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Record W4392781455 · doi:10.1063/5.0209294

Laser-written waveguide-integrated coherent spins in diamond

2024· preprint· en· W4392781455 on OpenAlexaff
Yanzhao Guo, John P. Hadden, Federico Gorrini, Giulio Coccia, Vibhav Bharadwaj, Vinaya K. Kavatamane, Mohammad Sahnawaz Alam, Roberta Ramponi, Paul E. Barclay, Andrea Chiappini, Maurizio Ferrari, Alexander Kubanek, Angelo Bifone, Shane M. Eaton, A. J. Bennett

Bibliographic record

VenueAPL Photonics · 2024
Typepreprint
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Calgary
FundersH2020 Marie Skłodowska-Curie ActionsEngineering and Physical Sciences Research CouncilHorizon 2020 Framework ProgrammeMinistero dell'Università e della RicercaEPSRC Centre for Doctoral Training in Additive ManufacturingEuropean CommissionAlexander von Humboldt-Stiftung
KeywordsSpinsDiamondLaserWaveguideOpticsPhysicsMaterials scienceOptoelectronicsCondensed matter physics

Abstract

fetched live from OpenAlex

Quantum emitters, such as the negatively charged nitrogen-vacancy center in diamond, are attractive for quantum technologies, such as nano-sensing, quantum information processing, and as a non-classical light source. However, it is still challenging to position individual emitters in photonic structures while preserving the spin coherence properties of the defect. In this paper, we investigate single and ensemble waveguide-integrated nitrogen-vacancy centers in diamond fabricated by femtosecond laser writing followed by thermal annealing. Their spin coherence properties are systematically investigated and are shown to be comparable to native nitrogen-vacancy centers in diamond. This method paves the way for the fabrication of coherent spins integrated within photonic devices.

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)0.0010.000

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 head0.020
GPT teacher head0.289
Teacher spread0.270 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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