Synthesis, Characterisation, and Catalytic Evaluation of Novel Mesoporous Zeolite H‐BEA Catalysts for n‐Hexyl Levulinate Synthesis: Effect of Templates and Kinetic Studies
Bibliographic record
Abstract
Abstract Amongst the biomass derived chemicals, levulinic acid esters are promising because of their vital use as solvents, fuel additives, and plasticizers. n‐Hexyl levulinate (HL) is a promising fuel additive, has been synthesised through the catalytic esterification of levulinic acid with n‐hexanol, employing novel mesozeolite H‐BEA as an effective solid acid catalysts. The zeolite H‐BEA catalyst has been modified using the tetra‐n‐butylammonium bromide (TBAB), designated as MBT catalyst and another with TBAB surfactant and yeast, designated as MBTY catalyst. Mesozeolites are characterised by XRD, FT‐IR, solid state NMR ( 27 Al, 29 Si, 1 H), SEM, TGA‐DTG, NH 3 ‐TPD, and N 2 sorption isotherms analysis. Parametric studies were performed to optimise LA conversion by altering various reaction parameters, that include the molar ratio of acid to n ‐hexanol, catalyst concentration, and reaction duration. Best catalytic performance has been demonstrated by MBTY catalyst wherein it has afforded 99.44% conversion of LA and 94% yield under optimal reaction conditions. Further, a pseudo‐homogeneous model was adopted to describe the reaction kinetics of LA esterification over the parent and modified catalysts. Also, an experimental activation energy value for MBTY catalyst was found lowest (38.6 kJ/mol) as compared to other catalysts under study.
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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".