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Record W3011493023 · doi:10.1117/12.2551249

Harnessing femtosecond laser filaments for nano-structuring of "Lab-in-Fibre" sensors and "Spectrometer-in-Fibre" microsystems (Conference Presentation)

2020· article· en· W3011493023 on OpenAlexaff
Peter R. Herman, Ehsan Alimonhammadian, Keivan Mahmoud Aghdami, Erdem Yigit Ertorer, Young Hwan Kim, Jianzhao Li, Abdullah Rahnama

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFemtosecondSpectrometerMicrosystemStructuringMaterials scienceNano-LaserPresentation (obstetrics)NanotechnologyOptoelectronicsOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

Beam shaping of femtosecond lasers was applied in the Kerr-lensing and aberration regime to enable high-aspect-ratio filament tracks to form uniformly through the silica cladding and core waveguide of single-mode fiber (SMF28/450). One- and two-dimensional filament arrays were embedded along the waveguide to form weak to strong photonic stopbands. The filament shape enhanced transverse light scattering into narrow azimuthal radiation zones. Tailoring of chirp and 2D patterns further facilitated high-resolution (~350 pm) spectral focusing onto a CCD camera, defining a compact “Spectrometer-in-fibre” over the visible spectrum. At higher exposure, the filaments opened into narrow nano-channels (200-400 nm diameter) presenting a novel Bragg grating for refractive index sensing of the ambient environment. This lab-in-fiber technology presents a robust, flexible, and ubiquitous communication platform for nano-scale sensing across expansive networks or into tightly confined, sinuous spaces.

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 score0.018
Threshold uncertainty score0.814

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 head0.021
GPT teacher head0.241
Teacher spread0.220 · 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
Published2020
Admission routes1
Has abstractyes

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