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Register a new userPersonal Ultraviolet Respiratory Germ Eliminating Machine (PUR$diamond$GEM) for COVID-19: Prototype Development
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Nausheen R. Shah
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In this article we detail the prototype development of the Personal Ultraviolet Respiratory Germ Eliminating Machine(PUR$diamond$GEM) to safely, efficiently and economically, continuously disinfect inhaled/exhaled air using Ultraviolet (UV) radiation with possible 99.99% virus elimination. The PUR$diamond$GEM consists of a series of UV disinfection chambers through which constant airflow is maintained via fans. A minimum air flow rate of $sim20-30$ L/min is sufficient to keep CO$_2$ levels $lesssim$ 0.5% in the hood/helmet. We validated that using easily available PTFE wrap, a factor of $sim 18$ enhancement in UV power can be obtained in our spherical chambers. Detailed analysis is presented for the air travel time distributions through the cavities, and the expectation value for actual pathogen elimination is computed. We provide the scaling of pathogen elimination with the number of cavities in series, reflective enhancement, UV source power, sphere radius and airflow rate. We show that disinfection greater than 4-log is achievable for a series of three or more spheres. We 3D printed our prototype, consisting of two spheres of 10 cm diameter each in series for each direction of disinfection. Using UVC LEDs emitting $sim$ 40 mW of power each with an airflow rate of 30 L/min, actual SARS-COV2 virus elimination of $sim$ 98% is expected. While not manually feasible to construct smaller spheres in the lab, smaller cavities can be commercially manufactured, leading to significantly higher actual pathogen elimination, as well as reducing fingerprint and cost of cavity manufacture. Patent pending.
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The current COVID-19 pandemic has highlighted the need for cheap reusable personal protective equipment. The disinfection properties of Ultraviolet (UV) radiation in the 200-300 nm have been long known and documented. Many solutions using UV radiation, such as cavity disinfection and whole room decontamination between uses, are in use in various industries, including healthcare. Here we propose a portable wearable device which can safely, efficiently and economically, continuously disinfect inhaled/exhaled air using UV radiation with possible 99.99% virus elimination. We utilize UV radiation in the 260 nm range where no ozone is produced, and because of the self-contained UV chamber, there would be no UV exposure to the user. We have optimized the cavity design such that an amplification of 10-50 times the irradiated UV power may be obtained. This is crucial in ensuring enough UV dosage is delivered to the air flow during breathing. Further, due to the turbulent nature of airflow, a series of cavities is proposed to ensure efficient actual disinfection. The Personal Ultraviolet Respiratory Germ Eliminating Machine (PUR$diamond$GEM) can be worn by people or attached to devices such as ventilator exhausts/intakes, or be used free-standing as a portable local air disinfection unit, offering modularity with multiple avenues of usage. Patent pending.
The use of GEM foils for the amplification stage of a TPC instead of a con- ventional MWPC allows one to bypass the necessity of gating, as the backdrift is suppressed thanks to the asymmetric field configuration. This way, a novel continuously running TPC, which represents one option for the PANDA central tracker, can be realized. A medium sized prototype with a diameter of 300 mm and a length of 600 mm will be tested inside the FOPI spectrometer at GSI using a carbon or lithium beam at intermediate energies (E = 1-3AGeV). This detector test under realistic experimental conditions should allow us to verify the spatial resolution for single tracks and the reconstruction capability for displaced vertexes. A series of physics measurement implying pion beams is scheduled with the FOPI spectrometer together with the GEM-TPC as well.
This document contains the pre-design of the beam diagnostics components Tracking Detectors for the Super-FRS. A GEM-TPC detector has been suggested as suitable tracking detector for the ion/fragment beams produced at the in-flight separator Super-FRS under construction at the FAIR facility. The detector concept combines two widely used approaches in gas filled detectors, the Time Projection Chamber (TPC) and the Gas Electron Multiplication (GEM). Three detector generations (prototypes) have been tested in 2011, 2012 and 2014 with relativistic ion beams at GSI. Due to the high-resolution achromatic mode of the Super-FRS, highly homogeneous transmission tracking detectors are crucial to tag the momentum of the ion/fragment beam. They must be able to provide precise information on the (horizontal and vertical) deviation from nominal beam optics, while operated with slow-extracted beam on event-by event basis, in order to provide unambiguous identification of the fragments. The main requirements are a maximum active area horizontally and vertically of (380x80) mm2, a position resolution of < 1 mm, a maximum rate capability of 1 MHz, a dynamic range of about 600 fC. About 32 tracking detectors operating in vacuum are needed along the Super-FRS beam line.
After emerging in China in late 2019, the novel Severe acute respiratory syndrome-like coronavirus 2 (SARS-CoV-2) spread worldwide and as of early 2021, continues to significantly impact most countries. Only a small number of coronaviruses are known to infect humans, and only two are associated with the severe outcomes associated with SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a closely related species of SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Both of these previous epidemics were controlled fairly rapidly through public health measures, and no vaccines or robust therapeutic interventions were identified. However, previous insights into the immune response to coronaviruses gained during the outbreaks of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have proved beneficial to identifying approaches to the treatment and prophylaxis of novel coronavirus disease 2019 (COVID-19). A number of potential therapeutics against SARS-CoV-2 and the resultant COVID-19 illness were rapidly identified, leading to a large number of clinical trials investigating a variety of possible therapeutic approaches being initiated early on in the pandemic. As a result, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA) in the United States, and many other therapeutics remain under investigation. Here, we describe a range of approaches for the treatment of COVID-19, along with their proposed mechanisms of action and the current status of clinical investigation into each candidate. The status of these investigations will continue to evolve, and this review will be updated as progress is made.
Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
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