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Record W2605718278 · doi:10.1088/1361-6528/aa6612

Design guidelines of triboelectric nanogenerator for water wave energy harvesters

2017· article· en· W2605718278 on OpenAlexaff
Abdelsalam Ahmed, Islam Hassan, Tao Jiang, Khaled Youssef, Lian Liu, Mohammed Hedaya, Taher Abu Yazid, Jean W. Zu, Zhong Lin Wang

Bibliographic record

VenueNanotechnology · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
FundersKing Abdullah University of Science and Technology
KeywordsTriboelectric effectNanogeneratorMaterials scienceEnergy harvestingMechanical energyPower (physics)Energy (signal processing)Wave powerRADIUSAcousticsElectrical engineeringComputer sciencePhysicsEngineeringComposite materialPiezoelectricity

Abstract

fetched live from OpenAlex

Ocean waves are one of the cleanest and most abundant energy sources on earth, and wave energy has the potential for future power generation. Triboelectric nanogenerator (TENG) technology has recently been proposed as a promising technology to harvest wave energy. In this paper, a theoretical study is performed on a duck-shaped TENG wave harvester recently introduced in our work. To enhance the design of the duck-shaped TENG wave harvester, the mechanical and electrical characteristics of the harvester's overall structure, as well as its inner configuration, are analyzed, respectively, under different wave conditions, to optimize parameters such as duck radius and mass. Furthermore, a comprehensive hybrid 3D model is introduced to quantify the performance of the TENG wave harvester. Finally, the influence of different TENG parameters is validated by comparing the performance of several existing TENG wave harvesters. This study can be applied as a guideline for enhancing the performance of TENG wave energy harvesters.

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: none
Teacher disagreement score0.553
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.061
GPT teacher head0.268
Teacher spread0.207 · 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
Published2017
Admission routes1
Has abstractyes

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