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Record W4255399170 · doi:10.1002/9781119381860.ch4

Applications of Impedance Spectroscopy

2018· other· en· W4255399170 on OpenAlexaff
Nikolaos Bonanos, B.C.H. Steele, E. P. Butler, J. Ross Macdonald, W.B. Johnson, W. L. Worrell, Gunnar A. Niklasson, Sara Malmgren, Maria Strømme, S. K. Sundaram, Michael C. H. McKubre, Digby D. Macdonald, George R. Engelhardt, Evgenij Barsoukov, Brian E. Conway, Wendy Pell, Norbert Wagner, C. M. Roland, Bob Eisenberg

Bibliographic record

Venuenot available
Typeother
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDielectric spectroscopyMaterials scienceElectrolyteSemiconductorInsulator (electricity)Electrical impedancePorosityCeramicElectrodeGrain boundarySpectroscopyCorrosionNanotechnologyOptoelectronicsComposite materialElectrical engineeringMicrostructureChemistryEngineeringPhysicsElectrochemistry

Abstract

fetched live from OpenAlex

A wide range of materials can be usefully characterized by impedance spectroscopy (IS), namely, electrical and structural ceramics, magnetic ferrites, semiconductors, membranes, polymeric materials, and protective paint films. The measurement techniques used to characterize materials are generally simpler than those used for electrode processes. This chapter first discusses microstructural models describing grains and grain boundaries of differing phase composition, suspensions of one phase within another, and porosity. It then gives examples of the combined use of IS and electron microscopy. The chapter then discusses the models that have been proposed for describing the conductive-system dispersive responses. It then presents examples of several different applications of IS. Four different devices have been chosen: solid electrolyte chemical sensors (SECSs), secondary batteries, photoelectrochemical devices, and semiconductor-insulator-electrolyte sensors. The chapter further reviews the application of IS to the study of corrosion phenomena.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.109
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0280.001

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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations46
Published2018
Admission routes1
Has abstractyes

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