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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Increasing Functional Diversity in a Global Land Surface Model Illustrates Uncertainties Related to Parameter Simplification

Abstract Simulations of the land surface carbon cycle typically compress functional diversity into a small set of plant functional types (PFT), with parameters defined by the average value of measurements of functional traits. In most earth system models, all wild plant life is represented by between five and 14 PFTs and a typical grid cell (≈100 × 100 km) may contain a single PFT. Model logic applied to this coarse representation of ecological functional diversity provides a reasonable proxy for the carbon cycle, but does not capture the non‐linear influence of functional traits on productivity. Here we show through simulations using the Energy Exascale Land Surface Model in 15 diverse terrestrial landscapes, that better accounting for functional diversity markedly alters predicted total carbon uptake. The shift in carbon uptake is as great as 30% and 10% in boreal and tropical regions, respectively, when compared to a single PFT parameterized with the trait means. The traits that best predict gross primary production vary based on vegetation phenology, which broadly determines where traits fall within the global distribution. Carbon uptake is more closely associated with specific leaf area for evergreen PFTs and the leaf carbon to nitrogen ratio in deciduous PFTs.

58 GEOSCIENCES↗
DOE OSTI2020

Elevation distributed micro-climatology data in a coastal glaciated watershed

Air temperature, ground temperature and relative humidity data were collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 to 1800 m above sea level. Data were collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Select sites included relative humidity sensors to indicate whether precipitation was occurring. Data were collected every 3-4 h from December 2015 to Sept 2018 (with ongoing collection). Code for analysis of daily mean, minimum, maximum, and temperature change with elevation (lapse rates) are available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare at (http://www.hydroshare.org/resource/222e832d3df24dea9bae9bbeb6f4219d) with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org. Hydrologic models are generally structured with a single annual average lapse rate parameter which assumes a linear temperature gradient with elevation. The daily data (2016-2018) is used as part of ongoing studies on the non-linear dynamics and temporal variability of temperature with elevation to improve assessments of watershed function and salmon habitat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Parametric analysis on optimized design of hybrid solar power plants

There is increasing interest in utility-scale solar power plants with storage which can flexibly dispatch renewable energy to the grid. However, plant design possesses many degrees of freedom and non-obvious trade-offs in performance. Software tools can estimate or optimize the performance of a specific plant configuration under market and weather conditions of interest; the associated cost parameters and operating assumptions strongly influence estimates of plant performance and decisions regarding optimal sizing. We employ the National Renewable Energy Laboratory's Hybrid Optimization and Performance Platform, which incorporates optimal dispatch when evaluating plant performance, and investigate the sensitivity to weather and market conditions, operating limitations, and the presence of a capacity-based incentive. We demonstrate changes in plant performance and optimal sizing with respect to these inputs and discuss implications. Here results show that PV-with-battery designs are more profitable under our assumptions, but that designs including a concentrated solar power (CSP) system produce significantly greater annual energy; and that CSP-with-thermal energy storage designs maximizing the benefit-to-cost ratio have an input-dependent linear relationship between the CSP field solar multiple and the hours of storage as the project budget varies.

14 SOLAR ENERGY↗
DOE OSTI2023

Thermomechanical Stress and Creep-Fatigue Analysis of a High-Temperature Prototype Receiver for Heating Particles

This work presents a three-dimensional (3D) thermomechanical model of a prototype-scale enclosed light trapping solar receiver for heating particles. Results of the thermoelastic model are used to estimate receiver lifetime under maximum flux conditions. A computational fluid dynamics (CFD) model is first developed to predict the temperature fields in a multi-panel assembly under steady operating conditions. Solar flux distributions on the receiver are obtained from the software package SolTrace and applied to the 3D thermal model. The subsequent particle heating is captured through a simplified 1D energy balance. Panel reradiation is considered through a surface-to-surface radiation model and natural convection loss to the surrounding air is captured in a representative fluid domain surrounding the receiver. The resulting temperature fields from the CFD analysis are used as inputs for a thermoelastic mechanical model with representative boundary conditions. With the resultant temperature and stress fields, a creep-fatigue damage and lifetime analysis is performed using the linear damage accumulation (LDA) theory. The Manson-Coffin formula and Larson Miller correlation are used to calculate the fatigue and creep, respectively. A maximum damage (corresponding to a 30-year service life) is defined for design assessment. The model was first developed and verified in detail by comparing with published results in the literature (temperature and stress profiles and distributions, and creep/fatigue damage fractions) for tubular solar receivers with supercritical carbon dioxide as the working fluid. It was then implemented to model a planar-cavity receiver with various design parameters. Specifically, three different design geometries are considered, and the results show that a maximum temperature of approximately 1200 K could be reached for each design with the given incident solar flux, with the main difference being the distribution of these temperatures. Preliminary resulting stresses for the small-scale prototype without design optimization vary from 20 MPa to 250 MPa for each design, with the maximum stresses occurring on the front face and concave geometry on the side of the panel. In future work, the developed methodology shown here will be applied to analyze a full-scale (50-150 MWth) receiver.

concentrated solar power↗
DOE OSTI2021

Fracture Toughness Characterization of Generation II FeCrAl Alloys after ~18 dpa Irradiation

FeCrAl alloys are promising candidate materials for the accident tolerant fuel (ATF) cladding applications due to their excellent corrosion resistance to the elevated temperature steam environment. Currently, the handbook on FeCrAl material properties contains only limited data regarding the fracture toughness properties of any FeCrAl alloy. This includes alloys currently under investigation within the Advanced Fuels Campaign (AFC) at Oak Ridge National Laboratory (ORNL). In this project, a series of irradiation capsules have been irradiated in the High Flux Isotope Reactor (HFIR) at ORNL with two Generation II FeCrAl candidate alloys, i.e., C06M and C36M, to assess the fracture response of these alloys after neutron irradiation. These alloys represent the “book-end” compositions for C26M, the alloy currently being developed as the leading candidate for LWR cladding. A total of six irradiation capsules were irradiated in HFIR at target temperatures of 200°C, 330°C, and 500°C up to target damage doses of 8 displacements per atom (dpa) and 16 dpa. These damage doses represent the expected middle and end of life damage levels for typical LWR cladding while the irradiation temperature regimes will provide insight into the role of varying microstructural features on the fracture toughness properties of neutron irradiated FeCrAl alloys. To date, irradiation of all capsules has been completed in HFIR. This report summarizes the latest results of microhardness and fracture toughness PIE for the 16 dpa capsules (FCAB2, FCAB4, and FCAB6), for which the measured irradiation conditions were: 204°C/17.6dpa, 343°C/18.3dpa, and 507°C/18.6dpa. The main conclusions of this study can be summarized as follows: 1) After the 204°C/17.6dpa irradiation, both C06M and C36M exhibited significant irradiation hardening and embrittlement 2) After the 343°C/18.3dpa irradiation, both C06M and C36M exhibited small irradiation hardening without irradiation embrittlement 3) After the 507°C/18.6dpa irradiation, both C06M and C36M exhibited irradiation softening without irradiation embrittlement 4) Comparing the microhardness and Master Curve reference temperature T 0q before and after neutron irradiation, we did not observe a linear correlation between the two parameters for both C06M and C36M. This should be mainly due to a flat response of the Master Curve reference temperature T 0q to the irradiations at 166-204°C and 315-343°C ranges 5) C06M showed a lower T 0q , meaning better toughness, than C36M at the unirradiated condition and such trend was kept even after neutron irradiation except for the 166-204°C irradiation where both materials had similar T 0q . 6) In terms of hardening and embrittlement, the irradiation effect on both C06M and C36M appeared to saturate after an irradiation dose of 7 dpa.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms

Quantum state preparation involves preparing a target state from an initial system, a process integral to applications such as quantum machine learning and solving systems of linear equations. Recently, there has been a growing interest in qumodes due to advancements in the field and their potential applications. However there is a notable gap in the literature specifically addressing this area. This paper aims to bridge this gap by providing performance benchmarks of various optimizers used in state preparation with Variational Quantum Algorithms. We conducted extensive testing across multiple scenarios, including different target states, both ideal and sampling simulations, and varying numbers of basis gate layers. Our evaluations offer insights into the complexity of learning each type of target state and demonstrate that some optimizers perform better than others in this context. Notably, the Powell optimizer was found to be exceptionally robust against sampling errors, making it a preferred choice in scenarios prone to such inaccuracies. Additionally, the Simultaneous Perturbation Stochastic Approximation optimizer was distinguished for its efficiency and ability to handle increased parameter dimensionality effectively.

Kan, Shuwen [Fordham University]↗
DOE OSTI2024

Constraining primordial non-Gaussianity from DESI quasar targets and Planck CMB lensing

We detect the cross-correlation between 2.7 million DESI quasar targets across 14,700 deg 2 (180 quasars deg -2 ) and Planck 2018 CMB lensing at ~30σ. We use the cross-correlation on very large scales to constrain local primordial non-Gaussianity via the scale dependence of quasar bias. The DESI quasar targets lie at an effective redshift of 1.51 and are separated into four imaging regions of varying depth and image quality. We select quasar targets from Legacy Survey DR9 imaging, apply additional flux and photometric redshift cuts to improve the purity and reduce the fraction of unclassified redshifts, and use early DESI spectroscopy of 194,000 quasar targets to determine their redshift distribution and stellar contamination fraction (2.6%). Due to significant excess large-scale power in the quasar autocorrelation, we apply weights to mitigate contamination from imaging systematics such as depth, extinction, and stellar density. We use realistic contaminated mocks to determine the greatest number of systematic modes that we can fit, before we are biased by overfitting and spuriously remove real power. We find that linear regression with one to seven imaging templates removed per region accurately recovers the input cross-power, f NL and linear bias. As in previous analyses, our fNL constraint depends on the linear primordial non-Gaussianity bias parameter, b Φ = 2(b - p)δ c assuming universality of the halo mass function. We measure f NL = -26 +45 -40 with p = 1.6 (f NL = -18 +29 -27 with p = 1.0), and find that this result is robust under several systematics tests. Future spectroscopic quasar cross-correlations with Planck lensing can tighten the f NL constraints by a factor of 2 if they can remove the excess power on large scales in the quasar auto power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys

Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].

Lin, Yan-Ru [ORNL] (ORCID:0000000339991473)↗
DOE OSTI2023

Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics

Abstract We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of $$C^{\infty }$$ C ∞ bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Stabilizing dynamic subsea power cables using Bi-stable nonlinear energy sinks

This study investigates vibration mitigation of dynamic subsea cables through passive bi-stable nonlinear energy sinks (B-NESs). These devices suppress vibration energy in a broadband way, and can be regarded as extensions of classical linear tuned mass dampers (TMDs) which are narrowband devices. Through the open-source MoorDyn library, we simulated the vibrations of a vertical subsea cable equipped with a set of B-NESs. Multi-objective optimization was performed to detect the B-NES parameters for optimal mitigation of the cable vibrations. Advanced signal processing verified the efficacy of the optimized B-NESs not only to rapidly absorb and locally dissipate vibration energy, but also to nonlinearly scatter vibration energy from low to high frequencies within the cable itself. This last feature is especially beneficial for vibration mitigation of the undersea cable, as at higher frequencies the cable vibrations exhibit drastically reduced amplitudes and are more effectively dissipated by inherent structural damping and hydrodynamic radiation damping. This contrasts with traditional TMDs which can mitigate vibration only at a single frequency. Furthermore, our robustness study confirms the B-NES's effectiveness under even varying environmental conditions. Overall, the B-NES's capacity for broadband vibration mitigation renders it a promising retrofit solution for improving the performance and operational safety of dynamic power cables in offshore wind farms and other marine applications.

17 WIND ENERGY↗
DOE OSTI2020

Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hubbard-corrected oxide formation enthalpies without adjustable parameters

A density functional theory (DFT) approach to computing transition metal oxide heat of formation without adjustable parameters is presented. Different degrees of d-electron localization in oxides are treated within the DFT+U approach with site-dependent, first-principles Hubbard U-parameters obtained from linear response theory, and delocalized states in the metallic phases are treated without Hubbard corrections. Comparison of relative stabilities of these differently treated phases is enabled by a local d-electron density matrix-dependent model, which was found by genetic programming against experimental reference formation enthalpies. This mathematically simple model does not explicitly depend on the Hubbard-corrected ionic species and is shown to reproduce the heats of formation of the Mott insulators Ca 2 RuO 4 and Y 2 Ru 2 O 7 within ~3% of experimental results, where the experimental training data did not contain Ru oxides. This newly developed method thus absolves from the need for element-specific corrections fitted to experiments in existing Hubbard-corrected approaches to the prediction of reaction energies of transition metal oxides and metals. The absence of fitting parameters opens up here the possibility to predict relative thermodynamic stabilities and reaction energies involving d-states of varying degree of localization at transition metal oxide interfaces and defects, where site-dependent U-parameters will be particularly important and devising a fitting scheme against experimental data with predictive power would be exceedingly difficult.

transition metal oxides↗
DOE OSTI2024

The influence of the cloud virial parameter on the initial mass function

ABSTRACT Crucial for star formation is the interplay between gravity and turbulence. The observed cloud virial parameter, $\alpha _{\mathrm{vir}}$, which is the ratio of twice the turbulent kinetic energy to the gravitational energy, is found to vary significantly in different environments, where the scatter among individual star-forming clouds can exceed an order of magnitude. Therefore, a strong dependence of the initial mass function (IMF) on $\alpha _{\mathrm{vir}}$ may challenge the notion of a universal IMF. To determine the role of $\alpha _{\mathrm{vir}}$ on the IMF, we compare the star-particle mass functions obtained in high-resolution magnetohydrodynamical simulations including jet and heating feedback, with $\alpha _{\mathrm{vir}}=0.0625$, 0.125, and 0.5. We find that varying $\alpha _{\mathrm{vir}}$ from $\alpha _{\mathrm{vir}}\sim 0.5$ to $\alpha _{\mathrm{vir}}< 0.1$ shifts the peak of the IMF to lower masses by a factor of $\sim 2$ and increases the star formation rate by a similar factor. The dependence of the IMF and star formation rate on $\alpha _{\mathrm{vir}}$ is non-linear, with the dependence subsiding at $\alpha _{\mathrm{vir}}< 0.1$. Our study shows a systematic dependence of the IMF on $\alpha _{\mathrm{vir}}$. Yet, it may not be measurable easily in observations, considering the uncertainties, and the relatively weak dependence found in this study.

Mathew, Sajay Sunny (ORCID:0000000283818195)↗
DOE OSTI2025

Porosity, swelling, and composition evolution in high-burnup monolithic U-Mo fuel

The microstructural progression of very high-burnup (>8 × 10 21 fissions/cm 3 ) monolithic uranium-molybdenum (U-10wt.%Mo) was analyzed, providing crucial insights into the behavior of post-recrystallized nuclear fuel, where scant data exists. Three focused ion beam cuboids sourced from a fuel plate with varying local burnups of 8.86 × 10 21 , 9.05 × 10 21 , and 9.36 × 10 21 fissions/cm 3 were characterized. The porosity and composition of the samples were evaluated to characterize the evolution of the microstructure as a function of fission density and different locations on the fuel plate, while simultaneously isolating plate-specific parameters such as Zr diffusion barrier thickness, hot-isostatic-pressing conditions, enrichment, and reactor conditions. The porosity was segmented, and the three-dimensional distribution of the porosity was extrapolated from the two-dimensional segmentation. The composition was assessed and quantified using energy-dispersive X-ray spectroscopy areal mapping. The porosity fraction increased as a function of the burnup from 27.77±0.51, 35.12±1.54, and 37.71±0.44 % for 8.86 × 10 21 , 9.05 × 10 21 , and 9.36 × 10 21 fissions/cm 3 , respectively. When compared to literature, the porosity volume fraction plateaus at burnups greater than 6 × 10 21 fissions/cm 3 , while the pore size grows linearly as a function of fission density. The number of large pores increased in number density as a function of burnup, while the smallest pores (<0.3 µm) increased up to 9.05 × 10 21 fissions/cm 3 , followed by a decrease at 9.36 × 10 21 fissions/cm 3 . The delamination and cracking in the fuel plate propagated through an interconnected porosity sublayer identified ∼5 µm from the diffusion barrier. The local swelling of the specimens was within or near the prediction bounds of the Robinson-Williams model for local swelling. The fission products, strontium, barium, cerium, and cesium, precipitated into the pores, while neodymium accumulated adjacent to the pores. Furthermore, these findings have direct implications for the development of fuel performance codes and the accurate documentation of the microstructure evolution in high burnup U-Mo, thus enhancing the safety and efficiency of nuclear fuel usage.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Metastable doubly threaded [3]rotaxanes with a large macrocycle

Ring size is a critically important parameter in many interlocked molecules as it directly impacts many of the unique molecular motions that they exhibit. Reported herein are studies using one of the largest macrocycles reported to date to synthesize doubly threaded [3]rotaxanes. A large ditopic 46 atom macrocycle containing two 2,6-bis(N-alkyl-benzimidazolyl)pyridine ligands has been used to synthesize several metastable doubly threaded [3]rotaxanes in high yield (65–75% isolated) via metal templating. Macrocycle and linear thread components were synthesized and self-assembled upon addition of iron(II) ions to form the doubly threaded pseudo[3]rotaxanes that could be subsequently stoppered using azide–alkyne cycloaddition chemistry. Following demetallation with base, these doubly threaded [3]rotaxanes were fully characterized utilizing a variety of NMR spectroscopy, mass spectrometry, size-exclusion chromatography, and all-atom simulation techniques. Critical to the success of accessing a metastable [3]rotaxane with such a large macrocycle was the nature of the stopper group employed. By varying the size of the stopper group it was possible to access metastable [3]rotaxanes with stabilities in deuterated chloroform ranging from a half-life of <1 minute to ca. 6 months at room temperature potentially opening the door to interlocked materials with controllable degradation rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Kinetic and temperature sensitivity properties of soil exoenzymes through the soil profile down to one-meter depth at a temperate coniferous forest (Blodgett, CA)

This dataset contains data on kinetic and temperature sensitivity parameters of the exoenzymes β-glucosidase (BG), leucine/leucyl aminopeptidase (LAP) and acid phosphatase (AP) across a soil profile down to 90 cm depth at Blodgett forest, CA, USA. These data were generated to determine if kinetic and thermal properties of microbial exoenzymes involved in organic matter decomposition varied with soil depth, following variation in soil properties and microbial communities, as part of the study: Alves et al. (2021). Kinetic Properties of Microbial Exoenzymes Vary with Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile. Frontiers in Microbiology 12:3618. https://doi.org/10.3389/fmicb.2021.735282. This research was performed within the framework of the TES Belowground Biogeochemistry SFA project, in particular association with a long-term field warming experiment of the whole soil profile at Blodgett forest. Samples for this work were collected from locations representative of the field experimental plots. Potential enzyme activity rates were measured using laboratory fluorometric assays with soils collected at 0-10, 10-20, 30-40, 50-60, 60-70 and 80-90 cm deep in biological triplicates (i.e., three soil cores collected at different representative locations). Assays with each soil were conducted over a gradient of eight substrate concentrations per enzyme, and incubated at 4, 10, 16, 25, 35 or 50°C. Enzyme Michaelis-Menten kinetics were modeled over the eight substrate concentrations at each temperature, and the temperature sensitivity of the kinetic parameters was modeled over the six temperatures using linear Arrhenius/Q10 and non-linear Macromolecular Rate Theory (MMRT) models. The dataset includes the fully processed enzyme activity rate data used to model Michaelis-Menten kinetics, calculated kinetic and temperature sensitivity parameters, and basic soil and microbial biomass chemistry for each sample. All data is provided for each individual biological replicate, and kinetic and temperature sensitivity parameters are also provided as means of the biological replicates. The dataset also includes all raw measurement data and code used to parse, combine and perform the analyses described by Alves et al. (2021). For file descriptions, see the file-level metadata files: “enzymes_dataProcessed_flmd.xlsx” (processed data); “enzymes_dataRaw_flmd.xlsx” (compressed raw data and metadata); and “enzymes_code_flmd.xlsx” (compressed code). The experimental design, list of parameters measured, soil and microbial biomass chemistry data, and means of biological replicates for kinetic and temperature sensitivity parameters are also provided as human-readable tables in file “enzymeTraits_design_results_tables.xlsx”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Pit rim decomposition into multiple quantum dots on surfaces of epitaxial thin films grown on pit-patterned substrates

Here, we report results of dynamical simulations according to an experimentally validated surface morphological evolution model on the formation of regular arrays of quantum dot molecules (QDMs) consisting of 1D arrays of smaller interacting quantum dots (QDs). These QD arrays form along the sides of each pit rim on the surface of a coherently strained thin film epitaxially deposited on a semiconductor substrate, the surface of which consists of a periodic pattern of inverted prismatic pits with rectangular pit openings. We find that this complex QDM pattern results from the decomposition of the pit rim from a “quantum fortress” with four elongated QDs into four 1D arrays of multiple smaller QDs arranged along each side of the pit rim. Systematic parametric analysis indicates that varying the pit opening dimensions and the pit wall inclination directly impacts the number of QDs in the resulting QDM pattern, while varying the pit depth only affects the dimensions of the QDs in the nanostructure pattern. Therefore, the number, arrangement, and sizes of QDs in the resulting pattern of QDMs on the film surface can be engineered precisely by proper tuning of the pit design parameters. Our simulation results are supported by predictions of morphological stability analysis, which explains the pit rim decomposition into multiple QDs as the outcome of a tip-splitting instability and provides a fundamental characterization of the post-instability nanostructure pattern. Our theoretical findings can play a vital role in designing optimal semiconductor surface patterns toward enabling future nanofabrication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Examination of stiff ion temperature gradient mode physics in simulations of DIII-D H-mode transport

A systematic evaluation of gyrokinetic and gyrofluid model predictions of ion temperature gradient (ITG) stability and transport using parameters from DIII-D high confinement mode (H-mode) plasmas has been performed. The nonlinear CGYRO code is used to make the gyrokinetic predictions, and the quasilinear TGLF model for the corresponding gyrofluid predictions. The assessments are made at three radii (normalized toroidal flux ρtor = 0.4, 0.55, and 0.7) in three different plasma scenarios with varying levels of neutral beam heating and torque. For each of the nine cases (3 radii × 3 scenarios) considered, ITG turbulence is found to be the dominant long-wavelength instability and transport mechanism. The inclusions of both transverse magnetic fluctuations and dynamic fast beam ions are stabilizing for all cases considered, with strongest effects seen at ρor = 0.4 where the fast ion population and normalized plasma pressure β = 2μ0nT/B2 are highest. The further inclusion of parallel magnetic fluctuations does not have a meaningful impact on the ITG turbulence in these scenarios, but does destabilize (in combination with fast ions) new high-frequency instabilities at ρtor = 0.4 in the high power scenarios. In each case the linear and nonlinear ITG critical gradients are predicted to be lower than the measured ITG scale lengths and their associated uncertainties. Inclusion of equilibrium flow shear in the transport predictions generally leads to an upshift in effective critical gradient rather than a qualitative change in the predicted stiffness, with stronger responses typically seen in the gyrokinetic predictions than in the gyrofluid results. However, in most cases these upshifted gradients still remain below the measured values and their uncertainties. Although the predicted critical gradients are below the measured gradients, both models predicted flux-matching gradients consistent with measured values in six of the nine cases considered, with no clear systematic over- or underprediction. Thus, while the experimental ion temperature profiles do not appear to be closely pinned to the ITG critical gradient, both gyrokinetic and gyrofluid models are able to accurately match the measured gradients reasonably well in most cases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗