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Record W4402768681 · doi:10.1063/5.0219194

Nitrogen-vacancy centers as a self-gauged micro-scale heater and its application for multi-modal sensing

2024· article· en· W4402768681 on OpenAlexaff
Dewen Duan, Vinaya K. Kavatamane, Sri Ranjini Arumugam, Huan‐Cheng Chang, Gopalakrishnan Balasubramanian

Bibliographic record

VenueApplied Physics Letters · 2024
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Calgary
FundersMax-Planck-Gesellschaft
KeywordsNitrogenScale (ratio)Vacancy defectModalMaterials scienceEnvironmental scienceNanotechnologyPhysicsComposite materialCondensed matter physics

Abstract

fetched live from OpenAlex

The optical excitation of nitrogen-vacancy (NV) color centers in diamonds mostly results in fluorescence emission. During this process, a portion of the incident energy is transferred to phonon vibration, which heats the diamond crystal. For single NV color centers, the heat generated by the optical cycle is negligible, while for an ensemble of NV defects, the generated heat accumulates rapidly and heats the diamond. The temperature rise is rapid due to the high thermal conductivity of the diamond. In addition to the ability to be heated by light, the NV defect's unique properties also allow for the precise measurement of temperature using optically detected magnetic resonance. Here, we experimentally demonstrate that microcrystalline diamond containing NV center ensembles can be used as a self-gauged microheater. We attached a microcrystal diamond to an optical fiber in an endoscope configuration, evaluated its performance as a self-gauged heater under varied biologically relevant environments, and discussed its potential applications. In addition to the aforementioned capabilities, the NV defect enables the precise measurement of local magnetic fields. This provides a unique multimodal sensor to probe temperature-controlled magnetic phenomena at microscopic scales.

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 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 score0.006
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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