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At least 1,801 records · Page 100

The Imprint of Southern Ocean Storms on Modeled Surface Chlorophyll, Their Drivers and Satellite Biases

Southern Ocean (SO) phytoplankton chlorophyll is highly variable on sub-seasonal time scales. Although the SO is the windiest ocean basin globally, it is not conclusively understood how storms impact SO phytoplankton dynamics. Much of our existing knowledge stems from satellites, but biases due to data gaps from cloud cover and low solar angles remain unquantified. Here, we use ocean–sea-ice simulations with the Community Earth System Model to quantify the climatological 1997–2018 imprint of storms on chlorophyll and phytoplankton dynamics in the ice-free SO. Additionally, by comparing the full-field model output to synthetic satellite observations, we quantify sampling biases in satellite-derived estimates. We find that both the sign and the magnitude of the average surface chlorophyll imprint vary substantially across storms but last for at least 4 days after the storm passing. Based on our analysis, more than one third of the storms explain the majority of local non-seasonal chlorophyll variability, but satellite-derived storm imprints are often too large in magnitude. On the day of the storm passing, changes in vertical mixing predominantly cause surface chlorophyll anomalies, and reduced light availability due to enhanced cloud cover outweighs the enhanced nutrient availability due to entrainment. Interestingly, storms imprint differently on total net primary production than on surface chlorophyll, demonstrating the difficulty to derive carbon-cycle impacts from a surface-chlorophyll assessment. With SO future storm activity projected to increase, complementing satellite observations with other observing technologies, for example, profiling floats, is necessary to better constrain how storms impact biological carbon cycling in the SO.

Nissen, Cara [University of Colorado, Boulder, CO ↗

The effect of changes in sea surface temperature on linear growth of Porites coral in Ambon Bay

Coral is one of the most important organisms in the coral reef ecosystem. There are several factors affecting coral growth, one of them is changes in sea surface temperature (SST). The purpose of this research is to understand the influence of SST variability on the annual linear growth of Porites coral taken from Ambon Bay. The annual coral linear growth was calculated and compared to the annual SST from the Extended Reconstructed Sea Surface Temperature version 3b (ERSST v3b) model. Coral growth was calculated by using Coral X-radiograph Density System (CoralXDS) software. Coral sample X-radiographs were used as input data. Chronology was developed by calculating the coral’s annual growth bands. A pair of high and low density banding patterns observed in the coral’s X-radiograph represent one year of coral growth. The results of this study shows that Porites coral extents from 2001-2009 and had an average growth rate of 1.46 cm/year. Statistical analysis shows that the annual coral linear growth declined by 0.015 cm/year while the annual SST declined by 0.013°C/year. SST and the annual linear growth of Porites coral in the Ambon Bay is insignificantly correlated with r=0.304 (n=9, p>0.05). This indicates that annual SST variability does not significantly influence the linear growth of Porites coral from Ambon Bay. It is suggested that sedimentation load, salinity, pH or other environmental factors may affect annual linear coral growth.

54 ENVIRONMENTAL SCIENCES↗

Statistical Analyses of Post Irradiation Experiments of the BWR Forsmark

Computer codes which are to be used in nuclear safety research need to be validated prior to their application. For depletion codes, this validation can be done using data from post irradiation experiments (PIE). These experiments consist of a radiochemical analysis of small parts of a fuel rod (FR), which has been irradiated in a nuclear power plant as part of a fuel assembly (FA). This analysis allows the deduction of the isotopic composition of nuclides important for the desired application case. In addition the operation history at the sample location needs to be known and reported with the analysis results. In our work, we perform uncertainty and sensitivity analyses of a model based on a sample from the boiling water reactor (BWR) Forsmark-3 in order to deduce the influence of uncertain system parameters on the nuclide composition of the depleted fuel. In a second step we compare the resulting uncertainties of the nuclide densities with the deviation between their calculated and experimental values. The goal is to obtain quantitative uncertainty data which can be accounted for in a validation procedure. We use the Monte Carlo sampling approach, i.e. repeating the model calculation using statistically varied sets of the uncertain input parameters and analyzing the distributions of the result values statistically. BWR depletion calculations are especially demanding due to the increased complexity and heterogeneity compared to pressurized water reactors (PWR): extensively variable enrichments, complex FA geometries, partial length FRs, strong axially varying moderator density and the common use of gadolinium as burnable absorber in some of the FRs. However, the precise and correct calculation of BWR-depletion is extremely important in view of a possible application in burnup credit for BWR FAs. Therein credit is taken for the reduced reactivity of spent fuel due to the depletion of {sup 235}U for transport and intermediate or final storage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Exploration of multifidelity UQ sampling strategies for computer network applications.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Here, two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

97 MATHEMATICS AND COMPUTING↗

Detecting Magnon-phonon coupling in yttrium iron garnet with variable temperature STEM-EELS

Magnons, quanta of spin wave excitations in magnetically ordered materials, have been identified as candidates for several potentially transformative technologies in recent years. Macroscopic techniques, such as neutron scattering or Raman spectroscopy, can be used to identify and analyze magnons, but provide relatively delocalized information about the sample. Understanding how the bonding and local structure of a material interacts with, and influences, the magnon population in a material is a crucial step toward the ability to produce any real-world application utilizing magnons. By leveraging the combined spatial resolution of scanning transmission electron microscopy (STEM) and the energy resolution of monochromated electron energy-loss spectroscopy (EELS) nanoscale analysis of magnons can be performed. While the weak interaction of magnons with the electron beam makes magnon EELS challenging on reasonable timescales, magnon-phonon coupling can be leveraged to understand magnons through their effect on the more easily measured phonons. Here, we examine yttrium iron garnet (YIG) flakes, and demonstrate non-linear, temperature-dependent shifts in the phonon frequencies, consistent with previously described magnon-phonon coupling effects. The ability to measure the temperature-dependence of vibrational frequencies with high precision in individual nanoscale flakes, demonstrates the ability to study magnon-phonon coupling in the STEM with unprecedented spatial resolution.

Reifsnyder, Alexander [ORNL]↗

Anaerobic digestion of organic fraction of municipal solid waste combining two pretreatment modalities, high temperature microwave and hydrogen peroxide

Highlights: Black-Right-Pointing-Pointer Microwave and H{sub 2}O{sub 2} pretreatment were studied to enhance anaerobic digestion of organic waste. Black-Right-Pointing-Pointer The whole waste pretreated at 115 Degree-Sign C or 145 Degree-Sign C had the highest biogas production. Black-Right-Pointing-Pointer Biogas production of the whole waste decreased at 175 Degree-Sign C due to formation of refractory compounds. Black-Right-Pointing-Pointer Pretreatment to 145 Degree-Sign C and 175 Degree-Sign C were the best when considering only the free liquid fraction. Black-Right-Pointing-Pointer H{sub 2}O{sub 2} pretreatment had a lag phase and the biogas production was not higher than MW pretreated samples. - Abstract: In order to enhance anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW), pretreatment combining two modalities, microwave (MW) heating in presence or absence of hydrogen peroxide (H{sub 2}O{sub 2}) were investigated. The main pretreatment variables affecting the characteristics of the OFMSW were temperature (T) via MW irradiation and supplemental water additions of 20% and 30% (SWA20 and SW30). Subsequently, the focus of this study was to evaluate mesophilic batch AD performance in terms of biogas production, as well as changes in the characteristics of the OFMSW post digestion. A high MW induced temperature range (115-175 Degree-Sign C) was applied, using sealed vessels and a bench scale MW unit equipped with temperature and pressure controls. Biochemical methane potential (BMP) tests were conducted on the whole OFMSW as well as the liquid fractions. The whole OFMSW pretreated at 115 Degree-Sign C and 145 Degree-Sign C showed 4-7% improvement in biogas production over untreated OFMSW (control). When pretreated at 175 Degree-Sign C, biogas production decreased due to formation of refractory compounds, inhibiting the digestion. For the liquid fraction of OFMSW, the effect of pretreatment on the cumulative biogas production (CBP) was more pronounced for SWA20 at 145 Degree-Sign C, with a 26% increase in biogas production after 8 days of digestion, compared to the control. When considering the increased substrate availability in the liquid fraction after MW pretreatment, a 78% improvement in biogas production vs. the control was achieved. Combining MW and H{sub 2}O{sub 2} modalities did not have a positive impact on OFMSW stabilization and enhanced biogas production. In general, all samples pretreated with H{sub 2}O{sub 2} displayed a long lag phase and the CBP was usually lower than MW irradiated only samples. First order rate constant was calculated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Exploring unsupervised top tagging using Bayesian inference

Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable \tilde{A}_{3} A ̃ 3 , and in the other we consider the more traditional \tau_{3}/\tau_{2} τ 3 / τ 2 N N -subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC \sim 0.80-0.81 ∼ 0.80 − 0.81 and accuracies of about 69% - − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.

Alvarez, Ezequiel↗

ComStock Measure Documentation: Variable-Speed Pumps

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - variable speed pumps - and briefly introduces key results. The full public data set can be accessed on the ComStock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Montana Cropland Enrolled Farm Fields Carbon Sequestration Field Sampling, Measurement, Monitoring, and Verification: Application of Visible-Near Infrared Diffuse Reflectance Spectroscopy (VNIR) and Laser-induced Breakdown Spectroscopy (LIBS)

There is growing need for rapid, accurate, and inexpensive methods to measure, and verify soil organic carbon (SOC) change for national greenhouse gas accounting and the development of a soil carbon trading market. Laboratory based soil characterization typically requires significant soil processing, which is time and resource intensive. This severely limits application for large-region soil characterization. Thus, development of rapid and accurate methods for characterizing soils are needed to map soil properties for precision agriculture applications, improve regional and global soil carbon (C) stock and flux estimates and efficiently map sub-surface metal contamination, among others. The greatest gains for efficient soil characterization will come from collecting soil data in situ, thus minimizing soil sample transportation, processing, and lab-based measurement costs. Visible and near-infrared diffuse reflectance spectroscopy (VisNIR) and laser-induced breakdown spectroscopy (LIBS) are two complementary, yet fundamentally different spectroscopic techniques that have the potential to meet this need. These sensors have the potential to be mounted on a soil penetrometer and deployed for rapid soil profile characterization at field and landscape scales. Details of sensor interaction, efficient data management, and appropriate statistical analysis techniques for model calibrations are first needed. In situ or on-the-go VisNIR spectroscopy has been proposed as a rapid and inexpensive tool for intensively mapping soil texture and organic carbon (SOC). While lab-based VisNIR has been established as a viable technique for estimating various soil properties, few experiments have compared the predictive accuracy of on-the-go and lab-based VisNIR. Eight north central Montana wheat fields were intensively interrogated using on-the-go and lab-based VisNIR. Lab-based spectral data consistently provided more accurate predictions than on-the-go data. However, neither in situ nor lab-based spectroscopy yielded even semi-quantitative SOC predictions. There was little SOC variability to explain across the eight fields, and on-the-go VisNIR was not able to capture the subtle SOC variability in these Montana soils. With more variation in soil clay content compared to SOC, both lab and on-the-go VisNIR showed better explanatory power. There are several potential explanations for poor on-the-go predictive accuracy: soil heterogeneity, field moisture, consistent sample presentation, and a difference between the spatial support of on-the-go measurements and soil samples collected for laboratory analyses. Though the current configuration of a commercially available on-the-go VisNIR system allows for rapid field scanning, on-the-go soil processing (i.e. drying, crushing, and sieving) could improve soil carbon predictions. Laser-induced breakdown spectroscopy (LIBS) is an emerging elemental analysis technology with the potential to provide rapid, accurate and precise analysis of soil constituents, such as carbon, in situ across landscapes. The research team evaluated the accuracy of LIBS for measuring soil profile carbon in field-moist, intact soil cores simulating conditions that might be encountered by a probe-mounted LIBS instrument measuring soil profile carbon in situ. Over the course of three experiments, more than120 intact soil cores from eight north central Montana wheat fields and the Washington State University (WSU) Cook Agronomy Farm near Pullman, WA were interrogated with LIBS for rapid total carbon (TC), inorganic carbon (IC), and SOC determination. Partial least squares regression models were derived and independently validated at field- and regional scales. Researchers obtained the best LIBS validation predictions for IC followed by TC and SOC. Laser-induced breakdown spectroscopy is fundamentally an elemental analysis technique, yet LIBS PLS2 models appeared to discriminate IC from TC. Regression coefficients from initial models suggested a reliance upon stoichiometric relationships between carbon (247.8 nm) and other elements related to total and inorganic carbon in the soil matrix [Ca (210.2 nm, 211.3 nm, and 220.9 nm), Mg (279.55-280.4 nm, 285.26 nm), and Si (251.6 nm, 288.1 nm)]. Expanding the LIBS spectral range to capture emissions from a broader range of elements related to soil organic matter was explored using two spectrometer systems to improve SOC predictions. Results for increasing the spectral range of LIBS to the full 200-800 nm found modest gains in prediction accuracy for IC, but no gains for predicting TC or SOC. Poor SOC predictions are likely a function of (1) the lack of a consistent/definable molecular composition of SOC, (2) relatively little variation in SOC across field sites, and (3) inorganic carbon constituting the primary form of soil carbon, particularly for Montana soils.

54 ENVIRONMENTAL SCIENCES↗

ARM - Midlatitude Continental Convective Clouds Microwave Radiometer Profiler (jensen-mwr)

A major component of the Mid-latitude Continental Convective Clouds Experiment (MC3E) field campaign was the deployment of an enhanced radiosonde array designed to capture the vertical profile of atmospheric state variables (pressure, temperature, humidity wind speed and wind direction) for the purpose of deriving the large-scale forcing for use in modeling studies. The radiosonde array included six sites (enhanced Central Facility [CF-1] plus five new sites) launching radiosondes at 3-6 hour sampling intervals. The network will cover an area of approximately (300)2 km2 with five outer sounding launch sites and one central launch location. The five outer sounding launch sites are: S01 Pratt, KS [ 37.7oN, 98.75oW]; S02 Chanute, KS [37.674, 95.488]; S03 Vici, Oklahoma [36.071, -99.204]; S04 Morris, Oklahoma [35.687, -95.856]; and S05 Purcell, Oklahoma [34.985, -97.522]. Soundings from the SGP Central Facility during MC3E can be retrieved from the regular ARM archive. During routine MC3E operations 4 radiosondes were launched from each of these sites (approx. 0130, 0730, 1330 and 1930 UTC). On days that were forecast to be convective up to four additional launches were launched at each site (approx. 0430, 1030, 1630, 2230 UTC). There were a total of approximately 14 of these high frequency launch days over the course of the experiment. These files contain brightness temperatures observed at Purcell during MC3E. The measurements were made with a 5 channel (22.235, 23.035, 23.835, 26.235, 30.000GHz) microwave radiometer at one minute intervals. The results have been separated into daily files and the day of observations is indicated in the file name. All observations were zenith pointing. Included in the files are the time variables base_time and time_offset. These follow the ARM time conventions. Base_time is the number seconds since January 1, 1970 at 00:00:00 for the first data point of the file and time_offset is the offset in seconds from base_time.

54 ENVIRONMENTAL SCIENCES↗

ARM - Midlatitude Continental Convective Clouds - Ultra High Sensitivity Aerosol Spectrometer(tomlinson-uhsas)

Ultra High Sensitivity Aerosol Spectrometer (UHSASA) A major component of the Mid-latitude Continental Convective Clouds Experiment (MC3E) field campaign was the deployment of an enhanced radiosonde array designed to capture the vertical profile of atmospheric state variables (pressure, temperature, humidity wind speed and wind direction) for the purpose of deriving the large-scale forcing for use in modeling studies. The radiosonde array included six sites (enhanced Central Facility [CF-1] plus five new sites) launching radiosondes at 3-6 hour sampling intervals. The network will cover an area of approximately (300)2 km2 with five outer sounding launch sites and one central launch location. The five outer sounding launch sites are: S01 Pratt, KS [ 37.7oN, 98.75oW]; S02 Chanute, KS [37.674, 95.488]; S03 Vici, Oklahoma [36.071, -99.204]; S04 Morris, Oklahoma [35.687, -95.856]; and S05 Purcell, Oklahoma [34.985, -97.522]. Soundings from the SGP Central Facility during MC3E can be retrieved from the regular ARM archive. During routine MC3E operations 4 radiosondes were launched from each of these sites (approx. 0130, 0730, 1330 and 1930 UTC). On days that were forecast to be convective up to four additional launches were launched at each site (approx. 0430, 1030, 1630, 2230 UTC). There were a total of approximately 14 of these high frequency launch days over the course of the experiment. These files contain brightness temperatures observed at Purcell during MC3E. The measurements were made with a 5 channel (22.235, 23.035, 23.835, 26.235, 30.000GHz) microwave radiometer at one minute intervals. The results have been separated into daily files and the day of observations is indicated in the file name. All observations were zenith pointing. Included in the files are the time variables base_time and time_offset. These follow the ARM time conventions. Base_time is the number seconds since January 1, 1970 at 00:00:00 for the first data point of the file and time_offset is the offset in seconds from base_time.

54 ENVIRONMENTAL SCIENCES↗

A multi-century tree-ring record of spring flooding on the Mississippi River

Widespread destructive flooding is a common phenomenon along the Lower Mississippi River, and river managers have long sought to understand the temporal variability and relevant climatic factors of the system. One of the important drawbacks to better understanding the flood regime of this and similar large river systems is the relatively short instrumental record of flooding. In this study, we present a novel, annually-resolved tree-ring record of spring flooding based on anatomically anomalous “flood rings” preserved in trees growing about 60 km downstream of the Mississippi and Ohio River confluence. Our chronology records 39 flood-ring years between 1770 and 2009 including nearly all of the observed significant floods of the 20th century as well as severe floods documented in prior centuries. Comparison of the flood ring record with stream gage observations suggests that large-magnitude floods lasting for more than 10 days, during the spring flood season, are most likely to cause a flood ring in sampled trees. Instrumental and paleo-proxy records of atmospheric circulation features relevant to spring flooding on the Lower Mississippi were also examined. Results of this research suggest that similar flood-ring records could provide important insight into flood history elsewhere in the Mississippi River system and perhaps climate variability over North America.

Engineering↗

Variable Grid Method

NETL’s Variable Grid Method (VGM) is a novel approach that leverages GIS capabilities to simultaneously visualize and quantify spatial data trends and underlying data uncertainty. The flexible VGM approach utilizes a range of spatial datasets and uncertainty quantifications, which can be calculated using data related to sample density, sample variance, interpolation error, multiple simulations, etc., to create an integrated visualization of data and uncertainty. The intuitive manner of the VGM helps communicate the relationship between uncertainty and spatial data to effectively guide research, support advanced computation analyses, and helps inform research, management and policy decisions. The VGM approach is being developed into a standalone tool designed to integrate into a variety of research workflows, providing users the capabilities to utilize the VGM for their datasets, analysis, and models in support of their decision making needs. The VGM approach was developed as part of NETL’s Offshore portfolio. It was further matured and utilized in projects associated with both NETL’s Unconventional Resources and Carbon Storage portfolios, and is now being further developed by Varigrid, LLC.

Carbon Storage↗

First detection of mid-infrared variability from an ultraluminous X-ray source Holmberg II X-1

We present mid-infrared (IR) light curves of the Ultraluminous X-ray Source (ULX) Holmberg II X-1 from observations taken between 2014 January 13 and 2017 January 5 with the Spitzer Space Telescope at 3.6 and 4.5 μm in the Spitzer Infrared Intensive Transients Survey. The mid-IR light curves, which reveal the first detection of mid-IR variability from a ULX, is determined to arise primarily from dust emission rather than from a jet or an accretion disk outflow. We derived the evolution of the dust temperature (T{sub d}∼600--800 K), IR luminosity (L{sub IR}∼3×10{sup 4} L{sub ⊙}), mass (M{sub d}∼1--3×10{sup −6} M{sub ⊙}), and equilibrium temperature radius (R{sub eq}∼10--20 au). A comparison of X-1 with a sample of spectroscopically identified massive stars in the Large Magellanic Cloud on a mid-IR color–magnitude diagram suggests that the mass donor in X-1 is a supergiant (sg) B[e]-star. The sgB[e]-interpretation is consistent with the derived dust properties and the presence of the [Fe ii] (λ=1.644 μm) emission line revealed from previous near-IR studies of X-1. We attribute the mid-IR variability of X-1 to the increased heating of dust located in a circumbinary torus. It is unclear what physical processes are responsible for the increased dust heating; however, it does not appear to be associated with the X-ray flux from the ULX, given the constant X-ray luminosities provided by serendipitous, near-contemporaneous X-ray observations around the first mid-IR variability event in 2014. Our results highlight the importance of mid-IR observations of luminous X-ray sources traditionally studied at X-ray and radio wavelengths.

79 ASTRONOMY AND ASTROPHYSICS↗

Controllable Skyrmionic Phase Transition between Néel Skyrmions and Bloch Skyrmionic Bubbles in van der Waals Ferromagnet Fe 3-δ GeTe 2

The van der Waals (vdW) ferromagnet Fe 3-δ GeTe 2 has garnered significant research interest as a platform for skyrmionic spin configurations, that is, skyrmions and skyrmionic bubbles. However, despite extensive efforts, the origin of the Dzyaloshinskii–Moriya interaction (DMI) in Fe 3-δ GeTe 2 remains elusive, making it challenging to acquire these skyrmionic phases in a controlled manner. In this study, it is demonstrated that the Fe content in Fe 3-δ GeTe 2 has a profound effect on the crystal structure, DMI, and skyrmionic phase. For the first time, a marked increase in Fe atom displacement with decreasing Fe content is observed, transforming the original centrosymmetric crystal structure into a non-centrosymmetric symmetry, leading to a considerable DMI. Additionally, by varying the Fe content and sample thickness, a controllable transition between Néel-type skyrmions and Bloch-type skyrmionic bubbles is achieved, governed by a delicate interplay between dipole–dipole interaction and the DMI. The findings offer novel insights into the variable skyrmionic phases in Fe 3-δ GeTe 2 and provide the impetus for developing vdW ferromagnet-based spintronic devices.

2D ferromagnet Fe3-δGeTe2↗

Agile Allocation in the Tundra: A Single Growing Season of Warming Increases Nutrient Availability While Decreasing Fine-Root Length

The majority of plant biomass is located belowground in Arctic ecosystems and plant roots are responsible for the uptake of the nutrients that constrain plant growth in these infertile ecosystems. Despite performing a crucial role connecting primary producers to the soil, roots are relatively understudied in the Arctic and their functional response to a rapidly warming and increasingly variable climate is unknown. Here, we assessed whether one growing season with elevated temperatures would have an impact on nutrient uptake and allocation by applying a warming technique that increased daily air temperatures by 3.2 °C. Destructive sampling was performed at the peak of the growing season to quantify biomass pools of carbon (C) and nitrogen (N), root traits, and uptake of a 15 N tracer ( 15 NH 4 + ) for the dominant plant species, Arctagrostis latifolia. We found that soil nutrient availability increased with short-term warming, but A. latifolia NH 4 + uptake remained unchanged. Fine-root length density and root biomass within the soil profile, however, were both reduced by warming. N allocation patterns across plant tissues were also altered by warming. NH 4 + uptake was best fit with a logistic model that captured the spatial relationship between roots and soil (NH 4 + uptake expressed per length fine root and NH 4 + availability expressed per unit soil volume) rather than a traditional Michaelis–Menten model. Our results indicate that short-term experimental warming can shift plant–soil interactions, suggesting that the tundra’s belowground response to elevated temperatures may be more dynamic than previously recognized.

15N tracer↗

Using an Artificial Neural Network for Nondestructive Evaluation of the Heat Treating Processes for D2 Tool Steels

In nondestructive evaluation (NDE) of heat-treated steels, different variables of the heat treating process have complex effects on outputs of NDE methods, and hence, the effect of one desired variable on NDE outputs should be evaluated to help interpreting the changes. In the present paper, the potential of the magnetic hysteresis method was evaluated for simultaneous detection of the austenitizing and tempering temperatures of AISI D2 samples parts subjected to different heat treatment conditions. To produce the microstructural changes, five groups of the samples were austenitized at 1025-1130 °C, quenched in oil and finally each group was tempered in the range 200-650 °C. SEM and x-ray diffractometry techniques were used to characterize different produced microstructures. For accurate and simultaneous prediction of tempering and austenitizing temperatures, an artificial neural network (ANN) was implemented for magnetic hysteresis outputs including magnetic saturation, coercivity and maximum differential permeability. The study revealed that the magnetic NDE system coupled to ANN has the ability to be adopted as an effective expert NDE tool to predict heat treatment effects on D2 tool steels.

36 MATERIALS SCIENCE↗

Quantifying the hierarchy of structural and mechanical length scales in granular systems

Continuum modeling of granular media is made possible by the existence of a length scale at and above which grain-resolved properties can be meaningfully homogenized. Progress has been made in identifying such length scales relevant to local structural properties such as porosity. However, a systematic analysis of scales above which different mechanical properties can be homogenized has yet to emerge. Here, X-ray tomography and 3D X-ray diffraction data are examined to identify such length scales. The data was obtained in-situ in compressed granular materials with rigid and flexible confinement. The experimental data are supplemented with validated discrete element simulations which examine different system sizes and different boundary conditions. Overall, our study reveals a hierarchy in the length scales of granular solids, with lengths governing structural variables being the shortest, lengths of stress variables being intermediate, and lengths of energy dissipation being the longest. All structural and mechanical length scales obey a power law based on the theory of Geostatistics, implying that the length scales can be found by analyzing samples significantly smaller than the length scales themselves. The length scales are also found to be sensitive to boundary conditions, implying that they are extrinsic features of granular media.

36 MATERIALS SCIENCE↗

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