RESUMEN
Hydrodynamics, which generally describes the flow of a fluid, is expected to hold even for fundamental particles such as electrons when inter-particle interactions dominate1. Although various aspects of electron hydrodynamics have been revealed in recent experiments2-11, the fundamental spatial structure of hydrodynamic electrons-the Poiseuille flow profile-has remained elusive. Here we provide direct imaging of the Poiseuille flow of an electronic fluid, as well as a visualization of its evolution from ballistic flow. Using a scanning carbon nanotube single-electron transistor12, we image the Hall voltage of electronic flow through channels of high-mobility graphene. We find that the profile of the Hall field across the channel is a key physical quantity for distinguishing ballistic from hydrodynamic flow. We image the transition from flat, ballistic field profiles at low temperatures into parabolic field profiles at elevated temperatures, which is the hallmark of Poiseuille flow. The curvature of the imaged profiles is qualitatively reproduced by Boltzmann calculations, which allow us to create a 'phase diagram' that characterizes the electron flow regimes. Our results provide direct confirmation of Poiseuille flow in the solid state, and enable exploration of the rich physics of interacting electrons in real space.
RESUMEN
The exchange bias phenomenon, inherent in exchange-coupled ferromagnetic and antiferromagnetic systems, has intrigued researchers for decades. Van der Waals materials, with their layered structures, offer an ideal platform for exploring exchange bias. However, effectively manipulating exchange bias in van der Waals heterostructures remains challenging. This study investigates the origin of exchange bias in MnPS3/Fe3GeTe2 van der Waals heterostructures, demonstrating a method to modulate nearly 1000% variation in magnitude through simple thermal cycling. Despite the compensated interfacial spin configuration of MnPS3, a substantial 170 mT exchange bias is observed at 5 K, one of the largest observed in van der Waals heterostructures. This significant exchange bias is linked to anomalous weak ferromagnetic ordering in MnPS3 below 40 K. The tunability of exchange bias during thermal cycling is attributed to the amorphization and changes in the van der Waals gap during field cooling. The findings highlight a robust and adjustable exchange bias in van der Waals heterostructures, presenting a straightforward method to enhance other interface-related spintronic phenomena for practical applications. Detailed interface analysis reveals atom migration between layers, forming amorphous regions on either side of the van der Waals gap, emphasizing the importance of precise interface characterization in these heterostructures.
RESUMEN
With 6.93M confirmed cases of COVID-19 worldwide, making individuals aware of their sanitary health and ongoing pandemic remains the only way to prevent the spread of this virus. Wearing masks is an important step in this prevention. Hence, there is a need for monitoring if people are wearing masks or not. Closed circuit television (CCTV) cameras endowed with computer vision function by embedded systems, have become popular in a wide range of applications, and can be used in this case for real time monitoring of people wearing masks or not. In this paper, we propose to model this task of monitoring as a special case of object detection. However, real-time scene parsing through object detection running on edge devices is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, we used a few popular object detection algorithms such as YOLOv3, YOLOv3Tiny, SSD and Faster R-CNN and evaluated them on Moxa3K benchmark dataset. The results obtained from these evaluations help us to determine methods that are more efficient, faster, and thus are more suitable for real-time object detection specialized for this task.