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Record W4388449324 · doi:10.1021/acs.nanolett.3c02120

Multicone Diamond Waveguides for Nanoscale Quantum Sensing

2023· article· en· W4388449324 on OpenAlexafffund
Tianqi Zhu, J. Rhensius, Konstantin Herb, Viraj G. Damle, Gabriel Puebla‐Hellmann, Christian L. Degen, Erika Janitz

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Calgary
FundersH2020 European Research CouncilNational Center of Competence in Research Quantum Science and TechnologyNatural Sciences and Engineering Research Council of CanadaH2020 Future and Emerging TechnologiesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsDiamondNanopillarQuantum sensorMaterials scienceNanolithographyOptoelectronicsNanoscopic scaleNumerical apertureOpticsNanotechnologyQuantumFabricationWavelengthQuantum simulatorPhysicsNanostructureQuantum computer

Abstract

fetched live from OpenAlex

The long-lived electronic spin of the nitrogen-vacancy (NV) center in diamonds is a promising quantum sensor for detecting nanoscopic magnetic and electric fields in various environments. However, the poor signal-to-noise ratio (SNR) of prevalent optical spin-readout techniques presents a critical challenge in improving measurement sensitivity. Here, we address this limitation by coupling individual NVs to optimized diamond nanopillars, thereby enhancing the collection efficiency of fluorescence. Guided by near-field optical simulations, we predict improved performance for tall (≥5 μm) pillars with tapered sidewalls. This is subsequently verified by fabricating and characterizing a representative set of structures using a newly developed nanofabrication process. We observe increased SNR for optimized devices, owing to improved emission collimation and directionality. Promisingly, these devices are compatible with low-numerical-aperture collection optics and a reduced tip radius, reducing experimental overhead and facilitating improved spatial resolution for scanning applications.

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 categoriesInsufficient payload (model declined to judge)
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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

Citations15
Published2023
Admission routes2
Has abstractyes

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