Density Functional Theory Study of the Direct Conversion of Methane to Acetic Acid by RhCl<sub>3</sub>
Bibliographic record
Abstract
It has recently been reported by Sen et al. that dioxygen can functionalize methane directly at low temperatures with RhCl 3 as the catalyst and I - as the promoter. The main products are acetic acid and methanol, with formic acid as a side product. The active form of the catalyst is considered to be [Rh(CO) 2 I 2 ] - . We propose here a mechanism for the Sen process and investigated it theoretically with DFT. The proposed mechanism is as follows. In the first step, a methane C−H bond is activated by [Rh(CO) 2 I 2 ] - either through an oxidative-addition process or by a σ-bond metathesis mechanism, leading in both cases to a Rh−CH 3 complex ( a ). In the next step a facile insertion of CO into the Rh−CH 3 bond leads to a Rh−COCH 3 complex ( b ). Finally, the hydrolysis of a and b produces methanol and acetic acid, respectively, and forms [Rh(CO) 2 IH] - ( c ). The oxidation of c by O 2 leads to the peroxo complex [(HOO)Rh(CO) 2 I] -, which can react with another c to yield two hydroxo complexes of the form [(HO)Rh(CO) 2 I] - . Substitution of OH - by I - finally regenerates the [Rh(CO) 2 I 2 ] - catalyst.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".