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Effect of Inevitable Heat Leap on the Conversion Efficiency of Thermoelectric Generators

2023· article· en· W4388693724 on OpenAlexafffund
Kun Song, Shuang Wang, Yiwei Duan, Xiang Ling, Peter Schiavone

Bibliographic record

VenuePhysical Review Letters · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Jiangsu ProvinceNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of ChinaNatural Science Foundation of Guangzhou City
KeywordsThermoelectric generatorMaterials scienceEnergy conversion efficiencyThermoelectric effectThermoelectric materialsThermodynamicsComposite materialThermal conductivityOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Discrepancies between experimental and theoretical results in the study of thermoelectric generators (TEGs) have been a major long-standing problem in thermoelectric technology. In this Letter, we report that, besides interfacial resistance, the inevitable heat leap caused by the Peltier effect is the main factor affecting the conversion efficiency of TEGs. In fact, the heat leap is proven to have an impact of approximately 10% on the conversion efficiency of common TEGs. In addition, we enhance the formula for maximum conversion efficiency with heat leap from the classical expression to allow for the prediction of the performance of advanced materials in TEGs. For the first time, the experimental data from conversion efficiency corresponds exactly to that obtained theoretically by considering both the heat leap and interfacial resistivity.

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)0.004
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.276
Teacher spread0.266 · 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

Citations31
Published2023
Admission routes2
Has abstractyes

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