Development of the CO<sub>2</sub> Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms
Bibliographic record
Abstract
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 carbon-based, 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".