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Washable hydrophobic smart textiles and multi-material fibers for wireless communication

2016· article· en· W2538139547 on OpenAlex
Stepan Gorgutsa, Kyle J. Bachus, Sophie LaRochelle, Richard D. Oleschuk, Younès Messaddeq

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSmart Materials and Structures · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsQueen's UniversityUniversité Laval
FundersCanada Research ChairsUniversité Laval
KeywordsMaterials scienceFiberComposite materialBluetoothWirelessCore (optical fiber)WeavingSmart materialComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This paper reports on the performance and environmental endurance of the recently presented wirelessly communicating smart textiles with integrated multi-material fiber antennas. Metal–glass–polymer fiber composites were fabricated using sub-1 mm hollow-core silica fibers and liquid state silver deposition technique. These fibers were then integrated into textiles in the form of center-fed dipole and loop antennas during standard weaving procedure. Fiber antennas performance was found to be directly comparable to classic 'rigid' solutions in terms of return loss, gain and radiation patterns, which allowed transmitting data through Bluetooth protocol at 2.4 GHz frequency. Applied superhydrophobic coatings (water contact angle = 152°, sliding angle = 6°) allow uninterrupted wireless communication of the textiles under direct water application even after multiple washing cycles.

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.

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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.624

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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