Data-Guided Exploration of Process Control in Carbon-Based Catalyst Design for Two-Electron Oxygen Reduction.

Journal: Langmuir : the ACS journal of surfaces and colloids
Published Date: Aug 5, 2025

Abstract

Electrochemical hydrogen peroxide (HO) synthesis via the two-electron oxygen reduction reaction (2e ORR) is a promising alternative to the energy-intensive and high-pollution anthraquinone oxidation process. Identifying a carbon-based electrocatalyst with high selectivity and activity for 2e ORR is crucial to large-scale electrochemical HO synthesis. However, optimizing catalyst composition and process parameters through experimental studies has been resource-intensive and machine learning techniques provide a solution to this problem. In this study, machine learning models were developed to enhance our comprehension of how process control and carbon-based catalyst design impact the performance of 2e ORR. The values of the optimal 2e ORR models for HO selectivity and current density are 0.959 and 0.831, respectively. It revealed that nitrogen doping and oxygen content significantly enhance HO selectivity by modifying the catalyst's electronic structure and stabilizing reaction intermediates. When it comes to current density, the / ratio and carbon content were found to be the key factors. Higher defect densities along with suitable carbon content can enhance catalytic activity by boosting active site density and conductivity. The practical applicability of the model, preliminary validation was conducted using catalyst compositions and process parameters different from those in the data set, confirming the good accuracy of the model in real scenarios. Our findings provide a new perspective on the influence of process control and catalyst design.

Authors

  • Zihao Jiang
    School of Marxism, Shenzhen Polytechnic University, Shenzhen, Guangdong 518055, P.R. China.
  • Lin Cong
    PingAn Health Technology, Beijing, China.
  • Xinrui Zhou
    School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
  • Shengchun Hu
    Key Lab of Biomass Energy and Material, Jiangsu Province; Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry, Nanjing 210042, China.
  • Yuying Zhao
    Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
  • Qixin Yuan
    Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China.
  • Yuhan Wu
    State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
  • Kang Sun
    Department of Civil, Structural and Environmental Engineering, University at Buffalo, 230 Jarvis Hall, Buffalo, NY, 14260, USA.
  • Shule Wang
    Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China.
  • Jianchun Jiang
    Key Lab of Biomass Energy and Material, Jiangsu Province, Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry, Nanjing, 210042, P. R. China.
  • Mengmeng Fan
    College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China.

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