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Record W4412721445 · doi:10.1364/oe.571078

Vat photopolymerization of Ka-band Luneburg lenses: design, fabrication methods, and characterization

2025· article· en· W4412721445 on OpenAlexaff
Hojjat Jamshidi-Zarmehri, Bhavana Deore, Tabitha Arulpragasam, Nicolas Milliken, Mohammed Labadlia, Amir Akbari, J. Shaker, Eqab Almajali, Chantal Paquet, Rony E. Amaya

Bibliographic record

VenueOptics Express · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOpticsFabricationPhotopolymerMaterials scienceCharacterization (materials science)Refractive indexOptical materialsLuneburg lensOptoelectronicsPhysicsPolymer

Abstract

fetched live from OpenAlex

This study investigates the performance of 3D-printed Luneburg lenses using various printable dielectric materials, including commercial resins such as Radix from Rogers Corporation and Rigid 4000 from Formlabs, as well as what we believe to be a new in-house formulated liquid crystal polymer (LCP) resin. Three Luneburg lenses were successfully designed and fabricated using these materials. Step-by-step design processes of 3D printed Luneburg lenses are fully described along with their electrical characterization in the frequency range of 26.5 to 38 GHz. The measurement results reveal that the Luneburg lens fabricated with Radix exhibits the best performance thanks to its low loss. Improvement of gain performance in excess of 14 dB is achieved using a Radix-made in the fabrication process. On the other hand, the Luneburg lens from in-house-formulated LCP demonstrates a similar superior gain improvement up to 13.5 dB. In contrast, the lens fabricated with standard Formlabs resin exhibits inferior performance due to higher loss of the material, which renders it a lower grade candidate for the fabrication of microwave lenses. The paper also offers a comprehensive comparison of the physical properties of the materials in terms of the complexity of the specialized printing process.

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 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 score0.247
Threshold uncertainty score0.412

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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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