Feasibility of zeolites in converting butyric acid into propylene for biorefineries

The biorefinery has been recognized as a new industry to produce both energy and chemical materials such as olefins and BTX from renewable resources. In this context the conversion of butyric acid over zeolites was investigated for establishing a new production route of propylene. Propylene was mainly generated by decarbonylation and dehydration of butyric acid. Our study proved that H-ZSM-5 (750) and silicalite were the best industrial catalyst among the tested ones. For H-ZSM-5 (750), the selectivity of propylene reached 64.2 C% and the ratio of the yield for propylene to theoretical yield (75 C%) became 85.6%.     

Connection‐Strength Estimation of Neuronal Networks by Fitting for Izhikevich Model

Recently, there has been abundant research using multineuron recording, but there are many problems with extracting the features from the obtained spike time series, which are huge in volume and complex. Here we introduce a new method of estimating synaptic connection strengths between neurons by fitting to the Izhikevich model by maximum likelihood estimation. We demonstrate that our method can estimate connection strengths from spike time series given by a simulated neural ensemble and can estimate nonconnectivity between two independent cultured neuronal networks. These results suggest that our method is applicable to network and plasticity analysis of neuronal networks.     

Investigation of the Aroma Profile of Green Tea Leaves Using Organic Synthesis and Conventional Analytical Techniques

Although many aroma components have been identified in green tea leaves, the aroma compounds contributing to green tea＇s characteristic odor have not yet been reported. The authors recently reported that aroma components with a matcha-like odor are present in both green tea and black tea prepared from the Sayamakaori tea cuttivar. This matcha-like odor is similar to the odor of commercial available matcha （high-quality powdered green tea）, and is a specific odor feature of green tea leaves. At present, the green-tea odor is thought to arise from the combination of a large number of constituents. Recent reports indicate that a complex interaction between olfactory receptors and odorants is important for the evaluation of the odors. Taking into consideration these findings, the authors investigated the aroma profile of green tea, focusing on the characteristic molecular structures of the constituents that give matcha-like odor. Using a combination of organic synthesis and gas chromatography-mass spectrometry plus gas chromatography-olfactometry, the authors identified aroma components with matcha-like odors in five other tea cultivars. This investigation also revealed that several compounds with a formyl group were important constituents of the aroma of green tea leaves, although the odor of each constituent was not individually similar to the tea＇s overall aroma. The authors found for the first time a group of key components that have the matcha-like odor.     

Experimental investigation of melt pool behaviour during selective laser melting by high speed imaging

Melt pool behaviour and the characteristics of the surrounding metal powder during selective laser melting (SLM) are captured using high-speed imaging, and the influence of the substrate temperature and the line building sequence on the formation of the built structure and the generation of spatter particles is experimentally investigated. The results indicate that the substrate temperature has a measurable effect on the formation of droplets and the generation of spatter particles, while the volume specific energy density of the process was the principal factor affecting the built structure.     

Probabilistic enhancement of approximate indexing in metric spaces

Some approximate indexing schemes have been recently proposed in metric spaces which sort the objects in the database according to pseudo-scores. It is known that (1) some of them provide a very good trade-off between response time and accuracy, and (2) probability-based pseudo-scores can provide an optimal trade-off in range queries if the probabilities are correctly estimated. Based on these facts, we propose a probabilistic enhancement scheme which can be applied to any pseudo-score based scheme. Our scheme computes probability-based pseudo-scores using pseudo-scores obtained from a pseudo-score based scheme. In order to estimate the probability-based pseudo-scores, we use the object-specific parameters in logistic regression and learn the parameters using MAP (Maximum a Posteriori) estimation and the empirical Bayes method. We also propose a technique which speeds up learning the parameters using pseudo-scores. We applied our scheme to the two state-of-the-art schemes: the standard pivot-based scheme and the permutation-based scheme, and evaluated them using various kinds of datasets from the Metric Space Library. The results showed that our scheme outperformed the conventional schemes, with regard to both the number of distance computations and the CPU time, in all the datasets.     

New high‐luminance 50‐ and 43‐in. ACPDPs with an improved panel structure

New 50‐ and 43‐in. ACPDPs, which have been developed and commercialized in 2001, show high luminance with improved cell structure and higher Xe‐content gas. The specific features of the cell structure are “T”‐shaped electrodes and waffle‐structured ribs, which are same as those of the previous model. Both the cell structure and gas conditions have been optimized. New green and blue phosphors have also been adopted. As a result, the luminous efficacy has been improved up to 1.8 lm/W by using a black stripe. The peak luminance of the 50‐ and 43‐in. PDPs have reached 900 and 1000 cd/m², respectively, while the power consumption of the 50‐in. PDP has been decreased to 380 W, which is 20% lower than that of our previous 50‐in. PDP.     

Cationic or anionic sites? Selectivity optimization of ion-selective electrodes based on charged ionophores

The influence of ionic sites on the selectivities of ionophore-based ion-selective electrodes (ISEs) is described on the basis of a phase boundary potential model. The discussion presented here is significantly more general than previous ones. It is formulated for primary and interfering ions of any charges and it is valid for ISEs based on electrically charged or neutral ionophores. Furthermore, it also applies to membranes that contain more than one type of complex of the primary or interfering ion. It has been believed thus far that only ionic sites of the same charge sign as the primary ion improve the selectivities of ISEs based on charged ionophores. However, it is shown here that the charge sign of the ionic sites that give the highest potentiometric selectivities depends on the charge number of the primary and interfering ions and on the stoichiometry of their complexes with the ionophore. The validity of our model was confirmed experimentally with three ISEs based on different charged ionophores. ISEs based on lasalocid or 1-(N,N-dicyclohexylcarbamoyl)-2- (N,N-dioctadecylcarbamoyl)ethylphosphonic acid monomethyl ester (ETH 5639) as the ionophore responded selectively to Sr2+ or Mg2+, respectively, and discriminated well against other alkaline earth cations when their membranes contained anionic sites. These two electrodes are the first examples of ISEs based on charged ionophores for which maximum selectivities are obtained with membranes containing ionic sites with a charge sign opposite to that of the primary ion. On the other hand, the experimental F- selectivities of membranes based on oxo(5,10,15,20-tetraphenylporphyrinato)molybdenum-(V) improved gradually when the concentration of anionic sites was increased from 0 to 75 mol%. The selectivity-modifying influence of ionic sites for these three types of ISEs can be explained by considering the different stabilities of the 1:2 ion-ionophore complexes of the primary and of the interfering ions.