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At least 1,531 records · Page 85

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NASA NTRS2009

Evolutionary Computing Methods for Spectral Retrieval

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Terrile, Richard↗

DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗

DOE OSTI2021

Nanoscale biochemical sample preparation and analysis

Provided herein are methods and systems for biochemical analysis, including compositions and methods for processing and analysis of small cell populations and biological samples (e.g., a robotically controlled chip-based nanodroplet platform). In particular aspects, the methods described herein can reduce total processing volumes from conventional volumes to nanoliter volumes within a single reactor vessel (e.g., within a single droplet reactor) while minimizing losses, such as due to sample evaporation.

Kelly, Ryan T.↗

DOE OSTI2022

Exploring New Ways to Classify Industries for Energy Analysis and Modeling

As the US moves closer to embracing a net zero greenhouse gas emissions position, combustion processes outside the power sector are becoming urgent concerns. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning that even a single product group can vary broadly in its production routes and associated energy usage. In the US, the North American Industry Classification System (NAICS) serves as the basis for data collection and reporting. In turn, data based on NAICS is the foundation of most US energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for plans to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data to build more detail into heterogeneous sectors is scarce. This work explores alternative classification schemes for industry based on energy use characteristics, and provides a validation of an approach to make facility-level energy use estimates based on publicly available data from the greenhouse gas reporting program. First, several approaches to industrial taxonomies and their usefulness for industrial energy modeling are summarized. Data from Industrial Assessment Centers is analyzed using unsupervised machine learning techniques to detect clusters. Cladistics, an approach from biology, is adapted to energy and process characteristics of industries. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation. Finally, validation is performed for facility-level energy estimates from the US EPA Greenhouse Gas Reporting Program. This validation assists in making this data source available for use in energy modeling. Together, this work explores alternative approaches for categorizing industries in a way that aids understanding energy use, and presenting pathways for the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications

Natural products derived from microbes are crucial innovations that would help in reaching sustainability development goals worldwide while achieving bioeconomic growth. Trichoderma species are well-studied model fungal organisms used for their biocontrol properties with great potential to alleviate the use of agrochemicals in agriculture. However, identifying and characterizing effective natural products in novel species or strains as biological control products remains a meticulous process with many known challenges to be navigated. Integration of recent advancements in various “omics” technologies, next generation biodesign, machine learning, and artificial intelligence approaches could greatly advance bioprospecting goals. Herein, we propose a roadmap for assessing the potential impact of already known or newly discovered Trichoderma species for biocontrol applications. By screening publicly available Trichoderma genome sequences, we first highlight the prevalence of putative biosynthetic gene clusters and antimicrobial peptides among genomes as an initial step toward predicting which organisms could increase the diversity of natural products. Next, we discuss high-throughput methods for screening organisms to discover and characterize natural products and how these findings impact both fundamental and applied research fields.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Quantitative soil zymography: Mechanisms, processes of substrate and enzyme diffusion in porous media

Soil membrane zymography enables 2D mapping of enzyme activities on the surface of soil samples. The method is according to diffusion of components of enzymatically-mediated reactions to/from membrane, and, hence, reflects the distribution of enzyme activities at the intact soil surface. Zymography has been already successfully implemented in numerous soil ecology applications. Here we identify two methodological aspects for further improvement and expansion of the method at micro and macro scales: first, accounting for the area of contact between the soil surface and the zymography membranes and, second, accounting for diffusion effects during the zymography procedure. We tested three methods, namely, laser-scanning, staining with a fluorescent product (e.g. MUF: 4-methylumbelliferone), and X-ray computed micro-tomography, for assessing the area of the soil surface in contact with the membranes. We quantified diffusion of MUF, enzymes and substrate between the substrate-saturated membrane and soil as well as diffusion processes during membrane zymography via HP2 software. Diffusion of the substrate from the membrane and of the MUF-product to the membrane was detected, while there were no clear evidence of enzyme diffusion to/in the membrane. According to the model simulations, the enzyme activities detected via 2D zymography probably represent only a small portion, about 20%, of the actual reactions within the soil volume that is in both direct contact and in hydrological contact with zymography membranes. This is a result of omnidirectional diffusion of reaction products. The membrane contact with the soil surface estimated by three methods ranged from 3.4 to 36.5% further signifying that only a fraction of enzymes activity is detectable in a course of 2D soil zymography.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Elviz – exploration of metagenome assemblies with an interactive visualization tool

Metagenomics, the sequencing of DNA collected from an entire microbial community, enables the study of natural microbial consortia in their native habitats. Metagenomics studies produce huge volumes of data, including both the sequences themselves and metadata describing their abundance, assembly, predicted functional characteristics and environmental parameters. The ability to explore these data visually is critically important to meaningful biological interpretation. Current genomics applications cannot effectively integrate sequence data, assembly metadata, and annotation to support both genome and community-level inquiry. Elviz (Environmental Laboratory Visualization) is an interactive web-based tool for the visual exploration of assembled metagenomes and their complex metadata. Elviz allows scientists to navigate metagenome assemblies across multiple dimensions and scales, plotting parameters such as GC content, relative abundance, phylogenetic affiliation and assembled contig length. Furthermore Elviz enables interactive exploration using real-time plot navigation, search, filters, axis selection, and the ability to drill from a whole-community profile down to individual gene annotations. Thus scientists engage in a rapid feedback loop of visual pattern identification, hypothesis generation, and hypothesis testing. Compared to the current alternative of generating a succession of static figures, Elviz can greatly accelerate the speed of metagenome analysis. Elviz can be used to explore both user-submitted datasets and numerous metagenome studies publicly available at the Joint Genome Institute (JGI). Elviz is freely available at http://genome.jgi.doe.gov/viz and runs on most current web-browsers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Omics-Lethal Human Viruses, West Nile Experiment WCN002

The purpose of this experiment was to evaluate the host response to wild-type West Nile virus (WNV NY99 clone 382) and mutant (WNVE218A) virus infection. Sample data was obtained from mouse cortical neuron tissue and processed for mRNA and miRNA expression analysis. This data set comprises two complete biological replicate experiments conducted in the same conditions and with data processed independently (WCN003). Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics dataset downloads have a direct relationship to a primary sample submission corresponding to a specific West Nile virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges

Abstract Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

Biochemistry & Molecular Biology↗

NASA NTRS2002

Radiation Effect on Human Tissue

Predicting the occurrence of human cancer following exposure of an epidemiologic population to any agent causing genetic damage is a difficult task. To an approximation, this is because the uncertainty of uniform exposure to the damaging agent, and the uncertainty of uniform processing of that damage within a complex set of biological variables, degrade the confidence of predicting the delayed expression of cancer as a relatively rare event within clinically normal individuals. This situation begs the need for alternate controlled experimental models that are predictive for the development of human cancer following exposures to agents causing genetic damage. Such models historically have not been of substantial proven value. It is more recently encouraging, however, that developments in molecular and cell biology have led to an expanded knowledge of human carcinogenesis, and of molecular markers associated with that process. It is therefore appropriate to consider new laboratory models developed to accomodate that expanded knowledge in order to assess the cancer risks associated with exposures to genotoxic agents. When ionizing radiation of space is the genotoxic agent, then a series of additional considerations for human cancer risk assessment must also be applied. These include the dose of radiation absorbed by tissue at different locations in the body, the quality of the absorbed radiation, the rate at which absorbed dose accumulates in tissue, the way in which absorbed dose is measured and calculated, and the alterations in incident radiation caused by shielding materials. It is clear that human cancer risk assessment for damage caused by ionizing radiation is a multidisciplinary responsibility, and that within this responsibility no single discipline can hold disproportionate sway if a risk assessment model of radiation-induced human cancer is to be developed that has proven value. Biomolecular and cellular markers from the work reported here are considered for use in assessing human cancer risk related to exposure to space radiation. This potential use must be integrated within the specified multidisciplinary context in order to create a new tool of molecular epidemiology that can hopefully then realistically assess this cancer risk.

Richmond, Robert C.↗

DOE OSTI2015

Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury

Objective: Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury. Methods: Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances. Results: 99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers. Conclusion: We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Structures of MERS1, the 5' processing enzyme of mitochondrial mRNAs in Trypanosoma brucei

Most mitochondrial mRNAs are transcribed as polycistronic precursors that are cleaved by endonucleases to produce mature mRNA transcripts. However, recent studies have shown that mitochondrial transcripts in the kinetoplastid protozoan, Trypanosoma brucei, are transcribed individually. Also unlike most mitochondrial mRNAs, the 5' end of these transcripts harbor a triphosphate that is hydrolyzed. This modification is carried out by a putative Nudix hydrolase called MERS1. The Nudix motif in MERS1 is degenerate, lacking a conserved glutamic acid, thus it is unclear how it may bind its substrates and whether it contains a Nudix fold. To obtain insight into this unusual hydrolase, we determined structures of apo, GTP-bound and RNA-bound T. brucei MERS1 to 2.30 Å, 2.45 Å, and 2.60 Å, respectively. The MERS1 structure has a unique fold that indeed contains a Nudix motif. The nucleotide bound structures combined with binding studies reveal that MERS1 shows preference for RNA sequences with a central guanine repeat which it binds in a single-stranded conformation. The apo MERS1 structure indicates that a significant portion of its nucleotide binding site folds upon substrate binding. Finally, a potential interaction region for a binding partner, MERS2, that activates MERS1 was identified. The MERS2-like peptide inserts a glutamate near the missing Nudix acidic residue in the RNA binding pocket, suggesting how the enzyme may be activated. Thus, the combined studies reveal insight into the structure and enzyme properties of MERS1 and its substrate-binding activities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Nuclear Protein-Only Ribonuclease P2 Structure and Biochemical Characterization Provide Insight into the Conserved Properties of tRNA 5' End Processing Enzymes

Protein-only RNase Ps (PRORPs) are a recently discovered class of RNA processing enzymes that catalyze maturation of the 5' end of precursor tRNAs in Eukaryotes. PRORPs are found in the nucleus and/or organelles of most eukaryotic organisms. Arabidopsis thaliana is a representative organism that contains PRORP enzymes (PRORP1, PRORP2 and PRORP3) in both its nucleus and its organelles; PRORP2 and PRORP3 localize to the nucleus and PRORP1 localizes to the chloroplast and the mitochondria. Apart from their identification, almost nothing is known about the structure and function of PRORPs that act in the nucleus. Here, we use a combination of biochemical assays and X-ray crystallography to characterize A. thaliana PRORP2. We solved the crystal structure of PRORP2 (3.2 Å) revealing an overall V-shaped protein and conserved metallonuclease active-site structure. Our biochemical studies indicate that PRORP2 requires Mg 2+ for catalysis and catalyzes the maturation of nuclear encoded substrates up to 10-fold faster than mitochondrial encoded precursor nad6 t-element under single-turnover conditions. We also demonstrate that PRORP2 preferentially binds precursor tRNAs containing short 5' leaders and 3' trailers; however, leader and trailer lengths do not significantly alter the observed rate constants of PRORP2 in single-turnover cleavage assays. In conclusion, our data provide a biochemical and structural framework to begin understanding how nuclear localized PRORPs recognize and cleave their substrates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

N-terminal additions to the WE14 peptide of chromogranin A create strong autoantigen agonists in type 1 diabetes

Chromogranin A (ChgA) is an autoantigen for CD4 + T cells in the nonobese diabetic (NOD) mouse model of type 1 diabetes (T1D). The natural ChgA-processed peptide, WE14, is a weak agonist for the prototypical T cell, BDC-2.5, and other ChgA-specific T-cell clones. Mimotope peptides with much higher activity share a C-terminal motif, WXRM(D/E), that is predicted to lie in the p5 to p9 position in the mouse MHC class II, IA g7 binding groove. This motif is also present in WE14 (WSRMD), but at its N terminus. Therefore, to place the WE14 motif into the same position as seen in the mimotopes, we added the amino acids RLGL to its N terminus. Like the other mimotopes, RLGL-WE14, is much more potent than WE14 in T-cell stimulation and activates a diverse population of CD4 + T cells, which also respond to WE14 as well as islets from WT, but not ChgA –/– mice. The crystal structure of the IA g7 –RLGL–WE14 complex confirmed the predicted placement of the peptide within the IA g7 groove. Fluorescent IA g7 –RLGL–WE14 tetramers bind to ChgA-specific T-cell clones and easily detect ChgA-specific T cells in the pancreas and pancreatic lymph nodes of NOD mice. Here, the prediction that many different N-terminal amino acid extensions to the WXRM(D/E) motif are sufficient to greatly improve T-cell stimulation leads us to propose that such a posttranslational modification may occur uniquely in the pancreas or pancreatic lymph nodes, perhaps via the mechanism of transpeptidation. This modification could account for the escape of these T cells from thymic negative selection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A New Method to Overcome Carboxyamide Formation During AFEX Pretreatment of Lignocellulosic Biomass

Lignin-carbohydrate complexes (LCCs) in the plant cell wall are responsible for providing resistance against biomass-degrading enzymes produced by microorganisms. Four major types of lignin-carbohydrate bonds are reported in the literature, namely, benzyl ethers, benzyl esters, phenyl glycosides, and acetyl ester linkages. Ester’s linkages in the plant cell wall are labile to alkaline pretreatments, such as ammonia fiber expansion (AFEX), which uses liquid or gaseous ammonia to cleave those linkages in the plant cell wall and reduce biomass recalcitrance. Two competing reactions, notably hydrolysis and ammonolysis, take place during AFEX pretreatment process, producing different aliphatic and aromatic acids, as well as their amide counterparts. AFEX pretreated grasses and agricultural residues are known to increase conversion of biomass to sugars by four- to five-fold when subjected to commercial enzyme hydrolysis, yielding a sustainable feedstock for producing biofuels, biomaterials, and animal feed. Animal feed trials on dairy cows have demonstrated a 27% increase in milk production when compared to a control feedstock. However, the presence of carboxamides in feedstocks could promote neurotoxicity in animals if consumed beyond a certain concentration. Thus, there is the need to overcome regulatory hurdles associated with commercializing AFEX pretreated biomass as animal feed in the United States. This manuscript demonstrates a modified pretreatment for increasing the digestibility of industrial byproducts such as Brewer’s spent grains (BSG) and high-fiber meal (HFM) produced from BSG and dry distillers grains with soluble (DDGS), while avoiding the production of carboxamides. The three industrial byproducts were first treated with calculated amounts of alkali such as NaOH, Ca(OH) 2 , or KOH followed by AFEX pretreatment. We found that 4% alkali was able to de-esterify BSG and DDGS more efficiently than using 2% alkali at both 10 and 20% solids loading. AFEX pretreatment of de-esterified BSG, HFM, and DDGS produced twofold higher glucan conversion than respective untreated biomass. This new discovery can help overcome potential regulatory issues associated with the presence of carboxamides in ammonia-pretreated animal feeds and is expected to benefit several farmers around the world.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Proton transfer in microbial electrolysis cells

Proton transfer and electron transfer are of prime importance in the development of microbial electrochemical cells. While electron transfer is primarily controlled by biology, proton transfer is controlled by process engineering and cell design. To develop commercially feasible technologies around the concept of a bioelectrochemical cell, real feedstocks have to be explored and associated limitations have to be identified. Here in this study, the proton transfer rate was quantified for a microbial electrolysis cell (MEC) and its dependence on process parameters was investigated using a proton balance model. The reaction system consisted of a biomass-derived pyrolytic aqueous stream as a substrate producing hydrogen in a flow-through MEC. The proton transfer rate increased with anode flow rate and organic loading rate up to a maximum of 0.36 ± 0.01 moles per m 2 per h, equivalent to a hydrogen production rate of 9.08 L per L per day. Higher rates of hydrogen production, reaching 11.7 ± 0.2 L per L per day were achieved, when additional protons were provided via the cathode buffer. Electrochemical impedance spectroscopy shows that proton transfer was the dominant resistance in the production of hydrogen. The quantification of proton transfer rates for MECs with potential for biorefinery application and the demonstration of high hydrogen production rates approaching those required for commercial consideration indicate the strong potential of this technology for renewable hydrogen production. Understanding the transport phenomenon in bioelectrochemical cells is of great significance since these systems have potential for wide-ranging applications including energy production, bioremediation, chemical and nanomaterial synthesis, electro-fermentation, energy storage, desalination, and produced water treatment. Electron transfer in anode biofilms has been investigated extensively, but proton transfer studies are also important, since many cathodic half reactions require protons as the reactant. Determination of transport rates via proton balance was investigated in microbial electrolysis cells, which can be applied to other forms of microbial electrochemical systems. Lastly, these systems have a unique niche in the development of future biorefineries as a means of recovering energy from waste streams with potential for water recycle, making them an integral part of the water–energy nexus focus area.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Salk Institute for Biological Studies Requirements (Analysis Report)

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Between February and March 2024, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from the Salk Institute for Biological Studies (Salk) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff better to understand the needs of the researchers within the community. Material for this event included the written documentation from each of the profiled research areas, documentation about the current state of technology support, and a write-up of the discussion that took place via e-mail and video conferencing. The case studies highlighted the ongoing challenges and opportunities that Salk Institute for Biological Studies have in supporting a cross-section of established and emerging research use cases. Each case study mentioned unique challenges which were summarized into common needs.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Modeling Evolvable Water Recovery Systems for Short and Long-Duration Missions in Partial Gravity

Water recovery technologies on the International Space Station (ISS) are designed and optimized for microgravity environments, thus creating a need for innovative systems optimized for mission operations in the presence of gravity. Alternative water recovery technologies under research by the Life Support Systems division were compiled into a series of Partial Gravity Water Recovery System (PGWRS) architectures. Individual unit processes were modeled via fundamental physical-chemical process equations to simulate their method of treatment, including chemical or biological oxidation, flash evaporation, filtration, or a combination of adsorption and ion exchange processes. Dimension and sizing data were then utilized to scale each architecture. Modeling was completed on an assortment of architectures assuming three scenarios in which treatment methods could take advantage of partial gravity: a short-duration mission with a temporary surface habitat, a longer duration mission with a more permanent habitat, and a long duration mission without resupply. Total system mass was estimated to quantitatively compare architectures within scenarios, but qualitative observations were also made regarding system robustness, flexibility, and capacity to meet more stringent demands. Technologies that traded most favorably between the scenarios were those that included a pre-oxidation stage to minimize mass loading to downstream absorption and ion exchange beds, exploited gravity in liquid-vapor separation processes, and reconstituted wastewater components into recyclable material streams.

Avery Carlson↗