MétaCan
Menu
Back to cohort
Record W2100964294 · doi:10.1002/cjce.21701

Silver‐coated copper nanowires with improved anti‐oxidation property as conductive fillers in low‐density polyethylene

2012· article· en· W2100964294 on OpenAlexafffundvenueabout
Xiaoxiong Luo, Genaro A. Gelves, Uttandaraman Sundararaj, Jing‐Li Luo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaXerox Foundation
KeywordsMaterials scienceCopperNanocompositeCoatingPolyethyleneElectrical resistivity and conductivityComposite materialNanowireCurrent densityLow-density polyethyleneReagentVolume (thermodynamics)Chemical engineeringMetallurgyNanotechnologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Silver‐coated copper nanowires (AgCuNWs) are prepared by chemical plating method with copper nanowires (CuNWs) and Ag‐amine reagent. The prepared AgCuNWs with silver content of 66.52 wt.%, diameter 28–33 nm exhibited improved anti‐oxidation behaviour. The silver coating on AgCuNWs can effectively reduce the formation of copper oxide under room temperature. The temperature at which nanowires begin to gain weight can be improved from 85 to 230°C and the maximum weight gain can be decreased from 20.3% to 3.2% by applying silver coating. The volume electrical resistivity of the AgCuNWs filled low‐density polyethylene nanocomposites is lower than that of the CuNWs filled low‐density polyethylene nanocomposites with same volume percentage of fillers because the silver content in the AgCuNWs is not oxidised during compression moulding. © 2012 Canadian Society for Chemical Engineering

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 score0.001

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)0.0000.000

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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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

Citations30
Published2012
Admission routes4
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207