Benchmark experiment of large-angle scattering reaction cross section of iron at 14 MeV using two shadow bars – Comparison of experimental results with ENDF/B-VIII –

ABSTRACT

It is important to perform neutron transport simulations with accurate nuclear data in the neutronics design of a fusion reactor. However, absolute values of large-angle scattering cross sections vary among nuclear data libraries even for well-examined nuclide of iron. Benchmark experiments focusing on large-angle scattering cross sections were thus performed to confirm the correctness of nuclear data libraries. The series benchmark experiments were performed at a DT neutron source facility, OKTAVIAN of Osaka University, Japan, by the unique experimental system established by the authors’ group, which can extract only the contribution of large-angle scattering reactions. This system consists of two shadow bars, target plate (iron), and neutron detector (niobium). Two types of shadow bars were used and four irradiations were conducted for one experiment, so that contribution of room-return neutrons was effectively removed and only large-angle scattering neutrons were extracted from the measured four Nb reaction rates. The obtained experimental results were compared with calculations for five nuclear data libraries including JENDL-4.0, JEFF.-3.3, FENDL-3.1, ENDF/B- VII, and recently released ENDF/B-VIII. It was found from the comparison that ENDF/B-VIII showed the best result, though ENDF/B-VII showed overestimation and others are in large underestimation at 14 MeV.     

Process Intensification of Living Cationic Polymerization of Isobutylene by a Flow Reaction System

Increasing the reaction temperature of the living cationic polymerization of isobutylene is crucial for industrial production due to the cost of refrigeration. The reaction temperature increase was achieved with an accelerated reaction rate using a flow reaction system. The polymerization conditions, including the flow reactor design, were based on the results of kinetic studies. Utilizing a milli‐scale flow reactor, polyisobutylene, which has a narrow molecular weight distribution, was obtained within a considerably short residence time at a high temperature. Furthermore, it was confirmed that the value of M_w/M_n correlates with the product of the Reynolds number and the angle of collision.     

Combustion synthesis of AlN doped with carbon and oxygen

Carbon-and-oxygen-doped AlN specimens were prepared by combustion synthesis using Al, graphite, and AlN. Graphite addition changed the product color from white to blue. By XRD, the lattice constant increased slightly with increasing carbon content. Blue AlN powder was synthesized with a molar ratio of the diluent AlN of 0.2-0.5 with a fixed graphite content of 0.05. At an AlN molar ratio exceeding 0.6, carbon was not successfully incorporated due to the lower reaction temperature. Calcination at 800°C in air removed residual graphite without changing the crystal structure or product color. Oxygen, nitrogen, and carbon analyses revealed that blue AlN powders contained 0.45-0.54 mass% carbon and 1.4-1.6 mass% oxygen, while the undoped AlN contained 0.021 mass% carbon and 0.94 mass% oxygen. The origin of the white-to-blue color change was investigated via reflection measurements. Blue AlN exhibits an absorption peak at 634 nm (1.96 eV). From first-principles electronic structure calculations, the C-doped AlN and carbon-and-oxygen-doped AlN with a 1:1 ratio could be classified as p-type, whereas the O-doped AlN and 1:3 carbon-and-oxygen-doped AlN were n-type. One reason for the absorption peak at 634 nm may be a transition from the conduction band to an upper unoccupied state. These results suggest the possible control of optical and electronic properties of AlN via carbon-and-oxygen doping.     

Fine-tuned,molecular-composite,organosilica membranes for highly efficient propylene/propane separation via suitable pore size

Fine-tuned, molecular-composite, organosilica membranes were fabricated via the co-condensation of organosilica precursors bis(triethoxysilyl)acetylene (BTESA) and bis(triethoxysilyl)benzene (BTESB). Fourier transform infrared and UV–vis spectra confirmed the co-condensation behaviors of BTESA and BTESB. The evolution of the network structure indicated that the incorporated BTESB decreased the membrane pore size, which was determined by a modified gas translation model according to the steric effect of the phenyl groups. The incorporation of BTESB to BTESA finely tuned the membrane structure and endowed the resultant composite membrane with improved separation properties. The BTESAB 9:1 membrane (molar ratio of BTESA/BTESB was 9:1) exhibited high C₃H₆ permeance at 4.5 × 10⁻⁸ mol m⁻² s⁻¹ Pa⁻¹ and a C₃H₆/C₃H₈ permeance ratio of 33 at 50°C. One of the most important developments of this study involved clearly defining the relationship between membrane pore size and C₃H₆/C₃H₈ separation performance for organosilica membranes in single and binary separation systems.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

Activity measurement of the constituents in molten Fe–B and Fe–B–C alloys

The distribution ratios of Fe and B between molten Fe–B alloy and molten Ag were measured at temperatures between 1573 and 1923 K. Also, distribution ratios of Fe and B between molten Fe–B–C_satd. alloys and molten Ag were measured at 1873 K. It was found that the excess Gibbs free energy of mixing in molten Fe–B and Fe–B–C alloys can be expressed by utilizing the Redlich–Kister polynomial. The activity curves of the elements in molten Fe–B alloy and Fe–B–C alloy were estimated.     

High-performance carbon counter electrode for dye-sensitized solar cells

Here, we reported that a new carbon electrode prepared with an activated carbon was superior to a Pt sputtered electrode as the counter electrode of dye-sensitized solar cells. The photovoltaic performance was largely influenced by the roughness factor of carbon electrode. The open-circuit voltage increased by about 60 mV using the carbon counter electrode compared to the Pt counter electrode because of positive shift of the formal potential for I₃⁻/I⁻ couple.     

Oligosaccharide structures formed during the hydrolysis of lactose by Aspergillus oryzae β-galactosidase

Di-, tri-, tetra-, penta- and hexasaccharides were formed during the hydrolysis of lactose by transgalactosylation reaction of Aspergillus oryzae β-galactosidase. In this study the isolation and characterization of the major constituents of tri-, tetra- and pentasaccharides are described. The structure elucidation of 3 tri-, 2 tetra- and 1 pentasaccharides was carried out by methylation analysis, mass spectrometry and ¹³C-nmr spectrometry. The trisaccharides are $\text{O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 3)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 4)-}\text{d}\text{-}\text{glucose}\text{(3′-}\text{galactosyl-lactose}\text{)}$, $\text{O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 4)-}\text{d}\text{-}\text{glucose}\text{(6′-}\text{galactosyl-lactose}\text{)}$ and $\text{O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 4)-O-[β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)]-}\text{d}\text{-}\text{glucose}\text{(4,6-}\text{digalactosyl-glucose}\text{)}$. Tetrasaccharides are $\text{O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 4)-}\text{d}\text{-}\text{glucose}$ and $\text{O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 3)}$ [or $\text{O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 3)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)]-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 4)-}\text{d}\text{-}\text{glucose}$. Pentasaccharide is $\text{O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 6)-O-β-}\text{d}\text{-}\text{galactopyranosyl}\text{-(1 → 4)-}\text{d}\text{-}\text{glucose}$.