Recent Advances in Polymeric Materials Used as Electron Mediators and Immobilizing Matrices in Developing Enzyme Electrodes

Different classes of polymeric materials such as nanomaterials, sol-gel materials, conducting polymers, functional polymers and biomaterials have been used in the design of sensors and biosensors. Various methods have been used, for example from direct adsorption, covalent bonding, crossing-linking with glutaraldehyde on composites to mixing the enzymes or use of functionalized beads for the design of sensors and biosensors using these polymeric materials in recent years. It is widely acknowledged that analytical sensing at electrodes modified with polymeric materials results in low detection limits, high sensitivities, lower applied potential, good stability, efficient electron transfer and easier immobilization of enzymes on electrodes such that sensing and biosensing of environmental pollutants is made easier. However, there are a number of challenges to be addressed in order to fulfill the applications of polymeric based polymers such as cost and shortening the long laboratory synthetic pathways involved in sensor preparation. Furthermore, the toxicological effects on flora and fauna of some of these polymeric materials have not been well studied. Given these disadvantages, efforts are now geared towards introducing low cost biomaterials that can serve as alternatives for the development of novel electrochemical sensors and biosensors. This review highlights recent contributions in the development of the electrochemical sensors and biosensors based on different polymeric material. The synergistic action of some of these polymeric materials and nanocomposites imposed when combined on electrode during sensing is discussed

An investigation of trade-off between the benefit of process improvements and the lost production time spent on the improvements

Includes bibliographical references (pages [80]-82)This study applies the existing quantitative models of process learning to investigate the economic trade-off associated with the benefits of gradual process improvement. These models describe an imperfect production process which produces defective parts randomly. The defects are indicative of abnormal variations in the process quality. At the end of production of each lot, if defects are discovered, two different policies are considered. One is to invest a small amount to prevent the occurrence of similar defects in subsequent lots by improving the quality of the production process. The other is to continue production of the next lot in the usual manner without any improvement. A stochastic dynamic programming model is used to determine the optimal policy which gives the minimal expected discounted present cost over an infinite horizon. The effects of lost production time spent in gradual process learning and improvement are scrutinized for investigating the economic trade-off between the benefit of process improvement and the cost of lost production time. The model is investigated numerically.M.S. (Master of Science