Ultrafiltration membranes from acrylonitrile copolymer were chemically modified with different concentrations of hydrogen peroxide (from 5 to 30% H 2 O 2 ). The amount of the amide groups in the modified membranes was determined. The water flow and permeability coefficients of the initial and modified membranes were also researched. The modified membranes were used as carriers for covalent immobilization of the dual enzyme system of glucose oxidase and catalase (GODϩCAT). It was found that the best matrices for immobilization of the dual system were membranes modified with 20% H 2 O 2 and the optimal activity ratio was GOD : CAT ϭ 1 : 5. The glucose conversion efficiency with the dual enzyme system was twice as high as that of bound GOD alone. Some of the basic characteristics (optimum pH, optimum temperature, pH, temperature stability, and storage stability) of the dual enzyme system were determined and compared with characteristics of free and bound enzymes. The catalytic parameters of the enzyme reaction (K m and V max ) were determined with GOD immobilized alone and with the dual system GODϩCAT. The higher rate observed with the dual enzyme system clearly showed the advantage and the efficiency of the immobilized system. Glucose oxidase without catalase was deactivated by H 2 O 2 more rapidly than the immobilized dual GODϩCAT system. These experimental evidences can be explained by the protecting effect of catalase on glucose oxidase from inhibition by H 2 O 2 .

Nanofibrous polyacrylonitrile membranes (PANNFM) were obtained by electrospinning and then prepared for immobilizing acetylcholinesterase (AChE). Initially, the chemical modification of PANNFM with ethylenediamine produced reactive groups to overcome their inertness and hydrophobicity. The natural polymer, chitosan, was then tethered on the nanofibrous membranes to improve their biocompatibility. Scanning electron microscopy (SEM) and cross-section SEM were used to determine morphological and porosity changes of the membranes. The immobilized AChE had greater relative activity as well as thermal and storage stability compared to the free enzyme. The bound AChE showed excellent reusability. Chitosan-modified PANNFM was shown to be a suitable strategy for facile immobilization of AChE to produce a promising system that effectively supports biocatalysts.

No abstract

A database of 140 diesel fuels having cetane numbers in the range of 10–70 points; densities at 15 °C; and distillation characteristics according to ASTM D-86 T 10%, T 50%, and T 90% was used to develop new procedures for predicting diesel cetane numbers by application of the least-squares method (LSM) using MAPLE software and an artificial neural network (ANN) using MATLAB. The existing standard methods of determining cetane-index values, ASTM D-976 and ASTM D-4737, which are correlations of the cetane number, confirmed the earlier conclusions that these methods predict the cetane number with a large variation. The four-variable ASTM D-4737 method was found to better approximate the diesel cetane number than the two-variable ASTM D-976 method. The developed four cetane-index models (one LSM and three ANN models) were found to better approximate the middle-distillate cetane numbers. Between 4% and 5% of the selected database of 140 middle distillates were samples with differences between their measured cetane numbers and the cetane-index values predicted by the four new procedures was higher than the specified reproducibility limit in the standard for measuring cetane number, ASTM D-613. In contrast, the cetane-index values calculated in accordance with standards ASTM D-976 and ASTM D-4737 demonstrated that 18% and 16% of the selected database of 140 middle distillates, respectively, were samples with differences between their measured cetane numbers and predicted cetane-index values higher than the specified reproducibility limit in standard ASTM D-613. The ASTM D-4737 method, LSM, and three ANN models were tested against 22 middle distillates not included in the database of 140 diesel fuels. The LSM cetane index showed the best cetane-number prediction capability among all of the models tested.

No abstract