Bound Chlorinated Residue in Chloropicrin-Treated Douglas-Fir

Douglas-fir wafers exposed to chloropicrin vapors, then aerated and heated or extracted with acetone, were analyzed under a scanning electron microscope equipped with an energy dispersive X-ray analyzer. Chlorinated residues appeared to be most concentrated in the middle lamellae and in areas where wood extractives were located, which indicates that the residues were selectively binding to phenolic materials. Thin layer chromatography of acetone extracts of the treated wood suggested that chlorinated residues were binding to extractives, particularly to a portion of the phenolic extractive dihydro-quercetin. Analysis of a mixture of vanillin (a phenolic lignin derivative) and chloropicrin showed the presence of two other compounds. Mass spectroscopy tentatively identified these as CCl3-vanillin and NO2-vanillin. This identification suggests that the chloropicrin molecule was fragmented and that the two components were chemically linked to the vanillin molecule at an unspecified point. The data suggest an explanation for the presence of a phenolic-bound chlorinated residue in chloropicrin-treated wood

Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201