Model Compound Studies on the Devulcanization of Natural Rubber Using 2, 3-Dimethyl-2-Butene

The mechanism of devulcanization of sulfur-vulcanized natural rubber with aromatic disulfides and aliphatic amines has been studied using 23-dimethyl-2-butene (C5H1,) as a low-molecular weight model compound. First C6H12 was vulcanized with a mixture of sulfur, zinc stearate and N-cyclohexyl-2-benzothiazylsulfenamide (CBS) as accelerator at 140 °C, resulting in a mixture of addition products (C(,H 1 i-S,-C5H 1 i ). The compounds were isolated and identified by High Performance Liquid Chromatography (HPLC) with respect to their various sulfur ranks. In it second stage, the vulcanized products were devulcanized using the agents mentioned above at 200 °C. The kinetics and chemistry of the breakdown of the sulfur-hridges were monitored. Both devulcanization agents decompose sulfidic vulcanization products with sulfur ranks equal or higher than 3 quite effectively and with comparable speed. Di phenyldisulfide as devulcanization agent gives rise to a high amount of mono- and disulfidic compounds formed during the devulcanization, hexadecylamine, as devulcanization agent, prevents these lower sulfur ranks from being formed.

Investigations On The Development Of An Ann Model & Visual Manipulation Approach For 2-D Dft Computation In Image Processing

This thesis is an outcome of the investigations carried out on the development of an Artificial Neural Network (ANN) model to implement 2-D DFT at high speed. A new definition of 2-D DFT relation is presented. This new definition enables DFT computation organized in stages involving only real addition except at the final stage of computation. The number of stages is always fixed at 4. Two different strategies are proposed. 1) A visual representation of 2-D DFT coefficients. 2) A neural network approach. The visual representation scheme can be used to compute, analyze and manipulate 2D signals such as images in the frequency domain in terms of symbols derived from 2x2 DFT. This, in turn, can be represented in terms of real data. This approach can help analyze signals in the frequency domain even without computing the DFT coefficients. A hierarchical neural network model is developed to implement 2-D DFT. Presently, this model is capable of implementing 2-D DFT for a particular order N such that ((N))4 = 2. The model can be developed into one that can implement the 2-D DFT for any order N upto a set maximum limited by the hardware constraints. The reported method shows a potential in implementing the 2-D DF T in hardware as a VLSI / ASIC