Spatio-Temporal Plasma Afterglow Induces Additional Neutral Drag Force on Microparticles

An emerging topic in complex plasma physics is the interaction between dust particles and afterglow plasmas. Control of plasma-particle interactions and specifically of the particle trajectories is especially relevant for plasma based contamination control applications. In systems where this contamination control is relevant, emerging or applied plasmas can be of highly transient nature, due to which contaminating particles interact with a combination of a spatial and a temporal afterglow plasma. Until now this type of plasmas and the possible interaction with embedded microparticles has remained far from fully explored in literature. In this work we visually record falling microparticles in a spatio-temporal afterglow of a low pressure inductively coupled plasma and observe a sudden and temporary reversal in their vertical velocity. Numerical simulations confirm that this effect is due to the cooling of the heated background gas in the former active plasma region, which creates a pressure wave and causes microparticles in the spatial afterglow to experience an additional neutral drag force in direction of the plasma bulk. Besides being an interesting principle phenomenon, the presence of this effect could have added value for developing plasma-driven particle contamination control applications. Moreover, for a well defined vacuum vessel geometry and plasma heating volume, this enables the use of microparticles in the spatio-temporal afterglow as probe for the neutral gas temperature in plasma

Charge neutralisation of microparticles by pulsing a low-pressure shielded spatial plasma afterglow

In this paper, it is shown that microparticles can be effectively neutralised in the (spatial) plasma afterglow of an inductively coupled plasma. A key element in the reported experiments is the utilisation of a grounded mesh grid separating the plasma bulk and the 'shielded' plasma afterglow. Once particles-being injected in and charged by the inductively coupled plasma-had passed this mesh grid, the plasma was switched off while the particles continued to be transported under the influence of both flow and gravity. In the shielded spatial plasma afterglow region, the particle charge was deducted from their acceleration in an externally applied electric field. Our experiments demonstrate that all particles were neutralised independently of the applied electric field magnitude. The achieved neutralisation is of primary importance for the further development of plasma-assisted contamination control strategies as well as for a wide range of other applications, such as colourimetric sensing, differential mobility analysers, and medical applications

The charge of micro-particles in a low pressure spatial plasma afterglow

In this letter, we present charge measurements of micro-particles in the spatial afterglow (remote plasma) of an inductively coupled low pressure radiofrequency plasma. The particle afterglow charge of (-30 ± 7) e, being deducted from their acceleration in an externally applied electric field, is about three orders of magnitude lower compared to the typical charge expected in the bulk of such plasmas. This difference is explained by a relatively simplistic analytical model applying orbital motion limited theory in the afterglow region. From an application perspective, our results enable further understanding and development of in situ plasma-based particle contamination control for ultra-clean low pressure environments

Charge of clustered microparticles measured in spatial plasma afterglows follows the smallest enclosing sphere model

The plasma-induced charge of non-spherical microparticles is a crucial parameter in complex plasma physics, aerosol science and astrophysics. Yet, the literature describes this charge by two competing models, neither of which has been experimentally verified or refuted. Here we offer experimental proof that the charge on a two-particle cluster (doublet) in the spatial afterglow of a low-pressure plasma equals the charge that would be obtained by the smallest enclosing sphere and that it should therefore not be based on its geometrical capacitance but rather on the capacitance of its smallest enclosing sphere. To support this conclusion, the size, mass and charge of single particles (singlets) and doublets are measured with high precision. The measured ratio between the plasma-afterglow-induced charges on doublets and singlets is compared to both models and shows perfect agreement with the predicted ratio using the capacitance of the smallest enclosing sphere, while being significantly dissimilar to the predicted ratio based on the particle’s geometrical capacitance

Vision-Based Machine Learning in Robot Soccer

Robots need to perceive their environment in order to properly interact with it. In the RoboCup Soccer Middle Size League (MSL) this happens primarily through cameras mounted on the robots. Machine Learning can be used to extract relevant features from camera imagery. The real-time analysis of camera data is a challenge for both traditional and Machine Learning algorithms, since all computations in the MSL have to be performed on the robot itself.This contribution shows that it is possible to process camera imagery in real-time using Machine Learning. It does this by presenting the current state of Machine Learning in MSL and providing two examples that won the Scientific and Technical Challenges at RoboCup 2021. Both examples focus on semantic detection of objects and humans in imagery. The Scientific Challenge winner presents how YOLOv5 can be used for object detection in the MSL. The Technical Challenge winner demonstrates how to improve interaction between robots and humans in soccer using OpenPose. This contributes towards the goal of RoboCup to arrive at robots that can beat the human soccer world champion by 2050

Vision-Based Machine Learning in Robot Soccer

Robots need to perceive their environment in order to properly interact with it. In the RoboCup Soccer Middle Size League (MSL) this happens primarily through cameras mounted on the robots. Machine Learning can be used to extract relevant features from camera imagery. The real-time analysis of camera data is a challenge for both traditional and Machine Learning algorithms, since all computations in the MSL have to be performed on the robot itself. This contribution shows that it is possible to process camera imagery in real-time using Machine Learning. It does this by presenting the current state of Machine Learning in MSL and providing two examples that won the Scientific and Technical Challenges at RoboCup 2021. Both examples focus on semantic detection of objects and humans in imagery. The Scientific Challenge winner presents how YOLOv5 can be used for object detection in the MSL. The Technical Challenge winner demonstrates how to improve interaction between robots and humans in soccer using OpenPose. This contributes towards the goal of RoboCup to arrive at robots that can beat the human soccer world champion by 2050