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Record W1982794522 · doi:10.2174/1876531901103010042

Femtosecond Laser-Induced Nanowelding: Fundamentals and Applications

2010· article· en· W1982794522 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

VenueThe Open Surface Science Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFemtosecondMaterials scienceNanoparticleLaserIrradiationNanoengineeringRaman spectroscopyNanometreNanotechnologyUltrashort pulseAnalytical Chemistry (journal)OpticsChemistryComposite material

Abstract

fetched live from OpenAlex

Fundamentals of femtosecond laser pulse and nanoparticles are analyzed by a two-temperature model. Ultrafast surface melting, surface nanoengineering and shock wave impact are evident in the surface of graphite by femtosecond irradiation. The interaction between femtosecond laser pulses and Au/Ag nanoparticles has been investigated. Two effects are identified at different intensities: photofragmentation at rather high intensity (~10 14 W/cm 2 ), nanojoining at low intensity (~10 10 W/cm 2 ). Photofragmentation forms a large number of tiny nanoparticles with an average size of tens of nanometers. Control over irradiation conditions at intensities near 10 10 W/cm 2 results in nanojoining of most of the nanoparticles. This nanojoining is obtained in both liquid solution and in solid state thin films assembled from nanoparticles. Nanojoining mechanism is further studied by joining Ag nanoparticles encapsulated by a polymer shell. Nonthermal melting is investigated. Nanojoined Au nanoparticles are expected to have numerous applications, such as probes for surface enhance Raman spectroscopy.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score0.018
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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