Elemental analysis of whole blood using proton-induced X-ray emmission.
A technique for the analysis of trace elements in whole blood was developed using proton-induced x-ray emission. Samples of 0.1 ml whole blood from humans and mice were dried, weighed, and then ashed in a plasma asher. Targets were prepared by placing drops made from the ash and a 100-ppM Pd solution onto Formvar backings. The samples were irradiated with 2.25-MeV protons, and the x-rays analyzed in a nondispersive x-ray detector. The elements Fe, Cu, Zn, Se, and Rb were detected with a precision of 7, 18, 7, 50, and 19%, respectively, in human whole blood. The precision of the technique was determined by statistical analyses of two different sets of 27 samples. The system was found to be linear for variations in elemental concentration. The accuracy for deterrmination of Zn was found to be within 10% by comparison to atomic absorption spectrometry.
Sensitivity and Detectability Limits for Elemental Analysis by Proton-Induced X-Ray Fluorescence with A 3 Mv Van De Graaff
Protons from a 3-MV Van de Graaff have been used to produce characteristic x-rays from 21 elements spanning the periodic table. Absolute K/ sub alpha / and L/sub alpha / x-ray production cross sections have been determined, allowing one to calculate sensitivities for any given sample, detector geometry, and proton beam parameters. Elemental detectability limits are discussed assuming various backings (e.g., mylar and kapton) and matrix materials. The large number of available small proton accelerators throughout the United States promises wide applicability of this technique to environmental and biomedical analysis. (auth)
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.