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Record W2272060414 · doi:10.1038/ncomms9097

Integration of photonic nanojets and semiconductor nanoparticles for enhanced all-optical switching

2015· article· en· W2272060414 on OpenAlexafffund
Brandon Born, Jeffrey D. A. Krupa, Simon Geoffroy-Gagnon, Jonathan F. Holzman

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotonicsOptical switchSemiconductorNanoparticleMaterials scienceFemtosecondOptoelectronicsSwitching timeDielectricNanotechnologyOpticsPhysicsLaser

Abstract

fetched live from OpenAlex

All-optical switching is the foundation of emerging all-optical (terabit-per-second) networks and processors. All-optical switching has attracted considerable attention, but it must ultimately support operation with femtojoule switching energies and femtosecond switching times to be effective. Here we introduce an all-optical switch architecture in the form of a dielectric sphere that focuses a high-intensity photonic nanojet into a peripheral coating of semiconductor nanoparticles. Milli-scale spheres coated with Si and SiC nanoparticles yield switching energies of 200 and 100 fJ with switching times of 10 ps and 350 fs, respectively. Micro-scale spheres coated with Si and SiC nanoparticles yield switching energies of 1 pJ and 20 fJ with switching times of 2 ps and 270 fs, respectively. We show that femtojoule switching energies are enabled by localized photoinjection from the photonic nanojets and that femtosecond switching times are enabled by localized recombination within the semiconductor nanoparticles.

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 score0.084
Threshold uncertainty score0.315

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 head0.039
GPT teacher head0.307
Teacher spread0.268 · 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

Citations55
Published2015
Admission routes2
Has abstractyes

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