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Record W1985195451 · doi:10.2494/photopolymer.15.497

Microlens Arrays for Optoelectronic Devices.

2002· article· en· W1985195451 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.

Bibliographic record

VenueJournal of Photopolymer Science and Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced optical system design
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsMaterials scienceMicrolensEpoxySiliconeMoldElastomerComposite materialPolydimethylsiloxaneCuring (chemistry)Refractive indexUV curingShrinkageLens (geology)OptoelectronicsOptics

Abstract

fetched live from OpenAlex

This paper reports on an improved method of fabricating microlens arrays using a low cost replication process. An accurate negative reproduction ("mold") of an existing high quality lens surface (master) is made in a soft silicone elastomer. This mold is formed by thermally curing Sylgard® 182 silicone elastomer (made by Dow Corning®) on the lens surface. To prevent distortion of the replica surface, the mold is made on a rigid backing plate. Dispensing a commercial epoxy ‘Polyset’ PCX 28-91B into the mold and curing it under UV radiation generates a replica lens array. The epoxy material is chosen to have minimal shrinkage upon curing. The epoxy material is also chosen to have lower intrinsic loss and have a refractive index tailored to the application. In addition, we have developed a procedure to enable the incorporation of commercially available SiO2 nanoparticles (Nissan IPA-ST-S, 9-11nm) into this epoxy material. The incorporation of nanoparticles allows the epoxide to be harder, have a refractive index closer to SiO2, have even smaller shrinkage while maintaining low intrinsic loss because of the small size of the SiO2 particles.

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 score0.126
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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