Formation of bromo-substituted triclosan during chlorination by chlorine in the presence of trace levels of bromide

The side reactions of triclosan (2,4,4'-trichloro-2'-hydroxydiphenyl ether, TC) and chlorine in the presence of sodium chloride were investigated. In the absence of sodium chloride, three chloro-derivatives of TC, 2',3,4,4'-tetrachloro-2-hydroxydiphenyl ether (3-Cl-TC), 2',4,4',5-tetrachloro-2-hydroxydiphenyl ether (5-Cl-TC), and 2',3,4,4',5-pentachloro-2-hydroxydiphenyl ether (3,5-Cl(2)-TC) were formed, whereas in the presence of sodium chloride, 3-bromo-2',4,4'-trichloro-2-hydroxydiphenyl ether (3-Br-TC), 5-bromo-2',4,4'-trichloro-2-hydroxydiphenyl ether (5-Br-TC), (3 or 5)-bromo-2',4,4',(5 or 3)-chloro-2-hydroxydiphenyl ether ((3,5)-(BrCl)-TC), and 3,5-dibromo-2',4,4'-trichloro-2-hydroxydiphenyl ether (3,5-Br(2)-TC) were additionally formed. Radiochemical neutron activation analysis indicated that 1g of commercially available sodium chloride contained 73 microg of bromide and the bromide ion was determined to be the source of the side reactions. The rate of decrease of TC due to reaction with chlorine was greatly accelerated by the presence of bromide ion in the system: the rate with only 1 x 10(-5) M bromide ion was three times the rate in the absence of bromide.     

Improving visualisation and prediction performance of supervised self-organising map by modified contradiction resolution

We have previously proposed a new type of information-theoretic method where a neuron is evaluated by itself (self-evaluation) and by its surrounding neurons (outer-evaluation). If contradiction between different types of evaluation exists, it is reduced as much as possible. In the present paper, we try to separate self- and outer-evaluation more explicitly and introduce the importance of neurons. First, we separate self- and outer-evaluation to enhance the characteristics shared by the two types of evaluation. Second, we introduce the importance of neurons in evaluation. By using a limited number of important neurons in evaluation, we expect the main characteristics in input patterns to emerge. We applied this contradiction resolution to two types of data, namely, the Senate data and the Euro-yen exchange rates. In both data sets, experimental results confirmed that improved prediction performance was obtained. Prediction performance was better than that obtained by the conventional self-organising map (SOM) and radial basis function networks. In addition, final representations obtained by contradiction resolution were easier to interpret than those given by the conventional SOM. Experimental results confirmed that improved interpretation and visualisation were accompanied by improved prediction performance.     

New approach for synthesis of activated carbon from bamboo

Unconventional pretreatment, that is, delignification and the addition of guanidine phosphate, was performed for the synthesis of activated carbon having a high specific surface area from bamboo by physical activation. The values of the specific surface area, total pore volume, and average pore size depended on the amount of added guanidine phosphate and the CO₂ activation time. The amount of the added guanidine phosphate under the optimum conditions for the highest specific surface area was much lower than that of the phosphorous acid chemical activator under conventional conditions. The N₂ adsorption isotherms of all the samples were type I, which means that micropores were dominant. The pore sizes of the samples in this study were similar to that of the physically-activated carbon. Therefore, the activation process was presumed to be essentially not chemical, but physical. The relation between the yield and the specific surface area improved with the addition of guanidine phosphate. The reason for the improvement may be the change in the reactivity of the carbon material generated during the heating process. The maximum specific surface area was ca. 2000 m² g^?1, which is a high value for a physically-activated carbon.     

Flexible feature discovery and structural information control

In this paper, we propose a new information theoretic method called structural information control for flexible feature discovery. The new method has three distinctive characteristics, which traditional competitive learning fails to offer. First, the new method can directly control competitive unit activation patterns, whereas traditional competitive learning does not have any means to control them. Thus, with the new method, it is possible to extract salient features not discovered by traditional methods. Second, competitive units compete witheach other by maximizing their information content about input patterns. Consequently, this information maximization makes it possible to control flexibly competition processes. Third, in structural information control, it is possible to define many different kinds of information content, and we can choose a specific type of information according to a given objective. When applied to competitive learning, structural information can be used to control the number of dead or spare units, and to extract macro as well as micro features of input patterns in explicit ways. We first applied this method to simple pattern classification to demonstrate that information can be controlled and that different neuron firing patterns can be generated. Second, a dipole problem was used to show that structural information could provide representations similar to those by the conventional competitive learning methods. Finally, we applied the method to a language acquisition problem in which networks must flexibly discover some linguistic rules by changing structural information. Especially, we attempted to examine the effect of the information parameter to control the number of dead neurons, and thus to examine how macro and micro features in input patterns can explicitly be discovered by structural information.     

Determination of optical purity and absolute configuration of natural 12‐hydroxystearic acid by the 1H NMR anisotropy method

The enantiopure of (R)‐(?) MαNPA was allowed to react with racemic 18‐(tert‐butyldimethylsilyloxy)‐5‐octadecayne‐7‐ol which was derived from dodecane‐1,12‐diol, yielding diastereomeric esters mixture. These racemic esters were easily separated by HPLC on silica‐gel. The absolute configurations of the first‐eluted diastereomeric esters from the separated esters were determined using ¹H NMR anisotropy method. Analysis on ¹H NMR spectra and HPLC elution time of the synthesized esters and those of MαNP ester derived from natural methyl‐12‐hydroxystealate showed that the absolute configuration of natural 12‐hydroxystearic acid was R form, and the enantiomeric excess was over 99%.     

Input information maximization for improving self-organizing maps

In this paper, we propose a new type of information-theoretic method called “input information maximization” to improve the performance of self-organizing maps. We consider outputs from input neurons by focusing on winning neurons. The outputs are based on the difference between input neurons and the corresponding winning neurons. Then, we compute the uncertainty of input neurons by normalizing the outputs. Input information is defined as a decrease in the uncertainty of input neurons from a maximum and observed value. When input information increases, fewer input neurons tend to be activated. In the maximum state, only one neuron is on, and all others are off. We applied the method to two data sets, namely, the Senate and voting attitude data sets. In both, experimental results confirmed that when input information increased, quantization and topographic errors decreased. In addition, clearer class structure could be extracted by increasing input information. In comparison to our previous methods to detect the importance of input neurons, the present method turned out to be good at producing faithful representations with much more simplified computational procedures.     

Raman spectra of high‐density,low‐density,and linear low‐density polyethylene pellets and prediction of their physical properties by multivariate data analysis

Raman spectra have been measured for pellets of five samples of high‐density polyethylene (HDPE), seven samples of low‐density polyethylene (LDPE), and six samples of linear low‐density polyethylene (LLDPE). The obtained Raman spectra have been compared to find out characteristic Raman bands of HDPE, LDPE, and LLDPE. Principal component analysis (PCA) was applied to the Raman spectra in the 1600–650 cm^?1 region after multiplicative scatter correction (MSC) to discriminate the Raman spectra of the three different PE species. They are classified into three groups by a score plot of PCA factor 1 vs. 2. HDPE with high density and high crystallinity gives high scores on the factor 1 axis, while LDPE with low density and low crystallinity yields negative scores on the same axis. It seems that factor 1 reflects the density or crystallinity. A PC weight loadings plot for factor 1 shows six upward peaks corresponding to the bands arising from the crystalline parts or all‐trans ? (CH₂)_n? groups and seven downward peaks ascribed to the bands of the amorphous or anisotropic regions and those arising from the short branches. Partial least‐squares (PLS‐1) regression was applied to the Raman spectra after MSC to propose calibration models that predict the density, crystallinity, and melting points of the polyethylenes. The correlation coefficient was calculated to be 0.9941, 0.9800, and 0.9709 for the density, crystallinity, and melting point, respectively, and their root‐mean‐square error of cross validation (RMSECV) was found to be 0.0015, 3.3707, and 2.3745, respectively. The loadings plot of factor 2 for the prediction of melting point is largely different from those for the prediction of density and crystallinity. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 443–448, 2002