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Soft capacitor fibers using conductive polymers for electronic textiles

2010· article· en· W2054611933 on OpenAlex

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.

Bibliographic record

VenueSmart Materials and Structures · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFiberCapacitanceFabricationElectrical conductorCoaxialPlastic optical fiberPlastic-clad silica fiberOptical fiber

Abstract

fetched live from OpenAlex

A novel, highly flexible, conductive polymer-based fiber with high electric capacitance is reported. In its crossection the fiber features a periodic sequence of hundreds of conductive and isolating plastic layers positioned around metallic electrodes. The fiber is fabricated using fiber drawing method, where a multi-material macroscopic preform is drawn into a sub-millimeter capacitor fiber in a single fabrication step. Several kilometres of fibers can be obtained from a single preform with fiber diameters ranging between 500um -1000um. A typical measured capacitance of our fibers is 60-100 nF/m and it is independent of the fiber diameter. For comparison, a coaxial cable of the comparable dimensions would have only ~0.06nF/m capacitance. Analysis of the fiber frequency response shows that in its simplest interrogation mode the capacitor fiber has a transverse resistance of 5 kOhm/L, which is inversely proportional to the fiber length L and is independent of the fiber diameter. Softness of the fiber materials, absence of liquid electrolyte in the fiber structure, ease of scalability to large production volumes, and high capacitance of our fibers make them interesting for various smart textile applications ranging from distributed sensing to energy storage.

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 score0.709

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 head0.010
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