Airborne silica dust (quartz) is common in coal mines and represents a respiratory hazard that can lead to silicosis, a potentially fatal lung disease. With an eye toward developing a portable monitoring device for rapid analysis of silica dust, laser-induced breakdown spectroscopy (LIBS) was used to quantify quartz in coal dust samples collected on filter media. Pure silica (Min-USil™ 5), Georgia kaolin, and Pittsburgh-4 and Illinois-6 coal dusts were deposited separately and at multiple mass loadings onto 37-mm polyvinylchloride (PVC) filters. LIBS-generated silicon emission was monitored at 288.16 nm, and non-silica contributions to that signal from kaolinite were removed by simultaneously detecting aluminum. Measurements of the four samples were used to calculate limits of detection (LOD) for silicon and aluminum of approximately 0.08 µg/cm 2 and 0.05 µg/cm 2 , respectively (corresponding to 0.16 µg/cm 2 and 0.20 µg/cm 2 for silica and kaolinite, respectively). Relative errors of prediction are around 10%. Results demonstrate that LIBS can dependably quantify silica on filter samples of coal dust and confirm that accurate quantification can be achieved for very lightly loaded samples, which supports the potential application of LIBS for rapid, in-field monitoring.
We present here a dataset of nearly 5000 small craters across roughly 1700 km2 of the Martian surface, in the MC-11 East quadrangle. The dataset covers twelve 2000-by-2000 pixel Context Camera images, each of which is comprehensively labelled by six annotators, whose results are combined using agglomerative clustering. Crater size-frequency distributions are centrally important to the estimation of planetary surface ages, in lieu of in-situ sampling. Older surfaces are exposed to meteoritic impactors for longer and, thus, are more densely cratered. However, whilst populations of larger craters are well understood, the processes governing the production and erosion of small (sub-km) craters are more poorly constrained. We argue that, by surveying larger numbers of small craters, the planetary science community can reduce some of the current uncertainties regarding their production and erosion rates. To this end, many have sought to use state-of-the-art object detection techniques utilising Deep Learning, which—although powerful—require very large amounts of labelled training data to perform optimally. This survey gives researchers a large dataset to analyse small crater statistics over MC-11 East, and allows them to better train and validate their crater detection algorithms. The collection of these data also demonstrates a multi-annotator method for the labelling of many small objects, which produces an estimated confidence score for each annotation and annotator.
Laboratory measurements of ultrafine titanium dioxide (TiO2) particulate matter loaded on filters were made using three field portable methods (X-ray fluorescence (XRF), laser-induced breakdown spectroscopy (LIBS), and Fourier-transform infrared (FTIR) spectroscopy) to assess their potential for determining end-of-shift exposure. Ultrafine TiO2 particles were aerosolized and collected onto 37 mm polycarbonate track-etched (PCTE) filters in the range of 3 to 578 μg titanium (Ti). Limit of detection (LOD), limit of quantification (LOQ), and calibration fit were determined for each measurement method. The LOD's were 11.8, 0.032, and 108 μg Ti per filter, for XRF, LIBS, and FTIR, respectively and the LOQ's were 39.2, 0.11, and 361 μg Ti per filter, respectively. The XRF calibration curve was linear over the widest dynamic range, up to the maximum loading tested (578 μg Ti per filter). LIBS was more sensitive but, due to the sample preparation method, the highest loaded filter measurable was 252 μg Ti per filter. XRF and LIBS had good predictability measured by regressing the predicted mass to the gravimetric mass on the filter. XRF and LIBS produced overestimations of 4% and 2%, respectively, with coefficients of determination (R(2)) of 0.995 and 0.998. FTIR measurements were less dependable due to interference from the PCTE filter media and overestimated mass by 2% with an R(2) of 0.831.
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