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Record W2119486655 · doi:10.2320/matertrans.md201222

Metal–Metal Bonding Process Using Cu+Ag Mixed Nanoparticles

2013· article· en· W2119486655 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

VenueMATERIALS TRANSACTIONS · 2013
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceNanoparticleSinteringMetalCopperMetallurgyChemical engineeringSilver nanoparticleShear strength (soil)Composite materialNanotechnology

Abstract

fetched live from OpenAlex

The Cu+Ag mixed nanoparticles were prepared based on the chemical reduction method. The polymer coated on the Cu+Ag mixed nanoparticles can protect Cu nanoparticles from oxidation. The metal–metal joint of silver plated Cu bulks was investigated with the use of Cu+Ag mixed nanoparticles. The bonding experiments show that joint with shear strength about 20 MPa was formed at the bonding temperature above 250°C under 5 MPa using Cu+Ag mixed nanoparticles. The strength of bonding using Cu+Ag mixed nanoparticles is lower than that of bonding using pure Ag nanoparticles. This may be due to the fact that the sintering between the Cu nanoparticles and Ag nanoparticles is more difficult than the sintering between Ag nanoparticles.

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 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 score0.030
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

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 head0.026
GPT teacher head0.246
Teacher spread0.221 · 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