Intrinsic and extrinsic factors in anion electron-stimulated desorption: D− from deuterated hydrocarbons condensed on Kr and water ice films
Bibliographic record
Abstract
The results of D(-) ion desorption induced by 3-20 eV electrons incident on condensed CD(4), C(2)D(6), C(3)D(8), C(2)D(4), and C(2)D(2) are presented. These compounds were deposited in submonolayer amounts on the surfaces of multilayer solid films of Kr and nonporous and porous amorphous ice. While desorption of the D(-) anions proceeds via well-known processes, i.e., dissociative electron attachment (DEA) and dipolar dissociation, significant perturbations of these processes due to presence of the different film substrates are observed. We have shown that it is possible to distinguish between the character and nature of these perturbations. The presence of the nonporous ice perturbs the D(-) desorption intensity by affecting the intrinsic properties of the intermediate anion states through which dissociation proceeds. On the other hand, the presence of the porous ice introduces extrinsic effects, which can affect electron energy losses prior to their interaction with the hydrocarbon molecule and/or the energies and intensities of the fragment species after dissociation. Simple mechanisms responsible for the observed variations in the intensities of desorbed anionic signals are proposed and discussed. Electron transfer from transient anion states to electron states of the substrate film or nearby hydrocarbon molecules appear as the most efficient mechanism to reduce the magnitude of the DEA process.
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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".