Decomposition Mechanism of Anisole on Pt(111): Combining Single-Crystal Experiments and First-Principles Calculations
Bibliographic record
Abstract
To valorize lignin as a renewable source of aromatics, it is necessary to develop selective heterogeneous catalysts for the hydrodeoxygenation reaction of aromatic oxygenates such as anisole. Most of the metal-supported catalysts tested so far exhibit a high conversion but a low selectivity toward valuable aromatic hydrocarbons, yielding mainly phenolic compounds. To gain insights into that catalytic system, we performed surface science experiments (X-ray photoelectron spectroscopy and temperature-programmed desorption) under ultrahigh-vacuum conditions (UHV). Dosing anisole on Pt(111) surprisingly gave benzene, carbon monoxide, and hydrogen as the main desorbing products of decomposition. With the help of density functional theory (DFT) we successfully explain the unexpected selectivity. In the present work we show in particular that phenoxy (PhO) stands as a key intermediate. Although the UHV conditions do not allow the hydrogenation of phenoxy into phenol, i.e. the catalytic product, they reveal the key role of both hydrogen and carbonaceous species. Under UHV conditions, anisole becomes extensively dehydrogenated: it results in the formation of carbonaceous fragments, which can actually perform the deoxygenation of phenoxy into benzene, but also, more importantly, coke. This detailed study opens the door to a rational design of hydrodeoxygenation catalysts based on supported metals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".