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Record W2132748048 · doi:10.1002/lpor.201500062

Chemical‐assisted femtosecond laser writing of optical resonator arrays

2015· article· en· W2132748048 on OpenAlexaff
Moez Haque, Peter R. Herman

Bibliographic record

VenueLaser & Photonics Review · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFemtosecondResonatorMaterials scienceFinesseInterferometryLaserOptoelectronicsRefractive indexOpticsDielectricWaveguideIsotropic etchingEtching (microfabrication)NanotechnologyFabry–Pérot interferometerWavelengthLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract The design of micro‐optical resonator arrays are introduced and tailored towards refractive index sensing applications, building on the previously unexplored benefits of open dielectric stacks. The resonant coupling of identical hollow cavities present strong and narrow spectral resonance bands beyond that available with a single Fabry Perot interferometer. Femtosecond laser irradiation with selective chemical etching is applied to precisely fabricate stacked and waveguide‐coupled open resonators into fused silica, taking advantage of small 12 nm rms surface roughness made available by the self‐alignment of nanograting planes. Refractive index sensing of methanol‐water solutions confirm a very attractive sensing resolution of 6.5 × 10 −5 RIU. Such high finesse optical elements open a new realm of optofluidic sensing and integrated optical circuit concepts for detecting minute changes in sample properties against a control solution that may find importance in chemical and biological sensors, telecom sensing networks, biomedical probes, and low‐cost health care products. image

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.262
Teacher spread0.234 · 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 designNot applicable
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

Citations14
Published2015
Admission routes1
Has abstractyes

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