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Record W2112417833 · doi:10.1149/1.3421714

In Situ Electrochemical Analysis of Surface Layers on a Pyrrhotite Electrode in Hydrochloric Acid Solution

2010· article· en· W2112417833 on OpenAlexafffund
Ahmad Ghahremaninezhad, Edouard Asselin, David G. Dixon

Bibliographic record

VenueJournal of The Electrochemical Society · 2010
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of British Columbia
FundersDivision of Ocean SciencesNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsDissolutionElectrodePassivationElectrochemistrySaturated calomel electrodeDielectric spectroscopyAnalytical Chemistry (journal)PyrrhotiteAnodeHydrochloric acidMaterials scienceLayer (electronics)ChemistryElectrolyteInorganic chemistryReference electrodeChemical engineeringMetallurgyNanotechnologyPhysical chemistry

Abstract

fetched live from OpenAlex

The present study considers the electrochemical dissolution of pyrrhotite electrodes in 1 M HCl solution. Conventional electrochemical techniques as well as electrochemical impedance spectroscopy (EIS) and in situ Mott–Schottky analysis have been applied for surface studies during the anodic dissolution of the electrode. EIS results showed the formation of two distinct surface layers on the electrode. The first layer forms during sample preparation and is stable up to around 600 mV vs saturated calomel electrode (SCE). The second layer starts to form at high anodic potentials ( vs SCE). Electrochemically active dissolution of the pyrrhotite occurs between the formation potential of the two surface layers (600–670 mV vs SCE). Mott–Schottky plots showed that both of the layers are n-type semiconductors with quite different donor densities. Moreover, the second surface layer is less conductive than the first and thus hinders the dissolution current more effectively. Three different equivalent electrochemical circuits were modeled for different dissolution potential ranges and the model regression results was compared to the experimental results of the EIS.

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 score0.002
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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

Citations15
Published2010
Admission routes2
Has abstractyes

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