Research on Adsorbents for CO<sub>2</sub> Capture：From Traditional Design to Machine Learning

YAN Wei; GAO Suihan; SONG Shuting; HU Yadi; LAN Shenyu; ZHANG Qian

doi:10.7652/xjtuxb202511001

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Research on Adsorbents for CO₂ Capture：From Traditional Design to Machine Learning

Special Topic Pollution Control and Resource Utilization in the“Dual Carbon”Context | 更新时间：2025-11-11

- Research on Adsorbents for CO₂ Capture：From Traditional Design to Machine Learning
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- Journal of Xi'an Jiaotong University Vol. 59, Issue 11, Pages: 1-18(2025)
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- DOI：10.7652/xjtuxb202511001
  CLC： X131.1
- Received：07 April 2025，
  
  Published：10 November 2025
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YAN Wei, GAO Suihan, SONG Shuting, et al. Research on Adsorbents for CO₂ Capture：From Traditional Design to Machine Learning[J]. Journal of Xi'an Jiaotong University, 2025, 59(11): 1-18. DOI: 10.7652/xjtuxb202511001.
DOI：

YAN Wei, GAO Suihan, SONG Shuting, et al. Research on Adsorbents for CO₂ Capture：From Traditional Design to Machine Learning[J]. Journal of Xi'an Jiaotong University, 2025, 59(11): 1-18. DOI: 10.7652/xjtuxb202511001. DOI：

摘要

随着全球气候问题日益严峻，碳捕集技术对于实现“双碳”目标的重要性愈加凸显。在众多碳捕集材料中，吸附剂因其优异的选择性、高吸附容量及长期稳定性等优势，已成为实现高效CO

捕集的核心材料体系。其中，面向碳捕集应用场景的CO

吸附剂主要涵盖碳基吸附剂、胺基复合吸附剂、沸石分子筛、介孔二氧化硅、金属有机框架材料、共价有机框架材料和磁性纳米颗粒等类型。首先，系统梳理了上述各类CO

吸附材料的研究进展，着重对比分析其吸附动力学、吸附机理及循环稳定性等关键性能指标；其次，深入剖析机器学习技术在CO

吸附剂开发中的研究进展，包括基于主动学习的新材料筛选系统开发、基于神经网络和高通量计算的吸附容量等性能评估以及通过特征工程建立吸附参数与孔隙结构等参数的机理分析；最后，指出该领域当前面临的一些瓶颈问题，包括构建标准化多指标数据库、跨尺度数据融合困难及实验验证滞后等。未来应聚焦材料基因工程与机器学习的深度融合，开发兼具解释性和预测性的新一代算法框架，建立机器学习-实验闭环验证系统。

Abstract

As global climate issues become increasingly severe

carbon capture technology has grown in importance for achieving the“dual carbon”goals. Among various carbon capture materials

adsorbents have emerged as a core material system for efficient CO

capture due to their advantages such as excellent selectivity

high adsorption capacity

and long-term stability. Specifically

adsorbents for carbon capture applications primarily include carbon-based adsorbents

amine-based composite adsorbents

zeolite molecular sieves

mesoporous silica

metal-organic framework (MOFs)

covale

nt organic frameworks (COFs)

and magnetic nanoparticles. First

this paper systematically reviews the latest research progress in these CO

adsorbent materials

with a focus on comparative analysis of key performance indicators such as adsorption kinetics

adsorption mechanisms

and cycling stability. Second

it provides an in-depth analysis of the advancements in machine learning technology for CO

adsorbent development

including the creation of new material screening systems based on active learning

the evaluation of performance metrics such as adsorption capacity using neural networks and high-throughput calculations

and mechanistic analysis linking adsorption parameters to pore structure through feature engineering. Finally

this paper highlights current bottlenecks in the field

such as the complexity of constructing standardized multi-indicator databases

challenges in cross-scale data integration

and delays in experimental validation. Future efforts should focus on the deep integration of materials genome engineering and machine learning

the development of next-generation algorithmic frameworks that balance interpretability and predictive power

and the establishment of closed-loop validation systems combining machine learning and experiments.

关键词

Keywords

references

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Research on Adsorbents for CO2 Capture：From Traditional Design to Machine Learning

DOI：10.7652/xjtuxb202511001

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Research on Adsorbents for CO₂ Capture：From Traditional Design to Machine Learning