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Formate-Free Metal-Organic Decomposition Inks of Copper Particles and Self-Reductive Copper Complex for the Fabrication of Conductive Copper Films

2016· article· en· W2530829344 on OpenAlexvenueno aff
Yuki Kawaguchi, Ryuichi Ryuichi, Hideya Kawasaki

Bibliographic record

VenueJournal of Coating Science and Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceKansai University
KeywordsCopperElectrical conductorFormateFabricationDecompositionMaterials scienceMetalChemical engineeringChemistryInorganic chemistryMetallurgyOrganic chemistryComposite materialCatalysis

Abstract

fetched live from OpenAlex

Metal-organic decomposition (MOD) inks have been developed for printed electronics applications. Cu-based MOD inks prevent the oxidation of the metal during storage, as the Cu is already present in an oxidized form (i.e. a salt). However, usually hazardous formates such as Cu (II) formate have to be used as the copper salt in order to ensure thermal decomposition and self-reduction of the metal salt at moderate temperatures (less than 150°C). In this study, a formate-free hybrid ink containing copper particles and a Cu/1-amino-2-propanol (AmIP)/acetate complex was developed for the fabrication of conductive copper films on flexible polymer substrates at low sintering temperatures. A hybrid ink with a weight ratio of 3:1copper particles to MOD ink produced a conductive copper film with close-packed copper particles and a low resistance of 7.3—10-5Ω cm after sintering at a temperature of 180°C for 60 min under a N2 gas flow. Good oxidation resistance of the copper films was observed after exposure to air at 23 °C for two months.

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 score0.002

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)0.0010.000

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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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