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Record W2340193635 · doi:10.1149/07235.0021ecst

Influence of Composition of Ti/SnO<sub>2</sub>-Sb<sub>2</sub>O<sub>5</sub> Anodes on the Kinetics of Electro-Oxidation of Aqueous Organic Species

2016· article· en· W2340193635 on OpenAlexaff
Qing Ni, Donald W. Kirk, Steven J. Thorpe

Bibliographic record

VenueECS Transactions · 2016
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOxalic acidAnodeCatalysisMaterials scienceKineticsInorganic chemistryAdsorptionAqueous solutionAnodic oxidationCoatingChemical engineeringElectrochemistryChemistryElectrodeOrganic chemistryNanotechnologyPhysical chemistry

Abstract

fetched live from OpenAlex

SnO 2 -Sb 2 O 5 coated Ti anodes can be used in the anodic oxidation of organic compounds for wastewater treatment. In this work, different SnO 2 -Sb 2 O 5 anode compositions were fabricated using various techniques, such that the composition of the coating was varied from a SnO 2 -based to a Sb 2 O 5 based material. These anodes were tested for the activity in the electro-oxidation of oxalic acid, which served as a model organic compound in wastewater. The activity of the anodes was correlated to the Sb doping level in the coating films. Specifically, the variation in the coverage of electrochemically active surface sites was explained by the arrangement of Sn and Sb on the SnO 2 surface, and the catalytic activity in organic oxidation was related to the detailed mechanism of electro-catalysis, modeled by considering a two-step oxidation with surface adsorbed intermediates. The modeled kinetics helps explain the behavior of these anodes in the anodic oxidation of oxalic acid.

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 score0.019
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.195
Teacher spread0.188 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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