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Record W4416875089 · doi:10.1109/qce65121.2025.10371

Towards Sub-Diffraction Mapping of NV Center Positions via Electrostatic Raster Scanning

2025· article· W4416875089 on OpenAlexaff
Kimy S Jaimes, Zahra Khatami

Bibliographic record

Venuenot available
Typearticle
Language
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsElectric fieldLissajous curveAmplitudeSaddleSaddle pointRaster scanMagnetic fieldPosition (finance)Hamiltonian (control theory)

Abstract

fetched live from OpenAlex

We present a theoretical method for the nanometric position mapping of single nitrogen-vacancy (NV) centers in diamond using electric-field-induced Stark shifts. A tunable saddle point in the electric field is generated by four symmetrically arranged electrodes and scanned across a defined area. As the saddle point moves, local field variations produce measurable shifts in the NV center's optically detected magnetic resonance (ODMR) spectrum. An analytical model was developed to calculate the voltage configurations needed to position the saddle point with nanometer scale precision. The spin Hamiltonian of the NV center is used to simulate its response under realistic field conditions. Results showed a linear relation between electric field amplitude and resonance frequency shift, with approximately 200 Hz per <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15 ~\mathrm{V} / \text{cm}$</tex>. These shifts are well within ODMR detection limits, and prior experiments confirm detectability down to 14 V/cm. This method enables sub-10 nanometer localization without optical imaging, offering a scalable solution for integrating NV centers into quantum 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 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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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