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+---
+title: "Recent Progress on Advanced Solid Adsorbents for CO2 Capture: From Mechanism to Machine Learning"
+collection: publications
+permalink: /publication/15
+excerpt: 'This review paper is about AI-assisted design and synthesis of adsorbents for sustainable applications including CO2 capturing.'
+date: 2024-06-29
+venue: 'Materials Today Sustainability'
+paperurl: 'https://doi.org/10.1016/j.mtsust.2024.100900'
+---
+<address class="author">Authors: Mobin Safarzadeh Khosrowshahi, Amirhossein Afshari Aghajari, Mohammad Rahimi, Farid Maleki, Elahe Ghiyabi, Armin Rezanezhad, 
+  <a rel="author" href="https://bakhshiali.github.io">Ali Bakhshi</a>, Ehsan Salari, Hadi Shayesteh, Hadi Mohammadi</address><br>
+
+<p align="justify" style="padding-left: 1em">
+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.
+</p>
+<cite> Safarzadeh Khosrowshahi, M., Afshari Aghajari, A., Rahimi, M., Maleki, F., Ghiyabi, E., Rezanezhad, A., Bakhshi, A., Salari, E., Shayesteh, H., Mohammadi, H.(2024). Recent Progress on Advanced Solid Adsorbents for CO2 Capture: From Mechanism to Machine Learning. Materials Today Sustainability. https://doi.org/10.1016/j.mtsust.2024.100900 </cite>
+
+<b>Full-Texts</b>
+<details>
+<summary>Sciencedirect</summary>
+  <a href="https://doi.org/10.1016/j.mtsust.2024.100900"> https://doi.org/10.1016/j.mtsust.2024.100900 </a>
+</details>
+<details>
+<summary>Researchgate</summary>
+  <a href="https://www.researchgate.net/publication/381829459_Recent_Progress_on_Advanced_Solid_Adsorbents_for_CO2_Capture_From_Mechanism_to_Machine_Learning"> https://www.researchgate.net/publication/381829459_Recent_Progress_on_Advanced_Solid_Adsorbents_for_CO2_Capture_From_Mechanism_to_Machine_Learning </a>
+</details>