A means of generating foams of high-temperature polymers, polyimides, has been developed for use in dielectric layers in microelectronics. In these systems, the pore sizes generated are in the tens of nanometers range, thus the term “nanofoams”. The foams are generated by preparing phase-separated block copolymers with the majority phase comprised of polyimide and the minor phase consisting of a thermally labile block. Films are cast, solvent is removed, and the copolymers are cured, causing phase separation of the two blocks. The labile blocks are subsequently removed via thermal treatments leaving pores having a size and shape commensurate with the size and shape of the original copolymer morphology. The polyimide derived from pyromellitic dianhydride (PMDA) and 2,2-bis[4-(4-aminophenoxy)phenyl]hexafluoropropane (4-BDAF) was used as the matrix materials for the generation of nanofoams, and poly(propylene oxide) oligomers were used as the thermally labile constituent. The synthesis and characterization of the copolymers were performed, and the process for obtaining nanofoams was optimized. The foams were characterized by a variety of techniques including TEM, SAXS, WAXD, DMTA, density, and refractive index measurements. Thin-film, high-modulus foams with good mechanical properties can be synthesized using the copolymer/nanofoam approach.
A series of polyol aeetals of aldehyde oils was prepared by an acetal interchange reaction between dimethyl and tetramethyl acetals of aldehyde oils derived by the reductive ozonolysis of linseed and soybean oils. The acetals interchanged with pentaerythritol, trimethylol propane and glycerol contained the equivalent of .93 and 1.88 aldehyde groups per oil molecule. The polyol aeetals were reacted with toluene diisocyanate in an NCO/OH of 2. These products were tested as film formers. Curing was by two reactions, namely oxidation polymerization of the residual unsaturation in the oil and moisture curing of the unreacted isocyanate group. Good quality films were formed.
A series of film formers based on aldehyde oils, derived by reduetive ozonolysis of soybean and linseed oils was prepared by a two-step reaction with a resinous polyol, styrene-allyl alcohol copolymer. The methyl acetals of the di-and monoaldehyde oils were interchanged with the hydroxyl functionality of the resinous polyoi with potassium acid sulfate as the catalyst as follows: (a) The heterogeneous mixture was heated only to clarity in the first step. This consumed about 20-25% of the hydroxyl groups of the resinous polyol. (b) Fihns of the partially reacted products of (a) were east and cured at various temperatures between 100 and 175 C for variable periods of time. The films showed good hardness, scratch and chemical resistance. Some yellowing was observed. The film former in the (a) stage was compatible with melamine and urea-formaldehyde resins and, upon baking, these compositions also produced clear, hard chemicalresistant films. D ~o mi~ To tu 4 J CURE TEMPERATURE°C I~IG. 2. Influence of cure-temperature on hardness aldehyde oil PJ-100 aeetals.
Redox flow batteries (RFBs) are a promising candidate that are capable of meeting the energy storage applications to fulfill the needs of renewable resources. Herein, we prepare an electrochemical device that holds higher energy density. In this work, a reusable glucose kit used as a flow cell which in turn helps to minimize the cost and also balance the pump losses in electrochemical systems. For fabricating RFB, ZnO, from the metal organic framework (Zn-MOF/ZnO), uses an electrode material: ZnCl2 in aqueous KOH used as both anolyte and catholyte solution. Upon the new cell fabricating in this investigation, we demonstrated the voltage efficiency of 92% at 5 mA cm−2, which reduces the cost of the cell upon being implemented in the flow battery application.
The development of smart metering technology empowers power reforms, which allows effective implementation of demand response programs to effectively operate the power grid. The systematic analysis of smart meter data plays a vital role for both consumers and utilities to reduce their costs and improve the efficiency of power management. In this paper, a machine learning algorithm is proposed to recommend the appropriate Demand Response (DR) program for the consumer in a real-time environment, tailored with dynamic pricing. The systematic recommendation can be made by integrating time series forecasting, consumer clustering, and DR analysis. The smart meter data of the 28 consumers for 108 weeks are recorded and applied to the ARIMA time series forecast algorithm. The smart meter data and ARIMA time series forecast data are combined and fed to the Agglomerative Hierarchical clustering algorithm to cluster consumers based on their usage and demand pattern. Clusters are analysed to identify a suitable DR program for the consumer. The results show that the proposed machine learning method effectively clusters consumers and implements the DR program in the smart grid environment.
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