A Theoretical Comparison of Lewis Acid vs Bronsted Acid Catalysis for <i>n-</i>Hexane → Propane + Propene
Bibliographic record
Abstract
Cracking of an all-trans n-alkane, via idealized Lewis acid and Bronsted acid catalysis, was examined using density functional theory. Optimized geometries and transitions states were determined for catalyst-reactant complexes, using AlCl3 and HCl.AlCl3 as the Lewis and Bronsted acids. For the Lewis acid cycle, hydride-transfer steps are seen to have large barriers in both forward and reverse directions, and an unstable physisorbed carbenium ion (lying 20 kcal mol(-1) above the chemisorbed intermediate) is the launching point for the beta-scission that leads to products. For the Bronsted acid cycle, proton-transfer steps have smaller barriers in both forward and reverse directions, and a semistable physisorbed alkanium ion is the launching point for the alkanium alpha-scission that leads to products. In the idealized Lewis cycle, formation of HCl units (and hence Bronsted acids) was found to be a common side reaction. A recent ionic-liquid catalysis study is mentioned as motivation, although our study is not a computational modeling study; we are more interested in the fundamental differences between Brosnted and Lewis mechanisms rather than merely mimicking a particular system. However, results of exploratory optimizations of various intermediates with Al2Cl7- as the catalyst are presented to provide the first step for future modeling studies on the ionic liquid system.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".