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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates
The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.
A database and framework for carbon ore resources and associated supply chain data
Not Available
Arsenic mobility and characterization in lakes impacted by gold ore roasting, Yellowknife, NWT, Canada
Abstract not provided
Goethite aging explains Ni depletion in upper units of ultramafic lateritic ores from New Caledonia
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The nature of hematite depression with corn starch in the reverse flotation of iron ore
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Fractionation of REE, U, and Th in natural ore-forming hydrothermal systems: Thermodynamic modeling
This contribution presents a thermodynamic model revealing the mechanisms responsible for separation of Heavy and Light Rare Earth (HREE and LREE) phosphates in natural hydrothermal systems at temperatures of 250–350 °C. Our calculations were performed for an isothermal column of rock containing 0.5 wt% of apatite (Ca phosphate), which served as an immobilizing agent for REE dissolved in the solution. REE were transported by 10 wt% NaCl acidic solution. The model accounted for formation of REE phosphate solid solutions through a regular mixing model. It demonstrates that hydrothermal flushing can efficiently separate REE forming xenotime (HREE phosphate solid solutions) at the beginning of the column, and re-transportation of monazite (LREE rich) to the end of the column. This separation is primarily due to the fact that at elevated temperatures stability LREE chloride complexes is significantly higher than that for HREE. The model also evaluates behavior of U and Th, which accompany REE in vast majority of natural locations. It was found that U strongly fractionates to xenotime, whereas Th fractionates to monazite. Finally, this phenomenon can be explained by the differences in crystal-chemical characteristics between monazite and xenotime, and the mobility of Th in aqueous solutions at elevated temperatures.
The ignored emission of volatile organic compounds from iron ore sinter process
Not Available
Zn speciation and fate in soils and sediments along the ground transportation route of Zn ore to a smelter
We discuss assessment of Zn toxicity/mobility based on its speciation and transformations in soils as critical for maintaining human and ecosystem health. Zn-concentrate (56% Zn as ZnS, sphalerite) has been imported through a seaport and transported to a Zn-smelter for several decades, and smelting processes resulted in aerial deposition of Zn and sulfuric acids in two geochemically distinct territories around the smelter (mountain-slope and riverside). XAFS analysis showed that the mountain-slope soils contained franklinite (ZnFe 2 O 4 ) and amorphous (e.g., sorbed) species of Zn(II), whereas the riverside sediments contained predominantly hydrozincite [Zn 5 (OH) 6 (CO 3 ) 2 ], sphalerite, and franklinite. The mountain-slope soils had low pH and moderate levels of total Zn (~1,514 ppm), whereas the riverside sediments had neutral pH and higher total Zn (12,363 ppm). The absence of sphalerite and the predominance of franklinite in the mountain-slope soils are attributed to the susceptibility of sphalerite and the resistance of franklinite to dissolution at acidic pH. These results are compared to previous Zn analyses along the transportation routes, which showed that Zn-concentrate spilled along the roadside in dust and soils underwent transformation to various O-coordinated Zn species. Overall, Zn-concentrate dispersed in soils and sediments during transportation and smelting transforms into Zn phases of diverse stability and bioavailability during long-term weathering.