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Record W2024685321 · doi:10.1149/2.009407jes

Ionic Liquid Electrolytes for Thermal Energy Harvesting Using a Cobalt Redox Couple

2014· article· en· W2024685321 on OpenAlexfundno aff
Na Jiao, Theodore J. Abraham, Douglas R. MacFarlane, Jennifer M. Pringle

Bibliographic record

VenueJournal of The Electrochemical Society · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsElectrolyteRedoxIonic liquidMaterials scienceCobaltFigure of meritThermoelectric effectThermal stabilityIonic bondingEnergy transformationThermal energyChemical engineeringChemistryInorganic chemistryIonElectrodeOptoelectronicsThermodynamicsOrganic chemistryPhysicsPhysical chemistryCatalysis

Abstract

fetched live from OpenAlex

Waste thermal energy, such as that released from industrial or manufacturing processes, is a promising but as-yet underutilized source of sustainable energy. As an alternative to traditional semi-conductor based thermoelectrics, thermoelectrochemical cells use a redox couple in an electrolyte to directly convert thermal energy to electricity using a very simple device design. The good thermal stability of many ionic liquids (ILs) makes them very promising electrolytes for these devices, but the influence of the nature of the cation and anion on the cell performance is not yet well understood. Here we report measurement of the Seebeck coefficient and the thermoelectrochemical device performance of a cobalt redox couple in a series of ILs, and comparison of the electrolyte performance using a modified figure of merit.

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)0.001
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 score0.131
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.242
Teacher spread0.232 · 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

Citations52
Published2014
Admission routes1
Has abstractyes

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