Real-time estimation of the optically detected magnetic resonance shift in diamond quantum thermometry toward biological applications
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2020-12-24 |
| Journal | Physical Review Research |
| Authors | Masazumi Fujiwara, Alexander Dohms, Ken Suto, Yushi Nishimura, Keisuke Oshimi |
| Institutions | Keio University, Humboldt-UniversitÀt zu Berlin |
| Citations | 35 |
| Analysis | Full AI Review Included |
Executive Summary
Section titled âExecutive SummaryâThis study investigates and validates real-time protocols for Optically Detected Magnetic Resonance (ODMR) quantum thermometry using Nitrogen-Vacancy (NV) centers in nanodiamonds (NDs), specifically addressing challenges for in-vivo biological applications.
- Core Problem Addressed: Multipoint ODMR measurements (3-, 4-, and 6-point methods) are susceptible to systematic artifacts caused by variations in counter photo-responsivity, which arise from both hardware instrumental errors and the intrinsic nature of the NV spin resonance.
- Solution Implemented: A practical error-correction filter is proposed and validated. This filter uses pre-characterized photo-responsivity curves (fitted with second-order polynomials) to subtract systematic errors from raw photon counts, effectively canceling signal drift.
- Performance Achieved: Using the error correction, the system successfully monitored single ND temperature dynamics during stepwise thermal events, even under dynamic fluorescence intensity variations (e.g., due to thermal drift or NV quantum efficiency changes).
- Precision and Accuracy: The 4-point and 6-point methods demonstrated high precision (0.14 K and 0.15 K, respectively) and an accuracy of less than 0.5 K for temperature estimation in the steady state.
- Method Comparison: The 4-point and 6-point methods showed superior robustness and precision compared to the 3-point method, whose estimation is highly sensitive to the assumption of a single Lorentzian spectral shape.
- Noise Analysis: Allan variance analysis identified four distinct noise regions, confirming that the optimal time window for the moving average filter is around 40 seconds, balancing white noise reduction and environmental instability tracking.
Technical Specifications
Section titled âTechnical Specificationsâ| Parameter | Value | Unit | Context |
|---|---|---|---|
| Excitation Laser | CW 532-nm | Wavelength | Used for NV center excitation. |
| Laser Intensity | ~2 | kW·cm-2 | Typical excitation intensity. |
| Nanodiamonds (NDs) | ~500 NV per particle | Count | Used ND type (100 nm Hi10ml). |
| Objective NA | 1.4 | Dimensionless | Oil-immersion objective. |
| Microwave Power | 10-50 (10-17) | mW (dBm) | Power fed to the linear antenna. |
| Microwave B-field | > 2-5 | Gauss | Magnetic field strength 20 ”m from antenna. |
| SP6T Switch Time | 250 | ns | Time required for microwave switching. |
| Measurement Time (tM) | 100 | ”s | Gate width for photon counting. |
| Interval Time (tint) | 5 | ”s | Time between measurement gates. |
| DAQ-1 Clock Speed | 100 | MHz | Data acquisition board 1 (DAQ-1). |
| DAQ-2 Clock Speed | 80 | MHz | Data acquisition board 2 (DAQ-2). |
| Tracking Period (ttrack) | 4 | s | Total time for tracking and re-positioning. |
| 4-point Precision (Ïexp) | 0.14 | K | Experimentally determined precision. |
| 6-point Precision (Ïexp) | 0.15 | K | Experimentally determined precision. |
| Accuracy (All Methods) | < 0.5 | K | Upper bound of RMS in steady state. |
| Temp. Dependency (α, 4-point) | -54.1 | kHz·K-1 | Zero-field splitting temperature coefficient. |
| Temp. Dependency (α, 3-point) | -95.0 | kHz·K-1 | Zero-field splitting temperature coefficient (highly sensitive to spectral shape assumption). |
| Temp. Dependency (α, 6-point) | -67.5 | kHz·K-1 | Zero-field splitting temperature coefficient. |
| Minimum Photon Count (Itot) | 0.5 | Mcps | Required minimum for negligible drift artifact. |
Key Methodologies
Section titled âKey MethodologiesâThe real-time thermometry protocol integrates confocal microscopy, particle tracking, and multipoint ODMR measurements, incorporating a critical error-correction step:
- Optical Setup and Excitation: NV centers in NDs are excited using a CW 532-nm laser via an oil-immersion objective (NA 1.4). Fluorescence is filtered and detected by an Avalanche Photodiode (APD).
- Microwave Control: Multiple microwave sources (MW1-MW6) are routed via an SP6T switch and amplified, delivering the signal to a linear copper wire antenna placed on the coverslip.
- Multipoint ODMR Acquisition: Photon counts are acquired at 3, 4, or 6 pre-selected microwave frequencies (on the linear slopes of the ODMR dip) using gated edge counting (tM = 100 ”s).
- Particle Tracking: A confocal microscope system tracks the target NDs by scanning the piezo stage in xyz, fitting the point spread function to determine position, and re-positioning the stage every 4 seconds.
- Photo-Responsivity Characterization: Before temperature measurement, the photo-responsivity of the DAQ counters is characterized by varying the total fluorescence intensity (Itot). The counter values (Ii) are fitted using second-order polynomials (Eq. 8).
- Error Correction Filter: The derived polynomial coefficients are used to correct the raw photon counts (Ii) into error-corrected counts (IiEC). This step eliminates systematic drift caused by intensity fluctuations and hardware differences.
- Frequency Shift Estimation: The error-corrected counts are used in the respective multipoint formulas (Eqs. 2, 3, or 5) to calculate the ODMR frequency shift (ÎΩi-pnt).
- Temperature Conversion: The frequency shift is converted to temperature change (ÎTNV) using the calibrated temperature dependency factor (α = dD/dT).
- Noise Reduction: A 20-point moving average filter is applied to the ÎTNV data to balance precision improvement against temporal resolution requirements, or a Kalman filter is used for transient dynamics.
Commercial Applications
Section titled âCommercial ApplicationsâThe developed real-time quantum thermometry techniques are crucial for applications requiring high-precision, localized temperature sensing in dynamic environments.
- Biological and Biomedical Sensing:
- In-vivo thermometry in model organisms (e.g., nematode worms) and cultured cells.
- Monitoring temperature dynamics related to fundamental biological processes (circadian rhythms, energy metabolism, developmental processes).
- Validating local heating effects during microwave or optical therapies.
- Quantum Sensing and Metrology:
- Development of robust, real-time quantum sensors capable of operating under noisy, fluctuating conditions (e.g., dynamic motion, varying optical power).
- Integration of NV centers into complex microfluidic or nanofiber structures for localized environmental monitoring.
- Material Science and Device Engineering:
- Localized thermal mapping of microelectronic devices or high-power RF components.
- Studying thermal transport mechanisms in nanoscale materials.
- Advanced Data Processing:
- Implementation of advanced noise filtering techniques (e.g., Kalman filters) for transient dynamics in noisy quantum measurement systems.
View Original Abstract
Real-time estimation protocols for the frequency shift of optically detected magnetic resonance (ODMR) of nitrogen-vacancy (NV) centers in nanodiamonds (NDs) are the key to the recent demonstrations of diamond quantum thermometry inside living animals. Here we analyze the estimation process in multipoint ODMR measurement techniques (3-, 4-, and 6-point methods) and quantify the amount of measurement artifact derived from the optical power-dependent ODMR spectral shape and instrumental errors of experimental hardware. We propose a practical approach to minimize the effect of these factors, which allows for measuring accurate temperatures of single ND during dynamic thermal events. Further, we discuss integration of noise filters, data estimation protocols, and possible artifacts for further developments in real-time temperature estimation. This study provides technical details regarding quantum diamond thermometry and analyzes the factors that may affect the temperature estimation in biological applications.