Facile modifications of polyimide via chloromethylation: II. Synthesis and characterization of thermocurable transparent polyimide having methylene acrylate side groups

From chloromethylated polyimide, a useful starting material for modification of aromatic polyimides, a thermocurable transparent polyimide having acrylate side groups was prepared. In the presence of 1,8‐diazabicyclo[5,4,0]undec‐7‐ene, chloromethylated polyimide was esterified with acrylic acid to synthesize poly(imide methylene acrylate). The polymer was soluble in organic solvent, which makes it possible to prepare a planar film by spin coating. The polymer film became insoluble after thermal treatment at 230 °C for 30 min. Optical transparency of the film at 400 nm (for 1 µm thickness) was higher than 98 % and not affected by further heating at 230 °C for 250 min. Adhesion properties measured by the ASTM D3359‐B method ranged from 4B to 5B. Preliminary results of planarization testing showed a high degree of planarization (DOP) value (>0.53). These properties demonstrate that poly(imide methylene acrylate) could be utilized as a thermocurable transparent material in fabricating display devices such as TFT‐LCD. Copyright © 2004 Society of Chemical Industry     

Holographic polymer‐dispersed liquid crystal fabrication under electric field

The transmission mode of holographic polymer‐dispersed liquid crystals (HPDLCs) was developed an under electric field. It is reported that orientation of LC molecules under an electric field induces orientation of oligomer molecules giving rise to low off‐state diffraction and small grating shrinkage. Copyright © 2005 Society of Chemical Industry     

Reliability and performance analysis of time – space – time switching networks with multiple separated space switches

Since electronic switching systems usually require very strict reliability requirements as well as good performance objectives, we need to jointly analyse the performance and reliability of switching systems. In this paper, we compare conventional time–space–time switches with single space switches with those with multiple separated space switches, from the viewpoints of reliability and performance. We consider time–space–time switching networks which consist of N incoming time switches, i.e. one NxN space switch, two (N/2)x(N/2) space switches, and four (N/4)x(N/4) space switches. We introduce a Markov reliability model to study the effect of failures and analyse the reliability and performance of three different types of switching networks in terms of average blocking probability and the mean time to unreliable operation, as we vary the offered traffic. As a result, T–S–T switching networks with multiple separated space switches exhibit better performance and reliability than those with single space switches.     

Formation of C4 species in the deactivation of a Pd/SiO2 catalyst during the selective hydrogenation of acetylene

The deactivation characteristics of Pd/SiO₂ in the selective hydrogenation of acetylene were correlated with changes in the amount of the C4 species produced. The amounts of butenes produced changed in parallel with the catalyst activity, indicating that the rate limiting step for butene production was the same as that for acetylene hydrogenation. On the other hand, the amount of 1,3-butadiene produced changed, showing a maximum with catalyst deactivation because 1,3-butadiene is an intermediate in the sequential reaction process which involves both the production and consumption of 1,3-butadiene. This was verified by a simultaneous TG/reaction experiment showing that 1,3-butadiene was a precursor of green oil. The catalyst showed a self-regenerative behavior in its activity and the amounts of C4 species produced during the early stage of deactivation because two opposite factors, which contributed to either the lowering or the promotion of activity, were involved in the process. A specific type of polymer species, produced during the initial period of deactivation, is proposed to be responsible for the promotion of catalyst activity.     

Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods.     

Extraction of penicillin G from simulated media by an emulsion liquid membrane process

The extraction of penicillin G from simulated media was performed by water/oil/water (w/o/w) emulsion liquid membranes (ELMs) and studied under various operational conditions in a batch system. The degree of extraction achieved was between 80% and 95% under specific conditions. A concentration of greater than nine times the initial concentration of penicillin G in the external phase was obtained in the internal phase. The pH of the internal aqueous solution, containing a basic salt, was theoretically calculated on the basis of the amount of penicillin G transported into the internal phase. The calculated results agreed with the experimental data well and were used to select a suitable type and concentration of a basic salt in the internal phase to give a pH within the range 5 to 8 where penicillin G was stable after the termination of extraction. The extraction of penicillin G was successfully performed by the ELM process with sodium carbonate in the internal phase.     

Shortest path algorithms: A computational study with the C programming language

The main purpose of this study is to evaluate the computational efficiency of algorithms for calculating shortest paths when they are correctly coded by using the C programming language. The eight algorithms that we selected for this experiment are the most efficient, either measured in terms of worst-case bounds or marked as such from previous computational studies; they include the redistributive heap algorithm. We suggest computer implementations that use the full power of C. In particular, the network representation and the various data structures used to keep the scan eligible list may be managed by using only additions and no multiplications, while it is not possible with FORTRAN. These capabilities, unique to C, yield several interesting conclusions: one may expect to speed up a shortest path algorithm by a factor of 20%; in some cases, this factor may reach 30%. Interestingly, the level of programming difficulty required to achieve these benefits is not greater than that required by implementations using arrays.     

Efficient particle filter-based processing algorithm for clutter suppression and IR point targets enhancement

A novel soft decision-based median subtraction filter is presented for clutter suppression and infrared (IR) point targets enhancement. The decision is made based on a jump Markov model and its state and parameter estimation using a particle filter. The scheme is compared with other conventional clutter background removal techniques and good results are obtained.     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry