Structure and properties of polyimide‐bonded magnets processed from prepolymers based on diacetyl derivatives of aromatic diamines and dianhydrides

We report a novel method of polyimide (PI) synthesis from prepolymers based on dianhydrides and diacetyl derivatives of aromatic diamines that facilitate the preparation of a melt processable mixture at 300 ± 10°C of the prepolymer and magnetic Nd‐Fe‐B alloy to provide PI‐bonded magnets with enhanced properties. It is shown that chemical structure of the prepolymers strongly influences viscosity behavior via crystallization of the oligoimide in the melt, leading to formation of PI with rigid‐rod like structure. This structural ordering of the prepolymers based on diacetyl derivative of diamine used in this study, if not controlled, leads to exponential increase of melt viscosity with time, making it practically impossible to prepare melt processable mixture of the magnetic particles and the PI prepolymers at elevated temperatures. The results obtained demonstrate that appropriate dianhydrides and diacetyl derivatives of diamines that do not lead to crystallization of oligoimides in prepolymer mixture can be used under controlled processing conditions to prepare melt‐processable PI‐bonded magnets containing rigid‐rod like PI structure that significantly increases thermal stability of the magnets. The temperature dependencies of the magnetic properties of the PI‐bonded magnets under conditions that they are likely to encounter during their service life were found to be remarkably similar to that of commercial thermoplastic magnets such as injection‐molded nylon magnets. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 478–485, 2006     

Low-Temperature Dielectric Properties of Ceramic Potassium Tantalate (KTaO3)

Dielectric properties of KTaO₃ ceramic fabricated by both conventional sintering and sintering followed by isostatic hot pressing (HIP) were measured at T =4.2 to 300 K over a frequency range from 400 Hz to 4 MHz. The weak field response shows a faint peak near 10 K which has a clear relaxation character, as in high-purity single crystals. The general response is well reproduced by the Barrett function, but additional polarizability with relaxation character is also in evidence at higher temperatures. No dielectric hysteresis was observed at high fields for cycling frequencies down to 0.04 Hz anywhere in the temperature range.     

Evaporation of polymer-solvent mixtures

Summary The Stefan-Winkelmann diffusion technique has been used to determine vapor pressures of high boiling point compound-solvents mixtures. Data are reported at a temperature of 67°C for the mixtures polyphenyl ether (6 rings) — benzene and carbowax 1500 — benzene and at a temperature of 100°C for the mixtures polyphenyl ether (6 rings) — toluene and tricresylphosphate-toluene. The range of concentration is only limited by the appearance of a solid phase, and results are in good agreement with those determined by vapor pressure osmometry.Nomenclature A Vapour source - B Flowing gas phase - c Concentration, g-mol/cm³ - D_AB Molecular diffusion coefficient, cm₂/s - h_i Total interface depth, cm - K Parameter defined by equation (6), dimensionless - I Polymer or high boiling point compound - M_A Molecular weight - N_AZ Molar flux, g-mol/(cm₂) (s) - P_A Partial pressure at the interface, atm - P Total pressure, atm - R Gas constant, (atm) (cm₃) / (°K) (g-mol) - T Temperature, °K - t Time, s - Y_A Molar fraction at the interface, dimensionless - z_o Initial interface depth, cm - z_i Drop of the interface depth, cm - z Interface position Greek Letters _A Density of the liquid phase, g/cm³     

Tungsten Oxide Nanorods Array and Nanobundle Prepared by Using Chemical Vapor Deposition Technique

Tungsten oxide (WO₃) nanorods array prepared using chemical vapor deposition techniques was studied. The influence of oxygen gas concentration on the nanoscale tungsten oxide structure was observed; it was responsible for the stoichiometric and morphology variation from nanoscale particle to nanorods array. Experimental results also indicated that the deposition temperature was highly related to the morphology; the chemical structure, however, was stable. The evolution of the crystalline structure and surface morphology was analyzed by scanning electron microscopy, Raman spectra and X-ray diffraction approaches. The stoichiometric variation was indicated by energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy.     

Derivation and analysis of a filtered backprojection algorithm for cone beam projection data

The authors address the problem of three-dimensional image reconstruction from cone beam projections. Modifying a result due to A.A. Kirillov (Soviet Math. Dokl., vol. 2, p.268-9, 1961), the authors derive an inversion formula for the case where the cone vertices form an unbounded curve. For the special case in which the cone vertices form a circle, an approximate reconstruction formula is developed and shown to be essentially equivalent to the practical cone-beam algorithm of L.A. Feldkamp et al. (1984). For this approximate inverse, the authors derive the resulting spatially varying point spread function, examine the effect of bandlimiting due to sampling, and compare the resulting image quality as a function of the radius of the circle formed by the cone vertices.     

Cation interdiffusion between thick film of YBa2Cu3O7-δ and ceramic substrate

Thick films of superconducting oxides, YBa₂Cu₃O_7-, were successfully made by conventional screen-printing technology on Al₂O₃, MgO, and ZrO₂ substrates. Interdiffusion between the superconductive film and substrate was investigated using analytical electron microscopy. The results indicate that MgO and ZrO₂ are superior to Al₂O₃ for substrate materials.     

The fate of 14C-radiolabelled diclofenac and 4'-hydroxydiclofenac in membrane bioreactor treatment of wastewater

This study aimed at enhancing knowledge on the fate of diclofenac (DF), together with its main human metabolite 4'-hydroxydiclofenac (4'OHDF), during wastewater treatment by using a laboratory-scale membrane bioreactor (MBR). The reactor was fed continuously with non-radiolabelled diclofenac for a one month period prior to a single pulse of a 14C-radiolabelled solution of DF and 4'OHDF. The solution spike contained approximately 25% 4'OHDF and 65% DF, which corresponds to the ratio observed in municipal sewage, as well as traces of two other metabolites. The radioactivity was monitored for a total of twelve days in the various output streams. The calculation of the complete mass balance in the system demonstrated that the major part of the radioactivity left the reactor with the permeate (88.7%), while 2.1% was recovered in the excess sludge. Negligible amounts were recovered in the off-gas traps and on the membranes. Chromatographic analyses of effluent samples, by means of HPLC-MS coupled in parallel to a radiodetector, displayed a different pattern than the one of the spiked solution. It showed the occurrence of three additional metabolites.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine

It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.