Sustainable Synthesis of Dibutyl Itaconate from Biomass Derived Acid via Esterification Reaction over Hierarchical Zeolite H-BEA Catalysts
Bibliographic record
Abstract
Abstract The current study concentrates on the synthesis of dibutyl itaconate (DBI) via esterification reaction of itaconic acid (ITA) and n-butanol over the synthesized novel hierarchical zeolite and the parent H-BEA acid catalysts. ITA is among the top platform scaffolds which are derived from biomass. DBI, has numerous industrial applications as, plasticizers, gelation accelerators, lubricants, antirust additives, adhesives, detergent additives etc. In the present study, tetradecyltrimethylammonium bromide (TTAB) surfactant is used as a structure directing agent and yeast as an additional modifier to create hierarchical zeolite H-BEA. Several characterization techniques [XRD, SEM-EDX, N2-sorption isotherms, NH3-TPD, FT-IR, solid-state NMR (27Al, 29Si, 1H)] were used to characterise the synthesized hierarchical structure involving both, microporosity and mesoporosity. Under optimal reaction conditions, hierarchical zeolite shows a higher % ITA yield as compared to its counterpart, parent H-BEA zeolite catalyst. This may be attributed to the enhanced physicochemical and catalytic properties of the resulting hierarchical zeolite catalyst.
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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) | 0.001 | 0.001 |
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".