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Record W2585611519 · doi:10.1021/acs.jpcc.6b11501

Method of the Four-Electrode Electrochemical Cell for the Characterization of Concentrated Binary Electrolytes: Theory and Application

2017· article· en· W2585611519 on OpenAlexafffund
M. Farkhondeh, Mark Pritzker, Charles Delacourt, S. S.-W. Liu, Michael Fowler

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteElectrodeEquivalent circuitBinary numberAuxiliary electrodeMaterials scienceWork (physics)Electrochemical cellMeasure (data warehouse)ElectrochemistryMechanicsPulse (music)Biological systemComputer scienceThermodynamicsChemistryVoltageElectrical engineeringMathematicsPhysicsData mining

Abstract

fetched live from OpenAlex

A novel method based on a four-electrode cell to determine the transport properties of concentrated binary electrolytes is presented. The cell contains two potential sensors in addition to the working and counter electrodes. The sensors measure the closed-circuit as well as the open-circuit potential in response to an input galvanostatic pulse across the working and counter electrodes. An important advantage of this new method is that it requires only the application of a single pulse in addition to the appropriate concentration cell experiments. By fitting a suitable model to the data obtained from these experiments, the three independent transport properties of a concentrated binary electrolyte and thermodynamic factor can be determined. The proposed technique benefits considerably from the measurement of closed-circuit data for estimation of the transference number. A comprehensive 2D axisymmetric model based on concentrated-solution theory is developed to account for faradaic convection as well as the bipolar effect at the surface of the sensors. It is shown that the bipolar effect has negligible impact on the potential measurements under operating conditions relevant to these experiments. Consequently, a simpler 1D model can be used in place of the 2D model to estimate the transport properties without any loss in accuracy.

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)0.000
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 score0.149
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.271
Teacher spread0.263 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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