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Record W4415651358 · doi:10.1002/admt.202501205

All‐Printed, Flexible, Organic Thermoelectric Generators for Ambient Operation with Enhanced Performance under Mechanical Bending

2025· article· en· W4415651358 on OpenAlexafffund
Loup Chopplet, Jing Jiang, Nicolas Battaglini, Samia Zrig, Vincent Noël, Emanuele Orgiu, Benoı̂t Piro, Giorgio Mattana

Bibliographic record

VenueAdvanced Materials Technologies · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesLaboratoire d'Excellence SEAMNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueConseil Régional, Île-de-FranceAgence Nationale de la Recherche
KeywordsThermocoupleFabricationThermoelectric effectSeebeck coefficientThermoelectric generatorBendingPower (physics)Thermoelectric materials

Abstract

fetched live from OpenAlex

Abstract This study reports the fabrication and characterization of bipolar, fully printed organic thermoelectric generators on flexible substrates. All fabrication and testing are carried out under ambient conditions, demonstrating the feasibility of low‐cost, scalable manufacturing. The thermoelectric performance is evaluated in both flat and bent configurations, revealing a clear enhancement under mechanical deformation. The Seebeck coefficient of a single thermocouple increases from 30 µV K −1 in the flat state to 38.9 µV K −1 when bent, while the maximum output power rises from 8.8 to 14.9 nW. The devices also exhibit good stability, retaining ≈90% of their output power after 60 days of ambient exposure. These results confirm that fully printed, flexible organic thermoelectric generators are robust and lightweight energy harvesters whose performance improves under mechanical stress, highlighting their potential for real‐world, mechanically dynamic applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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.

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
Published2025
Admission routes2
Has abstractyes

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