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Record W2005207941 · doi:10.1364/oe.18.013813

Spectral characterization of porous dielectric subwavelength THz fibers fabricated using a microstructured molding technique

2010· article· en· W2005207941 on OpenAlexafffund
Alexandre Dupuis, Anna Mazhorova, Frédéric Désévédavy, Mathieu Rozé, Maksim Skorobogatiy

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFabricationOpticsPorosityTerahertz radiationDielectricOptical fiberComposite materialSubwavelength-diameter optical fibreMolding (decorative)Transfer moldingOptoelectronicsMoldWavelength

Abstract

fetched live from OpenAlex

We report two novel fabrication techniques, as well as THz spectral transmission and propagation loss measurements of subwavelength plastic wires with highly porous (up to 86%) and non-porous transverse geometries. The two fabrication techniques we describe are based on the microstructured molding approach. In one technique the mold is made completely from silica by stacking and fusing silica capillaries to the bottom of a silica ampoule. The melted material is then poured into the silica mold to cast the microstructured preform. Another approach uses a microstructured mold made of a sacrificial plastic which is co-drawn with a cast preform. Material from the sacrificial mold is then dissolved after fi ber drawing. We also describe a novel THz-TDS setup with an easily adjustable optical path length, designed to perform cutback measurements using THz fibers of up to 50 cm in length. We fi nd that while both porous and non-porous subwavelength fibers of the same outside diameter have low propagation losses (alpha

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.006
GPT teacher head0.211
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

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

Citations58
Published2010
Admission routes2
Has abstractyes

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