Colelocklear4112

Our ab initio modeling of excited state properties in 2D perovskites conveys material designing strategies to fine-tune perovskite emissions for solid-state lighting applications.Tip-enhanced Raman (TER) spectral images of 4-thiobenzonitrile-coated Au nanorods map the spatial profiles and trace the resonances of dipolar and multipolar plasmonic modes that are characteristic of the imaged particles. For any particular rod, we observe sequential transitions between high-order modes at low frequency shifts and lower-order modes at higher frequencies. We also notice that higher-order modes (up to m = 4) are generally observed for long rods as compared to their shorter analogues, where longitudinal dipolar resonances (m = 1) are observable. In effect, this work adds a new dimension to local optical field mapping via TERS, which we have previously explored. Not only can the magnitudes, vector components, local/nonlocal characters of local optical fields be imaged through molecular TERS, but spatially varying local optical resonances are also direct observables.Electrons in quantum materials exhibiting coexistence of dispersionless (flat) bands piercing dispersive (steep) bands can give rise to strongly correlated phenomena, and are associated with unconventional superconductivity. In twisted trilayer graphene steep Dirac cones can coexist with band flattening, but the phenomenon is not stable under layer misalignments. Here we show that such a twisted sandwiched graphene (TSWG) - a three-layer van der Waals heterostructure with a twisted middle layer - can have very stable flat bands coexisting with Dirac cones near the Fermi energy when twisted to 1.5◦. These flat bands require a specific high-symmetry stacking order, and our atomistic calculations predict that TSWG always relaxes to it. Additionally, external fields change the relative energy offset between the Dirac cone vertex and the flat bands, enhancing band hybridization and controlling correlated phases. Our work establishes twisted sandwiched graphene as a new platform for research into strongly interacting two-dimensional quantum matter.Engineering protein-based hydrogels that can change their physical and mechanical properties in response to environmental stimuli have attracted considerable interest due to their promising applications in biomedical engineering. Among environmental stimuli, temperature is of particular interest. Most thermally responsive protein hydrogels are constructed from thermally responsive elastin-like polypeptides (ELPs), which exhibit a lower critical solution temperature (LCST) transition, or nonstructured elastomeric proteins fused with ELPs. selleck products Here we report the engineering of thermally responsive elastomeric protein-based hydrogels by fusing ELPs to elastomeric proteins made of tandemly arranged folded globular proteins. By fusing ELP sequence (VPGVG)n to an elastomeric protein (GR)4, which is made of small globular protein GB1 (G) and random coil sequence resilin (R), we engineered a series of protein block copolymers, Vn-(GR)4. The fusion proteins Vn-(GR)4 exhibit temperature-responsive behaviors in aqueous soluhains can regulate thermoresponsiveness of protein-based hydrogels. We anticipate that this method can be applied to other elastomeric proteins for potential biomedical applications.Previous studies demonstrated that per- and polyfluoroalkyl substances (PFASs) can cross the human placental barrier. However, their transplacental transfer efficiencies (TTEs) have not been investigated in preterm delivery, and the role of placental transport proteins has rarely been explored. Our study hypothesized that the TTEs of PFASs could differ between preterm and full-term deliveries, and some placental transporters could be involved in active maternofetal PFAS transfer. In the present study, the median TTEs of 16 individual PFAS chemicals or isomers were determined to be 0.23 to 1.72 in matched maternal-cord serum pairs with preterm delivery (N = 86), which were significantly lower than those (0.35 to 2.26) determined in full-term delivery (N = 187). Significant associations were determined between the TTEs of several PFASs and the mRNA expression levels of selected transporters located on the brush border membrane. The association patterns also significantly differed between preterm and full-term deliveries and exhibited a chemical-specific manner. For example, the expression of MRP2 exhibited significantly positive associations with the TTEs of linear and branched perfluorooctanesulfonic acid (PFOS) isomers in full-term delivery, but negative, nonsignificant associations were observed in preterm delivery. This is the first study to compare the transplacental transfer of PFASs between preterm and full-term deliveries and indicate that some placental transport proteins could be involved in active transmission. The mechanisms underlying the cross-placental transfer of PFASs require further investigations to better elucidate their risks to fetal health and birth outcomes.Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 41. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. This CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.