Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling.
ABSTRACT:Proteins are naturally occurring nanosystems endowed with diverse properties and functions. Biotechnology affords means of modifying proteins to alter their properties and hence their function. Bionanotechnology is a rapidly emerging area of research with challenging opportunities and formidable challenges. Light-activated proteins, e.g., bacteriorhodopsin, manifest state changes when subjected to light, which can be exploited to store information in the range of terabytes. We describe the retroengineering of bacteriorhodopsin by rational site-specific mutagenesis to enhance its thermal and photochemical properties. Bacteriorhodopsin-based memory systems are amenable to writing and reading information at comparable speeds. With silicon-based technologies reaching their technological limits, protein-based memory systems are one viable alternate technology for the future.
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