Notes

Modulation associated with PPAR signaling interferes with pancreas development in the actual zebrafish, Danio rerio.
Twisted multilayer graphene (tMLG), in contrast to twisted bilayer graphene, offers a range of angular rotations for tuning the properties of the system. In this work, a turbostratic graphene system with a high degree of two-dimensional (2D) crystallinity is chosen to represent tMLG. We have investigated the distribution and population of twist angles from distributed sextets in electron diffraction (SAED) patterns with the collective Raman behavior at the same locations. A descriptor, termed the turbostratic factor, was calculated on the basis of angular spacings in SAEDs, to account for their distribution; the greater the spread, the higher the turbostratic factor. Raman spectra have revealed that the turbostratic factor remains low (∼0°) for a graphitic region with a low 2D to G intensity ratio (I2D/IG) and increases rapidly at higher I2D/IG values, saturating at 60° for highly turbostratic systems. Relating the intensities associated with the sextets and I2D/IG values, we found the maximum achievable value of I2D/IG to be 17.92.As a thermodynamically stable semiconductor material, black phosphorus (BP) has potential application in the field of energy storage and conversion. The preparation of black phosphorus is still limited to the laboratory, which is far from adequate to meet the requirements of future industrial applications. Here, the gram-scale black phosphorus is synthesized in the ethylenediamine medium using a 120-200 °C low-temperature recyclable liquid phase method directly from red phosphorus. A crystallization mechanism from red to black phosphorus based on FTIR, XPS, and DFT calculations is proposed. Black phosphorus as the anode material for lithium ion batteries is superior in discharge specific capacity, rate capability, and cycling stability in comparison with red phosphorus. Autophagy activator The facile low-temperature synthesis of BP by the ethylenediamine-assisted liquid phase process will facilitate the extended application of BP in the field of energy storage and conversion.Transition metal dichalcogenides (TMDs) have attracted great interest owing to their fascinating properties with atomically thin nature. Although TMDs have been exploited for diverse applications, the effective role of TMDs in synthesis of metal nanowires has not been explored. Here, we propose a new approach to synthesize ultrathin metal nanowires using TMDs for the first time. High-quality ultrathin nanowires with an average diameter of 11.3 nm are successfully synthesized, for realizing high performance transparent conductor exhibiting excellent conductivity and transparency with low haze. The growth mechanism is carefully investigated using high-resolution transmission electron microscopy, and growth of nanowires with tunable diameters is achieved by controlling the nanosheet dimension. Finally, we unravel the important role of TMDs acting as both reducing and nucleating agents. Therefore, our work provides a new strategy of TMD as an innovative material for the growth of metal nanowire as a promising building block in next-generation optoelectronics.Narrow-spectrum antimicrobials specifically eradicate the target pathogens but suffer from significantly lagging development. Photodynamic therapy eliminates cells with reactive oxygen species (ROS) generated upon light irradiation but is intrinsically a wide-spectrum modality. We herein converted photodynamic therapy into a narrow-spectrum modality by taking advantage of a previously unnoticed physics recognition pathway. We found that negatively charged nanospheres undergo selective entropy gain-driven adsorption onto spherical bacteria, but not onto rod-like bacteria. This bacterial morphology-targeting selectivity, combined with the extremely limited effective radii of action of ROS, enabled photodynamic nanospheres to kill >99% of inoculated spherical bacteria upon light irradiation and less then 1% of rod-like bacteria under comparable conditions, indicative of narrow-spectrum activity against spherical bacteria. This work unveils the bacterial morphology selectivity in the adsorption of negatively charged nanospheres and suggests a new approach for treating infections characterized by overthriving spherical bacteria in niches naturally dominated by rod-like bacteria.The activation energy (EA) spectra of the potential energy landscape (PEL) provide a convenient perspective for interpreting complex phenomena in amorphous materials; however, the link between the EA spectra and other physical properties in metallic glasses is still mysterious. By systematically probing the EA spectra for numerous metallic glass samples with distinct local geometric ordering, which correspond to broad processing histories, we found that the shear moduli of the samples are strongly correlated with the arithmetic mean of the EA spectra rather than with the local geometrical ordering. Furthermore, we studied the correlation of the obtained EA spectra and various well-established physical parameters. The outcome of our research clearly demonstrates that the soft-mode parameter Ψ and the EA spectrum are correlated; therefore, this could be a good indicator of metallic glass properties and sheds important light on the structure-property relationship in metallic glass through the medium of the PEL.The binding site and energy is an invaluable descriptor in high-throughput screening of catalysts, as it is accessible and correlates with the activity and selectivity. Recently, comprehensive binding energy prediction machine-learning models have been demonstrated and promise to accelerate the catalyst screening. Here, we present a simple and versatile representation, applicable to any deep-learning models, to further accelerate such process. Our approach involves labeling the binding site atoms of the unrelaxed bare surface geometry, hence, for the model application, DFT calculations can be completely removed if the optimized bulk structure is available as is the case when using the Materials Project database. In addition, we present ensemble learning, where a set of predictions are used together to form a predictive distribution which reduces the model bias. We apply the labeled site approach and ensemble to crystal graph convolutional neural network and the ~40,000 data set of alloy catalysts for CO2 reduction.
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