Dianna Sue Blair

STUDY OF ANALYTE DIFFUSION INTO A SILICONE-CLAD FIBER-OPTIC CHEMICAL SENSOR BY EVANESCENT WAVE SPECTROSCOPY

The diffusion rates of various polar and nonpolar analytes in dimethylsiloxane were examined with the use of a commercially available 200-μm silica-core/300-μm silicone-clad fiber as the optical element for evanescent wave spectroscopy in the near-infrared spectral region. An analytical solution to Ficks second law was used to model the time-dependent analyte concentration at the core/cladding interface. Successful fit of the analytical solutions to infrared data verifies the assumption of constant diffusion coefficients that is necessary to solve the equation. Transport rates of polar analytes in silicone can be estimated with the use of a single-parameter model that results in diffusion coefficients of 3.2 × 10−1, 1.6 × 10−1, 8.1 × 10−7, and 3.9 × 10−7 cm2/s for methanol, ethanol, 2-propanol, and n-butanol, respectively. Estimating the transport of larger nonpolar analytes in the silicone cladding requires a two-parameter model that includes a diffusion coefficient and an interfacial conductance term. For pentane, hexane, heptane, and cyclohexane the resultant diffusion coefficients and interfacial conductance parameters are 6.9 × 10−7, 4.6 × 10−7, 4.4 × 10−7, and 2.3 × 10−7 cm2/s and 2500, 2000, 2000, and 600 μm−1, respectively.

Improvements in Methods for Spectral Combination of Gas Chromatography/Fourier Transform Infrared Spectroscopic Data

Coaddition of spectra in a single-component peak of a gas Chromatograph (GC) obtained with a Fourier transform infrared spectrometer is the method generally used to improve the signal-to-noise ratio (S/N) of the spectrum of the eluted analyte. It is commonly thought that coaddition of spectra to a relative intensity level of 40% of the GC peak will lead to the optimal improvement in S/N of the resulting composite spectrum. We have shown that this is not generally the case for either simulated Gaussian-shaped or experimentally obtained asymmetric GC bands. The optimal intensity level for coaddition is found to be a function of the shape of the GC band and the ratio of the number of background to sample scans used in generating the individual IR spectra. We have also introduced the use of classical least-squares (CLS) techniques as a superior method to improve the S/N of the composite analyte spectrum. With the use of CLS methods, spectra included in generating the composite spectrum can be a small fraction of the maximum intensity in the GC peak while still resulting in S/N improvements. The theoretical S/N of the composite spectrum with the use of CLS methods is shown to be always as good as or better than that achieved with the coaddition method. The improvements achieved in S/N when CLS methods are used can be more than a factor of two greater than results for the traditional coaddition method for the cases considered in this paper. Furthermore, it is shown that increasing the number of background to sample scans is a very convenient method to improve the S/N of the composite spectrum obtained by either method. The results presented here for GC/FT-IR are also generally applicable to LC/FT-IR, SFC/FT-IR, and TGA/FT-IR for bands that contain a single analyte.

Systematic evaluation of satellite remote sensing for identifying uranium mines and mills.

In this report, we systematically evaluate the ability of current-generation, satellite-based spectroscopic sensors to distinguish uranium mines and mills from other mineral mining and milling operations. We perform this systematic evaluation by (1) outlining the remote, spectroscopic signal generation process, (2) documenting the capabilities of current commercial satellite systems, (3) systematically comparing the uranium mining and milling process to other mineral mining and milling operations, and (4) identifying the most promising observables associated with uranium mining and milling that can be identified using satellite remote sensing. The Ranger uranium mine and mill in Australia serves as a case study where we apply and test the techniques developed in this systematic analysis. Based on literature research of mineral mining and milling practices, we develop a decision tree which utilizes the information contained in one or more observables to determine whether uranium is possibly being mined and/or milled at a given site. Promising observables associated with uranium mining and milling at the Ranger site included in the decision tree are uranium ore, sulfur, the uranium pregnant leach liquor, ammonia, and uranyl compounds and sulfate ion disposed of in the tailings pond. Based on the size, concentration, and spectral characteristics of these promising observables, we then determine whether these observables can be identified using current commercial satellite systems, namely Hyperion, ASTER, and Quickbird. We conclude that the only promising observables at Ranger that can be uniquely identified using a current commercial satellite system (notably Hyperion) are magnesium chlorite in the open pit mine and the sulfur stockpile. Based on the identified magnesium chlorite and sulfur observables, the decision tree narrows the possible mineral candidates at Ranger to uranium, copper, zinc, manganese, vanadium, the rare earths, and phosphorus, all of which are milled using sulfuric acid leaching.

Characterization of surface contaminants using infrared microspectroscopy

Infrared spectroscopy (IR) is a vibrational spectroscopic technique used for the nondestructive identification of molecular species. It provides information about molecular structure by determining the frequency and intensity of light a compound absorbs in the infrared region. The resultant IR absorption spectrum is characteristic of the compound. This spectrum is considered one of the compounds physical properties, like its density or boiling point. However, unlike these other properties only optical isomers have identical infrared absorption spectra. Infrared spectroscopy can be performed on molecular species in any physical state. Therefore, gases, liquids, and solids can all be sampled. Generally, because of the high information content of the infrared spectra of organic molecules IR is most useful for the identification of organic materials. However, many inorganics also exhibit IR detectable molecular vibrations and therefore can be identified by this technique. The paper deals briefly with the use of infrared spectrometers coupled to microscopes, and methods of compressing spectral data by integrating absorbance intensity over a characteristic frequency range.