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NASA NTRSDate not supplied

Designing Experiments for SpinSat, A Novel Variable-Gravity-and-Radiation Platform for Deep-Space Science

Conducting experiments to measure the effects of deep-space radiation and reduced gravity on biological and physical systems remains challenging. The result is a substantial knowledge gap that poses risks to our ability to sustain life and conduct critical operations in deep space. The SpinSat spacecraft platform is designed to bridge such gaps by providing low-cost, reliable, and frequent access to deep space. A disk-shaped rotating satellite that can provide artificial gravity and exposure to space radiation simultaneously, SpinSat is designed to accommodate payloads in a CubeSat form factor (with at least 48 “U” volume), providing power, communications, and a benign thermal environment. It is orbit-agnostic, enabling access to a variety of radiation environments (Van Allen belts, deep space, cis-lunar); and can be equipped with shielding to mimic planetary radiation environments, for both short- and long-term experiments. Because of its versatility and prioritization of late loading for biological payloads, it is well suited to host a wide range of ranging from human tissues and organoids to microorganisms, plants, chemistry, and regolith. Here, we present examples of potential experiment concepts for SpinSat, and discuss the details of how experimental designs could interact with the platform. Potential SpinSat studies have diverse applications, including fundamental radiation biology and DNA repair; cancer biology and countermeasure development; space agriculture; bioproduction of nutrients and pharmaceuticals; understanding regolith dynamics in low gravity; prebiotic chemistry and panspermia. We will further highlight ideas for SpinSat-compatible experimental hardware, existing and in development, and experiment-relevant details on SpinSat capabilities including artificial gravity, potential radiation environments, data, and power. This presentation will aim to provide investigators with the high-level technical information necessary to inspire experiments for SpinSat. We also seek to stimulate conversation and to gain community input on accommodations needs to help guide the evolving design of this platform.

experiment design↗

NASA NTRSDate not supplied

Two-Phase Flow in Filters and Orifices: In preparation for Measurements on ISS Packed Bed Reactor Experiment-Water Recovery Series (PBRE-WRS)

Understanding the hydrodynamics of adiabatic two-phase flows in packed bed reactors offers numerous benefits. It enables the optimization of chemical reactions rates and products, crucial for pharmaceuticals and energy production industries. Additionally, this understanding aids in designing more efficient and compact reactors, reducing space and resource requirements. Insights gained from studying such flows in microgravity contribute to advancements of space technologies and the enhancement of our capabilities for undertaking future long duration safe, and sustainable space exploration missions. The Packed Bed Reactor Experiment-Water Recovery Series (PBRE-WRS) is a flight experiment planned for operation on the Microgravity Science Glove box (MSG) facility of the International Space Station (ISS). The objective of the experiment is to validate hydrodynamic models at a range of gas and liquid flow rates. The microgravity environment allows for measurement and observation of aspects of fluid dynamics that are unique when compared to observations made in full or partial gravity environments. The experiment consists of testing two-phase flow hydrodynamics in three different filters, four (4) orifices and one check valve test articles. The experiment utilizes the hardware of the previously flown PBRE and PBRE-2 with packed beds of different packings materials and sizes. The fluid system of the PBRE-WRS consists of a nitrogen gas delivery and water delivery subsystems. The gas delivery subsystem can accommodate two ranges of gas flow rates, just as for the water delivery system. The nitrogen gas loop is open whereas the water loop is closed. Gas is separated from the water using a gas-liquid phase separator which is located in the water delivery module. Preliminary results from ground testing show the pressure drop increasing at different rates with the liquid and gas flow rates in the Brine filter. In this work, a detailed system description is presented along with a summary of results from ground performance testing of different test sections in an end-to-end preflight testing campaign.

Packed Bed Reactor Experiment↗

NASA NTRS2024

Expanding Ketamine Application for Treatment of Acute Suicidality in Long-Duration Spaceflight

Introduction. The transition to exploration missions places a heightened risk on behavioral health in spaceflight. Although serious psychiatric emergencies during spaceflight have been rare, longer duration missions increase the possibility of emergence in latent mental health disorders due to genetic predisposition, increased autonomy, isolation, helplessness, loss of family member, or catastrophic events. Complicated grief and bereavement have the highest rate of suicidal ideation. Recently, ketamine has been used as an emergent intervention for acute suicidality, promoting its stability, ease of administration, favorable safety profile, and outcomes for reduction of suicidal intent. The goal of this study was to review current literature and collate the understanding of ketamine as a safe, effective pharmacological adjunct for acute suicidality in spaceflight. Methods. This literature review was conducted to collate data on ketamine use for acute suicidality and inform on stability, limitations and utilization of ketamine within extreme environments. Results. 122 publications were reviewed for relevance including 23 randomized-control trials for ketamine use in behavioral emergencies. Discussion. Ketamine is a diverse pharmaceutical with multiple advantageous indications, including acute suicidality, pain, and sedation. Terrestrial use of ketamine suggests a rapidly efficacious medication for reduction in acute suicidality. As behavioral stressors expand related to extended missions, contingencies for behavioral emergencies become increasingly important. Although this review is not intended to re-develop current International Space Station (ISS) protocols, it is the first to discuss the benefits of ketamine in spaceflight as a potential safe, effective multifaceted tool for future exploration missions and treatment for acute suicidal ideation.

Ketamine, Suicidality, Spaceflight↗

NASA NTRSDate not supplied

Harnessing Synthetic Communities and Microbial Recycling of Space Waste Streams for Biomanufacturing Applications

The long-term habitation of extraterrestrial environments such as the Moon or Mars presents significant challenges including supplying materials to sustain life. Off-world recycling of waste materials into biomanufacturer products may ameliorate this. Current literature highlights the need for efficient waste recycling systems to support the bioproduction of essential materials including foods, pharmaceuticals, and biomaterials. The study herein concerns itself with the investigation of three key aspects: 1) formulating an optimal wastewater media on which to grow recombinant microbes for bioproduction in space, 2) examining the potential for constructing stable and metabolically synergistic synthetic microbial communities for largescale and multi-tiered biomanufacturing, and 3) testing the efficacy of one such bioengineered enzyme, cutinase, on the degradation of PET plastics characteristic of those found in ISS waste as a model for recombinant recycling-based biomanufacturing of mission-critical substrates. Formulation of an optimal wastewater media involved growing several microorganisms on mixtures of synthetic planetary wastes representative of those found in space waste systems, combined with simple carbon sources derived from a physio-chemical CO conversion system to determine their growth potential. Potential synthetic microbial communities were conceptually designed, and their stability and metabolic synergism was evaluated within the context of co-cultures. Cutinase activity assays were utilized to determine the efficacy of bioengineered cutinase on PET plastic degradation. Findings will contribute to optimization of wastewater-based media formulations, data on stable synthetic microbial communities for bioproduction, and effective methods for measuring cutinase-based PET plastic degradation. These outcomes support the development of sustainable waste recycling systems for space habitation and aim to fill gaps in the current literature and proposing innovative solutions for waste recycling in space environments. By leveraging synthetic biology this study seeks to enhance the feasibility of long-term extraterrestrial habitation through sustainable resource management.

Bioprocessing↗

NASA NTRSDate not supplied

Developing a Genetic Variant Calling Pipeline for Quantifying the Complex Mutagenic Load Accumulated in BioNutrients-1 Production Pack Samples

Microorganisms hold great promise for on demand production of labile nutrients and pharmaceuticals as well recycling and in situ resource utilization. The utilization of microorganisms for such tasks on space missions is hindered by the limited data on how microbes respond to spaceflight. For example, the genetic stability of microorganisms, and the genomic engineered traits added to deliver desired functions, over long-term storage in the spacecraft environment is poorly understood. The BioNutrients-1 (BN-1) mission conducted a 5-year study of desiccated storage in Low Earth Orbit (LEO) to evaluate the suitability of eight synthetic biology chassis organisms for long-duration space missions. We are employing high-depth, whole genome sequencing (WGS) to determine the mutagenic load that accumulated during long-term storage. Mutation analysis pipelines are well established for homogenous culture grown from a single colony, but the mutational landscape of the BN-1 samples present a unique analysis challenge, as every cell in the BN-1 samples had a unique genetic journey of DNA damage and repair. Consequently, sequence variants are expected at low allele frequency within samples. To address this genetic complexity, we apply two distinct computational approaches to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO. For reference genome free mutation detection, we utilized DiscoSNP++, which is a de Bruijn graph approach. For reference genome-based mutation detection we utilize GATK for Microbes, which is a Bayesian probabilistic approach. We will benchmark these approaches against the mutations originally identified using CRISP, a method optimized for pooled samples. Ultimately, quantifying the mutation load imposed by storage or growth on the ISS will help identify chassis organisms with both high levels of genome stability and viability, which are desirable traits for implementation of bioproduction in long-duration missions.

SNP↗

NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗

NASA NTRSDate not supplied

Two-Phase Flow in Packed Beds, Filters and Orifices: Summary of Findings of the Packed Bed Reactor Experiment Onboard of the International Space Station

Understanding the hydrodynamics of adiabatic two-phase flows in packed bed reactors enables the optimization of chemical reaction rates and products that crucial in pharmaceuticals and energy production industries. Additionally, this understanding aids in designing more efficient and compact reactors and reducing space and resource requirements. Insights gained from studying and understanding such flows in microgravity contribute to advancements of space technologies and the enhancement of our capabilities for undertaking future long duration safe, and sustainable space exploration missions. The objective of the Packed Bed Reactor Experiment series (PBRE and PBRE-2) was to develop a fundamental understanding of the hydrodynamics of adiabatic two-phase flows through porous media in microgravity. This work provides a description of the PBRE fluid system and presents the highlights of the PBRE and PBRE-2 previously flown with different packings and operated onboard of the International Space Station Microgravity Science Glovebox (ISS-MSG). These highlights include flow patterns and two-phase pressure drop, and the impact of capillary effects on pressure drop at various gas and liquid flow rates. This work also presents preliminary results from the Packed Bed Reactor Experiment-Water Recovery Series (PBRE-WRS) recently flown with filters and orifices, which was also operated on the ISS-MSG. The objective of the experiment is to validate hydrodynamic models at a range of gas and liquid flow rates pertinent to the operation of these test articles. The microgravity environment allows for measurement and observation of aspects of fluid dynamics that are unique when compared to observations made in full or partial gravity environments.

ISS↗

DOE OSTI2020

Multiphase Reacting Flow Simulations and Optimization of Commercial-Scale Aerobic Bioreactors

Aerobic bioreactors are widely used in the synthesis of value-added products in pharmaceutical and biotechnology industries, wherein microbial bioreactions aid in the conversion of sugars to macromolecules. These reactors are also being actively investigated as a cost-effective pathway for the production of low-value commodities such as biofuels and animal feed. Reactor scale-up is one of the important challenges when designing these systems. Gas and liquid phase transport, mass-transfer, and mixing physics at large length scales can significantly affect microbial conversion rates, particularly when the microbial reaction requires a narrow set of conditions. These phenomena are difficult to study in small-scale bench-top reactors that are typically well-mixed. Predictive computational fluid dynamics (CFD) based simulations can therefore aid in the design and optimization of these reactors. This work presents multiphase Euler-Euler CFD simulations of commercial-scale (~ 500 m3) aerobic bioreactors. Our mathematical model treats the gas and liquid as interpenetrating phases. This approach reduces the computational complexity of tracking individual gas bubbles that are several orders of magnitude smaller than reactor dimensions. We solve the Reynolds averaged Navier-Stokes (RANS) multiphase equations that account for phase and chemical species transport, interphase mass and momentum transfer and uses a phenomenological model for oxygen uptake by microbes. We use a customized solver derived from open-source CFD toolbox, OpenFOAM, to perform these simulations, which has been validated against small-scale reactors in our previous work. This work examines the performance of three different reactor designs, viz. bubble column reactor, airlift reactor with an internal draft tube, and a stirred-tank reactor with Rushton impellers. Reactor oxygen mass-transfer coefficient, gas hold-up, and oxygen distribution are critically analyzed among reactors, and sensitivity studies pertaining to gas flow rates and reactor geometry will be presented.

09 BIOMASS FUELS↗

DOE OSTI2020

Carbon fixation systems in plants and algae

Provided are methods for elevating cyclic electron transfer activity, improving carbon concentration, and enhancing carbon fixation in C3 and C4 plants, and algae, and producing biomass or other products from C3 or C4 plants, and algae, selected from among, for example, starches, oils, fatty acids, lipids, cellulose or other carbohydrates, alcohols, sugars, nutraceuticals, pharmaceuticals, fragrance and flavoring compounds, and organic acids, as well as transgenic plants produced thereby. These methods and transgenic plants and algae encompass the expression, or overexpression, of various combinations of genes that improve carbon concentrating systems in plants and algae, such as bicarbonate transport proteins, carbonic anhydrase, light driven proton pump, cyclic electron flow regulators, etc.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Multiple-objective Reinforcement Learning for Inverse Design and Identification

The aim of inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are considered as the state-of-the-art in inverse chemical design and have achieved early success in generating molecular structures with desired properties in the pharmaceutical and material chemistry fields. However, satisfying a large number (> 10 objectives) of molecular objectives is a limitation of current generative models. To improve the model’s ability to handle a large number of molecule design objectives, we developed a Reinforcement Learning (RL) based generative framework to optimize chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune the pre-trained generative network allowed the model to satisfy up to 21 objectives and increase the generative network’s robustness. The experiments show that the proposed multiple-objective RL-based generative model can correctly identify unknown molecules with an 83% to 100% success rate, compared to the baseline approach of 0%. Additionally, this proposed generative model is not limited to just chemistry research challenges; we anticipate that problems that utilize RL with multiple objectives will benefit from this framework.

reinforcement learning, generative model, inverse ↗

DOE OSTI2020

COVID-19 Joint Pandemic Modeling and Analysis Platform

The non-pharmaceutical intervention to reduce the impact and spread of COVID-19 requires the development of policies and guidance through a collaborative effort among government, academia, medicine, and citizens. To operationalize this effort, we have developed an all-encompassing situational awareness platform that can process multi-modal and multi-source data allowing informed decision making. Besides, showing the current spread of infection, the platform also captures the impact of human dynamics on the infection spread, location, and availability of critical infrastructure, prediction, and high-performance computing driven simulation. The platform is extensible, allowing third-party integration and services to consume the curated data and analytics in near real-time. We believe the platform will augment critical decision making for reducing the impact and spread of the pandemic.

Thakur, Gautam↗

DOE OSTI2020

High molecular weight polysaccharide that binds and inhibits virus

This invention provides a high molecular weight polysaccharide capable of binding to and inhibiting virus and related pharmaceutical formulations and methods of inhibiting viral infectivity and/or pathogenicity, as well as immunogenic compositions. The invention further includes methods of inhibiting the growth of cancer cells and of ameliorating a symptom of aging. Additionally, the invention provides methods of detecting and/or quantifying and/or isolating viruses.

Konowalchuk, Thomas W.↗

DOE OSTI2020

Non-coding RNAs linked to immortality and associated methods and compositions

Aspects of the present disclosure include RNAs that confer a mortal phenotype and nucleic acids encoding same. Liposomes, recombinant cells, and pharmaceutical compositions that include the RNAs or nucleic acids encoding same are also provided. Further provided are methods involving quantifying MORT RNAs and/or determining the methylation status of the MORT promoter, as well as methods that employ the RNAs or nucleic acids encoding same.

Stampfer, Martha R.↗

DOE OSTI2021

Coupled Microbial-Conversion and Computational-Fluid-Dynamics (CFD) Models for Butanediol Production in Micro-Aerated Reactors

Microbial conversion of substrates to macromolecules has been widely used in the synthesis of value-added products in pharmaceutical and biotechnology industries. These bioreactions are also being investigated in the production of low-value commodities such as biofuels [1]. Gas-liquid mass-transfer and transport-reaction coupling are important challenges when designing and scaling up these reactor systems. Experiments in wellmixed small-scale reactors have enabled characterization of microbial reactivity, while their coupling with macroscale transport remains relatively unexplored. In this work, we use a coupled metabolic-CFD model to study the action of a genetically engineered microbe Zymomonas mobilis [2] on sugars to produce 2,3-Butanediol (BDO). BDO is an important hydrocarbon intermediate that can be catalytically upgraded to several fuels and chemicals [3]. An important aspect to this particular microbial conversion is the need for micro-aerated environments as opposed to traditional aerobic fermentation. Slight variations in oxygen concentration can result in competing reaction pathways that disable BDO production. Hence, gas-liquid mass transfer and transport need to be optimized in large-scale reactors to maximize BDO production, for which CFD is a valuable tool. The aerobic-fermentation CFD model previously developed by the authors [5] for simulating bubble-column and airlift reactors at scale was used in this study. The Reynolds-averaged mass, momentum and energy transport equations for interpenetrating gas and liquid phase are solved in this model along with the transport and interphase mass transfer of oxygen. Our previous work used a phenomenological model for microbial oxygen uptake that neglected microbial growth and other reaction pathways. In this work, a detailed metabolic model enabled prediction of product formation and inhibition pathways. In order to manage computational cost, we used a subcycling technique [6] that takes advantage of the clear separation in transport (~ 200 sec) and reaction (~ 2-3 hours) timescales. The CFD model is first solved to steady state, after which the metabolic model is advanced at every cell in the computational domain using the local oxygen concentration. The CFD model is then run to achieve a new steady state that provides a new oxygen distribution for the metabolic model. This process, where reaction and fluid updates are interleaved together, is iterated until reactants are completely exhausted. This work will examine the performance of different reactor designs such as bubble column and airlift reactors at scale (250-500 m3). Oxygen mass-transfer coefficient and distribution are critically analyzed among reactors, and optimization studies pertaining to aeration is presented. Furthermore, it has been observed in experiments that high BDO production may be achieved by manipulating the aerobic environment over the course of reaction, such that oxygen concentration is high during the growth phase, and very low as sugar is depleted. This characteristic will be addressed by our simulations for which a timedependent scheduling strategy for aeration is presented that maximizes BDO production. [1] Humbird, D., Davis, R., and McMillan, J., Aeration costs in stirred-tank and bubble column bioreactors, Biochemical Engineering Journal, 127, 161—166, 2017 [2] Yang, S., Mohagheghi, A., Franden, M. A., Chou, Y.-C., Chen, X., Dowe, N., Himmel, M. E., and Zhang, M., Metabolic engineering of zymomonas mobilis for 2, 3-butanediol production from lignocellulosic biomass sugars. Biotechnology for biofuels, 9(1):189, 2016 [3] Kim, S. J., Sim, H. J., Kim, J. W., Lee, Y. G., Park, Y. C., and Seo, J. H., Enhanced production of 2,3-butanediol from xylose by combinatorial engineering of xylose metabolic pathway and cofactor regeneration in pyruvate decarboxylase-deficient Saccharomyces cerevisiae. Bioresource Technology, 245:1551–1557, 2017 [4] Weller, H., Tabor, G., Jasak, H. and Fureby, C., A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics, 12, 6, 620--631, 1998

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

AGR-5/6/7 Fuel Fabrication Report

The U.S. Department of Energy Office of Nuclear Energy (DOE NE) and the Idaho National Laboratory (INL) Advanced Reactor Technologies (ART) Advanced Gas Reactor (AGR) Fuel Development and Qualification program (referred to as AGR Fuel program hereafter) are pursuing qualification of tristructural isotropic (TRISO) coated particle fuel for use in high temperature gas cooled reactors (HTGRs). The AGR Fuel program was established to provide a fuel qualification data set in support of the licensing and operation of an HTGR. BWX Technologies Nuclear Operations Group (BWXT-NOG) was subcontracted to fabricate the fuel for the AGR program. Several investments and innovations were realized in preparation to fabricate fuel for the AGR-5/6/7 irradiation experiments that brought fuel fabrication fully out of the laboratory and into engineering-scale operations. These included: • Increased the kernel fabrication line capacity and uniformity • Upgraded ancillary support equipment and processes for the tristructural isotropic (TRISO) coating furnace • Demonstrated efficient production of the matrix precursor powder by dry jet milling of co mingled components • Demonstrated an engineering-scale method for quick and efficient overcoating TRISO particles with the matrix precursor • Demonstrated an automated, multi cavity compacting system with a volumetric feed system • Demonstrated a combined-cycle thermal treatment furnace These changes increased production rates of some of these processes by an order of magnitude or more while eliminating the use of flammable solvents, multiple grinding and sorting operations, and the weighing out of individual die charges. Three fuel kernel lots were fabricated for production of the fuel for AGR-5/6/7. The initial lot (J52R-16-39316) was certified to fuel specifications but was not used because the kernels had a high fraction of internal fissures that caused an unacceptable fraction of the kernels to fragment when charged to the coating furnace where the TRISO coating would be deposited. Fragmented kernels increased the dispersed uranium in the particles and produced a worrisome fraction of dimpled particles with an elevated probability of in-pile failure. After some efforts to identify the cause of the fissure formation, two additional lots were produced with much lower fissure fractions, J52R-16-69317 and 69318. The latter kernel lot was a backup to the first and was not needed. Multiple kernel batches were composited to form each of the lots so as to simulate a commercial-scale operation where kernel batches would also be composited. Multiple TRISO coating runs were performed and the product characterized so that several could be composited into a TRISO lot. TRISO lot J52R-16-98005 conformed to all fuel specifications except the mean outer pyrocarbon (OPyC) layer thickness was thinner than specified. Furthermore, it was determined that the TRISO lot had a dispersed uranium fraction (DUF) that might result in the compacts not meeting the DUF specification. A review of the role of the OPyC layer and consequences of the DUF by the Technical Coordination Team and INL concluded that the fuel was acceptable for use in the AGR-5/6/7 irradiation experiment. The TRISO particles were overcoated with the matrix precursor that had been produced in a jet-milling operation. The overcoating was performed in equipment originally designed to coat pharmaceuticals. The overcoating process performed well; producing highly spherical and uniform overcoats requiring little upgrading and no recycle or rework. TRISO particles were overcoated with the matrix precursor to achieve nominal volumetric packing fractions (PFs) of TRISO particles of 25% and 40% for the irradiation experiments. The 40% PF compacts occupy the first and fifth test capsule in the test train while the inner three capsules are loaded with 25% PF compacts. The resinated graphite matrix precursor powder was a derivative of the German A3-27 matrix formulation, which differs from previous AGR irradiation campaigns that used an A3-3 formulation. Jet milling of the matrix powder precursor produced a finer mean graphite particle size than the milling operations used for the A3-3 matrix powder precursor. Changes made in the matrix formula and equipment yielded compacts with significantly higher matrix density than was attained in previous AGR irradiation campaigns. The changes in the matrix formulation and the means of milling the powders also complicated resolution of the three fuel compact defect fractions, DUF, exposed kernel fraction (EKF), and the silicon carbide defect fraction (SDF). Characterization data from BWXT-NOG had some anomalous results, so samples of the fuel compacts and overcoated TRISO particles were also analyzed by Oak Ridge National Laboratory (ORNL) to ensure that the defect fractions were accurately characterized.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Reduction of Ultrafine Hematite for a Spouting Bed Chemical Looping Process - Presentation

Chemical looping combustion research has been a topic of great interest since the mid-1990s, although the concept can be traced much further back. It is a combustion technology for fuel utilization that uses a two-reactor system to produce a carbon free effluent stream from the air reactor and a carbon dioxide rich stream from the fuel reactor with oxygen shuttled between the reactors with an oxygen carrier. Chemical looping combustion has been studied extensively because of this inherent separation of the carbon dioxide combustion product from the atmospheric nitrogen over that past 25 years with hundreds of research papers and tens of books. The oxygen carrier is, in general, a solid such as naturally occurring hematite or an engineered carrier like a copper and iron oxide supported on alumina. System technical and economic research activities conducted at the National Energy Technology Laboratory have shown that the costs associated with the carrier, initial and replacement, are a significant factor in the economic viability of the technology. To overcome this economic challenge, the work reported on in this paper investigates the use of a waste iron oxide as the chemical looping oxygen carrier. This waste material is quite unique as it has a particle size of 0.7 um. Given the particle size, the reactions are quite fast. However, the development of a contactor reactor to mix the fuel with the carrier is a fluidization challenge as the material is a Geldart group C material and highly cohesive. Normally, Geldart group C materials are fluidized with either a vibrating bed. This technique is acceptable for low temperature pharmaceuticals production but does not lend itself to much larger reactors used for power production or operation at temperatures in the order of 1100 to 1200 C. Therefore, a hydrodynamic solution was needed to break up the cohesive structures to replace the vibration. In this instance, it was hypothesized that a spouting bed of large Geldart group B or Geldart group D particles could provide the necessary energy to break up the cohesive structures of the Geldart group C material. This paper presents the reduction performance for this novel carrier material in a laboratory spouting bed reactor.

Breault, Ronald↗

DOE OSTI2021

Carbon fixation systems in plants and algae

Provided are heterologous nucleic acid constructs, vectors and methods for elevating cyclic electron transfer activity, improving carbon concentration, and enhancing carbon fixation in C3 and C4 plants, and algae, and producing biomass or other products from C3 or C4 plants, and algae, selected from among, for example, starches, oils, fatty acids, lipids, cellulose or other carbohydrates, alcohols, sugars, nutraceuticals, pharmaceuticals, fragrance and flavoring compounds, and organic acids, as well as transgenic plants produced thereby. These methods and transgenic plants and algae encompass the expression, or overexpression, of various combinations of genes that improve carbon concentrating systems in plants and algae, such as bicarbonate transport proteins, carbonic anhydrase, light driven proton pump, cyclic electron flow regulators, etc.

Sayre, Richard Thomas↗

DOE OSTI2020

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

Swenson, Nicolas↗