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Record W3168404958 · doi:10.1557/s43577-021-00117-0

Conducting materials as building blocks for electronic textiles

2021· review· en· W3168404958 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMRS Bulletin · 2021
Typereview
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaKnut och Alice Wallenbergs StiftelseEngineering and Physical Sciences Research CouncilCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaChalmers Tekniska HögskolaImperial College London
KeywordsMaterials scienceNanotechnologyArchitectural engineeringEngineering physicsEngineering

Abstract

fetched live from OpenAlex

ABSTRACT: To realize the full gamut of functions that are envisaged for electronic textiles (e-textiles) a range of semiconducting, conducting and electrochemically active materials are needed. This article will discuss how metals, conducting polymers, carbon nanotubes, and two-dimensional (2D) materials, including graphene and MXenes, can be used in concert to create e-textile materials, from fibers and yarns to patterned fabrics. Many of the most promising architectures utilize several classes of materials (e.g., elastic fibers composed of a conducting material and a stretchable polymer, or textile devices constructed with conducting polymers or 2D materials and metal electrodes). While an increasing number of materials and devices display a promising degree of wash and wear resistance, sustainability aspects of e-textiles will require greater attention.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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)0.0020.000

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 head0.049
GPT teacher head0.304
Teacher spread0.254 · 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