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Record W1993092280 · doi:10.1063/1.1394733

Effects of morphology on the low-energy electron stimulated desorption of O− from O2 deposited on benzene and water ices

2001· article· en· W1993092280 on OpenAlexaff
A. D. Bass, L. Parenteau, F. Weik, Léon Sanche

Bibliographic record

VenueThe Journal of Chemical Physics · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDesorptionAmorphous solidBenzeneMaterials scienceDissociation (chemistry)Analytical Chemistry (journal)Yield (engineering)ElectronDiffusionQuenching (fluorescence)Activation energyMoleculeCrystallographyChemistryAdsorptionPhysical chemistryFluorescenceOrganic chemistryThermodynamicsComposite materialOptics

Abstract

fetched live from OpenAlex

We investigate the effects of the geometrical structure (phase and porosity) of multilayer benzene films on the desorption of O− induced by 2–20 eV electron impact on varying quantities of absorbed O2. Differences in the yield of O− from O2 doped amorphous and crystalline benzene films are attributed to the ability of O2 to diffuse into the amorphous solid via pores and defects formed during its deposition at 20 K. In contrast, diffusion into crystalline benzene is limited and deposited O2 molecules remain at the surface of the film. Thermal desorption measurements support this analysis. The data are also compared with results of similar experiments for O2 on water. While it is apparent that some of the variation in O− yield observed from ice films is similarly related to morphology, a substantial suppression of the O− yield is likely to result from energy loss by electrons prior to dissociation. Quenching of intermediate O2− states by water ice may also contribute to this suppression in the range 5–12 eV.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.217
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2001
Admission routes1
Has abstractyes

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