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Record W4389938024 · doi:10.24425/amm.2023.146212

Study on Electrochemical Performance of Dimensionally Stable Anodes during Zinc Electrowinning

2023· article· en· W4389938024 on OpenAlexaff
Wei Zhang, Georges Houlachi, Sanae Haskouri, Edward Ghali

Bibliographic record

VenueArchives of Metallurgy and Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsHydro-QuébecCollège ShawiniganUniversité Laval
Fundersnot available
KeywordsPhosphoric acidZincElectrolyteElectrochemistryInorganic chemistryAnodeElectrowinningMaterials scienceManganeseElectrolysisSulfuric acidChemistryMetallurgyElectrode

Abstract

fetched live from OpenAlex

In zinc electrowinning, small amounts of manganese ions additives are needed in the electrolyte to reduce the corrosion of anodes and minimize the contamination of cathodic zinc by dissolved lead. However, excess manganese oxide could cover the dimensionally stable anodes (DSA) surface and decrease their service life. Additives of phosphoric acid are put in the electrolyte to complex the manganic Mn3+ ion and hence reduce its disproportionation to MnO2. In the investigation, phosphoric acid was added to sulfuric acid or zinc electrolytes, and conventional and recent electrochemical measurements were carried out to examine electrochemical behaviour of DSA (Ti/IrO2-Ta2O5) anode during zinc electrolysis at 48 mA/cm2 and 39°C. It was observed that the anodic potentials of DSA anodes were lower by 27 mV after 5 h polarization in the zinc electrolyte containing 35 g/L phosphoric acid at 39°C. Electrochemical impedance measurements show that the addition of 35 ml/L H3PO4 to the zinc electrolyte can increase impedance resistances of the DSA mesh anodes. Cyclic voltammogram studies (CV) at a scan rate of 5 mV/s without agitation show that the oxidation peak in the solution with 35 ml/L phosphoric acid addition is highest, followed by that with 17 ml/L phosphoric acid addition and that without addition of phosphoric acid.

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 score0.033
Threshold uncertainty score0.409

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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