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Record W2035340804 · doi:10.2320/matertrans.mi201412

Interfacial Nano-Mechanical Properties of Copper Joints Bonded with Silver Nanopaste near Room Temperature

2015· article· en· W2035340804 on OpenAlexafffund
Peng Peng, Peng He, Guisheng Zou, Lei Liu, Y. Zhou

Bibliographic record

VenueMATERIALS TRANSACTIONS · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsMaterials scienceCopperNano-Composite materialMetallurgySilver NanoNanotechnology

Abstract

fetched live from OpenAlex

Sintering of nanomaterials at low temperatures has been demonstrated as an alternative for flexible electronic packaging. Silver nanowires were synthesized via polyol method and used as filler material for bonding copper to copper near room temperature. The experimental results indicated that both silver-to-silver and copper-to-silver formed metallurgical bonds. The elastic modulus and nano-hardness of copper joints at the copper-silver interface were characterized using nanoindentation. A transition layer at the interface was observed and its thickness was determined. Sintered silver filler material showed good elasticity both inside and out of the transition layer.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.020
GPT teacher head0.189
Teacher spread0.168 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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