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Record W4378373258 · doi:10.5539/jmsr.v12n1p51

Characterization of Clay Materials from Côte d’Ivoire: Possible Application for the Electrochemical Analysis

2023· article· en· W4378373258 on OpenAlexvenueno aff
S. Soro, Mariame Coulibaly, Légré Paul Gauly, Seiny Roger N Dri, Ali Sanou, Albert Trokourey

Bibliographic record

VenueJournal of Materials Science Research · 2023
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceFourier transform infrared spectroscopyCyclic voltammetryFerrocyanideScanning electron microscopeElectrochemistryKaoliniteElectrodeAnalytical Chemistry (journal)Clay mineralsCarbon fibersChemical engineeringNuclear chemistryMineralogyComposite numberComposite materialMetallurgyChemistryEnvironmental chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The utilization of clay minerals as electrode modifiers is based on their unique structure and properties. In this study, clays from various regions of Côte d'Ivoire were characterized for their potential use in developing electrochemical sensors. The clay samples underwent analysis via X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) mapping analysis, Fourier transform infrared spectroscopy (FTIR), and X-ray fluorescence (XRF). Results from XRD, FTIR, SEM, and XRF indicated that kaolinite was the primary component mineral phase in all samples. These clays were then employed as modifying agents to prepare modified carbon paste electrodes, and the electrochemical behavior of ferri/ferrocyanide was studied via cyclic voltammetry on the modified electrodes. The composite electrodes generated from clays and carbon pastes exhibited a well-defined redox peak of ferri/ferrocyanide and appeared to be more sensitive than bare carbon paste electrodes.

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)0.011
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.407
Teacher spread0.343 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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