Identification of dominant factors contributing to photocurrent density of BiVO4 photoanodes using Machine learning

Kobayashi, Kazuki; Nagai, Yuya; Pan, Zhenhua; Katayama, Kenji

doi:10.1016/j.jphotochem.2023.114651

Cited by 6 publications

(13 citation statements)

References 24 publications

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“…Previous studies have documented significant variability in the PEC performance of hematite samples, even when prepared under identical conditions. This variability was linked to certain features identified through analytical data. , It was also observed that some hematite samples exhibited no photoelectrode activity. Utilizing a categorization approach based on ML, we were able to not only detect these inactive samples but also trace their lack of activity to specific causes .…”

Section: Introductionmentioning

confidence: 99%

Combined Effect of Underlayer and Deposition Solution to Optimize the Alignment of Hematite Photoanodes

Idei,

Pan,

Katayama

2024

Langmuir

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This study investigates the optimization of hematite (α-Fe 2 O 3 ) photoanodes for enhanced photoelectrochemical (PEC) performance and reproducibility, which are crucial for photocatalytic applications. Despite hematite's potential, hindered by inherent limitations, significant improvements were realized by introducing a titanium dioxide (TiO 2 ) underlayer and ethanolmodified deposition. The influence of the deposition methods was understood by potential-dependent photoelectrochemical impedance spectroscopy analysis. The introduction of the TiO 2 underlayer effectively increased the density of states, preferable for the electron transport in the bulk hematite, and the ethanol deposition on a TiO 2 underlayer led to a stable surface state formation (S1 state) for the photoexcited hole transfer. This analysis illuminated the intricate interplay between electron transport in the bulk and photogenerated hole transfer at the solution interface, thereby facilitating smoother charge transfer. These findings underscore the viability of surface engineering and meticulous process optimization in addressing critical challenges in photocatalyst development.

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Section: Introductionmentioning

confidence: 99%

Combined Effect of Underlayer and Deposition Solution to Optimize the Alignment of Hematite Photoanodes

Idei,

Pan,

Katayama

2024

Langmuir

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“…Our strategy confronts the paucity of data head-on by integrating analytical data with ML approaches, a method that has seen success in several photocatalytic materials and devices. − The studies generated dozens of samples using a consistent method and analyzed them through techniques such as X-ray diffraction (XRD), Raman spectroscopy, UV/vis absorption spectroscopy, and photoelectrochemical impedance spectroscopy (PEIS). By using data descriptors such as peaks and patterns from these analyses, the research successfully predicted photocurrent values and pinpointed key factors affecting performance.…”

Section: Introductionmentioning

confidence: 99%

Subtraction Descriptors in Machine Learning for Optimizing the Cocatalyst Effect of Cobalt Phosphate on Hematite Photoanodes

Chen,

Nagai,

Pan

et al. 2024

ACS Appl. Mater. Interfaces

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In the quest for sustainable energy solutions, the optimization of the photoelectrochemical (PEC) performance of hematite photoanodes through cocatalysts represents a promising avenue. This study introduces a novel machine learning approach, leveraging subtraction descriptors, to isolate and quantify the specific effects of cobalt phosphate (Co-Pi) as a cocatalyst on hematite's PEC performance. By integrating data from various analytical techniques, including photoelectrochemical impedance spectroscopy and ultraviolet−visible spectroscopy, with advanced machine learning models, we successfully predicted the PEC performance enhancement attributed to Co-Pi. The Gaussian process regression (GPR) model emerged as the most effective, revealing the critical influence of the interfacial resistance, bulk resistance, and interfacial capacitance on the PEC performance. These findings underscore the potential of cocatalysts in improving charge separation and extending charge carrier lifetimes, thereby boosting the efficiency of photocatalytic reactions. This study not only advances our understanding of the cocatalyst effect in photocatalytic systems but also demonstrates the power of machine learning in modifying complex materials and guiding the development of optimized photocatalytic materials. The implications of this research extend beyond hematite photoanodes, offering a generalizable framework for enhancing the photoelectrochemical properties of a wide range of material modifications such as cocatalyst deposition, doping, and passivation.

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“…The analytical data for the samples are sophisticated descriptors to represent samples to predict the physical parameters even by using a small amount of data. 20,30 We prepared dozens of samples under the same preparation procedure, and they were analyzed with various analytical methods, such as X-ray diffraction (XRD), Raman spectroscopy, UV/vis absorption spectroscopy, and photoelectrochemical impedance spectroscopy (PEIS). The peaks/patterns in the analytical data were used as the descriptors of the samples, and they were used for the prediction of the photocurrent values representing their performance.…”

Section: ■ Introductionmentioning

confidence: 99%

“…To avoid the problem, we used a combination of analytical data for representing the samples, and these combinations were applicable for the performance prediction via ML, even for a small amount of data. The analytical data for the samples are sophisticated descriptors to represent samples to predict the physical parameters even by using a small amount of data. , …”

Section: Introductionmentioning

confidence: 99%

Identification of the Contributing Factors to the Photoelectric Conversion Efficiency for Hematite Photoanodes by Using Machine Learning

Idei,

Nagai,

Pan

et al. 2023

ACS Appl. Mater. Interfaces

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Identification of dominant factors contributing to photocurrent density of BiVO4 photoanodes using Machine learning

Cited by 6 publications

References 24 publications

Combined Effect of Underlayer and Deposition Solution to Optimize the Alignment of Hematite Photoanodes

Combined Effect of Underlayer and Deposition Solution to Optimize the Alignment of Hematite Photoanodes

Subtraction Descriptors in Machine Learning for Optimizing the Cocatalyst Effect of Cobalt Phosphate on Hematite Photoanodes

Identification of the Contributing Factors to the Photoelectric Conversion Efficiency for Hematite Photoanodes by Using Machine Learning

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