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Record W2761151651 · doi:10.1021/acsami.7b10748

Hybrid Copper–Silver–Graphene Nanoplatelet Conductive Inks on PDMS for Oxidation Resistance Under Intensive Pulsed Light

2017· article· en· W2761151651 on OpenAlexafffund
Changyong Yim, Zachary Kockerbeck, Sae Byeok Jo, Simon S. Park

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesUniversity of Calgary
KeywordsMaterials scienceGrapheneCopperElectrical conductorNanotechnologyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

A simple, low-cost, and reliable process of production for conductive tracks and their transfer to poly(dimethylsiloxane) (PDMS) substrate has been proposed. Flexible electrodes were fabricated using conductive nanoparticulates under intensive pulsed light, which were then transferred on to a PDMS substrate via a pouring, curing, and peeling process. The combination of copper-silver nitrate-graphene nanoplatelets (GnPs) provided multiple benefits to the conductive tracks, such as oxidation resistance and increased durability on PDMS. The addition of silver nitrate reduced the speed of oxidation during the curing process of PDMS in the presence of heat and air. The addition of GnPs then increased the stability of conductive tracks on PDMS, whereas the films without GnPs were not conductive on PDMS due to mechanical cracks. The copper-silver-GnP electrodes on PDMS were successfully demonstrated as flexible electrodes and reveal the enhancement of oxidation resistance during thermal oxidation for Joule heater application.

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 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 score0.005
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 head0.021
GPT teacher head0.249
Teacher spread0.228 · 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 teacher head, not a consensus.

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

Citations38
Published2017
Admission routes2
Has abstractyes

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