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Record W2083637066 · doi:10.1149/2.058303jes

The Role of Electrolyte Hydrodynamic Properties on the Performance of Lead-Based Anodes in Electrometallurgical Processes

2013· article· en· W2083637066 on OpenAlexafffund
Maysam Mohammadi, Farzad Mohammadi, Georges Houlachi, Akram Alfantazi

Bibliographic record

VenueJournal of The Electrochemical Society · 2013
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsHydro-QuébecUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnodeElectrowinningElectrolyteDegradation (telecommunications)Lead dioxideMaterials scienceCorrosionLead (geology)MetallurgyOxygen evolutionElectrochemistryChemistryChemical engineeringElectrode

Abstract

fetched live from OpenAlex

Integrity of lead anodes has always been a major concern for the electrowinning industry. This work investigates the effect of solution hydrodynamics on the degradation of Pb-Sn-Ca and Pb-Ca anodes. Galvanostatic experiments (50 mA/cm2) were performed at 37 ± 0.5°C using 0, 600, and 1000 rpm stirring rates for the durations of 24 and 72 hours. The PbO2 layer was reduced to PbSO4 (discharged) and the discharge duration was used in corrosion rate calculations. Weight loss measurements along with the discharge calculations revealed that the degradation rate of lead anodes increased with the solution stirring rate. In addition, the amount of PbO2 remaining on the surfaces was decreased as a function of solution velocity. Therefore, it was concluded that detachment of the corrosion products from the surface (flaking) affects the degradation rate of lead anodes significantly. Moreover, it was determined that the Pb-Sn-Ca anode performed better than the Pb-Ca anode in terms of both integrity and electrocatalytic properties for oxygen evolution reaction (OER). Surface morphologies of the Pb-Sn-Ca anodes indicated that the roughness decreased as the solution stirring rate increased.

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 score0.018
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.186
Teacher spread0.182 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

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