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Related Concept Videos

The Electrical Double Layer01:30

The Electrical Double Layer

In the region where two bulk phases meet, an intricate electric charge distribution arises due to charge transfer, ion adsorption, molecular orientation, and charge distortion. This complex distribution is commonly referred to as the electrical double layer.When a solid electrode interfaces with ions in an electrolyte solution, the speed of electron transfer dictates the rates of oxidation and reduction. The electrode acquires a charge through the escape of atoms into the solution as cations or...

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

Thi Hue Pham1, Eunsong Kim2, Kyoungmin Min2

  • 1Department of Physics, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 44610, Republic of Korea.

ACS Applied Materials & Interfaces
|May 26, 2023
PubMed
Summary

Engineered 2D materials show enhanced hydrogen evolution reaction (HER) activity. Machine learning models predict MoS2/ZnO as a top catalyst for sustainable hydrogen production.

Keywords:
DFTTMDCsexchange current densityhydrogen evolution reactionsinterfaceslateral heterostructuresmachine learningoverpotential

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Area of Science:

  • Materials Science
  • Catalysis
  • Computational Chemistry

Background:

  • Sustainable hydrogen production requires economical and effective catalysts.
  • Low-dimensional interfacial engineering enhances catalytic activity for the hydrogen evolution reaction (HER).
  • Two-dimensional lateral heterostructures (LHSs) offer tunable properties for catalysis.

Purpose of the Study:

  • To investigate hydrogen adsorption Gibbs free energy (ΔGH) in various 2D LHSs using DFT.
  • To develop universal descriptors for predicting HER catalytic activity in 2D materials.
  • To employ machine learning (ML) to identify promising HER catalyst candidates.

Main Methods:

  • Density Functional Theory (DFT) calculations to determine ΔGH for hydrogen adsorption in MX2/M'X'2 and MX2/M'X' LHSs.
  • Development of three universal descriptors based on atomic information near adsorption sites.
  • Training ML models (regression and classification) using DFT results and experimental data.

Main Results:

  • LHS MX2/M'X' interfaces exhibit enhanced HER reactivity due to metallic behavior and stronger hydrogen absorption.
  • ML models achieved high accuracy (R2=0.951 for regression, F1=0.749 for classification).
  • MoS2/ZnO identified as the most promising HER catalyst with ΔGH = -0.02 eV and low overpotential (-171 mV).

Conclusions:

  • Interfacial engineering of 2D LHSs is a viable strategy for developing efficient HER catalysts.
  • The developed descriptors and ML models can accurately predict catalytic performance.
  • MoS2/ZnO presents a superior candidate for sustainable hydrogen production.