Role of Low-Energy Electrons (<35 eV) in the Degradation of Fe(CO)<sub>5</sub> for Focused Electron Beam Induced Deposition Applications: Study by Electron Stimulated Desorption of Negative and Positive Ions
Bibliographic record
Abstract
Fe(CO) 5 is one of the most common precursors used in focused electron beam induced deposition (FEBID). Since high-energy electrons interacting with matter produce large amounts of secondary electrons with energies <50 eV, those generated within the substrate in FEBID are expected to play a major role in the dissociation process of the precursor molecules. The aim of this study is to identify the role of the secondary electrons in the deposition process of Fe(CO) 5 and the relevant dissociation mechanisms, using an electron stimulated desorption system with 4–33 eV electrons. The desorption of charged fragments from thin films of Fe(CO) 5, condensed on Xe or onto a Pt foil, was measured as a function of incident electron energy, incident current, and thickness of both Fe(CO) 5 and Xe films. Both dissociative electron attachment (DEA) and dipolar dissociation (DD) are involved in the production of anions, specifically, C – and O –, and Fe(CO) x – ( x = 0–4). Cations C +, O +, CO +, and Fe(CO) x + ( x = 0–4) were detected with desorption thresholds in the ∼15–25 eV range. These fragments are produced via direct dissociative processes. This study reveals the detailed dissociation mechanisms of Fe(CO) 5 induced by low-energy electron impact by the direct observation of Fe(CO) x fragments under different conditions, thus confirming the frequently proposed mechanisms of deposition of Fe by FEBID.
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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".