The effect of thiophene sequence separation on air-stable organic thin-film transistor materials

The relationship between thiophene sequences and organic thin-film transistor (OTFT) characteristics was studied to determine their effect on ionization potential, molecular orientation, and air stability. Two types of molecular structures were used: continuous sequence and divided sequence thiophenes. The length of thiophene sequence did not affect FET characteristics but did affect ionic potential and air stability. Furthermore, materials with divided thiophene sequences showed no change in OTFT characteristics when exposed to air. These results suggest that separation of thiophene sequences can improve air stability, which is a problem of thiophene-based materials.     

Optical Nanoscale Pool‐on‐Surface Design for Control Sensing Recognition of Multiple Cations

General design of optical chemical nanosensors is needed to develop efficient sensing systems with high flexibility, and low capital cost for control recognition of toxic analytes. Here, we designed optical chemical nanosensors for simple, high‐speed detection of multiple toxic metal ions. The systematic design of the nanosensors was based on densely patterned chromophores with intrinsic mobility, namely, “building‐blocks” onto three‐dimensional (3D) nanoscale structures. The ability to precisely modify the nanoscale pore surfaces by using a broad range of chromophores that have different molecular sizes and characteristics enables detection of multiple toxic ions. A key feature of this building‐blocks design strategy is that the surface functionality and good adsorption characteristics of the fabricated nanosensor arrays enabled the development of “pool‐on‐surface” sensing systems in which high flux of the metal analytes across the probe molecules was achieved without significant kinetic hindrance. Such a sensing design enabled sensitive recognition of metal ions up to sub‐picomolar detection limits (～10^?11 mol dm^?3), for first time, with rapid response time within few seconds. Moreover, because these sensing pools exhibited long‐term stability, reversibility and selectivity in detecting most pollutant cations, for example, Cr(VI), Pb(II), Co(II), and Pd(II) ions, they are practical and inexpensive. The key result in our study is that the pool‐on‐surface design for optical nanosensors exhibited significant ion‐selective ability of these target ions from environmental samples and waste disposals.     

Size dependence of THz region dielectric properties for barium titanate fine particles

Barium titanate (BaTiO₃) crystallites with various particle sizes from 22 to 500 nm were prepared by the two-step thermal decomposition method of barium titanyl oxalate. Various characterizations revealed that these particles were impurity-free, defect-free, dense BaTiO₃ particles. The powder dielectric measurement clarified that the dielectric constant of BaTiO₃ particles with a size of around 58 nm exhibited a maximum of over 15,000. To explain this size dependence, the THz region dielectric properties of BaTiO₃ fine particles, especially Slater mode frequency, were measured using the far infrared (FIR) reflection method. As the result, the lowest Slater mode frequency was obtained at 58 nm. This tendency was completely consistent with particle size dependence of the dielectric constant.     

Response of isotopically tailored titanium diboride to neutron irradiation

The response of polycrystalline TiB₂ to neutron irradiation was investigated. The material was fabricated using isotopically enriched ¹¹B powders to minimize helium production via the ¹⁰B(n, α)⁷Li reaction. Neutron irradiation was conducted at temperatures of ~200°C and ~600°C to a fast fluence of 2.4 × 10²⁵ n/m² (>0.1 MeV). The material exhibited some swelling, but less swelling at the higher irradiation temperature. No macroscopic damage was observed in the irradiated material, although moderate irradiation-induced micro-cracking was found in the irradiated TiB₂. This study demonstrated improved radiation resistance of isotopically tailored TiB₂ compared with natural boron TiB₂, which exhibited macroscopic fracture by irradiation.     

Protective Role of Glutathione in the Hippocampus after Brain Ischemia

Stroke is a major cause of death worldwide, leading to serious disability. Post-ischemic injury, especially in the cerebral ischemia-prone hippocampus, is a serious problem, as it contributes to vascular dementia. Many studies have shown that in the hippocampus, ischemia/reperfusion induces neuronal death through oxidative stress and neuronal zinc (Zn²⁺) dyshomeostasis. Glutathione (GSH) plays an important role in protecting neurons against oxidative stress as a major intracellular antioxidant. In addition, the thiol group of GSH can function as a principal Zn²⁺ chelator for the maintenance of Zn²⁺ homeostasis in neurons. These lines of evidence suggest that neuronal GSH levels could be a key factor in post-stroke neuronal survival. In neurons, excitatory amino acid carrier 1 (EAAC1) is involved in the influx of cysteine, and intracellular cysteine is the rate-limiting substrate for the synthesis of GSH. Recently, several studies have indicated that cysteine uptake through EAAC1 suppresses ischemia-induced neuronal death via the promotion of hippocampal GSH synthesis in ischemic animal models. In this article, we aimed to review and describe the role of GSH in hippocampal neuroprotection after ischemia/reperfusion, focusing on EAAC1.     

Mechanical property degradation of high crystalline SiC fiber–reinforced SiC matrix composite neutron irradiated to ∼100 displacements per atom

For the development of silicon carbide (SiC) materials for next-generation nuclear structural applications, degradation of material properties under intense neutron irradiation is a critical feasibility issue. This study evaluated the mechanical properties and microstructure of a chemical vapor infiltrated SiC matrix composite, reinforced with a multi-layer SiC/pyrolytic carbon–coated Hi-Nicalon^TM Type S SiC fiber, following neutron irradiation at 319 and 629?°C to ～100 displacements per atom. Both the proportional limit stress and ultimate flexural strength were significantly degraded as a result of irradiation at both temperatures. After irradiation at 319?°C, the quasi-ductile fracture behavior of the nonirradiated composite became brittle, a result that was explained by a loss of functionality of the fiber/matrix interface associated with the disappearance of the interphase due to irradiation. The specimens irradiated at 629?°C showed increased apparent failure strain because the fiber/matrix interphase was weakened by irradiation-induced partial debonding.     

High-dose,intermediate-temperature neutron irradiation effects on silicon carbide composites with varied fiber/matrix interfaces

SiC/SiC composites are promising structural candidate materials for various nuclear applications over the wide temperature range of 300–1000 °C. Accordingly, irradiation tolerance over this wide temperature range needs to be understood to ensure the performance of these composites. In this study, neutron irradiation effects on dimensional stability and mechanical properties to high doses (11–44 dpa) at intermediate irradiation temperatures (?600 °C) were evaluated for Hi-Nicalon Type-S or Tyranno-SA3 fiber–reinforced SiC matrix composites produced by chemical vapor infiltration. The influence of various fiber/matrix interfaces, such as a 50–120 nm thick pyrolytic carbon (PyC) monolayer interphase and 70–130 nm thick PyC with a subsequent PyC (?20 nm)/SiC (?100 nm) multilayer, was evaluated and compared with the previous results for a thin-layer PyC (?20 nm)/SiC (?100 nm) multilayer interphase. Four-point flexural tests were conducted to evaluate post-irradiation strength, and SEM and TEM were used to investigate microstructure. Regardless of the fiber type, monolayer composites showed considerable reduction of flexural properties after irradiation to 11–12 dpa at 450–500 °C; and neither type showed the deterioration identified at the same dose level at higher temperatures (>750 °C) in a previous study. After further irradiation to 44 dpa at 590–640 °C, the degradation was enhanced compared with conventional multilayer composites with a PyC thickness of ?20 nm. Multilayer composites have shown comparatively good strength retention for irradiation to ?40 dpa, with moderate mechanical property degradation beginning at 70–100 dpa. Irradiation-induced debonding at the F/M interface was found to be the major cause of deterioration of various composites.     

Bottom-up synthesis of oxygen-containing carbon materials using a Lewis acid catalyst

Oxygen-containing carbon materials have been studied extensively because of their excellent dispersibility, absorptivity, separability, and supportability of catalysts. However, structural control by existing top-down methods is almost impossible. Our group has demonstrated that phloroglucinol (PG, 1,3,5-trihydroxybenzene) can be a promising raw material to synthesize structurally controlled oxygen-containing carbon materials. In this study, in addition to PG, hexahydroxybenzene (HHB), which has more oxygen and high symmetry, was used as the raw material, and a Lewis acid catalyst, tris (pentafluorophenyl) borane (TPB), was used to enhance the structural control rate and the removability of catalysts from the carbonized samples. The solubility of heat-treated HHB was lower than that of heat-treated PG, but the oxygen content of heat-treated HHB was higher than that of heat-treated PG even at 673 K. By adding TPB to PG, dibenzofuran-like structures formed, and the structural control rate increased up to 93.6%. Besides, the content of fluorine in the catalyst was reduced to 0%, indicating that TPB can be a promising recyclable catalyst to promote the structural control rate of carbonized PG.

Graphical abstract

     

ADAPTIVE LEARNING CONTROL OF CUTTING PARAMETERS FOR SCULPTURED SURFACE CUTTING BASED ON GENETIC ALGORITHMS AND NEURAL NETWORK

An adaptive learning control scheme intended to the on-line optimization of sculptured surface cutting process is presented. The scheme uses a back-propagation neural network to learn the relationships between process inputs and process states. The cutting parameters of the process model are optimized through a genetic algorithms(GA). The capacity of the proposed scheme for determining optimum process inputs under a variety of process conditions and optimization strategies is evaluated on the basis of milling of a sculptured surface using a ball-end mill. The experimental results show that the neural network could model the cutting process efficiently, and the cutting conditions such as spindle speed could be regulated for achieving high efficiency and high quality. Therefore the proposed approach can be well applied to the manufacturing of dies and molds.