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Record W2002335527 · doi:10.1017/s0263034612000407

Femtosecond laser ablation of brass: A study of surface morphology and ablation rate

2012· article· en· W2002335527 on OpenAlexafffund
Mohamed E. Shaheen, Brian J. Fryer

Bibliographic record

VenueLaser and Particle Beams · 2012
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsFluenceMaterials scienceAblationFemtosecondLaserBrassNanosecondOpticsLaser ablationScanning electron microscopeComposite materialCopperMetallurgy

Abstract

fetched live from OpenAlex

Abstract The interaction of near infrared femtosecond laser pulses with a Cu based alloy (brass) in ambient air at atmospheric pressure and under different laser conditions was investigated. The effects of laser fluence and number of pulses on surface morphology and ablation rate were studied using scanning electron microscopy (SEM) and optical microscopy. Ablation rates were found to rapidly increase from 83 to 604 nm/pulse in the fluence range 1.14–12.21 J/cm2. At fluence >12.21 J/cm2, ablation rates increased slowly to a maximum (607 nm/pulse at 19.14 J/cm2), and then decreased at fluence higher than 20.47 J/cm2 to 564 nm/pulse at 24.89 J/cm2. Large amounts of ablated material in a form of agglomerated fine particles were observed around the ablation craters as the number of laser pulses and fluence increased. The study of surface morphology shows reduced thermal effects with femtosecond laser ablation in comparison to nanosecond laser ablation at low fluence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.243
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2012
Admission routes2
Has abstractyes

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