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Supplemental sequence processing and R statistical analysis for publication which compares the microbiome of 27 Zea cultivars: 12 Inbred maize genotypes, 9 hybrids, and 6 wild teosinte. The project contains amplicon data for various genes: 16S rRNA, ITS, bacterial amoA, Archeal amoA, nirS, nirK, and nosZ. In addition to functional potential assay data, and N2O flux.
Abstract Background The industrial feasibility of photosynthetic bioproduction using cyanobacterial platforms remains challenging due to insufficient yields, particularly due to competition between product formation and cellular carbon demands across different temporal phases of growth. This study investigates how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942, and provides insights that suggest potential strategies for enhanced bioproduction. Results After entrainment to light-dark cycles, PCC 7942 cultures transitioned to constant light revealed distinct temporal patterns in sucrose production, exhibiting three-fold higher productivity during subjective night compared to subjective day despite moderate down-regulation of genes from the photosynthetic apparatus. This enhanced productivity coincided with reduced glycogen accumulation and halted cell division at subjective night time, suggesting temporal separation of competing processes. Transcriptome analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism, with over 300 genes showing differential expression across four time points. The subjective night was characterized by altered expression of cell division-related genes and reduced expression of genes involved in glycogen synthesis, while showing upregulation of glycogen degradation pathways, alternative electron flow components, the pentose phosphate pathway, and oxidative decarboxylation of pyruvate. These molecular changes created favorable conditions for product formation through enhanced availability of major sucrose precursors (glucose-1-phosphate and fructose-6-phosphate) and maintained redox balance through multiple mechanisms. Conclusions Our analysis of circadian regulatory rewiring of carbon metabolism and redox balancing suggests two potential approaches that could be developed for improving cyanobacterial bioproduction: leveraging natural circadian rhythms for optimizing cultivation conditions and timing of pathway induction, and engineering strains that mimic circadian-driven metabolic shifts through controlled carbon flux redistribution and redox rebalancing. While these strategies remain to be tested, they could theoretically improve the efficiency of photosynthetic bioproduction by enabling better temporal separation between cell growth, carbon storage accumulation, and product synthesis phases.
The Savannah River Site (SRS) Liquid Waste System (LWS) contains approximately 66 tons of mercury within the liquids, salts, and sludges that are currently being processed into final wasteforms for disposal. Mercury concentrations within the system exceed those typically experienced in environmental or industrial systems; thus, management of mercury compounds continues to be a priority for SRS. In the LWS, waste is vitrified into a borosilicate glass wasteform that contains most of the radioactivity, while the lower activity solutions are dispositioned in a low-level grout wasteform, or “saltstone”. The alkaline, high ionic strength caustic wastes are pumped, evaporated, and otherwise managed throughout the LWS and Defense Waste Processing Facility (DWPF) as they are stored and prepared for conversion to the final wasteforms. Because of the complexity of this system, a key component of effective mercury management in the LWS requires analysis of mercury in various physical phases. The high concentration of mercury within the SRS LWS has the potential to generate vapor-phase contamination. Elemental mercury (Hg 0 ), dimethylmercury ((CH 3 ) 2 Hg), and methylmercury (CH 3 Hg + ) are among species known or suspected to contribute to the flux of mercury from liquid to vapor phase (Iverfeldt and Lindquist, 1982). Chemical speciation affects not only mercury behavior in LWS operations but may also affect the performance of mercury treatment and removal technologies in the LWS.
This project seeks to exploit volumetric absorption of concentrated solar irradiation in a thin, high-temperature, thermally conductive medium. High efficiency thermal conversion is achieved by volumetric absorption of both incoming solar irradiation (short wavelength) and emitted irradiation (long wavelength), demonstrating a volumetrically absorbing thermally insulating (VATI) effect. The thermalized energy is conducted to the back wall, enclosing the working fluid (molten salt or supercritical CO 2 ). Reliance on conductive thermal transport requires a thermally conductive medium, in the absence of which large temperature gradients drive losses due to emission. The project’s outcomes are important to realize a cost-effective approach to reduce optical and thermal losses from CSP receivers at high temperatures (720°C). High-temperature stable and commercially available porous SiC structures (open-cell foams) were explored as a volumetrically absorbing and thermally insulating layer. To the best of our knowledge, the current state-of-the-art for industrially deployed coatings is Pyromark. However, Pyromark suffers degradation at 700+°C and diurnal temperature changes. This necessitates periodic recoating, and the downtime increases the overall levelized cost of energy (LCOE) production. On the other hand, contemporary research activities have generated significant advances in the development of selective emitter coatings, which present challenges with costs, scalability, and stability. The pursued approach alleviates these concerns by developing a receiver that utilizes the inherent structure of high-temperature stable porous materials to enable robust and cost-effective receivers which require no periodic maintenance downtimes. The overall goal of the project is to experimentally demonstrate a figure of merit (FOM) of 0.92 at a temperature of 720°C and solar irradiation of 1000x concentration with porous receivers. The relevant crystallographic (phase) optical and thermal properties of porous SiC were first characterized. Second, the 3-D geometry of the porous SiC was analyzed using micro-X-ray tomography and converted to CAD data using image processing analysis. Utilizing this 3-D geometry and relevant optical/thermal properties, Monte Carlo- Ray Tracing (MCRT) analysis was performed to extract important parameters governing solar-thermal energy conversion such as extinction coefficient (β, 1/m), scattering albedo (ω) and the scattering phase function (Φ). These properties were then integrated into an in-house radiative and conduction transport model to solve for temperature and transport fluxes characterizing the solar-thermal energy conversion. This model was utilized to predict the FOM for various SiC porous geometries and to identify the highest possible FOM. Testing of the optimized porous structures will be accomplished with a custom-built high-accuracy (< ±4%) FOM measurement test-stand and a 1000x solar concentrator. When neglecting convective losses and resistance at the open boundary and the back wall, the optimized SiC foam leads to a FOM of 0.84. This FOM does not surpass the performance of Pyromark 2500. Yet, conversely to Pyromark 2500, SiC is stable at high temperatures and does not degrade over time. Also, it is possible to boost the FOM of SiC by engineering its effective thermal conductivity and its scattering albedo. A FOM of 0.92 is predicted for an optimized foam by accounting for convective losses and resistance at the open boundary and the back wall. This largely exceeds the FOM of Pyromark 2500 (~0.86) predicted by neglecting convective losses and resistance. Therefore, further engineering of the foam could lead to unprecedented FOMs.
The Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) Office of Science supports large-scale data generation efforts across its two divisions: Biological Systems Science and Earth and Environmental Systems Sciences. These efforts include user facilities in atmospheric radiation measurements, genomics, metabolomics, proteomics, compute, and imaging. In addition, BER supports the development of plant-based fuels; research in biosystems design, environmental microbiomes, and atmospheric systems; energy flux monitoring; climate-based ecosystem experiments; pathogen biopreparedness; and modeling of climate, urban interfaces, and interactions between people and energy resources. For data access, BER supports community data services at its user facilities, along with specialized data initiatives for Earth and environmental science, climate modeling, genomic and microbial analysis, and multisector dynamics modeling.
Engineering design studies are being performed to determine the feasibility of converting the High Flux Isotope Reactor (HFIR) from highly enriched uranium (HEU) to low-enriched uranium (LEU) fuel at Oak Ridge National Laboratory. This activity is sponsored by the Office of Reactor Conversion and Uranium Supply (ORCUS) under the auspices of the US Department of Energy National Nuclear Security Administration’s Office of Material Management and Minimization. HFIR is a very high flux, pressurized, light water–cooled and moderated, flux trap–type research reactor with a core made of involute shaped U 3 O 8 /Al cermet fuel plates and coolant channels. HFIR currently operates at a thermal power of 85 MW and supports key national and international missions in neutron scattering, isotope production, materials/fuels irradiation, neutron activation analysis, gamma irradiation, and neutrino research. Advanced multiphysics computational fluid dynamics models have been developed in the COMSOL Multiphysics software to simulate the steady-state operating conditions for the proposed low-and high-density LEU U 3 Si 2 -Al (uranium silicide dispersion) fuel designs. The COMSOL models for HFIR inner and outer fuel element models incorporate various essential inputs and physics such as spatially dependent nuclear heat deposition, multilayer heat conduction, conjugate heat transfer, turbulent flows (using Reynolds-averaged Navier Stokes turbulence models), structural mechanics (thermal–structural interactions and fuel swelling), and oxide layer build-up. This report presents the best-estimate thermal hydraulics results for the low- and high-density optimized silicide LEU core designs at 95 MW steady-state nominal operation.
We conduct X-ray spectral fits on 184 likely counterparts to Fermi-LAT 3FGL unassociated sources. Characterization and classification of these sources allows for more complete population studies of the high-energy sky. Most of these X-ray spectra are well fit by an absorbed power-law model, as expected for a population dominated by blazars and pulsars. A small subset of seven X-ray sources have spectra unlike the power law expected from a blazar or pulsar and may be linked to coincident stars or background emission. We develop a multiwavelength machine learning classifier to categorize unassociated sources into pulsars and blazars using gamma-ray and X-ray observations. Training a random forest (RF) procedure with known pulsars and blazars, we achieve a cross-validated classification accuracy of 98.6%. Applying the RF routine to the unassociated sources returned 126 likely blazar candidates (defined as P {sub bzr} ≥ 90%) and five likely pulsar candidates (P {sub bzr} ≤ 10%). Our new X-ray spectral analysis does not drastically alter the RF classifications of these sources compared to previous works, but it builds a more robust classification scheme and highlights the importance of X-ray spectral fitting. Our procedure can be further expanded with UV, visual, or radio spectral parameters or by measuring flux variability.
Radiation damage estimation is an important component of the post irradiation analysis of the Advanced Graphite Creep (AGC) experiment. It depends primarily on the fast fluence, which is determined using well established methods of spectral adjustment. These are based on best estimates from models such as Monte Carlo N-Particle (MCNP), input cross-sections, and measured activities from flux wires in the experiment. Each of these parameters can propagate uncertainties which will affect the uncertainty in the calculated dose levels for AGC, or any experiment irradiated within a reactor. While the methods of propagating uncertainty are well-established, the final uncertainty estimates they provide are only as good as the estimates of uncertainty in the inputs on which they are based. The purpose of this work is to outline some deficiencies in the ways these input uncertainties are presently estimated, and to outline a methodology by which they can be improved. The fast fluence and radiation damage received by graphite specimens irradiated in the Advanced Graphite Creep (AGC) experiments is presently estimated using spectral adjustment methods that are based on both flux wire activity measurements, and MCNP model predictions. This work describes an ongoing effort to quantify and propagate uncertainties in inputs to the spectral adjustment process, and thereby quantify the resultant error in radiation damage (dpa) estimates. The effort is multi-faceted, and we consider the impacts of both the set of flux wires selected, and the counting process. An expanded set of flux wires is identified that provides a more comprehensive data set on the fast spectrum. To address the counting process itself, a series of round-robin measurements in several reactor metrology laboratories across the Department of Energy (DOE) complex and nuclear industry are being undertaken to refine the American Society for Testing and Materials (ASTM) standards for flux wire measurements. To address the contribution of uncertainty in the MCNP model predictions, an uncertainty quantification (UQ) tool has been developed that statistically samples the model input parameters, runs a series of cases, and assimilates the results to provide an overall uncertainty. The impact of the MCNP UQ tool results is demonstrated by re-analyzing previous AGC flux wire and irradiation data. While the expanded flux wire set obviously cannot be added to these experiments retroactively, plans for future graphite irradiations are outlined.
A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable result in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates, in comparison to the measurement uncertainty. Range of true values depends on the physics of the experiment, while measurement uncertainty depends on the measurement method (properties of the detector not of the experiment). When data from one–of–a kind experiment are analyzed, only the measurement uncertainty is reported. It gives no information about the range, in which the true values of experiment would spread if the experiment was repeated. A frequently used approximation, that if a physical quantity is measured as a function of time, only measurement of this quantity, produces uncertainty is also in some real experiments fare to strong. Example: In reaction history time measurement uncertainty propagated to alpha dominated under certain conditions over the flux measurement uncertainty propagated to alpha. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable.
River networks sustain life and landscapes by carrying and distributing water, sediment, and nutrients throughout ecosystems and communities. At the largest scale, river networks drain continents through tree-like tributary networks. At typically smaller scales, river deltas and braided rivers form loopy, complex distributary river networks via avulsions and bifurcations.In order to model flows through these networks or analyze network structure, the topology, or connectivity, of the network must be resolved. Additionally, morphologic properties of each river channel as well as the direction of flow through the channel inform how fluxes travel through the network’s channels. Riv Graphis a Python package that automates the extraction and characterization of river channel networks from a user-provided binary image, or mask, of a channel network (Fig. 1). Masks may be derived from (typically remotely-sensed) imagery, simulations, or even hand-drawn. RivGraph will create explicit representations of the channel network by resolving river centerlines as links, and junctions as nodes. Flow directions are solved for each link of the network without using auxiliary data, e.g., a digital elevation model (DEM). Morphologic properties are computed as well, including link lengths, widths, sinuosities, branching angles,and braiding indices. If provided,RivGraph will preserve georeferencing information of the mask and will export results as ESRI shapefiles, GeoJSONs, and GeoTIFFs for easy import into GIS software.RivGraph can also return extracted networks as networkx objects for convenient interfacing with the full-featured networkx package (Hagberg et al., 2008). Finally, RivGraph offers a suite of topologic metrics that were specifically designed for river channel network analysis (Tejedor et al., 2015b).
Tungsten (W) is one of the leading candidate materials for plasma-facing components. However, its main drawback is its high radiative efficiency; if W penetrates the plasma, it can lead to core degradation or even collapse. Since eroded tungsten tends to ionize in the sheath and redeposit promptly, the net erosion flux that escapes prompt redeposition can differ significantly from the gross erosion. This work presents a modeling framework to estimate net erosion and photon emission from W coatings exposed to the lower divertor of DIII-D using the DiMES material exposure probe. The approach couples RustBCA for sputtering yields with a Monte Carlo transport code (LPTMC) that models redeposition and W emission. Computation is carried out with new atomic data, based on R-matrix and Mons calculations, leading to lower ionization probabilities and a twofold increase in net erosion estimates compared to calculations done with OPEN-ADAS atomic data. The model results are benchmarked against experimental measurements, showing quantitative agreement for erosion, although the trends in W emission are reproduced only qualitatively. The model is also used to assess whether W II emission can serve as a direct measurement of the net erosion of W in the lower divertor of DIII-D. Simulations show that this is not valid if the electron pressure is above ~120 Pa or if the toroidal length of the eroded material is smaller than the parallel-to-B distance traveled by impurity ions before steady-state conditions are reached. Finally, simulations suggest that when W is sputtered by carbon ions with high impact energies (≳300 eV) in DIII-D, W net erosion scales with W gross erosion and can be numerically approximated using W I flux alone as input.
Pebble power reconstruction has been explored to complement the conventional homogenized modeling approach in pebble bed reactor (PBR) analysis, as detailed heterogeneous geometry calculations are computationally expensive. The random distribution of pebble fuels within the core challenges the application of conventional pin power reconstruction methods. To address this, we introduce a machine learning approach based on the transformer model, composed of encoder and decoder layers, to estimate the flux and power form functions for reconstructing individual pebble neutron fluxes and powers. The homogeneous neutron flux distribution within each spectral zone (SZ) is obtained from finite element solutions of global diffusion or transport calculations. Verification tests demonstrate that the trained transformer model accurately predicts power form functions over a range of conditions, including variations in pebble enrichment, location, type, SZ size, and burnup. In particular, verification using a three-dimensional PBR benchmark with burned pebbles shows good agreement in heterogeneous pebble power distributions between Griffin and Serpent. These results highlight the potential of applying conventional pin power reconstruction approaches to PBR cores with randomly distributed pebbles.
Dark matter (DM) particles with sufficiently large cross sections may scatter as they travel through Earth’s bulk. The corresponding changes in the DM flux give rise to a characteristic daily modulation signal in detectors sensitive to DM-electron interactions. Here, we report results obtained from the first underground operation of the DAMIC-M prototype detector searching for such a signal from DM with MeV-scale mass. A model-independent analysis finds no modulation in the rate of 1 e − events with sidereal period, where a DM signal would appear. We then use these data to place exclusion limits on DM in the mass range [ 0.53 , 2.7 ] MeV / c 2 interacting with electrons via a dark photon mediator. Taking advantage of the time-dependent signal we improve by ∼ 2 orders of magnitude on our previous limit obtained from the total rate of 1 e − events, using the same dataset. This daily modulation search represents the current strongest limit on DM-electron scattering via ultralight mediators for DM masses around 1 MeV / c 2 . Published by the American Physical Society 2024
Recent advancements in metal additive manufacturing (AM) show great potential to revolutionize the design and manufacturing of electromagnetic components used in the field of electrical engineering. Lattice structures directly printed by AM processes typically offer better structural performance with reduced weight, such as high stiffness, surface area, elongation, energy absorption, and porosity, than the solid counterpart. This paper aims to study the electromagnetic modeling of lattice structures used in additively manufactured magnetic cores or windings for electric machines. Three dimensional (3D) electromagnetic finite element (FE) analysis with high performance computing (HPC) shows the highest fidelity in predicting the electromagnetic performance of designs with lattice structures by preserving complex geometry details. FE-based homogenization methods have also been explored to potentially speed up concept design. A case study based on an additively manufactured axial-flux permanent magnet machine with a Hilbert pattern stator validates the discussed electromagnetic modeling approaches.
Two titanium alloys, Ti 15-3-3-3 and Grade 5 Ti, have been analyzed for their neutronic differences after being exposed to neutron fluxes from a 5-year operational period in the Second Target Station (STS)moderators. There are several differences in the original composition of the two alloys where Ti 15-3-3-3contains Cr and Sn and Grade 5 Ti does not. The differences in original composition propagate through the analysis to yield discrepancies in decay power density and decay gamma intensity density of more than a factor of 3.5 at some times during operation. Immediately after the 5-year operational period, Ti 15-3-3-3has a factor of 3.59 higher intensity of high-energy decay gammas than Grade 5 Ti, and Ti 15-3-3-3 also has a factor of 3.53 higher decay power than Grade 5 Ti. After 1 year of shutdown, Ti 15-3-3-3 has a 48% higher intensity of high-energy decay gammas and a 86% greater decay power density when compared with Grade 5 Ti. The quantities-of-interest in this report are per unit volume and the overall volume of titanium should be considered when making engineering judgment on final titanium alloy selection.
The Fermi Large Area Telescope (LAT) lightcurve repository (LCR) is a publicly available, continually updated library of gamma-ray lightcurves of variable Fermi-LAT sources generated over multiple timescales. The Fermi-LAT LCR aims to provide publication-quality lightcurves binned on timescales of 3, 7, and 30 days for 1525 sources deemed variable in the source catalog of the first 10 yr of Fermi-LAT observations. The repository consists of lightcurves generated through full likelihood analyses that model the sources and the surrounding region, providing fluxes and photon indices for each time bin. The LCR is intended as a resource for the time-domain and multimessenger communities by allowing users to search LAT data quickly to identify correlated variability and flaring emission episodes from gamma-ray sources. We describe the sample selection and analysis employed by the LCR and provide an overview of the associated data access portal.
An improved fine-scale soil moisture (SM) dataset at 1 km grid spacing, covering much of the eastern continental US, was generated by assimilating 9 km Soil Moisture Active Passive (SMAP) SM data into the v4.0.1 Noah-MP land surface model. With 12 ensemble members, the assimilation was carried out using the ensemble Kalman filter algorithm within NASA's Land Information System. The SM analysis for 2016 was fully validated against in situ observations from four different networks and compared with four other existing datasets. Results indicate that this SM analysis surpasses other datasets in top-layer SM distribution, including a machine-learning-based product, despite all SM estimates being less heterogeneous than observed. The analysis of anomalous errors suggests that large similarity in intrinsic errors is likely due to overlapping data sources among the selected SM datasets. More detailed evaluations were performed over two geographic areas. The observations collected by the Atmospheric Radiation Measurement facility in Oklahoma suggest that soil temperature and surface heat fluxes are concurrently simulated with good accuracy. Investigation into the 2016 southeastern US drought response further indicates drier conditions and higher evapotranspiration estimates compared to GLEAMv4.1. Notably, large errors are associated with grids having clay soil textures, underscoring the need for refined model treatments for specific soil types to further improve SM estimates. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.14370563 (Tai et al., 2024).
Abstract. This article presents the C-grid implementation of the CICE sea ice model, including the C-grid discretization of the momentum equation, the boundary conditions (BCs), and the modifications to the code required to use the incremental remapping transport scheme. To validate the new C-grid implementation, many numerical experiments were conducted and compared to the B-grid solutions. In idealized experiments, the standard advection method (incremental remapping with C-grid velocities interpolated to the cell corners) leads to a checkerboard pattern. A modal analysis demonstrates that this computational noise originates from the spatial averaging of C-grid velocities at corners. The checkerboard pattern can be eliminated by adjusting the departure regions to match the divergence obtained from the solution of the momentum equation. We refer to this novel approach as the edge flux adjustment (EFA) method. The C-grid discretization with edge flux adjustment allows for transport in channels that are one grid cell wide – a capability that is not possible with the B-grid discretization nor with the C-grid and standard remapping advection. Simulation results match the predicted values of a novel analytical solution for one-grid-cell-wide channels.