Advanced intelligent techniques for modeling oxygen storage in zeolite-based porous materials
Bibliographic record
Abstract
Oxygen and nitrogen are essential gases with extensive applications in industrial and medical fields, making their separation from air a critical yet complex challenge. Zeolites, renowned for their highly porous structures, have garnered significant attention as promising materials for enhancing gas storage and separation processes. In this paper, advanced machine learning (ML) methods, including Generalized Regression Neural Network (GRNN), Cascade Forward Neural Network, and Multilayer Perceptron were utilized to forecast the O2 uptake capacity in zeolites. A comprehensive database of 750 experimental O2 uptake values was constructed, incorporating pressure, pore volume, temperature, and surface area as input features to develop robust predictive models. The findings demonstrated the superior performance of the GRNN model, achieving an exceptional root mean square error of 0.03 and a coefficient of determination (R2) of 0.9991, outperforming the other techniques. Additionally, further analyses confirmed the reliability of the presented models in accurately capturing physical trends of O2 uptake under varying pressure conditions at different constant temperatures. Sensitivity analysis further revealed that pressure positively influences O2 storage, while temperature exerts the most significant effect, with relevancy factors of 0.341 and − 0.848, respectively. These results underscore the effectiveness of ML techniques in precisely forecasting and enhancing gas storage processes in zeolites, offering meaningful insights to drive advancements in separation technologies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".