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Record W1971023975 · doi:10.1063/1.1574791

Degradation of functionalized alkanethiolate monolayers by 0–18 eV electrons

2003· article· en· W1971023975 on OpenAlexaff
Michael A. Huels, Pierre-Cyrille Dugal, Léon Sanche

Bibliographic record

VenueThe Journal of Chemical Physics · 2003
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsCanadian Institutes of Health ResearchUniversité de Sherbrooke
Fundersnot available
KeywordsDesorptionMonolayerElectronWork functionChemistryYield (engineering)AdsorptionAnalytical Chemistry (journal)Cleavage (geology)PhotochemistryMaterials scienceNanotechnologyPhysical chemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Electron stimulated desorption of neutral molecular fragments is used to study degradation of ordered organic thin films under low-energy (0–18 eV) electron impact, and total electron doses ranging between 180–550 μC/cm2. Different saturated linear thiols HS(CH2)nX (n=2 or 15, and X=CH3 or COOH) are adsorbed from solution onto a gold surface to produce a self-assembled monolayer (SAM). Here, we present yield function measurements for electron stimulated desorption of moities such as H2, CH3, CH3CH2, CH3CH2CH2, CO, and CO2 from such thin chemisorbed films. For CH3-terminated SAMs, neutral fragment desorption thresholds lie between 5–7 eV, whereas for COOH-terminated SAMs, desorption thresholds as low as 0.2 and 3–5 eV are observed. The results suggest that the incident electrons interact with functional groups localized at the film–vacuum interface, which then leads to predominantly methyl group C–H, and C–COOH bond cleavage. In addition to nonresonant degradation mechanisms, which vary monotonically from threshold with increasing incident electron energy, structures in the neutral fragment desorption yield functions are related to resonant electron attachment. Particularly for Au–S(CH2)15COOH monolayers, this mechanism leads to a desorption peak of CO fragments at incident electron energies near 1.0 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.060
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 teacher head, 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

Citations60
Published2003
Admission routes1
Has abstractyes

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