Monolayer WS2 has been a competitive candidate in electrical and optoelectronic devices due to its superior optoelectronic properties. To tackle the challenge of thermal management caused by the decreased size and concentrated heat in modern ICs, it
is of great significance to accurately characterize the thermal conductivity of the monolayer WS2, especially with substrate supported. In this work, the dual-wavelength flash Raman method is used to experimentally measure the thermal conductivity of the suspended and the Si/SiO2 substrate supported monolayer WS2 at a temperature range of 200 K - 400 K. The room-temperature thermal conductivity of suspended and supported WS2 are 28.45 W/mK and 15.39 W/mK, respectively, with a ~50% reduction due to substrate effect. To systematically study the underlying mechanism behind the striking reduction, we employed the Raman spatial mapping analysis combined with the molecular dynamics simulation. The analysis of Raman spectra showed the increase of doping level, reduction of phonon lifetime and suppression of out-of-plane vibration mode due to substrate effect. In addition, the phonon transmission coefficient was mutually verified with Raman spectra analysis and further revealed that the substrate effect significantly enhances the phonon scattering at the interface and mainly suppresses the acoustic phonon, thus leading to the reduction of thermal conductivity. The thermal conductivity of other suspended and supported monolayer TMDCs (e.g. MoS2, MoSe2 and WSe2) were also listed for comparison. Our researches can be extended to understand the substrate effect of other 2D TMDCs and provide guidance for future TMDCs-based electrical and optoelectronic devices.
Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mit
igate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.
This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming pow
er, even when the implant moves around inside the human body. To accomplish this, we exploit the unique energy ball pattern of distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile, the power levels on other body parts stay in low level, reducing the risk of overheating. We prototype In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Extensive experiments demonstrate that In-N-Out achieves 0.37~mW average charging power inside a 10~cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4$times$--18.1$times$ power gain when the implant is stationary, and 5.3$times$--7.4$times$ power gain when the implant is in motion.
