Self Diffusivity of n-Dodecane and Benzothiophene in ZSM-5 Zeolites. Its Significance for a New Catalytic Light Diesel Desulfurization Process
Bibliographic record
Abstract
This study provides theoretical support to a recent promising ZSM5 catalyst used for the selective desulfurization of light diesel type compounds (Al-Bogami and de Lasa 2013; Al-Bogami, Moreira, and de Lasa 2013). With this end, Molecular Dynamics (MD) simulations employing a rigid silicalite structure are developed to calculate self-diffusivities of n-Dodecane (n-C12) and Benzothiophene (BZT) in a silicalite structure. The simulations are performed at 573 K, 623 K, 673 K and 723 K at a fixed loading of 1 molecule per unit cell to study the temperature effect on diffusivity coefficient. In addition, a number of simulations which are developed to investigate four molecule loadings (corresponding to 0.25, 0.5, 0.75 and 1 molecule per zeolite unit cell) at 723 K. MD simulations, show a self diffusivity of BZT one order of magnitude higher than that of n-C12 self diffusivity at all temperatures investigated. This is the case in spite of BZT having a critical molecular diameter of 6 Å when compared to the 4.9 Å diameter of n-C12. In addition, the self diffusivity coefficient is found to increase with temperature for both n-C12 and BZT. Furthermore, the results obtained show that the self diffusivity of n-C12 decreases as the number of n-C12 molecules per zeolite unit cell increases. On the other hand, it is observed that the self-diffusivity coefficient for BZT remains fairly constant and drops at a loading of 1 molecule per zeolite unit cell only. These coefficients show that differences in n-C12 and benzothiophene diffusivities favours desulfurization with selective benzothiophene adsorption and sulfur species removal as coke (Al-Bogami and de Lasa 2013).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".