Development of Human-Safe, Miniature Far-UVC Microplasma Lamps for Personal Protective Respirators
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Aims To prospectively evaluate the incidence of myocardial injury after the administration of the fourth dose BNT162b2 mRNA vaccine (Pfizer‐BioNTech) against COVID‐19. Methods and results Health care workers who received the BNT162b2 vaccine during the fourth dose campaign had blood samples collected for high‐sensitivity cardiac troponin (hs‐cTn) during vaccine administration and 2–4 days afterward. Vaccine‐related myocardial injury was defined as hs‐cTn elevation above the 99th percentile upper reference limit and >50% increase from baseline measurement. Participants with evidence of myocardial injury underwent assessment for possible myocarditis. Of 324 participants, 192 (59.2%) were female and the mean age was 51.8 ± 15.0 years. Twenty‐one (6.5%) participants had prior COVID‐19 infection, the mean number of prior vaccine doses was 2.9 ± 0.4, and the median time from the last dose was 147 (142–157) days. Reported vaccine‐related adverse reactions included local pain at injection site in 57 (17.59%), fatigue in 39 (12.04%), myalgia in 32 (9.88%), sore throat in 21 (6.48%), headache in 18 (5.5%), fever ≥38°C in 16 (4.94%), chest pain in 12 (3.7%), palpitations in 7 (2.16%), and shortness of breath in one (0.3%) participant. Vaccine‐related myocardial injury was demonstrated in two (0.62%) participants, one had mild symptoms and one was asymptomatic; both had a normal electrocardiogram and echocardiography. Conclusion In a prospective investigation, an increase in serum troponin levels was documented among 0.62% of healthy health care workers receiving the fourth dose BNT162b2 vaccine. The two cases had mild or no symptoms and no clinical sequela. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT05308680.
This project is about flux line pinning in high T c superconductors using columnar defects as the pinning centers. The basic idea is that linear structural defects can pin the flux lines within HTSCs so that their critical current density (the maximal current density that these HTSCs can carry with zero resistance) can be enhanced. The question is how to create such linear structural defects within crystalline cuprate thin films. Scientists have already successfully demonstrated the concept by irradiating HTSC crystals with high-energy ions at synchrotron beamline facilities. However, such heavy ion irradiation is practically not viable for large-scale processes. Finally, we reasoned that maybe we could introduce some sort of linear nanoscopic objects into HTSC to form a composite, and then these linear nanoscopic objects would serve as the magnetic flux line pinning centers.
Dr. Amy Elliott has had unique opportunities to serve as a STEM role model through her various media production projects. Her debut on screen was as a competitor on an engineering reality show competition called “The Big Brain Theory,” aired by The Discovery Channel. Following this competition, Dr. Elliott served as an on-camera host for RoboNation’s water-based, annual collegiate competitions and for The Science Channel’s viral video show, “Outrageous Acts of Science.” Dr. Elliott also serves as volunteer pit-crew for Green Envy Racing, a woman-lead race team that seeks to set and speed records with electric motorcycles. Because of Dr. Elliott’s public exposure, she was selected as an IF/THEN Ambassador by the American Associate for the Advancement of Science (AAAS), a program that seeks to highlight women as STEM role models. Dr. Elliott’s work in 3D printing is frequently a source of inspiration for her.
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Here, in this work, hydrophobic magnetic deep eutectic solvents (HMDESs) were used in the development of a simple and rapid dispersive liquid-liquid microextraction (DLLME) approach coupled to high performance liquid chromatography with UV detection (HPLC-UV) for the determination of ten organic contaminants including five polycyclic aromatic hydrocarbons, four UV filters, and a pesticide from water at trace levels. The HMDESs were prepared by mixing a hydrogen bond acceptor, metal halide salt, and hydrogen bond donor in suitable molar ratios. Two HMDESs, 2 tetraoctylammonium bromide ([N 8888 + ][Br - ]): cobalt chloride (CoCl 2 ): 4 octanoic acid (OA) and 3 trioctylphosphine oxide (TOPO): neodymium chloride (NdCl 3 ): 3 OA, offered the highest analyte extraction efficiency overall and were chosen as suitable solvents for validation of the microextraction method. Under optimized extraction conditions, the method required 30 µL of HMDES as extraction solvent, acetone (87.5 µL) as disperser solvent, a NaCl concentration of 30% (w/v), and an extraction time of 120 s at 20°C. Enrichment factors of the analytes ranged from 44.6 for 3-(4-methylbenzylindene) camphor to 66.0 for 2-ethylhexyl-4-(dimethyl)aminobenzoate. The method provided low limits of detection (LODs) ranging from 0.5 to 4.5 µg L -1 , and acceptable precision, with RSD values lower than 9.6%. Furthermore, the validated method was successfully applied for tap and lake water analysis, resulting in relative recoveries of spiked samples ranging between 94.7 and 119.2%.
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While past research has shown that providing residents with feedback about their electricity usage can reduce demand and its associated environmental burdens, some questions remain regarding what makes such feedback most effective. We followed the electricity usage of 36 residents who each received 14 feedback messages over 2 months. Using approaches borrowed from Natural-Language-Processing, feedbacks were generated automatically, using 10 features in random combinations. Unlike in previous studies, each resident received varying types of messages over time. In 504 observations, the average prompted reduction in electricity usage was 11 ± 3%, compared to a control group of 89 residents who received no messages. Feedback types prompting the largest reductions were self-comparisons with one’s own earlier usage (average reduction 14%) and messages of high variety from one feedback-cycle to the next (average reduction 16%). Comparisons with neighbors did not prompt higher reductions on average. Instead, they prompted reductions only when a resident’s recent usage happened to be higher than the average usage of neighbors, and increases when the reverse was true. Finally, this behavior was exhibited by all residents and is likely explained by a norm-conforming mean reversion of residents to their neighbors’ average usage, rather than an anti-conform “boomerang” behavior previously suggested in similar contexts.
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