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Record W4247814667 · doi:10.5101/nml.v5i2.p88-92

Nano brazing of Pt-Ag nanoparticles under femtosecond laser irradiation

2013· article· en· W4247814667 on OpenAlex
Li Liu, Hong Huang, A. Hu, G. Zou, L. Quintino, Y. Zhou

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNano-Micro Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaTsinghua UniversityNational Natural Science Foundation of ChinaMcMaster University
KeywordsBrazingMaterials scienceFemtosecondNano-IrradiationLaserNanoparticleFiller metalComposite materialNanostructureNanotechnologyAlloyWeldingOptics

Abstract

fetched live from OpenAlex

Nano brazing of Pt-Ag nanoparticles with nano Ag filler metal is reported in this letter, which presents an effective way to join nanoobjects by femtosecond laser irradiation. The nano brazed interface between Pt-Ag and Ag showed good lattice matching along (111) Ag //(111) Ag-Pt . Lattice mismatch can hardly be observed at the interface between the filler metal and Pt-Ag nanoparticle, which is important for the joint strength and normally does not occur during joining. The very low mismatch also suggested that melting and solidification occurred during nano brazing by femtosecond laser. The role of Brownian motion on the nano joining process is also discussed in this paper.

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 score0.019
Threshold uncertainty score0.812

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 communication0.0000.001
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 head0.008
GPT teacher head0.198
Teacher spread0.191 · 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