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Record W1970784696 · doi:10.1021/jp066947q

Low-Energy (3−24 eV) Electron Damage to the Peptide Backbone

2007· article· en· W1970784696 on OpenAlexaff
Pierre Cloutier, Cécile Sicard‐Roselli, Emanuel Escher, Léon Sanche

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2007
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsChemistryFormamideExcited stateIonAmidePhotochemistryDissociation (chemistry)Fragmentation (computing)DesorptionElectron ionizationMoleculeCrystallographyPhysical chemistryOrganic chemistryIonizationAtomic physicsAdsorption

Abstract

fetched live from OpenAlex

We report the mass spectrometric measurement of anions desorbed by 3-24 eV electron impact on thin films of formamide-1-d (DCONH2) and on the self-assembled monolayer (SAM) of two different Lys amide molecules used as a molecular model of the peptide backbone. In the present SAM configuration, the amides are elevated from a gold substrate by hydrocarbon chains to remove the effects of the metal substrate. Electron irradiation produces H- and D- from the formamide-1-d film and H-, CH3-, O-, and OH- from the SAM Lys amides. Below 13 eV, the dependence of the anion yields on the incident electron energy exhibits structures indicative of the dissociative electron attachment process, which is responsible for molecular fragmentation via the initial formation of core-excited anions. Above 13 eV, anion desorption is dominated principally by non-resonant dipolar dissociation. Our results suggest that the sensitivity of the peptide backbone to secondary electrons produced by ionizing radiation depends on the chemical environment (i.e., the amino acids sequence).

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 score0.074
Threshold uncertainty score0.800

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 science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.255
Teacher spread0.249 · 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

Citations37
Published2007
Admission routes1
Has abstractyes

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