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Record W4323826885 · doi:10.1149/1945-7111/acc35f

Synergistic Effect of Vanadate and Nanoclay Hybrid Inhibitor on the Self-Corrosion and Discharge Activity of Al Anode in Alkaline Aluminum-Air Batteries

2023· article· en· W4323826885 on OpenAlexafffund
R. K. Harchegani, A.R. Riahi

Bibliographic record

VenueJournal of The Electrochemical Society · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnodeCorrosionVanadateMaterials scienceElectrolyteElectrochemistryAluminiumChemical engineeringVanadiumGalvanic anodeAqueous solutionMetallurgyChemistryCathodic protectionElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

The inherent safety and low cost of aqueous aluminum-air (Al-air) batteries have attracted significant attention. However, their lifespan is constrained due to the formation of passive layers and severe self-corrosion of the Al anode. This work addresses the Al anode issues using an innovative design strategy by adding vanadate and nanoclay to modify the interaction of Al and electrolyte. The results have shown that adding each vanadate, nanoclay, and a hybrid combination of both reduced Al anode corrosion considerably. However, the hybrid additive provided the highest inhibition efficiency of 72.6% compared to 57.6% for vanadium and 69.8% for nanoclay. The anode’s anodic efficiency and capacity density reached 81.4% and 2426 mAh.g −1 using a hybrid inhibitor. Electrochemical and microscopical analysis indicated that the corrosion inhibition of the additives was attributed to a protective film formed on the Al anode surface. Therefore, this technique has the potential for application in Al-air batteries to increase their lifespan by increasing the inhibition efficiency of the Al anode.

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)0.001
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 score0.002
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.234
Teacher spread0.227 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicCorrosion Behavior and InhibitionFrench-language works237,207