Self-protected nanoscale thermometry based on spin defects in silicon carbide

Quantum sensors with solid state electron spins have attracted considerable interest due to their nanoscale spatial resolution.A critical requirement is to suppress the environment noise of the solid state spin sensor.Here we demonstrate a nanoscale thermometer based on silicon carbide (SiC) electron spins.We experimentally demonstrate that the performance of the spin sensor is robust against dephasing due to a self protected machenism. The SiC thermometry may provide a promising platform for sensing in a noisy environment ,e.g. biological system sensing

High-efficiency generation of nanoscale single silicon vacancy defect array in silicon carbide

Color centers in silicon carbide have increasingly attracted attention in recent years owing to their excellent properties such as single photon emission, good photostability, and long spin coherence time even at room temperature. As compared to diamond which is widely used for holding Nitrogen-vacancy centers, SiC has the advantage in terms of large-scale, high-quality and low cost growth, as well as advanced fabrication technique in optoelectronics, leading to the prospects for large scale quantum engineering. In this paper, we report experimental demonstration of the generation of nanoscale $V_{Si}$ single defect array through ion implantation without the need of annealing. $V_{Si}$ defects are generated in pre-determined locations with resolution of tens of nanometers. This can help in integrating $V_{Si}$ defects with the photonic structures which, in turn, can improve the emission and collection efficiency of $V_{Si}$ defects when it is used in spin photonic quantum network. On the other hand, the defects are shallow and they are generated $\sim 40nm$ below the surface which can serve as critical resources in quantum sensing application

Multi-device wind turbine power generation forecasting based on hidden feature embedding

In recent years, the global installed capacity of wind power has grown rapidly. Wind power forecasting, as a key technology in wind turbine systems, has received widespread attention and extensive research. However, existing studies typically focus on the power prediction of individual devices. In the context of multi-turbine scenarios, employing individual models for each device may introduce challenges, encompassing data dilution and a substantial number of model parameters in power generation forecasting tasks. In this paper, a single-model method suitable for multi-device wind power forecasting is proposed. Firstly, this method allocates multi-dimensional random vectors to each device. Then, it utilizes space embedding techniques to iteratively evolve the random vectors into representative vectors corresponding to each device. Finally, the temporal features are concatenated with the corresponding representative vectors and inputted into the model, enabling the single model to accomplish multi-device wind power forecasting task based on device discrimination. Experimental results demonstrate that our method not only solves the data dilution issue and significantly reduces the number of model parameters but also maintains better predictive performance. Future research could focus on using more interpretable space embedding techniques to observe representation vectors of wind turbine equipment and further explore their semantic features

Metallic surface states in a correlated d-electron topological Kondo insulator candidate FeSb2

The resistance of a conventional insulator diverges as temperature approaches zero. The peculiar low temperature resistivity saturation in the 4f Kondo insulator (KI) SmB6 has spurred proposals of a correlation-driven topological Kondo insulator (TKI) with exotic ground states. However, the scarcity of model TKI material families leaves difficulties in disentangling key ingredients from irrelevant details. Here we use angle-resolved photoemission spectroscopy (ARPES) to study FeSb2, a correlated d-electron KI candidate that also exhibits a low temperature resistivity saturation. On the (010) surface, we find a rich assemblage of metallic states with two-dimensional dispersion. Measurements of the bulk band structure reveal band renormalization, a large temperature-dependent band shift, and flat spectral features along certain high symmetry directions, providing spectroscopic evidence for strong correlations. Our observations suggest that exotic insulating states resembling those in SmB6 and YbB12 may also exist in systems with d instead of f electrons

Quantitative assessment of otitis media with effusion with scanning laser Doppler vibrometer - A preliminary study

Otitis media with effusion (OME) is a common middle ear inflammation for preschool-age children, with the presence of fluid in the middle ear cavity (MEC). Although the biomechanical abnormalities induced by OME has been evaluated by many studies through measuring the vibration pattern of the tympanic membrane (TM), the precise and quantitative assessment of severity at different stages of OME is still not fully achieved, and the diagnosis still largely depends on the subjective rating of physicians. To provide reliable and objective evidence for the diagnosis, we developed a new algorithm to process the full-field TM surface motion data acquired by scanning laser Doppler vibrometer (SLDV). The frequency bands and areas on the TM highly sensitive to the effusion amount and viscosity in the MEC were identified.Oklahoma Louis Stokes Alliance for Minority Participation ProgramNational Science Foundation (U.S.)Aerospace and Mechanical Engineerin