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Title: Machine-Learning Assisted Exploration: Toward the Next-Generation Catalyst for Hydrogen Evolution Reaction
The development of active catalysts for hydrogen evolution reaction (HER) made from low-cost materials constitutes a crucial challenge in the utilization of hydrogen energy. Earth-abundant molybdenum disulfide (MoS 2 ) has been discovered recently with good activity and stability for HER. In this report, we employ a hydrothermal technique for MoS 2 synthesis which is a cost-effective and environmentally friendly approach and has the potential for future mass production. Machine-learning (ML) techniques are built and subsequently used within a Bayesian Optimization framework to validate the optimal parameter combinations for synthesizing high-quality MoS 2 catalyst within the limited parameter space. Compared with the heavy-labor and time-consuming trial-and-error approach, the ML techniques provide a more efficient toolkit to assist exploration of the most effective HER catalyst in hydrothermal synthesis. To investigate the structure-property relationship, scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and various electrochemical characterizations have been conducted to investigate the superiority of the ML validated optimized sample. A strong correlation between the material structure and the HER performance has been observed for the optimized MoS 2 catalyst.  more » « less
Award ID(s):
1944095
NSF-PAR ID:
10318035
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Journal of The Electrochemical Society
Volume:
168
Issue:
12
ISSN:
0013-4651
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Abstract

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    In addition, we investigate the effect of plasma treatment to tune the sensor response by modifying the defect density and chemistry. For example, we find that oxygen plasma treatment of screen-printed graphene ink greatly improves LOD of norepinephrine up to three orders of magnitude, which may be attributed to the increased defects and oxygen functional groups on the surface as evident by XPS measurements. Defects are known to play a key role in enhancing the sensitivity of 2D materials to surface interactions, and have been explored in tuning/enhancing the sensor sensitivity.5Building on our previous work,3we apply a custom machine learning-based data processing method to further improve that sensitivity and LOD, and also to automatically benchmark different molecule-material pairs.

    Future work includes expanding the plasma chemistry and conditions, studying the effect of precursor mixture in laser-induced solution-based functionalization, and understanding the interplay between molecule-material system. Work is also underway to improve the machine learning model by using nonlinear learning models such as neural networks to improve the sensor sensitivity, selectivity, and robustness.

    References

    A. J. Steckl, P. Ray, (2018), doi:10.1021/acssensors.8b00726.

    Y. Lei, D. Butler, M. C. Lucking, F. Zhang, T. Xia, K. Fujisawa, T. Granzier-Nakajima, R. Cruz-Silva, M. Endo, H. Terrones, M. Terrones, A. Ebrahimi,Sci. Adv.6, 4250–4257 (2020).

    V. Kammarchedu, D. Butler, A. Ebrahimi,Anal. Chim. Acta.1232, 340447 (2022).

    H. Yoon, J. Nah, H. Kim, S. Ko, M. Sharifuzzaman, S. C. Barman, X. Xuan, J. Kim, J. Y. Park,Sensors Actuators B Chem.311, 127866 (2020).

    T. Wu, A. Alharbi, R. Kiani, D. Shahrjerdi,Adv. Mater.31, 1–12 (2019).

     
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