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Record W2027347230 · doi:10.1139/v00-177

Excess electrons in aqueous glasses and crystalline ice

2001· article· en· W2027347230 on OpenAlexvenueno aff
Hugh A. Gillis, T. I. Quickenden

Bibliographic record

VenueCanadian Journal of Chemistry · 2001
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSolvated electronElectronChemistrySolvationAqueous solutionChemical physicsRadiolysisIrradiationVisible spectrumInfraredPhotochemistryPhysical chemistryOpticsMoleculePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Experimental studies of excess electrons in aqueous glasses and crystalline ice are reviewed. Emphasis is placed on studies of the two main optical absorption bands, the well known visible band, which is similar to that of the solvated electron in water, and the IR band which has λ max [Formula: see text] 2950 nm. Under some circumstances partial conversion of the IR-absorbing species to the visible-absorbing species has been observed. Evidence indicates that the two species are due to electrons trapped in distinctly different physical environments. Two mechanisms have been proposed for the formation of the visible-absorbing electron in crystalline ice, one involving naturally occurring vacancies and the other radiation produced vacancies. Studies of the UV and visible luminescence emitted when ice at low temperature is irradiated are summarized, and the mechanisms suggested for its production are discussed briefly. The studies on excess electrons in aqueous solids seem to the authors to be highly relevant to the more recent studies of electron solvation in water which are done on a much shorter time-scale. These latter studies are reviewed briefly with the aim of elucidating the relevance.Key words: visible-absorbing electrons, IR-absorbing electrons, irradiation of aqueous glasses, irradiation of crystalline ice, electron solvation in water.

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 score0.054
Threshold uncertainty score0.812

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 integrity0.0000.001
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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

Citations22
Published2001
Admission routes1
Has abstractyes

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