MétaCan
Menu
Back to cohort

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

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

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

Abstract 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.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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

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

Citations23
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueSmart Materials and StructuresSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207