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Record W2738623083 · doi:10.1149/ma2017-02/27/1172

(Invited) Flexible and Stretchable Thermoelectric Generators

2017· article· en· W2738623083 on OpenAlexaff
Muhammad M. Hussain, Jhonathan P. Rojas, Devendra Narain Singh, Galo A. Torres Sevilla, Hossain M. Fahad, Salman B. Inayat

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsThermoelectric generatorThermoelectric effectFlexibility (engineering)Thermoelectric materialsMechanical engineeringGenerator (circuit theory)Control reconfigurationMaterials scienceThermoelectric coolingPower (physics)Computer scienceEngineeringEmbedded systemPhysicsMathematics

Abstract

fetched live from OpenAlex

Thermoelectricity can be an interesting source of power from otherwise wasted heat. Therefore, for many decades discovery and optimization of new thermoelectric materials has shown important leap toward thermoelectric generator applications. However, from an engineering perspective, structural modifications at the device level can play an important role to maximize the power output. We base our study on device architecture reconfiguration by adopting various in-plane and out-of-plane fractal design to develop flexible and stretchable thermoelectric generator. Physical flexibility allows the devices to be conforming to asymmetric surfaces and mechanical stretching allows to dynamically controlling the distance between the hot and cold end in a thermoelectric generator. This way, one can tune the distance, maximize the temperature difference and maintain a high temperature difference which directly relates to efficiency of a thermoelectric generator. Adopting low cost materials like paper and Off-Stoichiometry Thiol-Enes (OSTE) as structural materials we demonstrate the integration strategy to rationally design materials, processes and devices for flexible and stretchable thermoelectric generators.

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 score0.030
Threshold uncertainty score0.770

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.017
GPT teacher head0.261
Teacher spread0.243 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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