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Record W2040199696 · doi:10.1364/oe.19.025632

Time dynamics of burst-train filamentation assisted femtosecond laser machining in glasses

2011· article· en· W2040199696 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

VenueOptics Express · 2011
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaDeutscher Akademischer Austauschdienst
KeywordsFilamentationMaterials scienceLaserOpticsFemtosecondSapphireLaser ablationIncandescenceOptoelectronicsPhysicsCombustion

Abstract

fetched live from OpenAlex

Bursts of femtosecond laser pulses with a repetition rate of f = 38.5MHz were created using a purpose-built optical resonator. Single Ti:Sapphire laser pulses, trapped inside a resonator and released into controllable burst profiles by computer generated trigger delays to a fast Pockels cell switch, drove filamentation-assisted laser machining of high aspect ratio holes deep into transparent glasses. The time dynamics of the hole formation and ablation plume physics on 2-ns to 400-ms time scales were examined in time-resolved side-view images recorded with an intensified-CCD camera during the laser machining process. Transient effects of photoluminescence and ablation plume emissions confirm the build-up of heat accumulation effects during the burst train, the formation of laser-generated filaments and plume-shielding effects inside the deeply etched vias. The small time interval between the pulses in the present burst train enabled a more gentle modification in the laser interaction volume that mitigated shock-induced microcracks compared with single pulses.

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 score0.122
Threshold uncertainty score0.538

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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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