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Record W2009295462 · doi:10.1149/2.032301jes

Reactions of Bromide and Iodide Ions with Silver Oxide Films on Ag Substrates

2012· article· en· W2009295462 on OpenAlexaff
Sarah Danielle Pretty, Ahmed Y. Musa, J.C. Wren

Bibliographic record

VenueJournal of The Electrochemical Society · 2012
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsHalideIodideChemistryBromideInorganic chemistryDissolutionSubstrate (aquarium)ElectrochemistryElectrodeRedoxSilver halideOxidePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Conversion of an Ag2O film on a Ag substrate to AgX in a halide solution occurs via three competing reaction pathways: (1) chemisorption of a halide ion, X−(aq) onto Ag2O followed by reaction to form AgX, (2) electrochemical oxidation of the Ag in the metal substrate to form AgX coupled with reduction of Ag2O to form Ag, and (3) dissolution of Ag2O followed by reaction of Ag+(aq) with X−(aq) to form AgX(aq) and deposition of the AgX. In this study, Ag2O films of different thicknesses were grown potentiostatically on Ag electrodes in 0.01 M NaOH solutions. Reactions of the Ag2O films with halide ions were initiated by transferring the electrodes into 0.01 M NaOH solutions containing I− or Br−. The reactions were followed by monitoring the open circuit potential (EOC) on the Ag electrode. The open circuit potential shows a sharp change upon completion of the reaction and this allows an easy determination of the reaction time. The reaction time as a function of halide concentration, the Ag2O film thickness, and the electrode rotation rate was measured to determine reaction kinetics parameters. Additionally, potentiodynamic and galvanostatic reduction of the AgX film was measured. The data from these different measurements were used to elucidate the contributions of the three pathways to the overall conversion process.

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 score0.003
Threshold uncertainty score0.351

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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