MétaCan
Menu
Back to cohort
Record W2735193266 · doi:10.5006/2160

Dependence of the Electrochemical and Passive Behavior of the Lead-Acid Battery Positive Grid on Electrode Surface Roughness

2017· article· en· W2735193266 on OpenAlexaff
Davood Nakhaie, Iman Taji, Mohammad Hadi Moayed, Edouard Asselin

Bibliographic record

VenueCORROSION · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDielectric spectroscopyMaterials scienceCorrosionSurface roughnessPolarization (electrochemistry)ElectrodeCyclic voltammetryElectrochemistryLead–acid batteryAlloySurface finishComposite materialMetallurgyBattery (electricity)Chemical engineeringChemistryThermodynamics

Abstract

fetched live from OpenAlex

The corrosion resistance of the positive grid alloy plays an important role on the performance and service life of lead-acid batteries. There are many parameters influencing the corrosion behavior of the grid alloy. Grid surface roughness, which is of importance from a manufacturing point of view, is a parameter that has received rather less attention. In the present study, the dependence of the electrochemical and the passive behavior of the positive grid alloy on electrode surface roughness was investigated. Cyclic voltammetry, potentiodynamic polarization, galvanostatic polarization, electrochemical impedance spectroscopy, and Mott-Schottky analysis were used to evaluate the influence of surface roughness on the electrochemical properties of the grid alloy. The results indicated that increasing the surface roughness hindered formation of the passive film and increased the corrosion resistance of the lead alloy electrode. Moreover, it was found that the donor density of the passive film is proportional to the surface roughness.

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 score0.048
Threshold uncertainty score0.358

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 science0.0010.000
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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCORROSIONSame topicCorrosion Behavior and InhibitionFrench-language works237,207