A Mechanistic Study of the Oxidative Reaction of Hydrogen-Terminated Si(111) Surfaces with Liquid Methanol
Bibliographic record
Abstract
H–Si(111) surfaces have been reacted with liquid methanol (CH 3 OH) in the absence or presence of a series of oxidants and/or illumination. Oxidant-activated methoxylation of H–Si(111) surfaces was observed in the dark after exposure to CH 3 OH solutions that contained the one-electron oxidants acetylferrocenium, ferrocenium, or 1,1′-dimethylferrocenium. The oxidant-activated reactivity toward CH 3 OH of intrinsic and n-type H–Si(111) surfaces increased upon exposure to ambient light. The results suggest that oxidant-activated methoxylation requires that two conditions be met: (1) the position of the quasi-Fermi levels must energetically favor oxidation of the H–Si(111) surface and (2) the position of the quasi-Fermi levels must energetically favor reduction of an oxidant in solution. Consistently, illuminated n-type H–Si(111) surfaces underwent methoxylation under applied external bias more rapidly and at more negative potentials than p-type H–Si(111) surfaces. The results under potentiostatic control indicate that only conditions that favor oxidation of the H–Si(111) surface need be met, with charge balance at the surface maintained by current flow at the back of the electrode. The results are described by a mechanistic framework that analyzes the positions of the quasi-Fermi levels relative to the energy levels relevant for each 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".