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At least 1,369 records · Page 76

Incommensurate magnetic orders and topological Hall effect in the square-net centrosymmetric EuGa 2 Al 2 system

Neutron diffraction on the centrosymmetric square-net magnet EuGa 2 Al 2 reveals multiple incommensurate magnetic states (AFM1, 2, 3) in zero field. In applied field, a new magnetic phase (A) is identified from magnetization and transport measurements, bounded by two of the μ 0 H=0 incommensurate magnetic phases (AFM1, helical, and AFM3, cycloidal) with different moment orientations. Moreover, magnetotransport measurements indicate the presence of a topological Hall effect, with maximum values centered in the A phase. Together, these results render EuGa 2 Al 2 a material with noncoplanar or topological spin texture in applied field. X-ray diffraction reveals an out-of-plane (OOP) charge density wave (CDW) below T CDW ~50 K while the magnetic propagation vector lies in plane below T N =19.5 K. Together these data point to a new route to realizing in-plane noncollinear spin textures through an OOP CDW. In turn, these noncollinear spin textures may be unstable against the formation of topological spin textures in an applied field.

36 MATERIALS SCIENCE↗

Epitaxial growth of hexagonal BaNi O 3–δ thin films on SrTi O 3 (111) substrates

Transition metal oxides containing nickel species in oxidation states higher than 3+ often exhibit high catalytic activity, which makes them promising for applications as advanced electrocatalysts for water splitting and fuel cells. Here, we examine the structure and properties of BaNiO 3 (BNO) thin films, containing formally Ni 4+ , grown on SrTiO 3 (111) substrates using oxygen-plasma–assisted molecular beam epitaxy. X-ray diffraction and scanning transmission electron microscopy measurements reveal that BNO films have a hexagonal structure with $c$- and $a$-axes of mixed textures showing epitaxial relationships BNO (0001) ∥ SrTiO 3 (111) and BNO (10$\overline{1}$0) ∥ SrTi O 3 (111), respectively. The formation of the $a$-axis texture is dominant due to the smaller lattice mismatch with the substrate. Here, density functional theory calculations confirm that the hexagonal BNO film with the $a$-axis texture is energetically more favorable than the competing $c$-axis texture. Detailed spectroscopy data analysis indicates that hexagonal BNO films contain mixtures of Ni 2+ , Ni 3+ , and Ni 4+ species, with Ni 4+ being dominant. Our study provides insights into stabilizing Ni 4+ in complex oxides, which is important for further exploration of the potential of materials containing Ni 4+ .

36 MATERIALS SCIENCE↗

Role of the PbTiO 3 Seed Layer on the Crystallization Behavior of PZT Thin Films

The role of a highly crystalline and oriented lead titanate ( PTO ) seed layer on the subsequent phase and texture evolution of lead zirconate titanate ( PZT ) thin films is investigated in situ using X‐ray diffraction ( XRD ) during crystallization. Crystalline PTO seed layers were first prepared via a 2‐methoxyethanol route. Amorphous PZT with a Zr/Ti ratio of 52/48 was then deposited on the seed layer using the same synthesis route and subsequently crystallized in situ during XRD . During heating, a tetragonal‐to‐cubic transformation of the seed layer occurs prior to the formation of perovskite PZT . Subsequent nucleation of the crystalline PZT occurs in the cubic phase. Simultaneous to nucleation of PZT , development of a dominant 100 texture component was observed in the PZT phase of the thin films. The results indicate that 100 textured PTO nucleates 100 texture of PZT thin films during crystallization.

Mhin, Sungwook↗

Foreign object detection and removal to improve automated analysis of chest radiographs

Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The method is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A{sub z} value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

SU-E-J-242: Volume-Dependence of Quantitative Imaging Features From CT and CE-CT Images of NSCLC

Purpose: To determine whether tumor volume plays a significant role in the values obtained for texture features when they are extracted from computed tomography (CT) images of non-small cell lung cancer (NSCLC). We also sought to identify whether features can be reliably measured at all volumes or if a minimum volume threshold should be recommended. Methods: Eleven features were measured on 40 CT and 32 contrast-enhanced CT (CECT) patient images for this study. Features were selected for their prognostic/diagnostic value in previous publications. Direct correlations between these textures and volume were evaluated using the Spearman correlation coefficient. Any texture that the Wilcoxon rank-sum test was used to compare the variation above and below a volume cutoff. Four different volume thresholds (5, 10, 15, and 20 cm{sup 3}) were tested. Results: Four textures were found to be significantly correlated with volume in both the CT and CE-CT images. These were busyness, coarseness, gray-level nonuniformity, and run-length nonuniformity with correlation coefficients of 0.92, −0.96, 0.94, and 0.98 for the CT images and 0.95, −0.97, 0.98, and 0.98 for the CE-CT images. After volume normalization, the correlation coefficients decreased substantially. For the data obtained from the CT images, the results of the Wilcoxon rank-sum test were significant when volume thresholds of 5–15 cm3 were used. No volume threshold was shown to be significant for the CE-CT data. Conclusion: Equations for four features that have been used in several published studies were found to be volume-dependent. Future studies should consider implementing normalization factors or removing these features entirely to prevent this potential source of redundancy or bias. This work was supported in part by National Cancer Institute grant R03CA178495-01. Xenia Fave is a recipient of the American Association of Physicists in Medicine Graduate Fellowship.

60 APPLIED LIFE SCIENCES↗

SU-E-J-248: Contributions of Tumor and Stroma Phenotyping in Computer-Aided Diagnosis

Purpose: To gain insight into the role of parenchyma stroma in the characterization of breast tumors by incorporating computerized mammographic parenchyma assessment into breast CADx in the task of distinguishing between malignant and benign lesions. Methods: This study was performed on 182 biopsy-proven breast mass lesions, including 76 benign and 106 malignant lesions. For each full-field digital mammogram (FFDM) case, our quantitative imaging analysis was performed on both the tumor and a region-of-interest (ROI) from the normal contralateral breast. The lesion characterization includes automatic lesion segmentation and feature extraction. Radiographic texture analysis (RTA) was applied on the normal ROIs to assess the mammographic parenchymal patterns of these contralateral normal breasts. Classification performance of both individual computer extracted features and the output from a Bayesian artificial neural network (BANN) were evaluated with a leave-one-lesion-out method using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the figure of merit. Results: Lesion characterization included computer-extracted phenotypes of spiculation, size, shape, and margin. For parenchymal pattern characterization, five texture features were selected, including power law beta, contrast, and edge gradient. Merging of these computer-selected features using BANN classifiers yielded AUC values of 0.79 (SE=0.03) and 0.67 (SE=0.04) in the task of distinguishing between malignant and benign lesions using only tumor phenotypes and texture features from the contralateral breasts, respectively. Incorporation of tumor phenotypes with parenchyma texture features into the BANN yielded improved classification performance with an AUC value of 0.83 (SE=0.03) in the task of differentiating malignant from benign lesions. Conclusion: Combining computerized tumor and parenchyma phenotyping was found to significantly improve breast cancer diagnostic accuracy highlighting the need to consider both tumor and stroma in decision making. Funding: University of Chicago Dean Bridge Fund, NCI U24-CA143848-05, P50-CA58223 Breast SPORE program, and Breast Cancer Research Foundation. COI: MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a cofounder and stockholder in Quantitative Insights.

60 APPLIED LIFE SCIENCES↗

SU-E-J-260: Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy

Purpose: To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy. Methods: Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver on pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student’s t-test to rank the effectiveness of features for the prediction of response. Results: The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09). Conclusion: Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT. This work was supported in part by the National Cancer Institute Grant R01CA172638.

60 APPLIED LIFE SCIENCES↗

Automatic anatomy recognition in whole-body PET/CT images

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.

61 RADIATION PROTECTION AND DOSIMETRY↗

SU-G-BRA-06: Quantification of Tracking Performance of a Multi-Layer Electronic Portal Imaging Device

Purpose: The purpose of this study was to quantify the improvement in tumor tracking, with and without fiducial markers, afforded by employing a multi-layer (MLI) electronic portal imaging device (EPID) over the current state-of-the-art, single-layer, digital megavolt imager (DMI) architecture. Methods: An ideal observer signal-to-noise ratio (d’) approach was used to quantify the ability of an MLI EPID and a current, state-of-the-art DMI EPID to track lung tumors from the treatment beam’s-eye-view. Using each detector modulation transfer function (MTF) and noise power spectrum (NPS) as inputs, a detection task was employed with object functions describing simple three-dimensional Cartesian shapes (spheres and cylinders). Marker-less tumor tracking algorithms often use texture discrimination to differentiate benign and malignant tissue. The performance of such algorithms is simulated by employing a discrimination task for the ideal observer, which measures the ability of a system to differentiate two image quantities. These were defined as the measured textures for benign and malignant lung tissue. Results: The NNPS of the MLI ∼25% of that of the DMI at the expense of decreased MTF at intermediate frequencies (0.25≤<f≤<0.5 cycles/mm). Lung tissue textures were found to follow a power-law trend in the frequency domain. It was found that for both fiducial marker tracking and lung tissue texture differentiation, the signal power of each task is retained at low frequencies. Thus, little difference may be observed in signal power when DMI and MLI are compared. In all cases, improvements in tracking were greater than a factor of 2. Conclusion: MLI performance in tumor tracking is greatly improved by the additional imager layers. This implies that further improvements in tracking may be gained through increasing the thickness of each MLI layer. For tracking, the MLI performance is limited by noise response. Losses in MTF result in negligible differences in d’. The project was partially supported by a grant from Varian Medical Systems, Inc. and grant No. R01CA188446-01 from the National Cancer Institute.

60 APPLIED LIFE SCIENCES↗

braggedgemodeling

braggedgemodeling (bem) is an open-source Python package for modeling neutron Bragg-edge imaging. It computes the wavelength-dependent total neutron cross-section of a material (coherent and incoherent elastic, coherent and incoherent inelastic scattering, and absorption) from its crystal structure, and implements the March-Dollase texture model and the Jorgensen peak profile, supporting quantitative analysis of energy-resolved neutron imaging data (phase, stress/strain, and texture). Published in the Journal of Open Source Software (2018).

Lin, Jiao [Oak Ridge National Laboratory (ORNL), O↗

Evaluation of soil manipulation to prepare engineered earthen waste covers for revegetation

Seven ripping treatments designed to improve soil physical conditions for revegetation were compared on a test pad simulating an earthen cover for a waste disposal cell. The field test was part of study of methods to convert compacted-soil waste covers into evapotranspiration covers. The test pad consisted of a compacted layer of fine-textured soil simulating a barrier protection layer overlain by a gravelly sand bedding layer and a cobble armor layer. Treatments included combinations of soil-ripping implements (conventional shank [CS], wing-tipped shank [WTS], and parabolic oscillating shank with wings [POS]), ripping depths, and number of passes. Dimensions, dry density, moisture content, and particle size distribution of disturbance zones were determined in two trenches excavated across rip rows. The goal was to create a root-zone dry density between 1.2 and 1.6 Mg m-3 and a seedbed soil texture ranging from clay loam to sandy loam with low rock content. All treatments created V-shaped disturbance zones as measured on trench faces. Disturbance zone size was most influenced by ripping depth. Winged implements created larger disturbance zones. All treatments lifted fines into the bedding layer, moved gravel and cobble down into the fine-textured protection layer, and thereby disrupted the capillary barrier at the interface. Changes in dry density within disturbance zones were comparable for the CS and WTS treatments but were highly variable among POS treatments. Water content increased in the bedding layer and decreased in the protection layer after ripping. The POS at 1.2-m depth and two passes created the largest zone with a low dry density (1.24 Mg m-3) and the most favorable seedbed soil texture (gravely silt loam). Furthermore, ripping also created large soil aggregates and voids in the protection layer that may produce preferential flow paths and reduce water storage capacity.

54 ENVIRONMENTAL SCIENCES↗

Modeling the thermal deformation of TATB-based explosives. Part 1: Thermal expansion of “neat-pressed” polycrystalline TATB

We detail a modeling approach to simulate the anisotropic thermal expansion of polycrystalline (1,3,5-triamino-2,4,6-trinitrobenzene) TATB-based explosives that utilizes microstructural information including porosity, crystal aspect ratio, and processing-induced texture. This report, the first in a series, focuses on nonlinear thermal expansion of “neat-pressed” polycrystalline TATB specimens which do not contain any binder; additional complexities related to polymeric binder and irreversible ratcheting behavior are briefly discussed, however detailed investigation of these aspects are deferred to subsequent reports. In this work we have, for the first time, developed a mesoscale continuum model relating the thermal expansion of polycrystal TATB specimens to their microstructural characteristics. A self-consistent homogenization procedure is used to relate macroscopic thermoelastic response to the constitutive behavior of single-crystal TATB. The model includes a representation of grain aspect ratio, porosity, and crystallographic texture attributed to the consolidation process. A quantitative model is proposed to describe the evolution of preferred orientation of graphitic planes in TATB during consolidation and an algorithm constructed to develop a discrete representation of the associated orientation distribution function. Analytical and numerical solutions using this model are shown to produce textures consistent with previous measurements and characterization for isostatic and uniaxial “die-pressed” specimens. Predicted thermal strain versus temperature for textured specimens are shown to be in agreement with corresponding experimental measurements. Using the developed modeling approach, several simulations have been run to investigate the influence of microstructure on macroscopic thermal expansion behavior. Results from these simulations are used to identify qualitative trends. Implications of the identified trends are discussed in the context of thermal deformation of engineered components whose consolidation process is generally more complex than isostatic or die-pressed specimens. Finally, an envisioned application of the modeling approach to simulating thermal expansion of weapon systems and components is outlined along with necessary future work to introduce the effects of binder and ratcheting behavior. Key conclusions from this work include the following. Both porosity and grain aspect ratio have an influence on the thermal expansion of polycrystal TATB considering realistic material variability. Thepreferred orientation of the single crystal TATB [001] poles within a polycrystal gives rise to pronounced anisotropy of the macroscopic thermal expansion. The extent of this preferred orientation depends on the magnitude of deformation, and consequently, is expected to vary spatially throughout manufactured components much like porosity. The modeling approach presented here has utility toward bringing spatially variable microstructural features into macroscale system engineering modelsAbstract Not Provided

36 MATERIALS SCIENCE↗

Interactive Effects of Climate Change and Decomposer Communities on the Stabilization of Wood-Derived Carbon Pools: Catalyst for a New Study

Globally, forest soils store ~two-thirds as much carbon (C) as the atmosphere. Although wood makes up the majority of forest biomass, the importance of wood contributions to soil C pools is unknown. Even with recent advances in the mechanistic understanding of soil processes, integrative studies tracing C input pathways and biological fluxes within and from soils are lacking. Therefore, our research objectives were to assess the impact of different fungal decay pathways (i.e., white-rot versus brown-rot)—in interaction with wood quality, soil temperature, wood location (i.e., soil surface and buried in mineral soil), and soil texture—on the transformation of woody material into soil CO 2 efflux, dissolved organic carbon (DOC), and soil C pools. The use of 13 C-depleted woody biomass harvested from the Rhinelander, WI free-air carbon dioxide enrichment (Aspen-FACE) experiment affords the unique opportunity to distinguish the wood-derived C from other soil C fluxes and pools. We established 168 treatment plots across six field sites (three sand and three loam textured soil). Treatment plots consisted of full-factorial design with the following treatments: 1. Wood chips from elevated CO 2 , elevated CO 2 + O 3 , or ambient atmosphere AspenFACE treatments; 2. Inoculated with white rot (Bjerkandera adusta) or brown rot (Gloeophyllum sepiarium) pure fungal cultures, or the original suite of endemic microbial community on the logs; and 3. Buried (15cm in soil as a proxy for coarse roots) or surface applied wood chips. We also created a warming treatment using open-topped, passive warming chambers on a subset of the above treatments. Control plots with no added wood (“no chip control”) were incorporated into the research design. Soils were sampled for initial δ 13 C values, CN concentrations, and bulk density. A subset of plots were instrumented with lysimeters for sampling soil water and temperature data loggers for measuring soil temperatures. To determine the early pathways of decomposition, we measured soil surface CO 2 efflux, dissolved organic C (DOC), and DO 13 C approximately monthly over two growing seasons from a subsample of the research plots. To determine the portion of soil surface CO 2 efflux attributable to wood-derived C, we used Keeling plot techniques to estimate the associated δ 13 C values of the soil CO 2 efflux. We measured the δ 13 CO 2 once during the peak of each growing season. Initial values for soil δ 13 C values and CN concentrations averaged across the six sites were -26.8‰ (standard error = 0.04), 2.46% (se = 0.11), and 0.15% (se = 0.01), respectively. The labeled wood chips from the Aspen FACE treatments had an average δ13C value of -39.5‰ (se 0.10). The >12 ‰ isotopic difference between the soil and wood chip δ 13 C values provides the basis for tracking the wood-derived C through the early stages of decomposition and subsequent storage in the soil. Across our six research sites, average soil surface CO 2 efflux ranged from 1.04 to 2.00 g CO 2 m -2 h -1 for the first two growing seasons. No wood chip controls had an average soil surface CO 2 efflux of 0.67 g CO 2 m -2 h -1 or about half of that of the wood chip treatment plots. Wood-derived CO 2 efflux was higher for loam textured soils relative to sands (0.70 and 0.54 g CO 2 m -2 h -1 , respectively; p = 0.045)), for surface relative to buried wood chip treatments (0.92 and 0.39 g CO 2 m -2 h -1 , respectively; p < 0.001), for warmed relative to ambient temperature treatments (0.99 and 0.78 g CO 2 m -2 h -1 , respectively; 0.004), and for natural rot relative to brown and white rots (0.93, 0.82, and 0.78 g CO 2 m -2 h -1 , respectively; p = 0.068). Our first two growing seasons of soil surface CO 2 efflux data show that wood chip location (i.e., surface vs. buried chip application) is very important, with surface chips loosing twice the wood-derived CO 2 . The DOC data support this trend for greater loss of ecosystem C from surface chips. This has strong implications for the importance of root and buried wood for ecosystem C retention. This strong chip location effect on wood-derived C loss was significantly modified by soil texture, soil temperature, decomposer communities, and wood quality as effected by potential future CO 2 and O 3 levels.

54 ENVIRONMENTAL SCIENCES↗

U-10ZR XRD Follow-on Report: Effects of Background; Extrusion, Heat Treatment

This report is a follow-on report to the previous report, INL/EXT-17-43392 “Modified Peak Ratio Method for Quantitative Texture Analysis of U10Zr Alloys” (Leah Squires, Kevin Tolman, Fidelma DiLemma, Thomas Hartman, and Douglas Porter, [June 2017]) The objective of this work is to investigate the effects of epoxy metallurgical (met) mounts on x-ray diffraction (XRD) and determine a method to account for background separate from the epoxy met mount contribution. Also, both the texturing effects of hot extrusion and cooling in ambient air (i.e., air quenching) on depleted uranium (DU)10Zr rods and the effects of heat treatments on these as-extruded DU10Zr rods are reported to determine the characteristics of texturing of the as-extruded materials and the degree of texture lost through heat treatment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Differential Organic Carbon Mineralization Responses to Soil Moisture in Three Different Soil Orders Under Mixed Forested System: Supporting Data

This data contains data from 90-day long incubation study which aimed to look at the soil moisture-texture relationship on soil organic carbon (SOC) cycling. Soils were collected from three distinct soil textures from mixed forests in 2017: sandy (Georgia, 2017-05-01), loamy (Missouri, 2017-06-14) and clayey (Texas, December 2017) were incubated at different soil moisture levels (air-dried, 25% water holding capacity (WHC), 50% WHC, 100% WHC and 175% WHC) at room temperature for a period of 90 days. Files contain microbial respiration, active and slow SOC pools, and their respective mineralization rates, extractable organic carbon (C), and C-acquiring extracellular enzymes. Findings from these data were used in Singh et al. (2021). This study aimed to examine the interactive effect of soil moisture and texture on SOC mineralization. Soil samples of three distinct textures (sandy, loamy, and clayey) were collected from mixed forests of Georgia, Missouri, and Texas, respectively. Soil cores of 5 cm diameter were collected from numerous random locations at each site from 0-15 cm depth after scraping the litter layer and mixed thoroughly to obtain a composite sample per site. Three additional soil cores were collected to determine the WHC using pressure plate extractors. Soil samples were composited, and triplicate soil samples were incubated in mason jars for a period of 90 days at room temperature under different moisture regimes: air dried, 25% WHC, 50% WHC, at WHC and 100% saturation. Soil respiration was measured weekly, and destructive sampling was conducted at 1, 15, 60, and 90 days to determine extractable organic C, C acquiring enzyme activity, and active and slow SOC pools with their respective mineralization rates. The C acquiring enzyme activity was the total activity of α-glucosidase, β-glucosidase, cellobiohydrolase, and β-xylosidase enzymes. Gas samples for microbial respiration measurements were collected from headspace of incubation jars through the sampling ports on the lids and then analyzed using a Shimadzu Gas Chromatograph (GC-2014). Prior to sampling, the vials were evacuated. Blank correction was also done by collecting gas samples from empty incubation jars. Double pool exponential decay model was used in SigmaPlot to determine the active and slow SOC pools and their mineralization rates (Farrar et al., 2012; Jagadamma et al., 2014). The C-acquiring extracellular enzymes were measured using the microplate method by German et al., (2011). Microbial community structure was determined using the phospholipid fatty acid (PLFA) and neutral lipid fatty acid (NLFA) analyses (Buyer and Sasser, 2012). This dataset has seven data files provided in comma-separate (*.csv) format. Additional metadata are provided: seven data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

Carbon acquiring enzyme activity↗

One-Pot Synthesis of Melamine Formaldehyde Resin-Derived N-Doped Porous Carbon for CO 2 Capture Application

The design and synthesis of porous carbons for CO 2 adsorption have attracted tremendous interest owing to the ever-soaring concerns regarding climate change and global warming. Herein, for the first time, nitrogen-rich porous carbon was prepared with chemical activation (KOH) of commercial melamine formaldehyde resin (MF) in a single step. It has been shown that the porosity parameters of the as-prepared carbons were successfully tuned by controlling the activating temperature and adjusting the amount of KOH. Thus, as-prepared N-rich porous carbon shows a large surface area of 1658 m 2 /g and a high N content of 16.07 wt%. Benefiting from the unique physical and textural features, the optimal sample depicted a CO 2 uptake of up to 4.95 and 3.30 mmol/g at 0 and 25 °C under 1 bar of pressure. More importantly, as-prepared adsorbents show great CO 2 selectivity over N 2 and outstanding recyclability, which was prominently important for CO 2 capture from the flue gases in practical application. An in-depth analysis illustrated that the synergetic effect of textural properties and surface nitrogen decoration mainly determined the CO 2 capture performance. However, the textural properties of carbons play a more important role than surface functionalities in deciding CO 2 uptake. In view of cost-effective synthesis, outstanding textural activity, and the high adsorption capacity together with good selectivity, this advanced approach becomes valid and convenient in fabricating a unique highly efficient N-rich carbon adsorbent for CO 2 uptake and separation from flue gases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modeling soil CO<sub>2</sub> production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0

Abstract. The flux of CO2 from the soil to the atmosphere (soil respiration, Rsoil) is a major component of the global carbon (C) cycle. Methods to measure and model Rsoil, or partition it into different components, often rely on the assumption that soil CO2 concentrations and fluxes are in steady state, implying that Rsoil is equal to the rate at which CO2 is produced by soil microbial and root respiration. Recent research, however, questions the validity of this assumption. Thus, the aim of this work was two-fold: (1) to describe a non-steady state (NSS) soil CO2 transport and production model, DETECT, and (2) to use this model to evaluate the environmental conditions under which Rsoil and CO2 production are likely in NSS. The backbone of DETECT is a non-homogeneous, partial differential equation (PDE) that describes production and transport of soil CO2, which we solve numerically at fine spatial and temporal resolution (e.g., 0.01 m increments down to 1 m, every 6 h). Production of soil CO2 is simulated for every depth and time increment as the sum of root respiration and microbial decomposition of soil organic matter. Both of these factors can be driven by current and antecedent soil water content and temperature, which can also vary by time and depth. We also analytically solved the ordinary differential equation (ODE) corresponding to the steady-state (SS) solution to the PDE model. We applied the DETECT NSS and SS models to the six-month growing season period representative of a native grassland in Wyoming. Simulation experiments were conducted with both model versions to evaluate factors that could affect departure from SS, such as (1) varying soil texture; (2) shifting the timing or frequency of precipitation; and (3) with and without the environmental antecedent drivers. For a coarse-textured soil, Rsoil from the SS model closely matched that of the NSS model. However, in a fine-textured (clay) soil, growing season Rsoil was ∼ 3 % higher under the assumption of NSS (versus SS). These differences were exaggerated in clay soil at daily time scales whereby Rsoil under the SS assumption deviated from NSS by up to 35 % on average in the 10 days following a major precipitation event. Incorporation of antecedent drivers increased the magnitude of Rsoil by 15 to 37 % for coarse- and fine-textured soils, respectively. However, the responses of Rsoil to the timing of precipitation and antecedent drivers did not differ between SS and NSS assumptions. In summary, the assumption of SS conditions can be violated depending on soil type and soil moisture status, as affected by precipitation inputs. The DETECT model provides a framework for accommodating NSS conditions to better predict Rsoil and associated soil carbon cycling processes.

54 ENVIRONMENTAL SCIENCES↗

Utilization of ERTS data to detect plant diseases and nutrient deficiencies, soil types and moisture levels

The author has identified the following significant results. Findings demonstrate the feasibility of delineating major terrain features, land uses, and crop species through computerized analyses. Channel 6 appears to give the most information for making separations of this type. By enlarging satellite imagery and visually comparing this with high altitude aerial photographs, locating small terrain features and cropland areas on satellite imagery is greatly facilitated. Forest types are discernable on the 2402 imagery with a #25 filter: pine stands have dark tones, hardwood stands have light tones, and pine-hardwood have intermediate tones. No textural differences are evident on this type of imagery. However, on the 2424 imagery with #89B filter, textural differences are evident but tonal differences are absent. Areas of considerable texture are interpreted as stands of high volume while areas of suppressed texture are of low volume. The 2402 imagery with a #57 filter appears to have little information of importance in timber inventory.

Parks, W. L.↗