Mobin Safarzadeh Khosrowshahi , Amirhossein Afshari Aghajari , Mohammad Rahimi , Farid Maleki , Elahe Ghiyabi , Armin Rezanezhad , Ali Bakhshi , Ehsan Salari , Hadi Shayesteh , Hadi Mohammadi
{"title":"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","authors":"Mobin Safarzadeh Khosrowshahi , Amirhossein Afshari Aghajari , Mohammad Rahimi , Farid Maleki , Elahe Ghiyabi , Armin Rezanezhad , Ali Bakhshi , Ehsan Salari , Hadi Shayesteh , Hadi Mohammadi","doi":"10.1016/j.mtsust.2024.100900","DOIUrl":null,"url":null,"abstract":"<div><p>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 CO<sub>2</sub> levels. To address this problem, various carbon capture and storage (CCS) technologies have been developed, aiming to reduce CO<sub>2</sub> emissions and mitigate their impact on climate change. Absorption using aqueous amines has long been recognized as a method for removing diluted CO<sub>2</sub> 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 CO<sub>2</sub> uptake behavior. In CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> adsorbents.</p></div>","PeriodicalId":18322,"journal":{"name":"Materials Today Sustainability","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning\",\"authors\":\"Mobin Safarzadeh Khosrowshahi , Amirhossein Afshari Aghajari , Mohammad Rahimi , Farid Maleki , Elahe Ghiyabi , Armin Rezanezhad , Ali Bakhshi , Ehsan Salari , Hadi Shayesteh , Hadi Mohammadi\",\"doi\":\"10.1016/j.mtsust.2024.100900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 CO<sub>2</sub> levels. To address this problem, various carbon capture and storage (CCS) technologies have been developed, aiming to reduce CO<sub>2</sub> emissions and mitigate their impact on climate change. Absorption using aqueous amines has long been recognized as a method for removing diluted CO<sub>2</sub> 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 CO<sub>2</sub> uptake behavior. In CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> adsorbents.</p></div>\",\"PeriodicalId\":18322,\"journal\":{\"name\":\"Materials Today Sustainability\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Today Sustainability\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2589234724002367\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Today Sustainability","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589234724002367","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
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.
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.