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Li G, Zhu Y, Guo Y, Mabuchi T, Li D, Huang S, Wang S, Sun H, Tokumasu T. Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5099-5108. [PMID: 36652634 DOI: 10.1021/acsami.2c17198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.
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Affiliation(s)
- Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Yonghong Zhu
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Yuting Guo
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takuya Mabuchi
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi980-8577, Japan
| | - Dong Li
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Shengfeng Huang
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Sirui Wang
- Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba263-8522, Japan
| | - Haiyi Sun
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takashi Tokumasu
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
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Guo Y, Mabuchi T, Li G, Tokumasu T. Morphology Evolution and Adsorption Behavior of Ionomers from Solution to Pt/C Substrates. Macromolecules 2022. [DOI: 10.1021/acs.macromol.2c00533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yuting Guo
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira,
Aoba-ku, Sendai 980-8577, Japan
| | - Takuya Mabuchi
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira,
Aoba-ku, Sendai 980-8577, Japan
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 2-1-1 Katahira Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira,
Aoba-ku, Sendai 980-8577, Japan
| | - Takashi Tokumasu
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira,
Aoba-ku, Sendai 980-8577, Japan
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Eliminating the need for craftsmanship: Facile and precise determination of oxygen reduction reaction activity by spraying catalyst ink on rotating disk electrode. J Electroanal Chem (Lausanne) 2021. [DOI: 10.1016/j.jelechem.2021.115115] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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