A Density Functional Theory Study on the Acid‐Catalyzed Transesterification Mechanism for Biodiesel Production from Waste Cooking Oils
Bibliographic record
Abstract
Abstract The acid‐catalyzed transesterification reaction has been one of the most effective methods to produce biodiesel, especially from waste cooking oils (WCO) with high contents of water and free fatty acids (FFA). However, in the acid‐catalyzed processes, the H+ action mechanism and the controlling step of the reaction rate have been ambiguous. To clearly understand the reaction process, the DMol3 module based on the density functional theory (DFT) was employed to investigate the acid (H+‐)‐catalyzed transesterification mechanism of methanol and oleic acid monoacylglycerol (OAM). The one step transesterification without a catalyst and the feasible paths of SN2 (substitution nucleophilic bimolecular) and SN1 (substitution nucleophilic unimolecular) reaction mechanisms with H+‐based catalysts were built as Path 1, Path 2, and Path 3, respectively. The calculated structures, thermodynamic, and kinetic data revealed that the H+‐based catalysts could effectively reduce the activation energy for the transesterification reaction, and the Path 2 based on the SN2 reaction mechanism was the optimal reaction path. A tetrahedral intermediate (IM2‐2) could be generated with the highest active energy of 15.383 kcal mol−1, implying that there was the most stable structure of IM2‐2 as the key species in the transesterification process. Hence, the increasing decomposition rate of IM2‐2 accelerated the forward reaction in H+‐based biodiesel processes. The calculated active energy of 15.383 kcal mol−1 was in good agreement with the kinetic data for the monoacylglycerol transesterification of 15.067 kcal mol−1. Our calculations should provide basic and reliable theoretical data for further understanding the mechanism of transesterification of WCO to biodiesel products in the future work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".