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Record W2275341195 · doi:10.1155/2016/5284048

Sintering Bonding Process with Ag Nanoparticle Paste and Joint Properties in High Temperature Environment

2016· article· en· W2275341195 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

VenueJournal of Nanomaterials · 2016
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Beijing MunicipalityTsinghua UniversityNational Natural Science Foundation of China
KeywordsMaterials scienceSinteringNanoparticleJoint (building)Composite materialSolderingBonding strengthPolyolNanotechnologyPolyurethane

Abstract

fetched live from OpenAlex

Ag nanoparticle paste is prepared based on the polyol method and subsequent concentration by centrifuging. The sintering bonding process using Ag nanoparticle paste at different bonding pressures is studied. The joint strengths are increased as the bonding pressure increases from 0 MPa to 7.5 MPa. This is due to the fact that the higher assistant bonding pressure is beneficial to the growth of neck size between the adjacent particles and forms denser sintered Ag layers. The joint strength bonded under 10 MPa is lower than that bonded under 7.5 MPa, which may be due to the residue of organic component in the sintered Ag layer. The joint properties bonded with Ag nanoparticle paste in high temperature environment are evaluated by heat treatments at temperatures ranges of 200–350°C for 50 hours. The results show that the mechanical properties of joint with Ag nanoparticle paste are better than the joint with Pb95Sn5 solder after storage at high temperatures.

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 score0.002
Threshold uncertainty score0.264

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 head0.009
GPT teacher head0.177
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