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Determining strain components in a diamond waveguide from zero-field optically detected magnetic resonance spectra of negatively charged nitrogen-vacancy-center ensembles

2024· article· en· W4401723010 on OpenAlexaff
M. Sahnawaz Alam, Federico Gorrini, Michał Gawełczyk, Daniel Wigger, Giulio Coccia, Yanzhao Guo, Sajedeh Shahbazi, Vibhav Bharadwaj, Alexander Kubanek, Roberta Ramponi, Paul E. Barclay, A. J. Bennett, John P. Hadden, Angelo Bifone, Shane M. Eaton, Paweł Machnikowski

Bibliographic record

VenuePhysical Review Applied · 2024
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Calgary
FundersEngineering and Physical Sciences Research CouncilMinistero dell'Università e della RicercaMinistero degli Affari Esteri e della Cooperazione InternazionaleBundesministerium für Bildung und ForschungEuropean CommissionNarodowe Centrum NaukiScience Foundation IrelandAlexander von Humboldt-Stiftung
KeywordsDiamondPhotonicsMaterials scienceSpectroscopyLaserMagnetic fieldVacancy defectSpectral lineQuantumOptoelectronicsMolecular physicsCondensed matter physicsOpticsPhysics

Abstract

fetched live from OpenAlex

Laser-written optical waveguides in diamonds are a key technology to enhance coupling between defect centers and light, boosting applications in nanoscale sensing and quantum information processing. However, laser writing of photonic structures produces strain in the diamond lattice, modifying the properties of defect centers in poorly understood ways. This study demonstrates that optically detected magnetic resonance spectroscopy provides sufficient information to fully characterize the spatial distribution of strain in such a device, even without a constant magnetic field. The work yields an accessible tool that could be very useful for advancing diamond-based quantum technologies.

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 categoriesMeta-epidemiology (narrow)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.291
Teacher spread0.267 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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