Kavitationsdetektion mittels self-sensing-ultraschallwandler

Many processes in chemistry can be enhanced by ultrasound assistance. In many cases ultrasound induced cavitation is the main reason for these enhancements. Therefore, it is desired to quantify cavitation activity during the process to optimize sonication for various processes and monitor cavitation activity throughout the process. One possibility to monitor cavitation activity is to measure the acoustic emissions of oscillating and collapsing cavitation bubbles by hydrophones in the liquid. However, harsh environments often coming along with chemical processes complicate the application of sensors in the liquid. Thus, this contribution discusses the applicability of the feedback of cavitation on the driving signals of the ultrasound transducer itself as possible alternative for cavitation monitoring. The measurement results show that the threshold of inertial cavitation could be detected based on the current signal of the transducer. Some indicators can even be used to distinguish between the two types of cavitation. However, to evaluate the strength of cavitation the application of a cavitation sensor is recommended

Controlled Sonication as a Route to in-situ Graphene Flake Size Control

Ultrasonication is widely used to exfoliate two dimensional (2D) van der Waals layered materials such as graphene. Its fundamental mechanism, inertial cavitation, is poorly understood and often ignored in ultrasonication strategies resulting in low exfoliation rates, low material yields and wide flake size distributions, making the graphene dispersions produced by ultrasonication less economically viable. Here we report that few-layer graphene yields of up to 18% in three hours can be achieved by optimising inertial cavitation dose during ultrasonication. We demonstrate that inertial cavitation preferentially exfoliates larger flakes and that the graphene exfoliation rate and flake dimensions are strongly correlated with, and therefore can be controlled by, inertial cavitation dose. Furthermore, inertial cavitation is shown to preferentially exfoliate larger graphene flakes which causes the exfoliation rate to decrease as a function of sonication time. This study demonstrates that measurement and control of inertial cavitation is critical in optimising the high yield sonication-assisted aqueous liquid phase exfoliation of size-selected nanomaterials. Future development of this method should lead to the development of high volume flow cell production of 2D van der Waals layered nanomaterials

Deep learning with R for beginners: design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep models using R