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Record W2508091830 · doi:10.1209/0295-5075/115/27003

Analysis on the energy harvesting cycle of dielectric elastomer generators for performance improvement

2016· article· en· W2508091830 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEurophysics Letters (EPL) · 2016
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsWestern University
Fundersnot available
KeywordsElastomerDielectricMaterials scienceEnergy harvestingEnergy (signal processing)Composite materialEngineering physicsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

With attractive features like high energy density and flexibility, dielectric elastomer generators (DEGs) have been designed to harvest mechanical energy from diverse sources. However, their energy harvesting performance could be limited by the material viscoelasticity and various failure modes. Adopting the finite-deformation viscoelasticity model, this work presents a theoretical framework for analyzing the performance of a DEG with a triangular harvesting scheme. Simulation results reveal that choosing an appropriate in-plane stretch ratio for the onset of the discharging process can raise the harvested energy of DEGs. It is also found that the energy conversion efficiency of a DEG can be markedly improved by avoiding loss-of-tension of elastomer during the operation of energy harvesting.

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.

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 score0.077
Threshold uncertainty score0.413

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 head0.005
GPT teacher head0.163
Teacher spread0.158 · 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