The effect of acid treatment and calcination on the modification of zeolite <scp>X</scp> in diesel fuel hydrodesulphurization
Bibliographic record
Abstract
Abstract Faujasite (X, Y) zeolites are considered the main and important catalysts in hydrorefining processes. In order to obtain zeolites with higher acidity and volume of mesopores, post‐synthesis modification, dealumination by different pickling techniques (using ethylenediamine tetraacetic acid [EDTA] chelating agent), and thermal treatment (calcination) were employed. The dealumination process led to the removal of the aluminum atoms from the zeolite structure and a rise in acidity while maintaining the zeolite crystalline lattice. X‐ray diffraction (XRD), atomic absorption spectrometry (AAS), Fourier‐transform infrared (FT‐IR), field‐emission scanning electron microscopy (FE‐SEM), Brunauer–Emmett–Teller (BET), and temperature‐programmed desorption of ammonia (NH3‐TPD) analyses were performed to study the physicochemical characteristics of the zeolite and catalysts prepared. Atomic absorption spectroscopy showed an increase in the Si/Al ratio in the modified zeolites. Measuring the surface area of zeolite and the volume of the pores through BET‐BJH and t‐plot methods indicated a reduction in the surface area and an increase in the volume of the pores. Under the influence of the dealumination process, the volume of the mesopores in the modified zeolites increased from 0.056 cm3 g−1 in the initial zeolite (Na‐X) to 0.2105 cm3 g−1 (zeolite X1). The acidity of the modified zeolites (X1) increased from 0.32–0.95 mmol NH3/g. The application of the catalysts containing X1 zeolite in the hydrodesulphurization (HDS) process yielded a product with less sulphur (conversion = 87.5%).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".