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Record W3200605617 · doi:10.3390/s21186297

Energy Harvesting Materials and Structures for Smart Textile Applications: Recent Progress and Path Forward

2021· review· en· W3200605617 on OpenAlexafffund
Patricia I. Dolez

Bibliographic record

VenueSensors · 2021
Typereview
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
FundersNational Research Council Canada
KeywordsEnergy harvestingTriboelectric effectTextilePhotovoltaic systemMaterials scienceNanotechnologyElectronicsWearable technologyEngineering physicsElectrical engineeringWearable computerEnergy (signal processing)Computer scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

A major challenge with current wearable electronics and e-textiles, including sensors, is power supply. As an alternative to batteries, energy can be harvested from various sources using garments or other textile products as a substrate. Four different energy-harvesting mechanisms relevant to smart textiles are described in this review. Photovoltaic energy harvesting technologies relevant to textile applications include the use of high efficiency flexible inorganic films, printable organic films, dye-sensitized solar cells, and photovoltaic fibers and filaments. In terms of piezoelectric systems, this article covers polymers, composites/nanocomposites, and piezoelectric nanogenerators. The latest developments for textile triboelectric energy harvesting comprise films/coatings, fibers/textiles, and triboelectric nanogenerators. Finally, thermoelectric energy harvesting applied to textiles can rely on inorganic and organic thermoelectric modules. The article ends with perspectives on the current challenges and possible strategies for further progress.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.284
Teacher spread0.257 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations53
Published2021
Admission routes2
Has abstractyes

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