Virological analysis is time-consuming and expensive. The aim of this work is to demonstrate the applicability of laser-induced fluorescence (LIF) to the classification of viruses, reducing the time for this analysis and its costs. Experimental tests were performed in which different viruses were irradiated with a UV laser emitting at 266 nm and the emitted spectra were recorded by a spectrometer. The classification techniques show the possibility of discriminating viruses. Although the application of the LIF technique to biological agents has been thoroughly studied by many researchers over the years, this work aims at validating for the first time its applicability to virological analyses. The development of a fast virological analysis may revolutionize this field, allowing fast responses to epidemiologic events, reducing their risks and improving the efficiency of monitoring environments. Moreover, a cost reduction may lead to an increase in the monitoring frequency, with an obvious enhancement of safety and prevention.
This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by drone propellers may influence the response of the sensors. Our initial focus was the detection of a small amount of radioactivity, such as that associated with a release of medical waste. Drones equipped with selective low-cost sensors could be quickly sent to dangerous areas that first responders might not have access to and be able to assess the level of danger in a few seconds, providing details about the source terms to Radiological-Nuclear (RN) advisors and decision-makers. Our ultimate application is the simulation of complex scenarios where fluid-dynamic instabilities are combined with elevated levels of radioactivity, as was the case during the Chernobyl and Fukushima nuclear power plant accidents. In similar circumstances, accurate mapping of the radioactive plume would provide invaluable input-data for the mathematical models that can predict the dispersion of radioactivity in time and space. This information could be used as input for predictive models and decision support systems (DSS) to get a full situational awareness. In particular, these models may be used either to guide the safe intervention of first responders or the later need to evacuate affected regions.
Abstract:The choice of materials for the future nuclear fusion reactors is a crucial issue. In the fusion reactors, the combination of very high temperatures, high radiation levels, intense production of transmuting elements and high thermomechanical loads requires very high-performance materials. Erosion of PFCs (Plasma Facing Components) determines their lifetime and generates a source of impurities (i.e., in-vessel tritium and dust inventories), which cool down and dilute the plasma. The resuspension of dust could be a consequences of LOss of Coolant Accidents (LOCA) and LOss of Vacuum Accidents (LOVA) and it can be dangerous because of dust radioactivity, toxicity, and capable of causing an explosion. These characteristics can jeopardize the plant safety and pose a serious threat to the operators. The purpose of this work is to determine the experimental and numerical steeps to develop a numerical model to predict the dust resuspension consequences in case of accidents through a comparison between the experimental results taken from campaigns carried out with STARDUST-U and the numerical simulation developed with CFD codes. The authors in this work will analyze the candidate materials for the future nuclear plants and the consequences of the resuspension of its dust in case of accidents through the experience with STARDUST-U.
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