Advanced computational modelling for biofuel catalyst optimization: enhancing beta zeolite acidity for oleic acid upgrading
Bibliographic record
Abstract
We explore here, through reactive force field (ReaxFF) molecular dynamics simulations, oleic acid upgrading on beta zeolite (BEA) regulated with nine silica-to-aluminium ratios (SARs) to investigate their acid-catalyzed deoxygenation and coking susceptibility. The selected computational descriptors involved conversion, hydrodeoxygenation, and decarboxylation/decarbonylation selectivity, biofuels yield, and coke deposition characteristics. Simulations were validated by 20.3 SAR available experimental data and used for the systematic study. High deoxygenation selectivity was found to be related to the structural sensitivity of BEA(100) on the upgrading mechanism. ReaxFF simulations revealed that altering the Al-substitution could greatly promote biofuel formation. Specifically, SARs towards the mid-region (SAR 47) favored gasoline production, while 31 SAR exploited diesel-like hydrocarbons. An optimum ratio of 37.4 SAR achieved maximum oleic acid conversion (69.8%), with high yields of gasoline and diesel fuels (15.6 and 20.4 wt%, respectively) and moderate coking (8.6 wt%). Density functional theory screening of metallic dopants allowed investigating of their deoxygenation and coke susceptibility, obtaining that Cu-BEA structure favored the optimal carbon and O-moiety adsorption (-3.89 and -1.8 eV, respectively). Furthermore, the economic and environmental analysis showed that Cu-dopped BEA displayed the lowest market price and global warming potential (71 USD/kg and 2.8 kg CO2-eq/kg).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".