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Familism as well as mental well-being between Latinx junior: The role regarding parental alcohol use.
The microwave spectrum of the fruit ester methyl valerate was recorded using two molecular jet Fourier transform spectrometers covering the frequency range from 2 to 40 GHz. Quantum chemical calculations yielded 11 minima for the anti ester configuration, among them two were identified in the experimental spectrum. The methyl group in the methoxy moiety undergoes internal rotation, leading to torsional splittings of all rotational transitions into doublets. The barrier to internal rotation of the methoxy methyl group was deduced to be 417.724(70) cm-1 and 418.059(27) cm-1 for the C1 and the Cs conformer, respectively, essentially the same values as those in methyl alkanoates with shorter alkyl chains, which are methyl acetate, methyl propionate and methyl butyrate. Geometry parameters such as the rotational and centrifugal distortion constants could be determined with very high accuracy. Optimisations at different levels of theory were performed for a comparison with the experimental results. The MP2/6-311++G(d,p) level of theory failed to calculate reliable rotational constants to guide the assigment of the C1 conformer, while the MP2/cc-pVDZ level fully succeeded.Silk fibroin (SF) based hydrogels have been exploited for years for their inherent biocompatibility and favorable mechanical properties which makes them interesting for biotechnology applications. In this study we investigate silk based composite hydrogels where pH-sensitive, anionic biosurfactant assemblies (sophorolipids SL-C18  1 and SL-C18  0), are employed to improve the present properties of SF. Results suggest that the presence of SL surfactant assemblies leads to faster gelling of SF by accelerating the refolding from random coil to β-sheet as shown by infrared and UV-visible spectroscopy. Small angle neutron scattering (SANS) including contrast matching studies show that SF and SL assemblies coexist in a fibrillary network that is, in the case of SL-C18  0, interpenetrating. The resulting overall network structure in composite gels is slightly more affected by SL-C18  1 than by SL-C18  0, whereas the structure of both SF and surfactant assemblies remains unchanged. No disassembly of SL surfactant structures is observed, which gives a new perspective on SF-surfactant interactions. The hydrophobic effect within SF is favored in the presence of SL, leading to faster refolding of SF into β-sheet conformation. The presented composite gels, being an interpenetrating network of which one compound (SL-C18  0) can be tweaked by pH, open an interesting option towards improved workability and stimuli responsive mechanical properties of SF based hydrogels with possible applications in controlled cell culture and tissue engineering or drug delivery. The presented SANS analysis approach has the potential to be expanded to other protein-surfactant systems and composite hydrogels.Halogens cause pronounced and systematic effects on the 13C NMR chemical shift (δ13C) of an adjacent carbon nucleus, usually leading to a decrease in the values across the halogen series. Although this normal halogen dependence (NHD) is known in organic and inorganic compounds containing the carbon atom in its neutral and cationic forms, information about carbanions is scarce. To understand how δ13C changes in molecules with different charges, the shielding mechanisms of CHX3, CX3+, and CX3- (X = Cl, Br, or I) systems are investigated via density functional theory calculations and further analyzed by decomposition into contributions of natural localized molecular orbitals. An inverse halogen dependence (IHD) is determined for the anion series as a result of the negative spin-orbit contribution instead of scalar paramagnetic effects. The presence of a carbon nonbonding orbital in anions allows magnetic couplings that generate a deshielding effect on the nucleus and contradicts the classical association between δ13C and atomic charge.C-reactive protein (CRP) is one of the most commonly used biomarkers for inflammation. The standardisation of a procedure for the detection of CRP has attracted significant attention globally, and primary reference materials of CRP based on the recombinant expression of E. coli that exist in the form of monomers have been developed. However, a primary reference material of natural CRP is still required to achieve the exact matching of CRP measurements in secondary reference materials (e.g. CRP in frozen human serum). Herein, the development process for a certified reference material of natural CRP is reported, namely GBW09228. The raw material employed in this study was CRP extracted and purified from human body fluid, and exhibits a natural and verified pentameric structure. Through the use of amino acid analysis isotope dilution mass spectrometry (AAA-IDMS) and signature peptide-IDMS, this reference material was certified, and its certification results can be traced to SI units. The developed method was evaluated for its accuracy using the international comparison tests of the National Metrology Institute of Japan (NMIJ) and the Korea Research Institute of Standards and Science (KRISS). Overall, a CRP primary certified reference material (CRM) of well-characterised purity was determined that could be used to calibrate an IDMS-based reference method, that could then be used to assign target values to secondary CRMs. These secondary CRMs could in turn be used to calibrate and verify the accuracy of immunoassays, thereby giving a good foundation for establishing a complete traceability chain for CRP.Optical design plays an important role in improving the performance of opto-electronic devices. However, conventional design processes using finite difference time domain (FDTD) or finite element methods are usually time and computing resource consuming, and often result in sub-optimal solutions due to an incomplete search of the parameter state space. In this paper, we propose a deep learning approach to predict and optimize the cell performance of perovskite/crystalline-silicon (c-Si) tandem solar cells. In particular, a deep neural network is established to predict the achievable short-circuit current for tandem solar cells with a given cell structure. After training on a FDTD numerical simulation data set, the proposed deep neural network achieves an accuracy of 98.3% and micro-second grade simulation time, which is an ultra-fast, highly accurate and computing resource-saving solution to investigate the current properties of tandem solar cells. Tamoxifen Heuristic algorithms are further adopted to inversely optimize the device structure, where the optimal set of layer thicknesses is obtained to maximize the achievable short-circuit current.
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