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DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Summary Data

Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to one meter in depth from irrigated and planted field treatments and analyzed using a suite of omics and chemical analyses. Carbon cycling processes in the surface soil were dominated by plant-microbe interactions that drive organic carbon cycling, whereas inorganic carbon chemistry dominated in deeper soil layers. Both irrigation and plant cover impacted organic and inorganic carbon pools in the soil profiles. This study reveals the complex interactions between water, plants, minerals, and microorganisms that govern organic and inorganic pools of soil carbon at different depths.

Naasko, Katherine I↗

DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Sequence Data

Naasko, Katherine I↗

DOE OSTI2020

Fast Evaluation of Aircraft Icing Severity Using Machine Learning Based on XGBoost

Aircraft icing represents a serious hazard in aviation which has caused a number of fatal accidents over the years. In addition, it can lead to substantial increase in drag and weight, thus reducing the aerodynamics performance of the airplane. The process of ice accretion on a solid surface is a complex interaction of aerodynamic and environmental variables. The complex relationship makes machine learning-based methods an attractive alternative to traditional numerical simulation-based approaches. In this study, we introduce a purely data-driven approach to find the complex pattern between different flight conditions and aircraft icing severity prediction. The supervised learning algorithm Extreme Gradient Boosting (XGBoost) is applied to establish the prediction framework which makes prediction based on any set of observations. The input flight conditions for the proposed prediction framework are liquid water content, droplet diameter and exposure time. The proposed approach is demonstrated in three cases: maximum ice thickness prediction, icing area prediction and icing severity level evaluation. Performance comparison studies and error analysis are also conducted to verify the effectiveness and performance of the proposed method. Results show that the proposed method has reasonable capability in evaluating aircraft icing severity.

Li, Sibo (ORCID:000000021705844X)↗

DOE OSTI2020

Potential Environmental Effects of Marine Renewable Energy Development—The State of the Science

Marine renewable energy (MRE) harnesses energy from the ocean and provides a low-carbon sustainable energy source for national grids and remote uses. The international MRE industry is in the early stages of development, focused largely on tidal and riverine turbines, and wave energy converters (WECs), to harness energy from tides, rivers, and waves, respectively. Although MRE supports climate change mitigation, there are concerns that MRE devices and systems could affect portions of the marine and river environments. The greatest concern for tidal and river turbines is the potential for animals to be injured or killed by collision with rotating blades. Other risks associated with MRE device operation include the potential for turbines and WECs to cause disruption from underwater noise emissions, generation of electromagnetic fields, changes in benthic and pelagic habitats, changes in oceanographic processes, and entanglement of large marine animals. The accumulated knowledge of interactions of MRE devices with animals and habitats to date is summarized here, along with a discussion of preferred management methods for encouraging MRE development in an environmentally responsible manner. As there are few devices in the water, understanding is gained largely from examining one to three MRE devices. This information indicates that there will be no significant effects on marine animals and habitats due to underwater noise from MRE devices or emissions of electromagnetic fields from cables, nor changes in benthic and pelagic habitats, or oceanographic systems. Ongoing research to understand potential collision risk of animals with turbine blades still shows significant uncertainty. There has been no significant field research undertaken on entanglement of large animals with mooring lines and cables associated with MRE devices.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ~ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ~ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

MicroBooNE's First Search for the MiniBooNE Anomalous Excess Under a Photon-Like Hypothesis with High-Sensitivity Search for Neutrino-Induced Neutral Current Delta Production and Radiative Decay

MicroBooNE is a liquid argon time projection chamber that collected neutrino data at Fermilab's Booster Neutrino Beam from 2015 to 2020. One of its primary goals is to investigate the “Low Energy Excess” of neutrino events observed by the MiniBooNE experiment, for which candidate photon-like interpretations include an underestimation of neutrino neutral current (NC) resonant $\Delta$ production with subsequent radiative decay or another anomalous source of single photon production in neutrino interactions. In particular, NC $\Delta$ radiative decay is poorly constrained background process to electron neutrino measurements and could be a sizable contribution to the “Low Energy Excess.” This thesis will present the analysis developed to search for NC $\Delta \rightarrow N\gamma$ events in MicroBooNE, consisting of a boosted decision tree based event selection with an NC neutral pion background constraint, using data from the first three years of operations corresponding to $6.9\times10^{20}$ POT.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Distributed Energy Resources Cybersecurity Framework & Risk Manager

Distributed Energy Resource Cybersecurity Framework (DER-CF) provides a holistic assessment for evaluating the cybersecurity posture of DER systems - filling an important gap that expands upon existing cybersecurity frameworks for more modern energy systems. The DER-CF is available as a written framework and an interactive Web tool. Distributed Energy Resource Risk Manager (DER-RM) process adheres closely to NIST's seven risk management steps: prepare, categorize, select, implement, assess, monitor, and authorize. This added feature is independent of the DER-CF's existing self-assessment and allows managers to focus on the RMF process.

cybersecurity↗

DOE OSTI2022

Simulations of Low Mach Number Reactive Flows Coupled with Electric Fields

Applying electric fields to flames can provide an effective, non-invasive control of the combustion process. In this work, an algorithm recently introduced to handle the interactions between reactive flows and an electric field is extended to handle multiple dimensions and adaptive mesh refinement (AMR), and applied to a series of laminar flames. Experimental data allows to validate the ability of the algorithm to capture the mechanisms driving the flame response to electric fields.

electric fields↗

DOE OSTI2022

Simulations of Low Mach Number Reactive Flows Coupled with Electric Fields: Preprint

electric field↗

DOE OSTI2021

Nuclear Transparency from Quasi-elastic ¹²C(e, e'p) scattering reaction up to Q2 = 14.2 (GeV/c)2 in Hall C at Jefferson Lab

Color Transparency (CT) is a unique prediction of Quantum Chromodynamics (QCD) where the final (and/or initial) state interactions of hadrons with the nuclear medium are suppressed for exclusive processes at high momentum transfers. While this phenomenon has been observed for mesons, there has never been a conclusive observation for baryons. A clear signal of CT for baryons would be the first evidence of baryons fluctuating to a small size in the nucleus, and the onset would show the transition from nucleon-meson picture to quark-gluon degrees of freedom. The experiment E1206107, searching for the onset of CT in protons was completed in Hall C at Jefferson Laboratory (JLab) using the upgraded 12 GeV e- beam. It used the High Momentum Spectrometer (HMS) and the new Super High Momentum spectrometer (SHMS) in coincidence to measure the e- + ¹²C ? e-' + p + X reaction in quasi-elastic kinematics. Data were collected on a ¹²C target over the range of Q2 = 8 - 14.3 (GeV/c)2, covering the region where a previous p + A ? p' + p + X experiment at Brookhaven National Laboratory (BNL) had observed an enhancement. Proton Transparency (PT) was extracted from these data. A rise in the PT as a function of Q2 (defined as the square of the negative of the 4-momentum transfer by the scattered electron) is predicted to be a signature of the onset of CT. Our data showed no significant increase and consistent with the traditional nuclear physics calculation. This dissertation discusses the theory and implementation of the CT experiment, summarizes the data analysis and presents results on hydrogen normalization and nuclear transparency.

Bhetuwal, Deepak↗

DOE OSTI2022

From light neutrino decay phenomenology to muon neutrino cross-section measurement at MINERvA experiment

This thesis tackles two essential topics of neutrino physics: neutrino decay and neutrino cross-section measurement. First, the invisible and visible neutrino decay is analyzed through a phenomenological approach, considering future long-baseline neutrino experiments such as DUNE and a hypothetical neutrino beam toward the ANDES laboratory. The study takes into account the ν μ and ν e disappearance and appearance, respectively, for both FHC and RHC flux modes. The results showed a negligible matter effect for DUNE but significantly more notable at ANDES. At 90% C.L., the sensitivity to the decay parameter α3 can be as small as 2 × 10 -6 eV 2 for a chosen coupling. The impact of neutrino decay in the determination of θ 23 and δ CP were also shown. Second, the double-differential cross-section measurement for ν μ -carbon interactions with three-momentum transfer |$\vec{q}$| < 1.2 GeV obtained with medium energy exposures in the NuMI beam at MINERvA experiment are reported. The measurement is presented as a function of |$\vec{q}$| and E avail and reviews different interaction models and nuclear effects along quasi-elastic to resonance processes to define a new model for a better agreement. The double differential cross sections are compared to the MnvTunes, GENIE, and NuWro predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Photoproduction of pion pairs at high energy and small angles

Here, we explore the photoproduction mechanisms for charged and neutral pion pairs off a heavy nucleus at threshold. We calculate the production of charged pairs in the Coulomb field of the nucleus in the Born approximation using explicit expressions for the differential cross sections and their connection with the total cross section σ(γγ→π + π - ). The production of σ mesons at threshold and their subsequent decay into pions is an important mechanism and is investigated quantitatively in a framework that can be used for both charged and neutral pions. σ mesons can be produced via electromagnetic (photon exchange) or strong (ω exchange) interactions. For neutral pions, another production mechanism consists of the two-step process where charged pions are produced first and then charge exchange into two neutral pions. Differential and total cross sections are computed for each production mechanism.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Tailoring the Selective Oxidation of Hydroxyl-Containing Compounds via Precisely Tuning the Hydrogen-Bond Strength of Catalyst H-Bond Acceptors

The unique performance of the enzyme is mainly achieved via weak interactions between the “outer coordination sphere” and the substrate. Inspired by this process, we developed 3D encapsulated-structure catalysts with hydrogen-bond engineering on the shell, which mimics the “outer coordination sphere” of an enzyme. Various hydrogen bond acceptors (C=O, S=O, and N–O groups) are imparted in the shell. Concentration-dependent 1H NMR, inverse-phase gas Chromatography (IGC) measurements, and DFT calculations underscore that the hydrogen bond strength between the acceptor groups and alcohol follows the order of C=O < S=O < N–O. The hydroxyl compound oxidation rate vs the hydrogen bond strength follows a volcano behavior, reminiscent of Sabatier’s principle. The performance variation among catalysts is attributed to the adsorption strength of the substrate. The proposed bioinspired design principle expands the scope of encapsulated catalysts, enabling fine regulation of catalytic activity through precise microenvironment control via weak interactions with substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Near-Field Imaging of Shallow Chemical Explosions in Granite Using Change Detection Methods with Surface and Borehole Seismic Data

Explosions detonated in geologic media damage it in various ways via processes that include vaporization, fracturing, crushing of interstitial pores, etc. Seismic waves interact with the altered media in ways that could be important to the discrimination, characterization, and location of the explosions. As part of the Source Physics Experiment, we acquired multiple pre- and post-explosion near-field seismic datasets and analyzed changes to seismic P-wave velocity. Our results indicate that the first explosion detonated in an intact media can cause fracturing and, consequently, a decrease in P-wave velocity. After the first explosion, subsequent detonations in the pre-damaged media have limited discernible effects. We hypothesize this is due to the stress-relief provided by a now pre-existing network of fractures into which gasses produced by the explosion migrate. We also see an overall increase in velocity of the damaged region over time, either due to a slow healing process or closing of the fractures by subsequent explosions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Variable optical properties of light-harvesting complex II revisited

Understanding photosynthetic light harvesting requires knowledge of the molecular mechanisms that dissipate excess energy in thylakoids. However, it remains unclear how the physical environment of light-harvesting complex II (LHCII) influences the process of chlorophyll de-excitation. Here, we demonstrate that protein-protein interactions between LHCIIs affect the optical properties of LHCII and thus influence the total energy budget. Aggregation of LHCII in the dark altered its absorption properties, independent of the amount of prior light exposure. We also revisited the triplet excited state involved in light-induced fluorescence quenching and found another relaxation pathway involving emission in the green region, which might be related to triplet excited energy transfer to neighboring carotenoids and annihilation processes that result in photoluminescence. LHCII- containing liposomes with different protein densities exhibited altered fluorescence and scattering properties. Our results suggest that macromolecular reorganization affects overall optical properties, which need to be addressed to compare the level of energy dissipation.

Iwai, Masakazu↗

DOE OSTI2022

Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control

Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.

deep reinforcement learning↗

DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Unraveling the role of EGR olefins at advanced combustion conditions in the presence of nitric oxide: Ethylene, propene and isobutene

We report the role of EGR (exhaust gas recirculation) olefinic constituents at advanced combustion conditions in the presence of nitric oxide is unraveled in this study through experimental and modeling efforts using a twin-piston rapid compression machine operating at a stochiometric fuel loading with 20% EGR by mass, pressures of 20 and 40 bar, and temperatures from 680 to 950 K. Five different levels of olefin addition, focusing on ethylene, propene and isobutene, with a fixed amount of NO at 70 ppm are doped into test mixtures of PACE-20, a multi-component gasoline surrogate, where olefin addition effects are characterized through changes in ignition times and heat release rates. Experiments indicate that all three EGR olefins inhibit autoignition reactivity and low-temperature heat release at Tc < 850 K, with isobutene exhibiting the greatest impact, while at Tc > 850 K, low ethylene and propene additions promote reactivity. A recently updated chemical kinetic model, with detailed gasoline/NO x interacting and olefin/NO x interacting chemistry incorporated, is adopted to simulate the experiments. Simulation results are somewhat inconsistent with the experiments, where the model captures the inhibiting effects of all olefins on first-stage ignition reactivity, while consistently predicting a promoting effect on main ignition reactivity. Sensitivity and rate of production analyses reveal that adding olefins greatly alters the role of the consuming pathways for the olefins and their primary derivatives at the initial stage of the oxidation process, particularly with the presence of NO, where the olefins and their derivatives interact with both NO x species such as NO 2 and other species such as OH and HO 2 . The olefin/NO x interactions are particularly pronounced with propene and isobutene addition, where these lead to increased ignition reactivity by facilitating NO production and are mostly responsible for the disagreement between the model and experiments. Further investigations of olefin interacting chemistry, particularly those with NOx species, are needed for chemistry models to accurately predict the complicated effects of EGR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗