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Record W2923767382 · doi:10.1017/s0263034619000132

Scanning electron microscope studies on laser ablation of solids

2019· article· en· W2923767382 on OpenAlexaff
Mohamed E. Shaheen, Joel E. Gagnon, Brian J. Fryer

Bibliographic record

VenueLaser and Particle Beams · 2019
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceSapphireLaserPicosecondOpticsScanning electron microscopeBrassFluenceNanosecondFemtosecondLaser ablationAblationOptoelectronicsX-ray laserWavelengthLaser power scalingComposite material

Abstract

fetched live from OpenAlex

Abstract This study investigates the interaction of picosecond laser pulses with sapphire and brass in air using scanning electron microscopy. A picosecond laser system operating at a wavelength of 785 nm, pulse width of 110 ps, and variable repetition rate (1–1000 Hz) was used in this study. The pulse width applied in this work was not widely investigated as it lies in the gap between ultrashort (femtosecond) and long (nanosecond) pulse width lasers. Different surface morphologies were identified using secondary electron and backscattered electron imaging of the ablated material. Thermal ablation effects were more dominant in brass than in sapphire. Exfoliation and fractures of sapphire were observed at high laser fluence. Compared with brass, multiple laser pulses were necessary to initiate ablation in sapphire due to its poor absorption to the incident laser wavelength. Ablation rate of sapphire was lower than that of brass due to the dissipation of a portion of the laser energy due to heating and fracturing of the surface.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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