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Record W6997091806

Towards compact photonic devices in highly nonlinear chalcogenide microwires

2014· dissertation· en· W6997091806 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2014
Typedissertation
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhotonicsChalcogenideChalcogenide glassBroadbandOptical powerOptical fiberFiber Bragg gratingDistributed Bragg reflector
DOInot available

Abstract

fetched live from OpenAlex

The future of fiber optic systems lies in the development of all fiber photonic devices thatare compact, power efficient, and can provide novel functionalities. Here, we propose anddemonstrate the realization of such photonic devices using chalcogenide microwires. Thedevices include Bragg gratings, wavelength converters, broadband amplifiers and lasers. Thedevices' operation relies on the ultrahigh nonlinear optical gain (>5 orders of magnitudelarger than in silica fibers) and the high photosensitivity of chalcogenide microwires. Theresulting photonic devices are a few centimeters in length, a few micrometers in diameter,and operate at record-low optical power levels (i.e., in the order of a few hundreds of microWatts). In addition, chalcogenide glasses are transparent over an ultrabroad wavelengthrange (1-10 microns in wavelength), making these devices capable of operation in the midinfraredwavelength region. Such photonic devices have been realized for the first time insuch a compact and power efficient geometry, and are thus leading candidates for replacingtheir electronic counterparts. The devices are applied with a polymer coating to add physicalstrength and for protection against any environmental damage. Owing to their compactness,power efficiency and ultra broadband operation window, these novel photonic devicescarry great commercial and research value for a wide range of fields, including biomedicine,instrumentation and mid-infrared spectroscopy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.426
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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.

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
Published2014
Admission routes1
Has abstractyes

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