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Record W4402413042 · doi:10.1021/acs.analchem.4c01476

Quantitative Interpretation of Potentiodynamic Polarization Curves Obtained at High Scan Rates in Scanning Electrochemical Cell Microscopy

2024· article· en· W4402413042 on OpenAlexafffund
Hu Zhou, Danny Chhin, Yuanjiao Li, Danick Gallant, Alban Morel, Janine Mauzeroll

Bibliographic record

VenueAnalytical Chemistry · 2024
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsNational Research Council CanadaMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCentre québécois de recherche et de développement de l’aluminium
KeywordsChemistryPolarization (electrochemistry)MicroscopyElectrochemistryInterpretation (philosophy)Analytical Chemistry (journal)OpticsChromatographyElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Scanning electrochemical cell microscopy is becoming the tool of choice for the investigation of localized metal corrosion. Typically, potentiodynamic polarization measurements in scanning electrochemical cell microscopy are performed at high potential scan rates. However, Tafel extrapolation applied to high-scan-rate potentiodynamic polarization curves would yield inaccurate corrosion kinetics due to the interference of double-layer charging current or mass transport of species in the metal oxide. Instead, the high field model was used to simulate the potentiodynamic polarization curves of pure aluminum at 25, 50, 100, and 200 mV/s in neutral and acidic phosphate solutions, thus enabling quantitative analysis of local corrosion kinetics by fitting the potentiodynamic polarization curve.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.272
Teacher spread0.267 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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