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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Critical behavior of the ferromagnetic-paramagnetic phase transition in Fe{sub 90−x}Ni{sub x}Zr{sub 10} alloy ribbons

This work presents a detailed study on the critical behavior of the ferromagnetic-paramagnetic (FM-PM) phase transition in Fe{sub 90−x}Ni{sub x}Zr{sub 10} (x = 0 and 5) alloy ribbons. Basing on field dependences of magnetization (M-H), M{sup 2} versus H/M plots prove the alloys exhibiting a second-order magnetic phase transition. To investigate the nature of the FM-PM phase transition at T{sub C} = 245 and 306 K for x = 0 and 5, respectively, we performed a critical-exponent study. The values of critical components β, γ, and δ determined by using the modified Arrott plots, Kouvel-Fisher (KF), and critical isotherm analyses agree with each other. For x = 0, the critical parameters β = 0.365 ± 0.013 and γ = 1.615 ± 0.033 are obtained by modified Arrott plots while β = 0.368 ± 0.008 and γ = 1.612 ± 0.016 are obtained by the KF method. These values are close to those expected for the 3D-Heisenberg model, revealing short-range FM interactions in Fe{sub 90}Zr{sub 10}. Meanwhile, for x = 5, the values of the critical parameters β = 0.423 ± 0.008 and γ = 1.325 ± 0.036 are obtained by modified Arrott plots, and β = 0.425 ± 0.006 and γ = 1.323 ± 0.012 are obtained by the KF method. The falling of the β value in between the values of the mean-field theory (β = 0.5) and the 3D-Heisenberg model (β = 0.365) indicates an existence of FM short-range order and magnetic inhomogeneity in Fe{sub 85}Ni{sub 5}Zr{sub 10}. With a partial replacement of Ni for Fe in Fe{sub 90−x}Ni{sub x}Zr{sub 10}, the value of the critical exponent β trends to shift towards that of the mean-field theory. Such the result proves the presence of Ni favors establishing FM long-range order. The nature of this phenomenon is carefully discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Effects of configurational changes on electrical resistivity during glass-liquid transition of two bulk metal-alloy glasses

Consequences of increase in structural fluctuations on heating Pd{sub 40}Ni{sub 10}Cu{sub 30}P{sub 20} and Zr{sub 46.75}Ti{sub 8.25}Cu{sub 7.5}Ni{sub 10}Be{sub 27.5} through their glass to liquid transition range were investigated by measuring the electrical resistivity, ρ, an electron scattering property. The temperature coefficient of resistivity (TCR = (1/ρ) dρ/dT) of the liquid and glassy states is negative. The plots of their ρ against T in the T{sub g} (glass to liquid transition) range show a gradual change in the slope similar to the change observed generally for the plots of the density, elastic modulus, and refractive index. As fluctuations in the melt structure involve fewer configurations on cooling, ρ increases. In the energy landscape description, the melt's structure explores fewer minima with decrease in T, vibrational frequencies increase, and electron scattering and ρ increase. Plots of (−dρ/dT) against T resemble the plot of the specific heat of other glasses and show a sub-T{sub g} feature and a rapid rise at T near T{sub g}. Analysis shows that the magnitude of negative TCR is dominated by change in the phonon characteristics, and configurational fluctuations make it more negative. The TCR of the liquid and glassy states seems qualitatively consistent with the variation in the structure factor in Ziman's model for pure liquid metals as extended by Nagel to metal alloys and used to explain the negative TCR of a two-component metal glass.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Effect of Cu substitution on the magnetic and dielectric properties of La{sub 2}NiMnO{sub 6}

In the continuation of our previous work [Biswal et al., J. Appl. Phys. 115, 194106 (2014)] of biphasic La{sub 2}NiMnO{sub 6} (LNM) prepared via sol-gel technique, here we are reporting the effect of Cu substitution at Ni site of LNM. The powder x-ray diffraction results of La{sub 2}Ni{sub 1−x}Cu{sub x}MnO{sub 6} (x = 0, 0.05, 0.1) confirm the biphasic nature (Pbnm + R-3c) for all samples along with increasing R-3c phase with Cu content in the sample. The temperature variation of magnetization (M) plot shows sharp ferromagnetic transitions of R-3c phase at 285 K, 278 K, and 270 K (obtained from dM/dT plot) and that of Pbnm phase at 165 K, 145 K, and 145 K for x = 0, 0.05, and 0.1, respectively. Weak antiferromagnetic type feature at 25 K (x = 0), is getting pronounced with Cu content in the sample and is now seen at 32 K and 266 K (for x = 0.05) and 32K and 257 K (for x = 0.1). Fitting of Curie-Weiss law in the paramagnetic region of χ{sup −1} vs T plot results slightly lower values of corresponding T{sub c}'s of R-3 c phase (277 K, 270 K, and 260 K), thus manifesting possibility of Griffiths like singularity. The onset temperature of Griffiths singularity occurs at 295 K (x = 0) and 281 K (x = 0.05 and 0.1), such that 295–277 K in x = 0, 281–270 K in x = 0.05, and 281–260 K in x = 0.1 sample is in Griffith's phase. Temperature dependent dielectric constant (ε{sub r}) shows usual step-like transitions with corresponding relaxation peaks in the dielectric loss (D) part. Cu substitution in LNM makes the entire dielectric plot shifts towards lower temperature such that high temperature plateau of step transition becomes evident. The peaks in dielectric loss for all compositions are found to obey Arrhenius law with activation energies 237 meV, 207 meV, and 180 meV for x = 0, 0.05, and 0.1, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

The Fowler-Nordheim behavior and mechanism of photo-sensitive field from SnS{sub 2} nanosheets

Here in, we report photo-sensitive field emission measurements of SnS{sub 2} nanosheets at base pressure of ∼1×10{sup −8} mbar are reported. The nonlinear Fowler-Nordheim (F-N) plot is elucidate according to a (F-N) model of calculation based on shift in a saturation of conduction band current density after light illumination and prevalence of valence band current density at high electric field values. The model of calculation suggests that the slope variation before and after visible light illumination of the F-N plot, in the high-field and low-field regions, does not depend on the magnitude of saturation but also depend on charge carrier (electron) concentration get increased in conduction band. The F-N model of calculation is important for the fundamental understanding of the photo-sensitive field emission mechanism of semiconducting SnS{sub 2}. The replicate F-N plots exhibit similar features to those observed experimentally. The model calculation suggests that the nonlinearity of the F-N plot is a characteristic of the photo-enhanced energy band structure of the photo-sensitive semiconductor material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Poole-Frenkel-effect as dominating current mechanism in thin oxide films—An illusion?!

In many of the publications, over 50 per year for the last five years, the Poole-Frenkel-effect (PFE) is identified or suggested as dominating current mechanism to explain measured current–electric field dependencies in metal-insulator-metal (MIM) thin film stacks. Very often, the insulating thin film is a metal oxide as this class of materials has many important applications, especially in information technology. In the overwhelming majority of the papers, the identification of the PFE as dominating current mechanism is made by the slope of the current–electric field curve in the so-called Poole-Frenkel plot, i.e., logarithm of current density, j, divided by the applied electric field, F, versus the square root of that field. This plot is suggested by the simplest current equation for the PFE, which comprises this proportionality (ln(j/F) vs. F{sup 1/2}) leading to a straight line in this plot. Only one other parameter (except natural constants) may influence this slope: the optical dielectric constant of the insulating film. In order to identify the importance of the PFE simulation studies of the current through MIM stacks with thin insulating films were performed and the current–electric field curves without and with implementation of the PFE were compared. For the simulation, an advanced current model has been used combining electronic carrier injection/ejection currents at the interfaces, described by thermionic emission, with the carrier transport in the dielectric, described by drift and diffusion of electrons and holes in a wide band gap semiconductor. Besides the applied electric field (or voltage), many other important parameters have been varied: the density of the traps (with donor- and acceptor-like behavior); the zero-field energy level of the traps within the energy gap, this energy level is changed by the PFE (also called internal Schottky effect); the thickness of the dielectric film; the permittivity of the dielectric film simulating different oxide materials; the barriers for electrons and holes at the interfaces simulating different electrode materials; the temperature. The main results and conclusions are: (1) For a single type of trap present only (donor-like or acceptor-like), none of the simulated current density curves shows the expected behavior of the PFE and in most cases within the tested parameter field the effect of PFE is negligibly small. (2) For both types of traps present (compensation) only in the case of exact compensation, the expected slope in the PF-plot was nearly found for a wider range of the applied electric field, but for a very small range of the tested parameter field because of the very restricting additional conditions: first, the quasi-fermi level of the current controlling particle (electrons or holes) has to be 0.1 to 0.5 eV closer to the respective band limit than the zero-field energy level of the respective traps and, second, the compensating trap energy level has to be shallow. The conclusion from all these results is: the observation of the PFE as dominating current mechanism in MIM stacks with thin dielectric (oxide) films (typically 30 nm) is rather improbable!.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

The Sloan Digital Sky Survey COADD: 275 deg{sup 2} of deep Sloan Digital Sky Survey imaging on stripe 82

We present details of the construction and characterization of the coaddition of the Sloan Digital Sky Survey (SDSS) Stripe 82 ugriz imaging data. This survey consists of 275 deg{sup 2} of repeated scanning by the SDSS camera over –50° ≤ α ≤ 60° and –1.°25 ≤ δ ≤ +1.°25 centered on the Celestial Equator. Each piece of sky has ∼20 runs contributing and thus reaches ∼2 mag fainter than the SDSS single pass data, i.e., to r ∼ 23.5 for galaxies. We discuss the image processing of the coaddition, the modeling of the point-spread function (PSF), the calibration, and the production of standard SDSS catalogs. The data have an r-band median seeing of 1.''1 and are calibrated to ≤1%. Star color-color, number counts, and PSF size versus modeled size plots show that the modeling of the PSF is good enough for precision five-band photometry. Structure in the PSF model versus magnitude plot indicates minor PSF modeling errors, leading to misclassification of stars as galaxies, as verified using VVDS spectroscopy. There are a variety of uses for this wide-angle deep imaging data, including galactic structure, photometric redshift computation, cluster finding and cross wavelength measurements, weak lensing cluster mass calibrations, and cosmic shear measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Taking Halo-Independent Dark Matter Methods Out of the Bin

We develop a new halo-independent strategy for analyzing emerging DM hints, utilizing the method of extended maximum likelihood. This approach does not require the binning of events, making it uniquely suited to the analysis of emerging DM direct detection hints. It determines a preferred envelope, at a given confidence level, for the DM velocity integral which best fits the data using all available information and can be used even in the case of a single anomalous scattering event. All of the halo-independent information from a direct detection result may then be presented in a single plot, allowing simple comparisons between multiple experiments. This results in the halo-independent analogue of the usual mass and cross-section plots found in typical direct detection analyses, where limit curves may be compared with best-fit regions in halo-space. The method is straightforward to implement, using already-established techniques, and its utility is demonstrated through the first unbinned halo-independent comparison of the three anomalous events observed in the CDMS-Si detector with recent limits from the LUX experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗
DOE OSTI2017

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon–climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming. Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO 2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Experimental warming and its legacy effects on root dynamics following two hurricane disturbances in a wet tropical forest

Abstract Tropical forests are expected to experience unprecedented warming and increases in hurricane disturbances in the coming decades; yet, our understanding of how these productive systems, especially their belowground component, will respond to the combined effects of varied environmental changes remains empirically limited. Here we evaluated the responses of root dynamics (production, mortality, and biomass) to soil and understory warming (+4°C) and after two consecutive tropical hurricanes in our in situ warming experiment in a tropical forest of Puerto Rico: Tropical Responses to Altered Climate Experiment (TRACE). We collected minirhizotron images from three warmed plots and three control plots of 12 m 2 . Following Hurricanes Irma and María in September 2017, the infrared heater warming treatment was suspended for repairs, which allowed us to explore potential legacy effects of prior warming on forest recovery. We found that warming significantly reduced root production and root biomass over time. Following hurricane disturbance, both root biomass and production increased substantially across all plots; the root biomass increased 2.8‐fold in controls but only 1.6‐fold in previously warmed plots. This pattern held true for both herbaceous and woody roots, suggesting that the consistent antecedent warming conditions reduced root capacity to recover following hurricane disturbance. Root production and mortality were both related to soil ammonium nitrogen and microbial biomass nitrogen before and after the hurricanes. This experiment has provided an unprecedented look at the complex interactive effects of disturbance and climate change on the root component of a tropical forested ecosystem. A decrease in root production in a warmer world and slower root recovery after a major hurricane disturbance, as observed here, are likely to have longer‐term consequences for tropical forest responses to future global change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Observed variation in soil properties can drive large variation in modelled forest functioning and composition during tropical forest secondary succession

Censuses of tropical forest plots reveal large variation in biomass and plant composition. This paper evaluates whether such variation can emerge solely from realistic variation in a set of commonly measured soil chemical and physical properties. Controlled simulations were performed using a mechanistic model that includes forest dynamics, microbe-mediated biogeochemistry, and competition for nitrogen and phosphorus. Observations from 18 forest inventory plots in Guanacaste, Costa Rica were used to determine realistic variation in soil properties. In simulations of secondary succession, the across-plot range in plant biomass reached 30% of the mean and was attributable primarily to nutrient limitation and secondarily to soil texture differences that affected water availability. The contributions of different plant functional types to total biomass varied widely across plots and depended on soil nutrient status. In Central America, soil-induced variation in plant biomass increased with mean annual precipitation because of changes in nutrient limitation. In Central America, large variation in plant biomass and ecosystem composition arises mechanistically from realistic variation in soil properties. Finally, the degree of biomass and compositional variation is climate sensitive. In general, model predictions can be improved through better representation of soil nutrient processes, including their spatial variation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

SU-D-207A-02: Possible Characterization of the Brain Tumor Vascular Environment by a Novel Strategy of Quantitative Analysis in Dynamic Contrast Enhanced MR Imaging: A Combination of Both Patlak and Logan Analyses

Purpose: The majority of quantitative analyses involving dynamic contrast enhanced (DCE) MRI have been performed to obtain kinetic parameters such as Ktrans and ve. Such analyses are generally performed assuming a “reversible” tissue compartment, where the tracer is assumed to be rapidly equilibrated between the plasma and tissue compartments. However, some tumor vascular environments may be more suited for a “non-reversible” tissue compartment, where, as with FDG PET imaging, the tracer is continuously deposited into the tissue compartment (or the return back to the plasma compartment is very slow in the imaging time scale). Therefore, Patlak and Logan analyses, which represent tools for the “non-reversible” and “reversible” modeling, respectively, were performed to better characterize the brain tumor vascular environment. Methods: A voxel-by-voxel analysis was performed to generate both Patlak and Logan plots in two brain tumor patients, one with grade III astrocytoma and the other with grade IV astrocytoma or glioblastoma. The slopes of plots and the r-square were then obtained by linear fitting and compared for each voxel. Results: The 2-dimensional scatter plots of Logan (Y-axis) vs. Patlak slopes (X-axis) clearly showed increased Logan slopes for glioblastoma (Figure 3A). The scatter plots of goodness-of-fit (Figure 3B) also suggested glioblastoma, relative to grade III astrocytoma, might consist of more voxels that are kinetically Logan-like (i.e. rapidly equilibrated extravascular space and active vascular environment). Therefore, the enhanced Logan-like behavior (and the Logan slope) in glioblastoma may imply an increased fraction of active vascular environment, while the enhanced Patlak-like behavior implies the vascular environment permitting a relatively slower washout of the tracer. Conclusion: Although further verification is required, the combination of Patlak and Logan analyses in DCE MRI may be useful in characterizing the tumor vascular environment, and thus, may have implications in tumor grading and monitoring response to anti-vascular therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Smisc - A collection of miscellaneous functions

A collection of functions for statistical computing and data manipulation. These include routines for rapidly aggregating heterogeneous matrices, manipulating file names, loading R objects, sourcing multiple R files, formatting datetimes, multi-core parallel computing, stream editing, specialized plotting, etc. Smisc-package A collection of miscellaneous functions allMissing Identifies missing rows or columns in a data frame or matrix as.numericSilent Silent wrapper for coercing a vector to numeric comboList Produces all possible combinations of a set of linear model predictors cumMax Computes the maximum of the vector up to the current index cumsumNA Computes the cummulative sum of a vector without propogating NAs d2binom Probability functions for the sum of two independent binomials dataIn A flexible way to import data into R. dbb The Beta-Binomial Distribution df2list Row-wise conversion of a data frame to a list dfplapply Parallelized single row processing of a data frame dframeEquiv Examines the equivalence of two dataframes or matrices dkbinom Probability functions for the sum of k independent binomials factor2character Converts all factor variables in a dataframe to character variables findDepMat Identify linearly dependent rows or columns in a matrix formatDT Converts date or datetime strings into alternate formats getExtension Filename manipulations: remove the extension or path, extract the extension or path getPath Filename manipulations: remove the extension or path, extract the extension or path grabLast Filename manipulations: remove the extension or path, extract the extension or path ifelse1 Non-vectorized version of ifelse integ Simple numerical integration routine interactionPlot Two-way Interaction Plot with Error Bar linearMap Linear mapping of a numerical vector or scalar list2df Convert a list to a data frame loadObject Loads and returns the object(s) in an ".Rdata" file more Display the contents of a file to the R terminal movAvg2 Calculate the moving average using a 2-sided window openDevice Opens a graphics device based on the filename extension p2binom Probability functions for the sum of two independent binomials padZero Pad a vector of numbers with zeros parseJob Parses a collection of elements into (almost) equal sized groups pbb The Beta-Binomial Distribution pcbinom A continuous version of the binomial cdf pkbinom Probability functions for the sum of k independent binomials plapply Simple parallelization of lapply plotFun Plot one or more functions on a single plot PowerData An example of power data pvar Prints the name and value of one or more objects qbb The Beta-Binomial Distribution rbb And numerous others (space limits reporting).

Landon Sego, PNNL↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2019

EMSL-Computing/fticRanalysis

fticRanalysis - a package to hand data formatting and storage of Fourier-transform ion cyclotron resonance (FTICR) data in a generalizable way. Additional functionality is available for manipulating and transforming the data, filtering the data based on different criteria, creating summary plots (e.g. Van Krevelen plots, Kendrick plots, etc.), summarizing group-level data, comparing treatment groups, and making complementary plotting functionality

White, Amanda↗
DOE OSTI2025

Data for Soil Fertility Management for Sustainable Miscanthus × giganteus Production: Increased Tiller Weight from Nitrogen Management Explains Yield Gains in Aged Miscanthus

Aging-related yield decline in Miscanthus × giganteus (miscanthus) remains a major constraint to sustainable biomass production. This study evaluated how nitrogen (N) management and soil fertility influence yield-component traits and productivity in aging miscanthus. Trials were conducted at two sites established in 2008 at the University of Illinois Energy Farm, Urbana, IL. (i) The Sun Grant trial received 0, 60, and 120 kg N ha−1 annually until 2015. Starting 2021, half of each plot received 60 or 120 kg N ha−1, resulting in six legacy-contemporary treatments: 0N–0N, 0N–120N, 60N–0N, 60N–60N, 120N–0N, 120N–120N. (ii) The Energy Farm trial remained unfertilized until 2014, when one half of each plot received 56 kg N ha−1, forming two treatments: 0N–0N, 0N–56N. Sun Grant trial results showed N fertilization increased tiller density (tillers m−2) and tiller weight (g tiller−1) in juvenile to early-mature miscanthus (2011–2015). After N withdrawal, both traits declined (20 % and 40 %), though legacy effects persisted in tiller weight in the aging stands (2020–2023). Contemporary N had little effect on tiller density but increased tiller weight by 34 %–77 %, resulting in 23 %–106 % higher machine-harvested biomass yield in 0–120N, 60-60N, and 120-120N plots. At the Energy Farm trial, 0N–56N plots yielded 59 %–108 % more biomass than 0N–0N. Soil total N increased (Sun Grant: 47 % by 2020; Energy Farm: 58 % by 2023), while Mehlich-3 P (42 %–44 %) and K (21 %–46 %) declined. These findings identify tiller weight as a key determinant of biomass yield in aging miscanthus and highlight the need for P and K management for long-term productivity.

miscanthus↗
DOE OSTI2026

Data for Genetic Variation in Zea mays Influences Microbial Nitrification and DeNitrification in Conventional Agroecosystems

Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N2O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under “real-world” conventional agricultural conditions. To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds ( Z. mays ssp. mays ), ex-PVP hybrids ( Z. mays ssp. may s), and teosinte ( Z. mays ssp. mexicana and Z. mays ssp. parviglumis ). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N2O fluxes, with teosinte plots producing less GHG than maize plots. Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N2O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

Nitrogen↗
DOE OSTI2016

The Ramachandran Number: An Order Parameter for Protein Geometry

Three-dimensional protein structures usually contain regions of local order, called secondary structure, such as α-helices and β-sheets. Secondary structure is characterized by the local rotational state of the protein backbone, quantified by two dihedral angles called Øand Ψ. Particular types of secondary structure can generally be described by a single (diffuse) location on a two-dimensional plot drawn in the space of the angles Ø andΨ, called a Ramachandran plot. By contrast, a recently-discovered nanomaterial made from peptoids, structural isomers of peptides, displays a secondary-structure motif corresponding to two regions on the Ramachandran plot [Mannige et al., Nature 526, 415 (2015)]. In order to describe such 'higher-order' secondary structure in a compact way we introduce here a means of describing regions on the Ramachandran plot in terms of a single Ramachandran number, R, which is a structurally meaningful combination of Ø andΨ. We show that the potential applications of R are numerous: it can be used to describe the geometric content of protein structures, and can be used to draw diagrams that reveal, at a glance, the frequency of occurrence of regular secondary structures and disordered regions in large protein datasets. We propose that R might be used as an order parameter for protein geometry for a wide range of applications.

59 BASIC BIOLOGICAL SCIENCES↗