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Record W2341184403 · doi:10.1108/ssmt-08-2015-0023

Effect of Sb on wetting behavior of near eutectic Sn-Cu solder micro-alloyed with Ni and Ge

2016· article· en· W2341184403 on OpenAlexaff
Amir Hossein Nobari, Mehran Maalekian, Karl Seelig, Mihriban Pekguleryuz

Bibliographic record

VenueSoldering and Surface Mount Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsWettingEutectic systemMaterials scienceContact angleSessile drop techniqueSolderingDifferential scanning calorimetryMetallurgyAlloyDrop (telecommunication)Composite materialThermodynamics

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate the effect of Sb (0, 0.2 and 2 wt.%) on wetting performance of lead-free solder of near eutectic Sn-Cu micro-alloyed with Ni and Ge. Design/methodology/approach The melting characteristic of the lead-free alloys was studied using differential scanning calorimetry. Wettability was examined using wetting balance test for two liquid fluxes, water based and alcohol based in two temperatures 265°C and 277°C. Also, contact angle was measured using sessile drop test. Findings It is shown that 0.2 wt.% Sb reduces the melting temperature and pasty range. Moreover, the addition of 0.2 wt.% Sb improves wetting behavior for alcohol-based flux. It is also demonstrated that the effect of Sb on meniscus height in wetting balance test and contact angle in sessile drop test follows the trend of wetting performance. Originality/value It is found that adding 0.2 wt.% Sb improves the wettability of Ni-Ge micro-alloyed Sn-Cu solder; however, higher concentration of Sb does not benefit the alloy.

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 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 score0.067
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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 teacher head, 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

Citations6
Published2016
Admission routes1
Has abstractyes

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