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Record W2070470128 · doi:10.1364/ol.40.002064

Burst train generator of high energy femtosecond laser pulses for driving heat accumulation effect during micromachining

2015· article· en· W2070470128 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 Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaserOpticsMaterials scienceFemtosecondPulse durationPulse waveOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

A new method for generating high-repetition-rate (12.7-38.2 MHz) burst trains of femtosecond laser pulses has been demonstrated for the purpose of tailoring ultrashort laser interactions in material processing that can harness the heat accumulation effect among pulses separated by a short interval (i.e., 26 ns). Computer-controlled time delays were applied to synchronously trigger the high frequency switching of a high voltage Pockels cell to specify distinctive values of polarization rotation for each round-trip of a laser pulse cycling within a passive resonator. Polarization dependent output coupling facilitated the flexible shaping of the burst envelope profile to provide burst trains of up to ∼1 mJ of burst energy divided over a selectable number (1 to 25) of pulses. Individual pulses of variable energy up to 150 μJ and with pulse duration tunable over 70 fs to 2 ps, were applied in burst trains to generate deep and high aspect ratio holes that could not form with low-repetition-rate laser 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 score0.037
Threshold uncertainty score0.775

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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