Novel Ultra-High Power Microwave Isolators
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Abstract Lanthanide triflates have been used to incorporate Nd III and Sm III ions into the 2.2.2‐cryptand ligand (crypt) to explore their reductive chemistry. The Ln(OTf) 3 complexes (Ln=Nd, Sm; OTf=SO 3 CF 3 ) react with crypt in THF to form the THF‐soluble complexes [Ln III (crypt)(OTf) 2 ][OTf] with two triflates bound to the metal encapsulated in the crypt. Reduction of these Ln III ‐in‐crypt complexes using KC 8 in THF forms the neutral Ln II ‐in‐crypt triflate complexes [Ln II (crypt)(OTf) 2 ]. DFT calculations on [Nd II (crypt)] 2+ ], the first Nd II cryptand complex, assign a 4f 4 electron configuration to this ion.
Abstract The methylamino diazonium cations [CH 3 N(H)N 2 ] + and [CF 3 N(H)N 2 ] + were prepared as their low‐temperature stable [AsF 6 ] − salts by protonation of azidomethane and azidotrifluoromethane in superacidic systems. They were characterized by NMR and Raman spectroscopy. Unequivocal proof of the protonation site was obtained by the crystal structures of both salts, confirming the formation of alkylamino diazonium ions. The Lewis adducts CH 3 N 3 ⋅AsF 5 and CF 3 N 3 ⋅AsF 5 were also prepared and characterized by low‐temperature NMR and Raman spectroscopy, and also by X‐ray structure determination for CH 3 N 3 ⋅AsF 5 . Electronic structure calculations were performed to provide additional insights. Attempted electrophilic amination of aromatics such as benzene and toluene with methyl‐ and trifluoromethylamino diazonium ions were unsuccessful.
The reductive cyclization of two linear hexasilanes is contrasted, where a methylated precursor yielded a cyclohexasilane while the tert-butyl-functionalized analog unexpectedly yielded a cyclopentasilane. A comprehensive analysis using X-ray diffraction, IR, HR, HRMS, and multinuclear (1H, 13C and 29Si) 1D and 2D NMR spectroscopy identified the 1,2-dihydrodisilane tBuPhHSi–SiHPhtBu as an additional product, which was interpreted as supportive of a mechanism involving silylene elimination. Here, the results of this study may prove informative about substituent effects in the practice of complex organosilicon molecular synthesis.
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conference paper submission
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Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there has been international concern about the emergence of virus variants with mutations that increase transmissibility, enhance escape from the human immune response, or otherwise alter biologically important phenotypes. In late 2020, several variants of concern emerged globally, including the UK variant (B.1.1.7), the South Africa variant (B.1.351), Brazil variants (P.1 and P.2), and two related California variants of interest (B.1.429 and B.1.427). These variants are believed to have enhanced transmissibility. For the South Africa and Brazil variants, there is evidence that mutations in spike protein permit it to escape from some vaccines and therapeutic monoclonal antibodies. On the basis of our extensive genome sequencing program involving 20,453 coronavirus disease 2019 patient samples collected from March 2020 to February 2021, we report identification of all six of these SARS-CoV-2 variants among Houston Methodist Hospital (Houston, TX) patients residing in the greater metropolitan area. Although these variants are currently at relatively low frequency (aggregate of 1.1%) in the population, they are geographically widespread. Houston is the first city in the United States in which active circulation of all six current variants of concern has been documented by genome sequencing. Finally, as vaccine deployment accelerates, increased genomic surveillance of SARS-CoV-2 is essential to understanding the presence, frequency, and medical impact of consequential variants and their patterns and trajectory of dissemination.
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Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. Here, this paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.