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Record W1966110050 · doi:10.1021/jp904602j

Kinetic Study of the Electrochemical Oxidation of Salicylic Acid and Salicylaldehyde Using UV/vis Spectroscopy and Multivariate Calibration

2009· article· en· W1966110050 on OpenAlexaff
Nelson Matyasovszky, Min Tian, Aicheng Chen

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2009
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsLakehead University
Fundersnot available
KeywordsChemistryElectrochemistrySalicylaldehydeElectrolyteKineticsSalicylic acidDielectric spectroscopySupporting electrolyteSpectroscopyElectrodeInorganic chemistryAnalytical Chemistry (journal)Physical chemistryOrganic chemistryPolymer chemistry

Abstract

fetched live from OpenAlex

The electrochemical oxidation of salicylic acid (SA), salicylaldehyde (SH), and their mixtures at Ti/IrO2-SnO2-Sb2O5 electrodes was studied using in situ UV/vis spectroscopy. Plackett-Burman's experimental design was employed to simultaneously investigate the effect of current density, temperature, mass transfer, composition of the electrode materials, initial concentration, and supporting electrolyte on the electrochemical oxidation of SA, revealing that temperature and the applied current density are the two major factors. The kinetics of the electrochemical oxidation of SA and SH was thus investigated at different temperatures and current densities, showing that the electrochemical oxidation of SA and SH is governed by the hydroxyl radical reaction and follows first-order kinetics with the apparent activation energy of 24.8 and 17.2 kJ/mol, respectively. The competitive effects of SA and SH during the electrochemical oxidation of their mixtures were further studied using UV/vis spectroscopy and multivariate calibration.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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

Citations33
Published2009
Admission routes1
Has abstractyes

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