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

Safarzadeh Khosrowshahi, Mobin; Afshari Aghajari, Amirhossein; Rahimi, Mohammad; Maleki, Farid; Ghiyabi, Elahe; Rezanezhad, Armin; Bakhshi, Ali; Salari, Ehsan; Shayesteh, Hadi; Mohammadi, Hadi

doi:10.1016/j.mtsust.2024.100900

Materials Today Sustainability

2024

DOI: 10.1016/j.mtsust.2024.100900

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

Mobin Safarzadeh Khosrowshahi,

Amirhossein Afshari Aghajari,

Mohammad Rahimi

et al.

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2024

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Cited by 2 publications

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Leveraging experimental and computational tools for advancing carbon capture adsorbents research

Ramasamy,

Raj,

Akula

et al. 2024

Environ Sci Pollut Res

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Leveraging experimental and computational tools for advancing carbon capture adsorbents research

Ramasamy,

Raj,

Akula

et al. 2024

Environ Sci Pollut Res

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Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Rahimi,

Mehrpanah,

Mouchani

et al. 2024

Ind. Eng. Chem. Res.

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Carbon materials possess active sites and functionalities on the surface that can attract prominent interest as solid adsorbents for diverse gas adsorption. This study aimed to predict the optimized methane uptake, adsorption energy (E ad ), and adsorbent rediscovery through multitechniques of neural, regression, classifier ML-DFT, and Uniform Manifold Approximation and Projection (UMAP). Nitrogen and oxygen (N/O) functionalities and graphene, graphene oxide (GO), and N-doped GO were applied to the methane storage medium. Multi-ML algorithms were employed for the adsorption energy of CH 4 uptake on (i) N/O functionalities such as pyridinic (N-py), carboxyl (O−II), oxidized (N-x), hydroxyl (O-h), Nitroso (N-ni), and Amine (primary, secondary, and tertiary). (ii) The graphene surfaces are decorated with N/O heteroatoms to construct graphene oxide (GO) and N-doped GO. The DFT calculations were applied by PW91 and the Dmol 3 package. N/O-functionalities in the distance of ∼2.0 to 3.1 Å groups obtained E ad of approximately −2.0 to −4 eV. Further, ML models accomplished the forthcoming rediscovery of CH 4 physisorption by using the multiadsorptive features of optimized adsorbents with an R 2 of 0.99. ML-derived sensitivity analysis approach was applied to specifications such as deformation adsorption energy, N/O functionality type, and optimized structure. CH 4 adsorption specifications indicate sensitivity levels of −0.03 to 0.02 eV. The synergetic DFT/ML approaches distinguished the modeled and rediscovered phases of CH 4 adsorption on N/O functional groups and graphene structures. UMAP is employed as a new adsorbent screening approach to play a complementary role in the ML modeling process.

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

Cited by 2 publications

References 304 publications

Leveraging experimental and computational tools for advancing carbon capture adsorbents research

Leveraging experimental and computational tools for advancing carbon capture adsorbents research

Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

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