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DIRECT CONVERSION OF WASTE HEAT TO ELECTRICITY USING PYROELECTRIC CONVERSION

2007· article· en· W2077493986 on OpenAlexvenueno aff
Jian Yu, M Ikura

Bibliographic record

VenueInternational Journal of Power and Energy Systems · 2007
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
Fundersnot available
KeywordsPyroelectricityMaterials scienceVoltageEnergy conversion efficiencyOptoelectronicsOverheating (electricity)Electrical engineeringDielectricFerroelectricityEngineering

Abstract

fetched live from OpenAlex

This paper presents information on how the pyroelectric effect could be applied to convert heat to electricity. Experimental results show how conversion output can be increased by properly controlling operating conditions. Pyroelectric coefficients of a thin 60/40 P(VDF-TrFE) film under high voltage were measured as a function of temperature. The extremely non-linear nature of pyroelectric coefficients was observed with respect to change in temperature. We also determined the electrical resistivity of 60/40 P(VDF-TrFE) copolymer. Subsequently, direct conversion of low-grade heat to electric power was conducted using a single 60/40 P(VDF-TrFE) film according to the Olsen cycle. Our experimental circuit directly measured pyroelectric output responding to synchronized temperature and voltage cyclings. Various operating parameters that affect pyroelectric conversion were examined in detail. The results showed that proper operating conditions are needed to ensure that the pyroelectric film reaches sufficient discharge at high temperature and also to avoid complete depolarization by overheating during the heating leg of the Olsen cycle. We determined that at least 5 MV/m of the electric field must be maintained at all times for effective pyroelectric conversion. By synchronizing temperature and voltage cyclings, we were able to achieve a net output energy density of 40 J/L of copolymer per cycle under applied voltage between 600 and 1000 V and film temperature 40-70°C.

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 score0.019
Threshold uncertainty score0.255

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 science0.0000.000
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 head0.013
GPT teacher head0.263
Teacher spread0.251 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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