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Record W2325534546 · doi:10.1021/jp506617f

Formation and Characterization of Femtosecond-Laser-Induced Subcluster Segregated Nanoalloys

2014· article· en· W2325534546 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsFemtosecondCharacterization (materials science)LaserMaterials scienceOptoelectronicsNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

We report the first synthesis of subcluster segregated nanoalloys formed through the joining of immiscible metallic nanoparticles (NPs) using femtosecond (fs) laser irradiation. Immiscible alloy components consisting of Ag and Ni, and Ag and Fe, all in the form of NPs, were first deposited on a carbon film in vacuum by fs laser ablation from the parent metals. These samples of randomly distributed NPs were then irradiated with multiple fs laser pulses at a fluence of 1.5 mJ/cm 2 . Transmission electron microscopy (TEM) observations indicate that Ag and Ni as well as Ag and Fe NPs were successfully joined under these conditions. Energy dispersive X-ray (EDX) results show that no mixing layer exists at the interface. The nanostructure in the interface reveals the assumption of a specific angle between two matching planes on either side of the interface. Calculation of the lattice mismatch indicates that the system adjusts to this angle so as to reduce surface energy. Structural ledges were also formed at the interface to further compensate for the atomic misfit.

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 score0.050
Threshold uncertainty score0.205

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 head0.010
GPT teacher head0.207
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