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Record W2024635643 · doi:10.1063/1.4808455

Femtosecond laser ablation of brass in air and liquid media

2013· article· en· W2024635643 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

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAblationMaterials scienceLaser ablationBrassLaserScanning electron microscopeDeposition (geology)FemtosecondOpticsAblation zoneAnalytical Chemistry (journal)Composite materialChemistryMetallurgyCopper

Abstract

fetched live from OpenAlex

Laser ablation of brass in air, water, and ethanol was investigated using a femtosecond laser system operating at a wavelength of 785 nm and a pulse width less than 130 fs. Scanning electron and optical microscopy were used to study the efficiency and quality of laser ablation in the three ablation media at two different ablation modes. With a liquid layer thickness of 3 mm above the target, ablation rate was found to be higher in water and ethanol than in air. Ablation under water and ethanol showed cleaner surfaces and less debris re-deposition compared to ablation in air. In addition to spherical particles that are normally formed from re-solidified molten material, micro-scale particles with varying morphologies were observed scattered in the ablated structures (craters and grooves) when ablation was conducted under water. The presence of such particles indicates the presence of a non-thermal ablation mechanism that becomes more apparent when ablation is conducted under water.

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 score0.028
Threshold uncertainty score0.250

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 head0.006
GPT teacher head0.196
Teacher spread0.189 · 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