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Record W2901376439 · doi:10.25071/10315/35308

Polymeric Triboelectric Nanogenerator: Effects Of Polymer Type, Geometry, And Porosity On Triboelectrification

2018· article· en· W2901376439 on OpenAlex
Hossein Abdoli, Siu N. Leung

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTriboelectric effectNanogeneratorPorosityMaterials sciencePolymerComposite material

Abstract

fetched live from OpenAlex

Triboelectric nanogenerator (TENGs), based on the principles of triboelectrification and electrostatic induction, have recently been viewed as a promising approach to harvest mechanical energy, which would otherwise be wasted through dissipation to surrounding. Large contact area and large surface area have been identified as the basic design principles to promote the performances of TENGs. In this study, particulate leaching method was used to introduce and tailor the open-cell morphology of negative triboelectric layers in TENGs. The effects of material type, material geometry, open-cell morphology on the level of triboelectrification induced by mechanical motions were also investigated. Experimental results revealed that polyvinylidene fluoride (PVDF) performed better than high density polyethylene (HDPE) as the negative triboelectric layer of the TENG. Furthermore, increasing the contact area, reducing the thickness, and introducing fine pores would enhance the performances of TENGs.

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 score0.083
Threshold uncertainty score0.825

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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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