Insight into the methylene C–H bond cleavage of ethylbenzene during ethylbenzene hydroxylation using EBDH as a catalyst, a DFT study
Bibliographic record
Abstract
The hydroxylation of ethylbenzene to ( S)-1-phenylethanol with the help of ethylbenzene dehydrogenase (EBDH) is a stereospecific catalytic reaction. This hydroxylation process involves the C–H bond cleavage of methylene part of ethylbenzene and transfer of its hydrogen to the oxygen atom attached with the metal at the active site of EBDH as a first step, which leads to the formation of an intermediate. The second step involves the transfer of OH from the active-site metal back to the carbon of intermediate, resulting in the formation of ( S)-1-phenylethanol. This C–H bond cleavage could be homolytic or heterolytic and directly affect the reaction mechanism of ethylbenzene hydroxylation. In this article, density functional theory studies were performed on the ethylbenzene-bound EBDH active-site model complexes to investigate the impact of the C–H bond cleavage of methylene part of ethylbenzene on the reaction mechanism of ethylbenzene hydroxylation. For this, different protonation states and participation of amino acid residues near the Mo center of EBDH were considered. Models with protonation of His192, Lys450, and Asp223 and model without protonation were investigated for comparison. Computed relative energies indicate that the overall lowest energy barrier pathway results when ionic (heterolytic) and radical (homolytic) pathways are combined.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".