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Record W4386813146 · doi:10.1016/j.nanoen.2023.108909

3D printing of conductive polymer aerogel thermoelectric generator with tertiary doping

2023· article· en· W4386813146 on OpenAlexafffund
Terek Li, Yuhang Huang, Jia Xi Mary Chen, Yu Sun, Zia Saadatnia, Hani E. Naguib

Bibliographic record

VenueNano Energy · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsOntario Tech UniversityUniversity of New BrunswickUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAerogelMaterials scienceThermoelectric effectDopingConductive polymerPolymerElectrical conductorComposite materialThermoelectric materialsThermoelectric generatorThermal conductivityNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

The rapid advancement of conductive polymer aerogel for thermoelectric application has predominantly depended on the use of performance-enhancing additives for improvement in electrical performance. Meanwhile, the intrinsic capability of the polymer matrix is often overlooked, and control over aerogel geometry remains limited. This arises from the challenges in fabricating conductive polymer aerogel film and the absence of an effective doping technique that does not compromise the aerogel’s fragile microstructure due to induced capillary stress. Herein, 3D printing is combined with a tertiary doping process to simultaneously enable synthesize of conductive polymer aerogel with controlled geometry, high electrical conductivity of over 10 Scm −1 , low thermal conductivity of < 100 mWm −1 K −1 , while increasing the thermoelectric output by four times compared to pristine aerogel. The result of this work opens new opportunities to enhance the performance of conductive polymer without relying on additives by unleashing the full potential for conductive polymer matrix.

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 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 score0.180
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.200
Teacher spread0.190 · 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

Citations38
Published2023
Admission routes2
Has abstractyes

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