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Record W1973635312 · doi:10.1134/s1061934811010163

Catalytic reduction currents in the stripping voltammetry of solutions of the system copper(II)-ascorbic acid and their application in analytical chemistry

2011· article· en· W1973635312 on OpenAlexaff
П. М. Зайцев, D. V. Krasnyi, K. V. Surmeleva

Bibliographic record

VenueJournal of Analytical Chemistry · 2011
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsEcoMetrix
Fundersnot available
KeywordsChemistryAscorbic acidCopperInorganic chemistryVoltammetryCatalysisElectrochemistryStripping (fiber)Detection limitCyclic voltammetryRedoxSupporting electrolyteReaction rate constantAnalytical Chemistry (journal)KineticsElectrodeChromatographyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

It has been found that the analytical signal from copper(II), cadmium(II), and zinc(II) in alternating-current stripping voltammetry in the presence of ascorbic acid ( AA ) increases only for copper(II) ions, which have significant oxidative potential. It is associated with the appearance of a catalytic current of the reducer in stripping voltammetry, where the reducer is AA. A scheme has been proposed for this process. The rate constant has been calculated for the chemical reduction of copper(I) ions, electrochemically produced in the diffusion layer as a result of the electrochemical oxidation of copper atoms, with AA. A procedure has been developed for the determination of copper in model solutions using the catalytic current of AA; the limit of copper detection is 0.01 μg/L, which is one order lower than the one achieved without AA.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes1
Has abstractyes

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