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Record W2128933705 · doi:10.1149/05836.0049ecst

Sensitivity Analysis of Impedance Characteristics of a Laminar Flow-Based Fuel Cell

2014· article· en· W2128933705 on OpenAlexaff
Seyed Mohammad Rezaei Niya, Paul Barry, Mina Hoorfar

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectrical impedanceLaminar flowDielectric spectroscopyVoltageSensitivity (control systems)Materials scienceOutput impedanceCurrent (fluid)Electronic circuitAnalytical Chemistry (journal)MechanicsElectrical engineeringChemistryElectronic engineeringElectrochemistryEngineeringPhysicsElectrodeChromatography

Abstract

fetched live from OpenAlex

The sensitivity of the impedance characteristics of a laminar flow-based fuel cell (LFFC) to the changes of the voltage, current and fuel concentration is analyzed using the electrochemical impedance spectroscopy (EIS) method. The impedance of the cell measured at different fuel concentrations and voltages is modeled using equivalent circuits. A t-test is performed on the values of the elements of the equivalent circuits estimated in successive voltages and fuel concentrations. As a result, intervals for the voltage, current and fuel concentration are identified in which the changes in the impedance characteristics of the cell cannot be distinguished statistically. These intervals, referred to as indifference intervals, show that the impedance characteristics of the cell is most sensitive to the changes in the voltage and current in the moderate frequency domains and least sensitive to the changes in the fuel concentration in the low frequency domains.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.005
GPT teacher head0.205
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations8
Published2014
Admission routes1
Has abstractyes

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