用于二氧化碳捕获的先进固体吸附剂的最新进展:从机理到机器学习

IF 7.1 3区 材料科学 Q1 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Materials Today Sustainability Pub Date : 2024-06-29 DOI:10.1016/j.mtsust.2024.100900
Mobin Safarzadeh Khosrowshahi , Amirhossein Afshari Aghajari , Mohammad Rahimi , Farid Maleki , Elahe Ghiyabi , Armin Rezanezhad , Ali Bakhshi , Ehsan Salari , Hadi Shayesteh , Hadi Mohammadi
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引用次数: 0

摘要

由于城市化、工业化和汽车交通的快速发展,环境污染已成为一个严重问题。值得注意的是,化石燃料燃烧大大加剧了大气中二氧化碳含量的上升。为解决这一问题,人们开发了各种碳捕集与封存(CCS)技术,旨在减少二氧化碳排放,减轻其对气候变化的影响。长期以来,人们一直认为使用水胺吸附是一种从气体流中去除稀释二氧化碳的方法,但这种方法也存在能源强度高和腐蚀问题等缺点。不过,现在人们正在认真考虑使用固体吸附剂作为一种潜在的替代方法,因为它可能是一种能耗较低的分离方法。本研究的主要重点是概述先进固体吸附剂的最新发展,包括沸石、碳基材料、MOFs、COFs、氮化硼、磁性纳米颗粒和介孔二氧化硅,以及它们的二氧化碳吸收行为。在二氧化碳捕集过程中,选择合适的吸附剂至关重要,但由于影响性能和经济性的各种因素,要事先确定最有前途的吸附剂并非易事。近年来,机器学习(ML)算法,特别是人工神经网络(ANN)和卷积神经网络(CNN)已成为预测物理性质、加快选择二氧化碳捕集最佳吸附剂、优化吸附剂合成条件以及了解气固相互作用有利变量的重要工具。本综述的第二个目的是在机器学习辅助二氧化碳吸附剂领域的最新进展与未来潜在突破之间建立联系。总之,本综述旨在根据最近的研究报告,为选择量身定制的固体吸附剂材料提供全面指导,以实现高性能的二氧化碳捕集。通过探讨各种材料、其特性以及影响其有效性的机制,本综述旨在为二氧化碳吸附剂的明智决策和创新解决方案提供帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning

Environmental pollution has become a serious issue due to the rapid development of urbanization, industrialization, and vehicle traffic. Notably, fossil fuel combustion significantly contributes to rising atmospheric CO2 levels. To address this problem, various carbon capture and storage (CCS) technologies have been developed, aiming to reduce CO2 emissions and mitigate their impact on climate change. Absorption using aqueous amines has long been recognized as a method for removing diluted CO2 from gas streams, but it comes with drawbacks such as high energy intensity and corrosion issues. The use of solid adsorbents, however, is now being seriously considered as a potential alternative, offering a possibly less energy-intensive separation method. The primary focus of this study is to outline the recent development of advanced solid adsorbents, including zeolites, carbon-based materials, MOFs, COFs, boron nitride, magnetic nanoparticles, and mesoporous silica, along with their CO2 uptake behavior. In CO2 capture procedures, selecting the appropriate adsorbent is crucial, yet it's not a straightforward task to determine the most promising sorbent beforehand due to various factors affecting performance and economy. In recent years, machine learning (ML) algorithms, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) have emerged as valuable tools for predicting physical properties, expediting the selection of optimal adsorbents for CO2 capture, optimizing synthesis conditions of adsorbents, and understanding advantageous variables for gas-solid interaction. The secondary objective of this review is to establish a correlation between recent advancements and potential future breakthroughs in the domain of machine learning-assisted CO2 adsorbents. In summary, this review aims to provide a comprehensive guideline for selecting tailored solid adsorbent materials according to recently reported research to achieve high-performance CO2 capture. By exploring various materials, their properties, and the mechanisms that influence their effectiveness, this review intends to facilitate informed decisions and innovative solutions for CO2 adsorbents.

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来源期刊
CiteScore
5.80
自引率
6.40%
发文量
174
审稿时长
32 days
期刊介绍: Materials Today Sustainability is a multi-disciplinary journal covering all aspects of sustainability through materials science. With a rapidly increasing population with growing demands, materials science has emerged as a critical discipline toward protecting of the environment and ensuring the long term survival of future generations.
期刊最新文献
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