Enhanced Hydrogen Evolution Performance at the Lateral Interface between Two Layered Materials Predicted with Machine Learning.

Pham, Thi Hue; Kim, Eunsong; Min, Kyoungmin; Shin, Young-Han

Pham, Thi Hue; Kim, Eunsong; Min, Kyoungmin; Shin, Young-Han.

Afiliación

Pham TH; Department of Physics, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 44610, Republic of Korea.
Kim E; School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.
Min K; School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.
Shin YH; Department of Physics, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 44610, Republic of Korea.

ACS Appl Mater Interfaces ; 15(23): 27995-28007, 2023 Jun 14.

Article en En | MEDLINE | ID: mdl-37233719

Palabras clave

DFT; TMDCs; exchange current density; hydrogen evolution reactions; interfaces; lateral heterostructures; machine learning; overpotential

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2023 Tipo del documento: Article

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