氧掺杂二维硼烯催化苄胺氧化脱氢的DFT计算与机器学习研究

司超伟; 占洁; 徐文静; 李波; 陈星秋

doi:10.11949/0438-1157.20260474

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氧掺杂二维硼烯催化苄胺氧化脱氢的DFT计算与机器学习研究

催化、动力学与反应器 | 更新时间：2026-05-18

- 氧掺杂二维硼烯催化苄胺氧化脱氢的DFT计算与机器学习研究
- DFT and machine learning study of oxygen-doped two-dimensional borophene catalyzed oxidative dehydrogenation of benzylamine
- 化工学报 2026年
- 作者机构：
  
  1.中国科学技术大学材料科学与工程学院，辽宁沈阳 110016
  2.中国科学院金属研究所，辽宁沈阳 110016
  3.沈阳师范大学化学化工学院，辽宁沈阳 110034
- 作者简介：
  
  司超伟（1998—），女，博士研究生，494182855@qq.com
  李波（1978—），男，博士，教授，boli@synu.edu.cn
  陈星秋（1975—），男，博士，研究员，xingqiu.chen@imr.ac.cn
- 基金信息：
  
  国家自然科学基金项目(22372105)
- DOI：10.11949/0438-1157.20260474
  中图分类号： TQ 032.4
- 收稿：2026-04-07，
  
  修回：2026-05-17，
  
  录用：2026-05-18，
- 稿件说明：
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司超伟, 占洁, 徐文静, 等. 氧掺杂二维硼烯催化苄胺氧化脱氢的DFT计算与机器学习研究[J/OL]. 化工学报, 2026.

SI Chaowei, ZHAN Jie, XU Wenjing, et al. DFT and machine learning study of oxygen-doped two-dimensional borophene catalyzed oxidative dehydrogenation of benzylamine[J/OL]. CIESC Journal, 2026.
司超伟, 占洁, 徐文静, 等. 氧掺杂二维硼烯催化苄胺氧化脱氢的DFT计算与机器学习研究[J/OL]. 化工学报, 2026. DOI： 10.11949/0438-1157.20260474.

SI Chaowei, ZHAN Jie, XU Wenjing, et al. DFT and machine learning study of oxygen-doped two-dimensional borophene catalyzed oxidative dehydrogenation of benzylamine[J/OL]. CIESC Journal, 2026. DOI： 10.11949/0438-1157.20260474.

摘要

本文基于密度泛函理论，研究了氧掺杂量与掺杂位点对二维硼烯结构及性能的影响。计算结果表明，适量氧掺杂能够优化硼烯几何与电子结构，促进苄胺的高效吸附与低能垒脱氢。其中，最上层氧掺杂量达33 wt%的二维硼烯催化剂活性最佳，其催化苄胺氧化脱氢能垒仅为0.33 eV，且过度脱氢副反应的能垒高达3.17 eV，表明该催化剂对目标产物苯甲亚胺具有高选择性。基于机器学习方法构建的预测模型成功预测了不同取代基底物的性能趋势，模型揭示的第一步脱氢能垒为最关键描述符。本研究结合理论计算与机器学习模型，系统阐明了氧掺杂硼烯催化剂的构效关系，为设计高效低维纳米催化体系提供了理论依据。

Abstract

Using density functional theory (DFT)

the effects of different doping amounts and doping sites on oxygen-doped two-dimensional borophene were systematically investigated. The calculation results show that an appropriate amount of oxygen doping can optimize the geometric and electronic structures of borophene

enabling efficient adsorption of benzylamine and low-energy-barrier dehydrogenation. The oxygen-doped two-dimensional borophene catalyst with 33 wt% oxygen doping on the top layer exhibits the best catalytic activity

with an energy barrier of only 0.33 eV for the oxidative dehydrogenation of benzylamine and an energy barrier as high as 3.17 eV for the over-dehydrogenation side reaction

indicating its high selectivity toward the target product

benzylideneimine. A prediction model based on machine learning successfully predicted the performance trends of substrates with different substituents

and the model revealed that the first-step dehydrogenation energy barrier is the most critical descriptor. By combining theoretical calculations and machine learning predictions

this study systematically elucidates the structure–activity relationship of oxygen-doped borophene catalysts

providing a complete theoretical framework for the rational design of efficient non-noble metal catalytic systems.

关键词

Keywords

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