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Record W2916274288 · doi:10.1002/aelm.201800648

Reduced Graphene Oxide Coated Silk Fabrics with Conductive Property for Wearable Electronic Textiles Application

2019· article· en· W2916274288 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

VenueAdvanced Electronic Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMinistry of Agriculture
FundersAgriculture Research System of ChinaRussian Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceSILKGrapheneFibroinElectrical conductorCoatingComposite materialElectronicsOxideNanotechnologyWearable computerElectrically conductiveWearable technologyFlexible electronicsElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The desire for this lightweight and flexible electronics has grown increasingly, and the flexible and wearable electronic textiles can be realized by coating traditional textiles with conductive materials. Here, the conductive silk fabrics are prepared by coating graphene oxide (GO) onto silk fabrics and followed by thermal reduction. The scanning electron microscope results show that the GO coated onto silk fabrics successfully forms a continuous thin film. The oxygen functional groups are removed by thermal reduction. The main structure (β‐sheet structure) of silk fabrics is not destroyed through a series of treatment, guaranteeing good mechanical properties. The resistivity and conductivity of silk fabrics using regenerated silk fibroin as a glue can reach 3.28 KΩ cm −1 , 3.06 × 10 −4 S cm −1 respectively, which can meet the electron conductive requirement of wearable electronics. Thus, it can be used for sensors, portable devices, and wearable electronic textiles.

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)
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 score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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