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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Electrocatalytic nitrate reduction to ammonia is vital for water remediation and energy applications.
  • High-entropy alloys (HEAs) are emerging as promising electrocatalysts.
  • Understanding HEA composition effects on nitrate reduction intermediates is crucial but limited.

Purpose of the Study:

  • To investigate how FeCoNiCuZn high-entropy alloy structures influence nitrate adsorption.
  • To develop predictive models for nitrate adsorption free energy (ΔGNO).
  • To identify key structural features governing catalyst performance.

Main Methods:

  • High-throughput density functional theory (DFT) calculations.
  • Machine learning regression models.
  • Analysis of 1268 sampled HEA structures and their surface microstructures.

Main Results:

  • Four accurate regression models were developed to predict ΔGNO based on HEA structure.
  • Active sites and local atomic environments significantly impact nitrate adsorption.
  • Compositional tuning of HEAs can facilitate adsorption and activation of reaction intermediates.

Conclusions:

  • Compositional modulation of HEAs is a viable strategy for optimizing electrocatalysts.
  • This work provides insights for probabilistic optimization of HEA microstructures for enhanced nitrate reduction.
  • Findings contribute to the development of efficient catalysts for water remediation and energy applications.