Mechanochemically functionalized and fibrillated microcrystalline cellulose as a filler in silicone foam: An integrated experimental and simulation investigation.

Fibrillated celluloses have gained significant attention due to their exceptional mechanical properties and eco-friendly characteristics, which make them suitable for various applications. In this study, we designed a precise approach for producing highly fibrillated microcrystalline cellulose (MCC) via ball-milling treatment using four typical silane coupling agents. The empirical data demonstrate that the fibrillization of MCC and the properties of fibrillated MCC are largely affected by the size and geometry of the functional groups of the silanes. After ball-milling, most MCC displayed enhanced e-beam tolerance and thermal stability, whereas the silane loading amount, surface area, and morphology of fibrillated MCC appeared to be random, which was exemplified by the proportional and non-proportional relationship between the loading amount and surface area of methyl silane- and phenyl silane-treated MCC, respectively. Density functional theory calculations and molecular dynamics simulations were employed to obtain the intricate details. The simulation results were in agreement with the experimental results. Finally, fibrillated MCC was incorporated into silicone foams as an additive. The thermal stability of fibrillated MCC with added silicone was greatly improved, and the tensile strength of fibrillated MCC-containing silicone foam was 44.1 and 5.4 times higher than that of the neat and MCC-containing silicone foams, respectively.

Synthesis of Superionic Conductive Li1+x+yAlxSiyTi2-xP3-yO12 Solid Electrolytes.

Commercial lithium-ion batteries using liquid electrolytes are still a safety hazard due to their poor chemical stability and other severe problems, such as electrolyte leakage and low thermal stability. To mitigate these critical issues, solid electrolytes are introduced. However, solid electrolytes have low ionic conductivity and inferior power density. This study reports the optimization of the synthesis of sodium superionic conductor-type Li1.5Al0.3Si0.2Ti1.7P2.8O12 (LASTP) solid electrolyte. The as-prepared powder was calcined at 650 °C, 700 °C, 750 °C, and 800 °C to optimize the synthesis conditions and yield high-quality LASTP powders. Later, LASTP was sintered at 950 °C, 1000 °C, 1050 °C, and 1100 °C to study the dependence of the relative density and ionic conductivity on the sintering temperature. Morphological changes were analyzed using field-emission scanning electron microscopy (FE-SEM), and structural changes were characterized using X-ray diffraction (XRD). Further, the ionic conductivity was measured using electrochemical impedance spectroscopy (EIS). Sintering at 1050 °C resulted in a high relative density and the highest ionic conductivity (9.455 × 10-4 S cm-1). These findings corroborate with the activation energies that are calculated using the Arrhenius plot. Therefore, the as-synthesized superionic LASTP solid electrolytes can be used to design high-performance and safe all-solid-state batteries.

Spin-Orbit Torque Switching in an All-Van der Waals Heterostructure.

Current-induced control of magnetization in ferromagnets using spin-orbit torque (SOT) has drawn attention as a new mechanism for fast and energy efficient magnetic memory devices. Energy-efficient spintronic devices require a spin-current source with a large SOT efficiency (ξ) and electrical conductivity (σ), and an efficient spin injection across a transparent interface. Herein, single crystals of the van der Waals (vdW) topological semimetal WTe2  and vdW ferromagnet Fe3 GeTe2 are used to satisfy the requirements in their all-vdW-heterostructure with an atomically sharp interface. The results exhibit values of ξ ≈ 4.6 and σ ≈ 2.25 × 105  Ω-1 m-1 for WTe2 . Moreover, the significantly reduced switching current density of 3.90 × 106 A cm-2 at 150 K is obtained, which is an order of magnitude smaller than those of conventional heavy-metal/ferromagnet thin films. These findings highlight that engineering vdW-type topological materials and magnets offers a promising route to energy-efficient magnetization control in SOT-based spintronics.

Deep-ultraviolet electroluminescence and photocurrent generation in graphene/hBN/graphene heterostructures.

Hexagonal boron nitride (hBN) is a van der Waals semiconductor with a wide bandgap of ~ 5.96 eV. Despite the indirect bandgap characteristics of hBN, charge carriers excited by high energy electrons or photons efficiently emit luminescence at deep-ultraviolet (DUV) frequencies via strong electron-phonon interaction, suggesting potential DUV light emitting device applications. However, electroluminescence from hBN has not been demonstrated at DUV frequencies so far. In this study, we report DUV electroluminescence and photocurrent generation in graphene/hBN/graphene heterostructures at room temperature. Tunneling carrier injection from graphene electrodes into the band edges of hBN enables prominent electroluminescence at DUV frequencies. On the other hand, under DUV laser illumination and external bias voltage, graphene electrodes efficiently collect photo-excited carriers in hBN, which generates high photocurrent. Laser excitation micro-spectroscopy shows that the radiative recombination and photocarrier excitation processes in the heterostructures mainly originate from the pristine structure and the stacking faults in hBN. Our work provides a pathway toward efficient DUV light emitting and detection devices based on hBN.

Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm.

The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve high-performance segmentation results in conjunctiva images with motion blur. Motion correction processes with two steps-registration and template matching-are used to correct for large displacements and fine movements. The image displacement values decrease to 4-7 µm during registration (first step) and less than 1 µm during template matching (second step). With the corrected images, the blood flow velocity is calculated for selected vessels considering temporal signal variances and vessel lengths. These methods for resolving motion artifacts contribute insights into studies quantifying the hemodynamics of the conjunctiva, as well as other tissues.