3
ER -
