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Analytical Methods for MIC Assessment
Published in Richard B. Eckert, Torben Lund Skovhus, Failure Analysis of Microbiologically Influenced Corrosion, 2021
Torben Lund Skovhus, Richard B. Eckert
For samples that contain different phases and various organic and inorganic materials, e.g., sludge from a cleaning pig run, EDS and XRD may not accurately describe the full nature of the sample if the water and hydrocarbons are removed. This situation is where wet chemical methods can be helpful. Wet chemistry is a general term that describes laboratory analysis techniques such as gravimetry (weighting), titrimetric (volume), and numerous separation techniques. Methods vary widely with sample type and composition and often require some experience to assemble the right analytical pieces. For example, solid organic phase samples are characterized by the percent weight of sample that is dissolved in water, acetone, toluene, and hexane. The inorganic percent weight of a mixed solid sample is sometime determined by combustion, i.e. heating to a high temperature to evaporate water and burn off the organic phase. Determining the percent water in a sample that is mostly solid can be performed through Karl Fischer moisture analysis as described in ASTM E203 (2006). Samples collected from pigging often contain multiple phases that require multiple extraction and analytical steps to characterize relative to the overall sample composition. Kagarise et al. (2017) discussed the importance of characterizing solid and sludge samples from pipelines in order to select the most appropriate means of mitigation.
Comparative assessment of blood glucose monitoring techniques: a review
Published in Journal of Medical Engineering & Technology, 2023
Nivad Ahmadian, Annamalai Manickavasagan, Amanat Ali
The methods of measuring blood glucose levels are mainly divided into invasive, minimally invasive, and non-invasive techniques regarding causing prick on the skin. Figure 1 gives an overview of various glucose monitoring techniques. Embracing the development and use of simple, less painful, non-invasive techniques for measuring blood glucose levels, is inevitable [8]. The invasive methods require collecting blood from diabetic patients. However, the amount of collected blood samples relies on the monitoring technique. The clinical laboratory tests (wet chemistry) require 1–3 ml of a blood sample to analyse the glucose levels, utilising the hexokinase method as a reference standard to diagnose diabetes [5]. Meanwhile, point-of-care testing (POCT) provides patients with at-home glucose monitoring devices known as the self-monitoring of blood glucose (SMBG), which requires a small drop of blood around 0.3–1 µL to analyse it through the glucose oxidase (GOx) procedure [6].
Performance measurements of machine learning and different neural network designs for prediction of geochemical properties based on hyperspectral core scans
Published in Australian Journal of Earth Sciences, 2022
H. Eichstaedt, C. Y. J. Ho, A. Kutzke, R. Kahnt
For this paper, only geochemical assays from established state databases were used, i.e. Queensland, New South Wales, Tasmania, South Australia and Western Australia. All available elements (major, trace, REE), not compounds, were extracted using various laboratory analysis techniques, including XRF, ICP-MS and wet chemistry. Of the total 70 million geochemical records, 110 000 were matched with 703 250 one-metre intervals of the TIR/SWIR hyperspectral scans and used for a performance test. Where geochemical samples were not from the same depth interval as the hyperspectral 1 m bins, the geochemical data were rounded to the majority covered by the 1 m spectral bin. Where geochemical samples covered a 2 m segment of the core, the record was duplicated to cover the 1 m hyperspectral bin. Larger geochemical sample intervals were not matched with the hyperspectral 1 m bins. Figure 1 shows the abundance of the selected minerals sorted by geochemical classes in a sample of copper as a heatmap. The brighter the colour, the higher the abundance of the specific mineral within the bin that was spectrally measured.
Applying 3D U-statistic method for modeling the iron mineralization in Baghak mine, central section of Sangan iron mines
Published in Geosystem Engineering, 2018
Seyyed Saeed Ghannadpour, Ardeshir Hezarkhani, Abbas Golmohammadi
The Sangan Iron Ore Complex (SIOC) has performed drilling (239 exploratory boreholes)in Baghak area from the beginning of exploration in this deposit. Core sampling and chemical analysis have provided elemental concentration data for Fe, S, and FeO in weight percent (Iran Eastern Iron Ore Iran Eastern Iron Ore Co, 2011). The samples have been collected by researchers in SIOC and were analyzed by wet chemistry method and wavelength dispersive X-ray fluorescence spectrometry on a LECO CS-230 XRF spectrometer at the central laboratory of the Sangan iron ore complex (Golmohammadi et al., 2015). Figures of boreholes (3D view) are also depicted in Figure 4.