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Record W1994997821 · doi:10.1149/2.081304jes

Electrochemical Reactions on Metal-Matrix Composite Anodes for Metal Electrowinning

2013· article· en· W1994997821 on OpenAlexafffund
Maysam Mohammadi, Farzad Mohammadi, Akram Alfantazi

Bibliographic record

VenueJournal of The Electrochemical Society · 2013
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsAnodeOxygen evolutionElectrowinningElectrochemistryManganeseComposite numberMaterials scienceElectrolyteCyclic voltammetryScanning electron microscopeInorganic chemistryMetalPolarization (electrochemistry)Chemical engineeringChemistryElectrodeMetallurgyComposite material

Abstract

fetched live from OpenAlex

In this study, Pb-MnO2 composite anodes were developed through single-action powder pressing. The effect of MnO2 content of the composite anodes on the physical and electrochemical properties of the samples were investigated and compared with those of the pure pressed Pb and a commercial Pb-Sn-Ca electrowinning anode. Surface morphology of the fabricated samples was examined using scanning electron microscopy (SEM). Cyclic voltammetry and potentiodynamic polarization experiments were performed in 180 g/l sulphuric acid solution at 37 ± 0.5°C to study the surface reactions of the anodes and their performance in terms of oxygen evolution reaction (OER) rate and potential. The results indicated that the addition of MnO2 improved the electrocatalytic activity of the lead anode for oxygen evolution. Oxidation of lead was also influenced by MnO2 particles. The effects of manganese ions in the electrolyte on the electrochemical performance of the composite anodes were also studied. Results showed that manganese ions, depending on concentration, can have both promoting and suppressive effects on the OER on the composite anodes.

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 score0.009
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.275
Teacher spread0.261 · 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

Citations42
Published2013
Admission routes2
Has abstractyes

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