Synthesis and Characterization of Zeolite NaY Using Kaolin With Different Synthesis Methods
Bibliographic record
Abstract
Abstract The benefits of using kaolin as a source of aluminosilicate in zeolite synthesis to obtain lowercost catalysts, adsorbents, or ion exchangers are widely known. Previous attempts to produce zeolite from natural Iranian kaolin resulted in the formation of zeolites A, X, and HS. Zeolite Y plays an important role in the petrochemical industry due to its application in the area of fluidized catalytic cracking; ~40% of gasoline production is obtained using this process. In the present study, different methods were used to prepare pure zeolite NaY from the Iranian kaolin available. The effects of different parameters such as aging time, crystallization time, kaolin calcination and crystallization temperature, and starting-material composition were investigated in order to obtain improved properties and maximize phase purity. In all cases, the crystal structure and microstructure were studied using X-ray diffraction and scanning electron microscopy. Among different synthesis approaches, the ‘guide-agent method’ resulted in the formation of zeolite NaY. The synthesis was generally sensitive to changes in kaolin calcination temperature and in hydrothermal synthesis parameters. The optimum parameters to prepare pure zeolite NaY were: kaolin calcination temperature = 680°C, aging time of guide agent = 48 h without an overall gel aging step, and crystallization at 90°C for 36 h.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".