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Record W2898382655 · doi:10.1038/s41598-018-34483-8

Photodiodes embedded within electronic textiles

2018· article· en· W2898382655 on OpenAlexfundno aff
Achala Satharasinghe, Theodore Hughes‐Riley, Tilak Dias

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsPhotodiodeTextileMaterials scienceUltravioletYarnElectronicsOptoelectronicsHumidityComputer scienceComposite materialBiomedical engineeringElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

A novel photodiode-embedded yarn has been presented and characterized for the first time, offering new possibilities for applications including monitoring body vital signs (including heart rate, blood oxygen and skin temperature) and environmental conditions (light, humidity and ultraviolet radiation). To create an E-Textile integrated with electronic devices that is comfortable, conformal, aesthetically pleasing and washable, electronic components are best integrated within the structure of a textile fabric in yarn form. The device is first encapsulated within a protective clear resin micro-pod before being covered in a fibrous sheath. The resin micro-pod and covering fibres have a significant effect on the nature of light received by the photoactive region of the device. This work characterised the effects of both encapsulating photodiodes within resin micro-pods and covering the micro-pod with a fibrous sheath on the opto-electronic parameters. A theoretical model is presented to provide an estimate for these effects and validated experimentally using two photodiode types and a range of different resin micro-pods. This knowledge may have wider applications to other devices with small-scale opto-electronic components. Wash tests confirmed that the yarns could survive multiple machine wash and drying cycles without deterioration in performance.

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 score0.036
Threshold uncertainty score0.500

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 studies0.0000.000
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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations41
Published2018
Admission routes1
Has abstractyes

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