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Record W2934533259 · doi:10.1149/2.0571906jes

Acceleration of the Cathodic Kinetics on Aluminum Alloys by Aluminum Ions

2019· article· en· W2934533259 on OpenAlexfundno aff
Chao Liu, Piyush Khullar, Robert G. Kelly

Bibliographic record

VenueJournal of The Electrochemical Society · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
FundersOffice of Naval ResearchMcMaster University
KeywordsKineticsCathodic protectionElectrochemistryDiffusionHydroxideElectrochemical kineticsOxideAlloyMaterials scienceInorganic chemistryProtonChemistryIonAluminiumMetallurgyElectrodeThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

The effect of Al 3+ on the cathodic kinetics of Al alloys as well as Pt and SS316L has been investigated by a number of electrochemical techniques. It has been observed that the addition of Al 3+ into NaCl solution can significantly increase the diffusion limited cathodic kinetics of Al alloys, and this increase is proportional to the [Al 3+ ]. The same phenomenon was also observed on Pt and SS316L, which indicates that this enhancement in cathodic kinetics is not related the surface structure of Al alloy, and it is the HER diffusion-limited kinetics that are increased rather than ORR kinetics as a result. Based on electrochemical studies on Pt, it is proposed that although the addition of Al 3+ can lead to the precipitation of an oxide/hydroxide film on Pt there is a greatly enhanced proton diffusivity which overwhelms the barrier effect of the precipitate film, leading to substantially increased cathodic kinetics. The results are interpreted in terms of an extension of the Grotthuss Theory in which Al 3+ can facilitate transport of the proton.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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