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Record W2756165395 · doi:10.14447/jnmes.v19i2.337

Electrodeposition of Iron from Kaolin Clay and the Effect of Mass Transport

2016· article· en· W2756165395 on OpenAlexaffvenue
Flores-Segura Juan Carlos, O. Savadogo, K Oishi, Víctor Esteban Reyes-Cruz, Maria Aurora Velóz Rodríguez

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2016
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsChronoamperometryElectrochemistryElectrodeCyclic voltammetryDeposition (geology)Analytical Chemistry (journal)Materials scienceHorizontal scan rateWorking electrodeChemistryMetallurgyChromatography

Abstract

fetched live from OpenAlex

The separation of iron from kaolin clay solutions using electro deposition was studied. Electrochemical studies of the iron electro-deposition were performed using the techniques of cyclic voltammetry, chronopotentiometry and chronoamperometry on a silver rotating disk electrode (RDE) as a working electrode. The effect of the kaolin solution pretreatment with ultrasonic method on the electrochemical reduction processes was studied. The influence of the disk speed of the electro-deposition performances was also studied. The morphology of the surface of the electro-deposit was observed by SEM. It was found that the ultrasonic pretreatment has an important effect on the reduction processes and on the morphology of the electrodeposited sample. The chemical composition of the electrode-posit was characterized by atomic absorption and ICP-ToFMS. It was shown that Fe2O3 content in the clay was 0.6% (weight). The voltammetric studies revealed that, during the electrochemical reduction processes of the kaolin solution, the iron reduction peaks were observed in the potential range from - 0.52V to -2.0V (E vs. SCE). Subsequently, the chronopotentiometry study showed that when a current of - 0.09A is applied to the electrochemical cell, the reduction of iron species occurred. It was also found that the variation of the amount of the electrodeposited iron is, of course, significantly dependent of the speed of the working electrode. For an electrode speed rate ranged from 1500 to 6500 rpm (revolutions per minute), an optimum amount of the electro deposited iron was obtained for an electrode speed of about 4000 rpm.

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 score0.005
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.239
Teacher spread0.233 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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