Wetland Soil Carbon in a Watershed Context for the Prairie Pothole Region.

Wetland restoration in the Prairie Pothole Region (PPR) often involves soil removal to enhance water storage volume and/or remove seedbanks of invasive species. Consequences of soil removal could include loss of soil organic carbon (SOC), which is important to ecosystem functions such as water-holding capacity and nutrient retention needed for plant re-establishment. We used watershed position and surface flow pathways to classify wetlands into headwater or network systems to address two questions relevant to carbon (C) cycling and wetland restoration practices: (i) Do SOC stocks and C mineralization rates vary with landscape position in the watershed (headwater vs. network systems) and land use (restored vs. native prairie grasslands)? (ii) How might soil removal affect plant emergence? We addressed these questions using wetlands at three large (?200 ha) study areas in the central North Dakota PPR. We found the cumulative amount of C mineralization over 90 d was 100% greater for network than headwater systems, but SOC stocks were similar, suggesting greater C inputs beneath wetlands connected by higher-order drainage lines are balanced by greater rates of C turnover. Land use significantly affected SOC, with greater stocks beneath native prairie than restored grasslands for both watershed positions. Removal of mineral soil negatively affected plant emergence. This watershed-based framework can be applied to guide restoration designs by (i) weighting wetlands based on surface flow connectivity and contributing area and (ii) mapping the effects of soil removal on plant and soil properties for network and headwater wetland systems in the PPR.

An end-to-end computer vision methodology for quantitative metallography.

Metallography is crucial for a proper assessment of material properties. It mainly involves investigating the spatial distribution of grains and the occurrence and characteristics of inclusions or precipitates. This work presents a holistic few-shot artificial intelligence model for Quantitative Metallography, including Anomaly Detection, that automatically quantifies the degree of the anomaly of impurities in alloys. We suggest the following examination process: (1) deep semantic segmentation is performed on the inclusions (based on a suitable metallographic dataset of alloys and corresponding tags of inclusions), producing inclusions masks that are saved into a separated dataset. (2) Deep image inpainting is performed to fill the removed inclusions parts, resulting in 'clean' metallographic images, which contain the background of grains. (3) Grains' boundaries are marked using deep semantic segmentation (based on another metallographic dataset of alloys), producing boundaries that are ready for further inspection on the distribution of grains' size. (4) Deep anomaly detection and pattern recognition is performed on the inclusions masks to determine spatial, shape, and area anomaly detection of the inclusions. Finally, the end-to-end model recommends an expert on areas of interest for further examination. The physical result can re-tune the model according to the specific material at hand. Although the techniques presented here were developed for metallography analysis, most of them can be generalized to a broader set of microscopy problems that require automation. All source-codes as well as the datasets that were created for this work, are publicly available at https://github.com/Scientific-Computing-Lab-NRCN/MLography .

The Initial Oxidation of HfNiSn Half-Heusler Alloy by Oxygen and Water Vapor.

The MNiSn (M = Ti, Zr, Hf) n-type semiconductor half-Heusler alloys are leading candidates for the use as highly efficient waste heat recovery devices at elevated temperatures. For practical applications, it is crucial to consider also the environmental stability of the alloys at working conditions, and therefore it is required to characterize and understand their oxidation behavior. This work is focused on studying the surface composition and the initial oxidation of HfNiSn alloy by oxygen and water vapor at room temperature and at 1000 K by utilizing X-ray photoelectron spectroscopy. During heating in vacuum, Sn segregated to the surface, creating a sub-nanometer overlayer. Exposing the surface to both oxygen and water vapor resulted mainly in Hf oxidation to HfO2 and only minor oxidation of Sn, in accordance with the oxide formation enthalpy of the components. The alloy was more susceptible to oxidation by water vapor compared to oxygen. Long exposure of HfNiSn and ZrNiSn samples to moderate water vapor pressure and temperature, during system bakeout, resulted also in a formation of a thin SnO2 overlayer. Some comparison to the oxidation of TiNiSn and ZrNiSn, previously reported, is given.

The Initial Stage in Oxidation of ZrNiSn (Half Heusler) Alloy by Oxygen.

The MNiSn (M = Ti; Zr; Hf); half-Heusler semiconducting alloys have a high potential for use as n-type thermoelectric materials at elevated temperatures (~1000 K). The alloys' durability is crucial for their commercial handling and use, and therefore it is required to characterize their surface oxidation behavior and stability at the working temperature. X-ray photoelectron spectroscopy was utilized to study the surface composition and oxidation of the ZrNiSn alloy at room and elevated temperatures. It was found that during heating in a vacuum, Sn segregates to the surface in order to reduce the surface energy. Exposing the alloy to oxygen resulted mainly in the oxidation of the zirconium to ZrO2, as well as some minor oxidation of Sn. At room temperature, the oxidation to ZrO2 was accompanied by the formation of a thin ZrO layer at the metal-oxide interface. In contrast to TiNiSn, where most of the oxide was formed on the surface due to oxygen-enhanced segregation of Ti, and in the case of ZrNiSn, the formed oxide layer was thinner. Part of the oxide is formed due to Zr segregation to the surface, and in part due to oxygen dissolved into the alloy.

Surface Oxidation of TiNiSn (Half-Heusler) Alloy by Oxygen and Water Vapor.

TiNiSn-based half-Heusler semiconducting compounds have the highest potential as n-type thermoelectric materials for the use at elevated temperatures. In order to use these compounds in a thermoelectric module, it is crucial to examine their behaviour at a working temperature (approximately 1000 K) under oxygen and a humid atmosphere. Auger electron spectroscopy (AES) and X-ray photoelectron spectroscopy (XPS) were utilized to study the surface composition and oxidation of the TiNiSn alloy at elevated temperatures. It was found that during heating in vacuum, Sn segregates to the surface. Exposing the alloy to oxygen at room temperature will cause surface oxidation of Ti to TiO2 and Ti2O3 and some minor oxidation of Sn. Oxidation at 1000 K induces Ti segregation to the surface, creating a titanium oxide layer composed of mainly TiO2 as well as Ti2O3 and TiO. Water vapor was found to be a weaker oxidative gas medium compared to oxygen.