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Time-resolved Fourier transform infrared (TRFTIR) emission spectroscopy has been used to study the 193 nm photolysis of vinyl bromide (C(2)H(3)Br) and vinyl chloride (C(2)H(3)Cl). Time-resolved IR emission was analysed to obtain nascent vibrational state populations of two primary photolysis products: HBr (v = 1-7) and HCl (v = 1-6). In both cases the nascent vibrational state populations monotonically decrease with increasing v and are in excellent agreement with previously published data. Time-resolved populations were analysed to yield rate constants for vibrational relaxation of HBr (v = 1-3) and HCl (v = 1-4) by parent vinyl bromide and vinyl chloride, respectively. In both cases the rate constants were found to increase with increasing vibrational quantum number, in agreement with a single quantum de-excitation via vibrational to vibrational energy transfer. Butadiene (C(4)H(6)) was identified as a secondary product of the photolysis of both vinyl halides, and shown to be formed from the reaction of parent vinyl halide with the vinyl radical. The presence of a buffer gas was found to produce a strong emission feature centred at 2,200 cm(-1), the intensity of which was dependent on the pressure of the buffer gas used, and whose kinetics are indicative of a secondary reaction product. We propose that this emission is from the vibrational progression of the electronic transition A(0, v, 1) --> X(0, v, 2) in the secondary reaction product C(2)H, whose formation route is favoured by the presence of buffer gas.

Obtaining rate constants and concentration profiles from spectroscopy is important in reaction monitoring. In this paper, we combined kinetic equations and Iterative Target Transformation Factor Analysis (ITTFA) to resolve spectroscopic data acquired during the course of a reaction. This approach is based on the fact that ITTFA needs a first guess (test vectors) of the parameters that will be estimated (target vectors). Three methods are compared. In the first, originally proposed by Furusjö and Danielsson, kinetic modelling is only used to provide the initial test vectors for ITTFA. In the second the rate constant used to provide the test vectors is optimised until a best fit is reached. In the third, a guess of the rate constant is used to provide the test vectors to ITTFA. The outcome of ITTFA is then used to fit the kinetic model and obtain a new guess of the rate constant. With this constant new concentration profiles are generated and provided to the ITTFA algorithm as new test vectors, in an iterative manner, minimising the residuals of the predicted dataset, until convergence. The second and third methods are new implementations of ITTFA and are compared to the first, established, method. First order (both one and two step) and second order reactions were simulated and instrumental noise was introduced. An experimental second order reaction was also employed to test the methods.

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Wireless Sensor Networks (WSNs) are commonly employed for environmental and wildlife monitoring. In these scenarios, mobile robots with specialized sensing, processing and actuation abilities may be employed to investigate relevant events in place. However, driving the robot to the event place is not a trivial task in the typical case where sensor nodes do not have positioning sensors such as GPS. In this work we propose a novel navigation algorithm for the mobile robot based solely on the Received Signal Strength Indication (RSSI) of communication packets. The proposed algorithm builds on a probabilistic signal propagation model recovered from real signal decay data, unlike alternative solutions found in literature. Simulations show a superior performance in comparison to similar related work.