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Record W2774429301 · doi:10.1038/s41598-017-17453-4

Self-adaptive Bioinspired Hummingbird-wing Stimulated Triboelectric Nanogenerators

2017· article· en· W2774429301 on OpenAlexaff
Abdelsalam Ahmed, Islam Hassan, Peiyi Song, Mohamed Gamaleldin, Ali Radhi, Nishtha Panwar, Swee Chuan Tjin, Ahmed Desoky, David Sinton, Ken‐Tye Yong, Jean W. Zu

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsToronto Metropolitan UniversityUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsHummingbirdTriboelectric effectNanogeneratorFlutterEnergy harvestingRenewable energyElectrificationWind powerElectric potential energyComputer scienceElectrical engineeringMaterials scienceAerospace engineeringMechanical engineeringEnergy (signal processing)VoltageEngineeringPhysicsAerodynamicsEcology

Abstract

fetched live from OpenAlex

Abstract Bio-inspired technologies have remarkable potential for energy harvesting from clean and sustainable energy sources. Inspired by the hummingbird-wing structure, we propose a shape-adaptive, lightweight triboelectric nanogenerator (TENG) designed to exploit the unique flutter mechanics of the hummingbird for small-scale wind energy harvesting. The flutter is confined between two surfaces for contact electrification upon oscillation. We investigate the flutter mechanics on multiple contact surfaces with several free-standing and lightweight electrification designs. The flutter driven-TENGs are deposited on simplified wing designs to match the electrical performance with variations in wind speed. The hummingbird TENG (H-TENG) device weighed 10 g, making it one of the lightest TENG harvesters in the literature. With a six TENG network, the hybrid design attained a 1.5 W m −2 peak electrical output at 7.5 m/s wind speed with an approximately linear increase in charge rate with the increased number of TENG harvesters. We demonstrate the ability of the H-TENG networks to operate Internet of Things (IoT) devices from sustainable and renewable energy sources.

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 score0.022
Threshold uncertainty score0.930

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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

Citations46
Published2017
Admission routes1
Has abstractyes

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