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Record W3090039869 · doi:10.5006/3625

Inhibiting Corrosion of Mg Alloy AZ31B-H24 Sheet Metal with Lithium Carbonate

2020· article· en· W3090039869 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCORROSION · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCorrosionCathodic protectionGravimetric analysisDissolutionMaterials scienceAnodeMetalPolarization (electrochemistry)AlloyInorganic chemistryCathodeMetallurgyChemical engineeringElectrodeChemistry

Abstract

fetched live from OpenAlex

The objective of this work was to determine the effectiveness of dissolved Li2CO3 as a corrosion inhibitor for AZ31B-H24 sheet metal when immersed in NaCl (aq) at ambient temperature. Corrosion rates were determined by gravimetric mass loss and volumetric H2 evolution measurements and the observed inhibition was investigated further using potentiodynamic polarization, scanning vibrating electrode technique, and x-ray photoelectron surface analytical measurements. It is shown that dissolved Li2CO3 significantly inhibits corrosion as it reduces the corrosion rate by a factor of 10. The manner in which inhibition is achieved is rationalized by the role played by the surface film produced during corrosion in inhibiting both the anode (anodic dissolution) and cathode (H2 evolution) kinetics. Inhibition involves the suppression of the filament-like corrosion mode, albeit on the macroscale, and associated cathodic activation. By process of elimination, it is proposed that the Li+ cations play a key role in inhibiting the anodic dissolution and associated cathodic activation that is required to drive the filament-like corrosion.

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.

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 score0.008
Threshold uncertainty score0.555

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 integrity0.0000.000
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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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