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Record W2993324064 · doi:10.3390/proceedings2019032001

Solar Energy-Harvesting E-Textiles to Power Wearable Devices

2019· article· en· W2993324064 on OpenAlexfundno aff
Achala Satharasinghe, Theodore Hughes‐Riley, Tilak Dias

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilTrent UniversityNottingham Trent University
KeywordsEnergy harvestingSolar energySupercapacitorSolar cellWearable computerMaterials scienceTextileElectricity generationEnvironmental scienceElectrical engineeringPower (physics)OptoelectronicsComputer scienceEngineeringElectrodeComposite materialPhysicsCapacitanceEmbedded system

Abstract

fetched live from OpenAlex

This work presents an innovative solar energy harvesting fabric and demonstrates its suitability for powering wearable and mobile devices. A large solar energy harvesting fabric containing 200 miniature solar cells has been shown to charge a 110 mF textile supercapacitor bank within 37 s. A series of solar energy harvesting fabrics with different design features, such as using red or black fibres, were tested and compared to a commercially available flexible solar panel outside under direct sunlight. The results showed that the solar energy harvesting fabrics had power densities that were favorable when compared to the commercially available solar cell.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2019
Admission routes1
Has abstractyes

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