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search.filters.applied.f.access: openAccess × Start date: 2021 × DDC: 620 × Publisher: Basel : MDPI × WGL Institute: INP ×

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    Development of a Mobile Sensory Device to Trace Treatment Conditions for Various Medical Plasma Source Devices
    (Basel : MDPI, 2022) Chaerony Siffa, Ihda; Gerling, Torsten; Masur, Kai; Eschenburg, Christian; Starkowski, Frank; Emmert, Steffen
    The emerging use of low-temperature plasma in medicine, especially in wound treatment, calls for a better way of documenting the treatment parameters. This paper describes the development of a mobile sensory device (referred to as MSD) that can be used during the treatment to ease the documentation of important parameters in a streamlined process. These parameters include the patient’s general information, plasma source device used in the treatment, plasma treatment time, ambient humidity and temperature. MSD was developed as a standalone Raspberry Pi-based version and attachable module version for laptops and tablets. Both versions feature a user-friendly GUI, temperature–humidity sensor, microphone, treatment report generation and export. For the logging of plasma treatment time, a sound-based plasma detection system was developed, initially for three medically certified plasma source devices: kINPen® MED, plasma care®, and PlasmaDerm® Flex. Experimental validation of the developed detection system shows accurate and reliable detection is achievable at 5 cm measurement distance in quiet and noisy environments for all devices. All in all, the developed tool is a first step to a more automated, integrated, and streamlined approach of plasma treatment documentation that can help prevent user variability.
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    Low-Cost Laser-Acoustic PVC Identification System Based on a Simple Neural Network
    (Basel : MDPI, 2022) Timmermann, Eric; Geißler, Philip; Bansemer, Robert
    Desktop laser cutters are an affordable and flexible rapid-prototyping tool, but some materials cannot be safely processed. Among them is polyvinyl chloride (PVC), which users usually cannot distinguish from other, unproblematic plastics. Therefore, an identification system for PVC applicable in a low-cost laser cutter has been developed. For the first time, this approach makes use of the laser-ablative sound generated by a low-power laser diode. Using a capacitor microphone, a preprocessing algorithm and a very simple neural network, black PVC could be detected with absolute reliability under ideal conditions. With ambient noise, the accuracy dropped to 80%. A different color of the material did not influence the accuracy to detect PVC, but a susceptibility of the method against a color change was found for other materials. The ablation characteristics for different materials were recorded using a fast-framing camera to get a better insight into the mechanisms behind the investigated process. Although there is still potential for improvements, the presented method was found to be promising to enhance the safety of future desktop laser cutters.