MétaCan
Menu
Back to cohort
Record W2185578371 · doi:10.4095/222399

Electrochemical double-layer capacitance of graphite electrodes: preliminary results

2006· report· en· W2185578371 on OpenAlexaff
N Scromeda, T J Katsube

Bibliographic record

Venuenot available
Typereport
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCapacitanceElectrodeMaterials scienceElectrochemistryLayer (electronics)GraphiteDouble layer (biology)Double-layer capacitanceDifferential capacitanceComposite materialChemistryDielectric spectroscopy

Abstract

fetched live from OpenAlex

Spectral--induced-polarization (spectral-IP) characteristics of 12 pairs of graphite electrodes (diameters of 5.74 and 3.77 cm), used in the two-electrode system for taking electrical measurements of rock and soil samples, have been measured to determine their electrochemical double-layer capacitances. This is a repetition of previous measurements of similar types. The purpose was to determine if specific capacitance values can be assigned to graphite electrodes so that their effect can be subtracted from the two-electrode measurements for increased accuracy. Electrode characteristics, represented by capacitance over electrode surface area (CE/A) and frequency-dependence coefficient (alphaE), are 1.61± 0.5 x 10-4 to 2.94 ± 0.93 x 10-4 F/cm2 and 0.23 ± 0.02 to 0.23 ± 0.07, respectively, for the pairs of electrodes with different diameters. These variations are too large to assign single values to each diameter of electrode. Nevertheless, the repeatability of individual electrode sets with previous measurements is extremely good with almost no errors, so values can be assigned with confidence to each specific set of 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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.277
Teacher spread0.254 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicElectrochemical Analysis and ApplicationsFrench-language works237,207