No Arabic abstract
The investigation of the spectral kinetic model of the Multipole Resonance Probe (MRP) is presented and discussed in this paper. The MRP is a radio-frequency driven probe of the particular spherical design, which is suitable for the supervision and control of low-temperature plasma. The importance of the kinetic effects was introduced in the previous study of the spectral kinetic model of the idealized MRP. Such effects particularly dominate the energy loss in a low-pressure regime. Unfortunately, they are absent in the Drude model. With the help of the spectral kinetic scheme, those energy losses can be predicted, and it enables us to obtain the electron temperature from the FWHM in the simulated resonance curve. Simultaneously, the electron density can be derived from the simulated resonance frequency. Good agreements in the comparison between the simulation and the measurement demonstrate the suitability of the presented model.
Magnetic impurities in diamond influence the relaxation properties and thus limit the sensitivity of magnetic, electric, strain, and temperature sensors based on nitrogen-vacancy color centers. Diamond samples may exhibit significant spatial variations in the impurity concentrations hindering the quantitative analysis of relaxation pathways. Here, we present a local measurement technique which can be used to determine the concentration of various species of defects by utilizing double electron-electron resonance. This method will help to improve the understanding of the physics underlying spin relaxation and guide the development of diamond samples, as well as offering protocols for optimized sensing.
The Numerical Advanced Model of Electron Cyclotron Resonance Ion Source (NAM-ECRIS) is applied for studies of the physical processes in the source. Solutions of separately operating electron and ion modules of NAM-ECRIS are matched in iterative way such as to obtain the spatial distributions of the plasma density and of the plasma potential. Results reveal the complicated profiles with the maximized plasma density close to the ECR surface and on the source axis. The ion-trapping potential dips are calculated to be on the level of ~(0.01-0.05) V being located at the plasma density maxima. The highly charged ions are also localized close to the ECR surface. The biased electrode effect is due to an electron string along the source axis formed by reflection of electrons from the biased electrode and the extraction aperture. The string makes profiles of the highly charged ions more peaked on the source axis, thus increasing the extracted ion currents.
We report the nanoscale spin detection and electron paramagnetic resonance (EPR) spectrum of copper (Cu$^{2+}$) ions via double electron-electron resonance with single spins in diamond at room temperature and low magnetic fields. We measure unexpectedly narrow EPR resonances with linewidths $sim 2-3$ MHz from copper-chloride molecules dissolved in poly-lysine. We also observe coherent Rabi oscillations and hyperfine splitting from single Cu$^{2+}$ ions, which could be used for dynamic nuclear spin polarization and higher sensitivity of spin detection. We interpret and analyze these observations using both spin hamiltonian modeling of the copper-chloride molecules and numerical simulations of the predicted DEER response, and obtain a sensing volume $sim (250 text{nm})^3$. This work will open the door for copper-labeled EPR measurements under ambient conditions in bio-molecules and nano-materials.
A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from operation of the DIII-D tokamak using extreme ultraviolet / vacuum ultraviolet (EUV/VUV) emission spectroscopy matched with high-accuracy divertor Thomson scattering measurements of the electron temperature, $T_e$. This NN is shown to be particularly good at predicting $T_e$ at low temperatures ($T_e < 10$ eV) where the NN demonstrated a mean average error of less than 1 eV. Trained to detect plasma detachment in the tokamak divertor, a NN classifier was able to correctly identify detached states ($T_e<5$ eV) with a 99% accuracy (F$_1$ score of 0.96) at an acquisition rate $10times$ faster than the Thomson scattering measurement. The performance of the model is understood by examining a set of 4800 theoretical spectra generated using collisional radiative modeling that was also used to predict the performance of a low-cost spectrometer viewing nitrogen emission in the visible wavelengths. These results provide a proof-of-principle that low-cost spectrometers leveraged with machine learning can be used both to boost the performance of more expensive diagnostics on fusion devices, and be used independently as a fast and accurate $T_e$ measurement and detachment classifier.
Electron dynamics in Electron Cyclotron Resonance Ion Source is numerically simulated by using Particle-In-Cell code combined with simulations of the ion dynamics. Mean electron energies are found to be around 70 keV close to values that are derived from spectra of X-ray emission out of the source. Electron life time is defined by losses of low-energy electrons created in ionizing collisions; the losses are regulated by electron heating rate, which depends on magnitude of the microwave electric field. Changes in ion confinement with variations in the microwave electric field and gas flow are simulated. Influence of electron dynamics on the afterglow and two-frequency heating effects is discussed.