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Record W4225640627 · doi:10.1021/acsnano.2c10974

Real-Time Ratiometric Optical Nanoscale Thermometry

2023· preprint· en· W4225640627 on OpenAlexafffund
Yongliang Chen, Chi Li, Tieshan Yang, Е. А. Екимов, Carlo Bradac, Milos Toth, Igor Aharonovich, Toan Trong Tran

Bibliographic record

VenueACS Nano · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsTrent University
FundersOffice of Naval Research GlobalAustralian Research CouncilRussian Science FoundationNatural Sciences and Engineering Research Council of CanadaAgency for Science, Technology and ResearchCanada Foundation for InnovationAustralian National Fabrication Facility
KeywordsNanophotonicsMaterials scienceNanoscopic scaleOptoelectronicsSensitivity (control systems)Temporal resolutionTransistorElectronic circuitMicrofluidicsNanotechnologyRangingLaserDiamondElectronic engineeringOpticsComputer sciencePhysicsVoltageElectrical engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

All-optical nanothermometry has become a powerful, remote tool for measuring nanoscale temperatures in applications ranging from medicine to nano-optics and solid-state nanodevices. The key features of any candidate nanothermometer are brightness, sensitivity, and (signal, spatial, and temporal) resolution. Here, we demonstrate a real-time, diamond-based nanothermometry technique with excellent sensitivity (1.8% K –1 ) and record-high resolution (5.8 × 10 4 K Hz –1/2 W cm –2 ) based on codoped nanodiamonds. The distinct performance of our approach stems from two factors: (i) temperature sensors─nanodiamonds cohosting two group IV color centers─engineered to emit spectrally separated Stokes and anti-Stokes fluorescence signals under excitation by a single laser source and (ii) a parallel detection scheme based on filtering optics and high-sensitivity photon counters for fast readout. We demonstrate the performance of our method by monitoring temporal changes in the local temperature of a microcircuit and a MoTe 2 field-effect transistor. Our work advances a powerful, alternative strategy for time-resolved temperature monitoring and mapping of micro-/nanoscale devices such as microfluidic channels, nanophotonic circuits, and nanoelectronic devices, as well as complex biological environments such as tissues and cells.

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), 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 score0.269
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.021
GPT teacher head0.277
Teacher spread0.256 · 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
Published2023
Admission routes2
Has abstractyes

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