Low-energy electron-induced decarbonylation of Fe(CO)<sub>5</sub> films adsorbed on Au(111) surfaces
Bibliographic record
Abstract
The decarbonylation of Fe(CO)5 adsorbed in monolayer and multilayer films on Au(111)/mica substrates has been induced by 0–20 eV electrons and studied by grazing incidence IR spectroscopy. Our results show that the cross sections for the initial stages of this process in as-deposited films range from 60–300 Å2 and show considerable variations with the incident electron energy. The high sensitivity to low-energy electrons is believed to be the result of secondary reactions of anion fragments in the film with the neighbouring Fe(CO)5 moieties, leading to increasingly massive heteronuclear Fen(CO)m species and progressive CO elimination. Continued exposure to the electron beam leads to the slower degradation of these newly created species into an Fe-rich deposit containing traces of CO. These traces are removed by subsequent heating to ~300 K. Fe(CO)5 films that have been subjected to temperatures exceeding 125 K have no measurable sensitivity to the electron beam in the 0–20 eV regime; this is believed to be due to the structural transformation of the as-deposited thin film structure into 3D aggregates. This structural motif presents a very limited quantity of the adsorbed Fe(CO)5 to the incident beam, and may also form a protective layer of the robust Fen(CO)m species during the initial stages of exposure to the electrons.
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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".