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Inactivation of airborne bacteria by plasma treatment and ionic wind for indoor air cleaning
Airborne bacteria are a general problem in medical or health care facilities with a high risk for nosocomial infections. Rooms with a continuous airflow, such as operation theaters, are of particular importance due to a possible dissemination and circulation of pathogens including multidrug-resistant microorganisms. In this regard, a cold atmospheric-pressure plasma (CAP) may be a possibility to support usual disinfection procedures due to its decontaminating properties. The aim of this study was to determine the antimicrobial efficacy of a plasma decontamination module that included a dielectric barrier discharge for plasma generation. Experimental parameters such as an airflow velocity of 4.5 m/s and microbial contaminations of approximately 6,000 colony-forming units (cfu)/m3 were used to simulate practical conditions of a ventilation system in an operating theater. The apathogenic microorganism Escherichia coli K12 DSM 11250/NCTC 10538 and the multidrug-resistant strains E. coli 21181 and 21182 (isolated from patients) were tested to determine the antimicrobial efficacy. In summary, the number of cfu was reduced by 31–89% for the tested E. coli strains, whereby E. coli K12 was the most susceptible strain toward inactivation by the designed plasma module. A possible correlation between the number or kind of resistances and susceptibility against plasma was discussed. The inactivation of microorganisms was affected by plasma intensity and size of the plasma treatment area. In addition, the differences of the antimicrobial efficacies caused through the nebulization of microorganisms in front (upstream) or behind (downstream) the plasma source were compared. The presence of ionic wind had no influence on the reduction of the number of cfu for E. coli K12, as the airflow velocity was too high for a successful precipitation, which would be a prerequisite for an increased antimicrobial efficacy. The inactivation of the tested microorganisms confirms the potential of CAP for the improvement of air quality. The scale-up of this model system may provide a novel tool for an effective air cleaning process.
Low-Cost Laser-Acoustic PVC Identification System Based on a Simple Neural Network
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.