The Role of Transition Metals on CeO<sub>2</sub>Supported for CO<sub>2</sub>Adsorption by DFT and Machine Learning Analysis

Mashhadimoslem, Hossein; Karimi, Peyman; Abdol, Mohammad Ali; Zanganeh, Kourosh; Shafeen, Ahmed; AlHammadi, Ali A.; Elkamel, Ali

doi:10.1021/acs.iecr.4c01200

Ind. Eng. Chem. Res.

2024

DOI: 10.1021/acs.iecr.4c01200

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The Role of Transition Metals on CeO₂Supported for CO₂Adsorption by DFT and Machine Learning Analysis

Hossein Mashhadimoslem,

Peyman Karimi,

Mohammad Ali Abdol

et al.

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Cited by 3 publications

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A comprehensive multiscale review of shaft furnace and reformer in direct reduction of iron oxide

Hosseinzadeh,

Kasiri,

Rezaei

2025

Minerals Engineering

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A comprehensive multiscale review of shaft furnace and reformer in direct reduction of iron oxide

Hosseinzadeh,

Kasiri,

Rezaei

2025

Minerals Engineering

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Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms

Mashhadimoslem,

Abdol,

Zanganeh

et al. 2024

ACS Appl. Energy Mater.

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Porous adsorbents have common characteristics, such as high porosity and a large specific surface area. These characteristics, attributed to the internal structure of the material, significantly affect their adsorption performance. In this research study, we created a data set and collected data points from porous adsorbents (2789) from 21 published papers, including carbonbased, porous polymers, metal−organic frameworks (MOFs), and zeolites, to understand their characteristics for CO 2 adsorption. Different machine learning (ML) algorithms, such as NN, MLP-GWO, XGBoost, RF, DT, and SVM, have been applied to display the CO 2 adsorption performance as a function of characteristics and adsorption isotherm parameters. XGBoost was selected as the best ML algorithm due to its highest accuracy (R 2 = 0.9980; MSE = 0.0001). The predicted results revealed that the adsorption pressure parameter is the most effective in all of the mentioned porous adsorbents. With regard to materials type, while carbon-based materials require higher pressures for a more effective CO 2 adsorption, MOFs exhibit a higher potential for adsorbing CO 2 under lower pressure conditions. The study also revealed that carbon-based adsorbents, zeolites, and porous polymers with smaller pore diameters demonstrate a high level of CO 2 uptake. In contrast, the adsorption performance of MOFs does not show a consistent trend with respect to pore sizes. Also, in all adsorbents, the effect of a pore size smaller than 1 nm on more CO 2 adsorption was evident.

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Machine learning-assisted DFT-prediction of pristine and endohedral doped (O and Se) Ge12C12 and Si12C12 nanostructures as anode materials for lithium-ion batteries

Egemonye,

Unimuke

2024

Sci Rep

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Nanostructured materials have gained significant attention as anode material in rechargeable lithium-ion batteries due to their large surface-to-volume ratio and efficient lithium-ion intercalation. Herein, we systematically investigated the electronic and electrochemical performance of pristine and endohedral doped (O and Se) Ge12C12 and Si12C12 nanocages as a prospective negative electrode for lithium-ion batteries using high-level density functional theory at the DFT/B3LYP-GD3(BJ)/6-311 + G(d, p)/GEN/LanL2DZ level of theory. Key findings from frontier molecular orbital (FMO) and density of states (DOS) revealed that endohedral doping of the studied nanocages with O and Se tremendously enhances their electrical conductivity. Furthermore, the pristine Si12C12 nanocage brilliantly exhibited the highest Vcell (1.49 V) and theoretical capacity (668.42 mAh g− 1) among the investigated nanocages and, hence, the most suitable negative electrode material for lithium-ion batteries. Moreover, we utilized four machine learning regression algorithms, namely, Linear, Lasso, Ridge, and ElasticNet regression, to predict the Vcell of the nanocages obtained from DFT simulation, achieving R2 scores close to 1 (R2 = 0.99) and lower RMSE values (RMSE < 0.05). Among the regression algorithms, Lasso regression demonstrated the best performance in predicting the Vcell of the nanocages, owing to its L1 regularization technique.

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The Role of Transition Metals on CeO₂Supported for CO₂Adsorption by DFT and Machine Learning Analysis

Cited by 3 publications

References 64 publications

A comprehensive multiscale review of shaft furnace and reformer in direct reduction of iron oxide

A comprehensive multiscale review of shaft furnace and reformer in direct reduction of iron oxide

Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms

Machine learning-assisted DFT-prediction of pristine and endohedral doped (O and Se) Ge12C12 and Si12C12 nanostructures as anode materials for lithium-ion batteries

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About

The Role of Transition Metals on CeO2Supported for CO2Adsorption by DFT and Machine Learning Analysis

Cited by 3 publications

References 64 publications

A comprehensive multiscale review of shaft furnace and reformer in direct reduction of iron oxide

A comprehensive multiscale review of shaft furnace and reformer in direct reduction of iron oxide

Development of the CO2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms

Machine learning-assisted DFT-prediction of pristine and endohedral doped (O and Se) Ge12C12 and Si12C12 nanostructures as anode materials for lithium-ion batteries

Contact Info

Product

Resources

About

The Role of Transition Metals on CeO₂Supported for CO₂Adsorption by DFT and Machine Learning Analysis

Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms