MétaCan
Menu
Back to cohort
Record W2785354616 · doi:10.1021/acs.nanolett.8b00550

Bright Single InAsP Quantum Dots at Telecom Wavelengths in Position-Controlled InP Nanowires: The Role of the Photonic Waveguide

2018· article· en· W2785354616 on OpenAlexaff
S. Haffouz, Katharina D. Zeuner, Dan Dalacu, Philip J. Poole, J. Lapointe, Daniel Poitras, Khaled Mnaymneh, Xiaohua Wu, Martin Couillard, Marek Korkusiński, Eva Schöll, Klaus D. Jöns, Valéry Zwiller, Robin L. Williams

Bibliographic record

VenueNano Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersFP7 Ideas: European Research CouncilH2020 Marie Skłodowska-Curie ActionsVetenskapsrådet
KeywordsQuantum dotNanowireOptoelectronicsWavelengthPhotonicsWaveguideMaterials scienceNanophotonicsOpticsPhysics

Abstract

fetched live from OpenAlex

We report on the site-selected growth of bright single InAsP quantum dots embedded within InP photonic nanowire waveguides emitting at telecom wavelengths. We demonstrate a dramatic dependence of the emission rate on both the emission wavelength and the nanowire diameter. With an appropriately designed waveguide, tailored to the emission wavelength of the dot, an increase in the count rate by nearly 2 orders of magnitude (0.4 to 35 kcps) is obtained for quantum dots emitting in the telecom O-band, showing high single-photon purity with multiphoton emission probabilities down to 2%. Using emission-wavelength-optimized waveguides, we demonstrate bright, narrow-line-width emission from single InAsP quantum dots with an unprecedented tuning range of 880 to 1550 nm. These results pave the way toward efficient single-photon sources at telecom wavelengths using deterministically grown InAsP/InP nanowire quantum dots.

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 score0.001
Threshold uncertainty score0.002

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations114
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueNano LettersSame topicPhotonic and Optical DevicesFrench-language works237,207