Optimizing Zeolite 13X Synthesis from Kaolin Clay for Higher CO <sub>2</sub> Adsorption: A Statistical Approach
Bibliographic record
Abstract
Abstract Zeolite 13X is a promising material for CO 2 capture due to its high adsorption kinetics and capacity. However, limited studies systematically evaluate the effect of synthesis parameters on performance. This study uses a design of experiments (DoE) approach to examine the influence of silica‐to‐alumina ratio, aging time, crystallization time, and temperature on CO 2 uptake. Kaolin, a low‐cost natural clay, served as the raw material. Synthesis conditions were selected based on extreme values reported in literature to cover a broad range. The resulting zeolite 13X showed up to three times higher CO 2 adsorption capacity than previously reported for kaolin‐derived variants. Characterization via XRD, CO 2 /N 2 isotherms, and SEM confirmed structural integrity and performance. Among variables, crystallization temperature had the strongest positive effect, with others influencing within specific ranges. Statistical modeling identified the linear model as significant (R 2 = 0.76, p = 1.43 × 10 −5 ), while the cubic model achieved the best fit (p = 0.014). Model selection was further refined using additional statistical metrics. Optimal conditions yielded a CO 2 adsorption capacity of 4.7 ± 0.3 mmol/g at atmospheric pressure and 30 °C temperature. These results emphasize the need for careful synthesis optimization to enhance the carbon capture performance of zeolite 13X.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".