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Record W2064825296 · doi:10.1088/1742-6596/59/1/150

Photochemical writing of silica optical waveguides in silicone rubber by F<sub>2</sub>laser

2007· article· en· W2064825296 on OpenAlexaff
Masayuki Okoshi, J Li, Peter R. Herman, Narumi Inoue

Bibliographic record

VenueJournal of Physics Conference Series · 2007
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
FundersMitutoyo Association for Science and Technology
KeywordsMaterials scienceLaserSilicone rubberSiliconeWavelengthFluenceOpticsWaveguideNatural rubberOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Photochemical writing of silica (SiO 2 ) optical waveguides in silicone [(SiO(CH 3 ) 2 ) n ] rubber has been successfully demonstrated by 157-nm F 2 laser-induced photochemical modification of silicone into silica. The 2-mm-thick or ∼40- m-thick silicone rubber was exposed to F 2 laser through a thin (∼0.2 mm) air layer. A proximity Cr-on-CaF 2 photomask with 8- to 16- m-wide slits controlled the exposure size to define the width of the silica waveguides. A laser processing window to generate crack-free waveguides with good optical transparency was found by varying the number laser pulse, pulse repetition rate and single pulse laser fluence. Otherwise, rapid or excess exposure of the F 2 laser caused cracking of the silica waveguides. The waveguides were found to guide both red (635-nm) and infrared (1550- nm) wavelength light with propagation loss estimated to be ∼15 and ∼6 dB/cm, respectively. Most of the loss originates in Rayleigh scattering from numerous inclusions originally present in the commercial 2-mm-thick silicone rubber.

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 score0.004

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

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