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Record W1597734246 · doi:10.1155/2015/897142

Low‐Temperature Sintering Bonding Using Silver Nanoparticle Paste for Electronics Packaging

2015· article· en· W1597734246 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 · 2015
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceSinteringComposite materialNanoparticleElectronic packagingElectronicsNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

Ag nanoparticles (NPs) with about 40 nm diameter covered with 5–8 nm organic shell were prepared by chemical reduction reaction. The thermal characteristics of Ag nanoparticle (NP) paste were measured by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The low‐temperature sintering bonding processes using Ag NP paste were carried out at the temperature range of 150–350°C for 5 min under the pressure of 3 MPa. The microstructures of the sintered joint and the fracture morphology were evaluated by scanning electron microscopy (SEM). The shear strength was used to evaluate the mechanical property of the sintered joint. TGA‐DSC test showed that the Ag content is approximately 95.5 mass% in Ag NP paste. The average shear strength of the joint fabricated at 250°C for 5 min under the pressure of 3 MPa was about 28 MPa, which could meet the requirements of electronics packaging working at high temperature. The joint shear strength increased with the increase of the sintering temperature due to much denser sintered Ag NPs and more comprehensive metallurgical bonds formed in the joint.

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)0.001
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 score0.004
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.258
Teacher spread0.232 · 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