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Record W2059506022 · doi:10.1063/1.4905588

Femtosecond laser-induced phase transformations in amorphous Cu77Ni6Sn10P7 alloy

2015· article· en· W2059506022 on OpenAlex
Y. Zhang, Lei Liu, Guisheng Zou, Nanguang Chen, Aihua Wu, H. Bai, 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.

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Beijing MunicipalityTsinghua UniversityNational Natural Science Foundation of China
KeywordsMaterials scienceCrystallizationAmorphous solidFemtosecondLaserSapphireFluencePhase (matter)DiffractionLaser ablationAmorphous metalOptoelectronicsOpticsCrystallographyAlloyChemical engineeringComposite materialChemistry

Abstract

fetched live from OpenAlex

In this study, the femtosecond laser-induced crystallization of CuNiSnP amorphous ribbons was investigated by utilizing an amplified Ti:sapphire laser system. X-ray diffraction and scanning electronic microscope were applied to examine the phase and morphology changes of the amorphous ribbons. Micromachining without crystallization, surface patterning, and selective crystallization were successfully achieved by changing laser parameters. Obvious crystallization occurred under the condition that the laser fluence was smaller than the ablation threshold, indicating that the structural evolution of the material depends strongly on the laser parameters. Back cooling method was used to inhibit heat accumulation; a reversible transformation between the disordered amorphous and crystalline phases can be achieved by using this method.

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 score0.134
Threshold uncertainty score0.536

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 head0.027
GPT teacher head0.260
Teacher spread0.233 · 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