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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

A Screening Model to Predict Microalgae Biomass Growth in Photobioreactors and Raceway Ponds

A microalgae biomass growth model was developed for screening novel strains for their potential to exhibit high biomass productivities under nutrient-replete conditions in photobioreactors or outdoor ponds. Growth is modeled by first estimating the light attenuation by biomass according to Beer-Lambert’s law, and then calculating the specific growth rate in discretized culture volume slices that receive declining light intensities due to attenuation. The model requires only two physical and two species-specific biological input parameters, all of which are relatively easy to determine: incident light intensity, culture depth, as well as the biomass light absorption coefficient and the specific growth rate as a function of light intensity. Roux bottle culture experiments were performed with Nannochloropsis salina at constant temperature (23 °C) at six different incident light intensities (5, 10, 25, 50, 100, 250, and 850 μmol/m2∙ sec) to determine both the specific growth rate under non-shading conditions and the biomass light absorption coefficient as a function of light intensity. The model was successful in predicting the biomass growth rate in these Roux bottle cultures during the light-limited linear phase at different incident light intensities. Model predictions were moderately sensitive to minor variations in the values of input parameters. The model was also successful in predicting the growth performance of Chlorella sp. cultured in LED-lighted 800 L raceway ponds operated at constant temperature (30 °C) and constant light intensity (1650 μmol/m2∙ sec). Measurements of oxygen concentrations as a function of time demonstrated that following exposure to darkness, it takes at least 5 seconds for cells to initiate dark respiration. As a result, biomass loss due to dark respiration in the aphotic zone of a culture is unlikely to occur in highly mixed small-scale photobioreactors where cells move rapidly in and out of the light. By contrast, as supported also by the growth model, biomass loss due to dark respiration occurs in the dark zones of the relatively less well mixed pond cultures. In addition to screening novel microalgae strains for high biomass productivities, the model can also be used for optimizing the pond design and operation. Additional research is needed to validate the biomass growth model for other microalgae species and for the more realistic case of fluctuating temperatures and light intensities observed in outdoor pond cultures.

biomass growth model↗
DOE OSTI2018

Novel Rapid Screening of Basic Immobilized Amine Sorbent/Catalyst Water Stability by a UV/Vis/Cu 2+ Technique

Abstract Time‐consuming thermogravimetric analysis (TGA) decomposition study is a typical practice to assess the stability of fresh and water‐treated basic immobilized amine sorbents (BIAS)/catalysts. This work presents a faster and more precise spectroscopic UV/Vis/Cu 2+ sorbent screening technique that quantifies aqueous amines washed from the BIAS by using UV‐active amine/Cu 2+ complexes. Six BIAS‐based catalysts, containing different amine species and a crosslinker within silica, were treated with ultrapure water and then analyzed for their CO 2 capture performance and amine leach resistance/stability by using TGA (catalysts, approximately 4 h) and UV/Vis/Cu 2+ techniques (wash solution, few minutes). A comparative analysis revealed that directly quantifying washed amines with UV/Vis/Cu 2+ is 9–127 times more precise than indirect testing of the sorbents by TGA. Similar trends in the H 2 O stability profiles of the catalysts [organic content retained values (OCR)] were reported by both analysis methods, allowing UV/Vis/Cu 2+ to replace TGA for quantifying unstable leached amines. The UV/Vis/Cu 2+ OCR results could be used to predict the CO 2 ‐capture stability profile of the sorbents, confirming the reliability of this technique to rapidly screen catalyst stability and performance.

Wilfong, Walter Christopher↗
DOE OSTI2013

Efficient Screening of Climate Model Sensitivity to a Large Number of Perturbed Input Parameters [plus supporting information]

Modern climate models contain numerous input parameters, each with a range of possible values. Since the volume of parameter space increases exponentially with the number of parameters N, it is generally impossible to directly evaluate a model throughout this space even if just 2-3 values are chosen for each parameter. Sensitivity screening algorithms, however, can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination.This can aid both model development and the uncertainty quantification (UQ) process. Here we report results from a parameter sensitivity screening algorithm hitherto untested in climate modeling, the Morris one-at-a-time (MOAT) method. This algorithm drastically reduces the computational cost of estimating sensitivities in a high dimensional parameter space because the sample size grows linearly rather than exponentially with N. It nevertheless samples over much of the N-dimensional volume and allows assessment of parameter interactions, unlike traditional elementary one-at-a-time (EOAT) parameter variation. We applied both EOAT and MOAT to the Community Atmosphere Model (CAM), assessing CAM’s behavior as a function of 27 uncertain input parameters related to the boundary layer, clouds, and other subgrid scale processes. For radiation balance at the top of the atmosphere, EOAT and MOAT rank most input parameters similarly, but MOAT identifies a sensitivity that EOAT underplays for two convection parameters that operate nonlinearly in the model. MOAT’s ranking of input parameters is robust to modest algorithmic variations, and it is qualitatively consistent with model development experience. Supporting information is also provided at the end of the full text of the article.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Screening a knowledge‐based library of low molecular weight compounds against the proline biosynthetic enzyme 1‐pyrroline‐5‐carboxylate 1 ( PYCR1)

Abstract Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 1 (PYCR1) is the last enzyme of proline biosynthesis and catalyzes the NAD(P)H‐dependent reduction of Δ 1 ‐pyrroline‐5‐carboxylate toL‐proline. High PYCR1 gene expression is observed in many cancers and linked to poor patient outcomes and tumor aggressiveness. The knockdown of thePYCR1gene or the inhibition of PYCR1 enzyme has been shown to inhibit tumorigenesis in cancer cells and animal models of cancer, motivating inhibitor discovery. We screened a library of 71 low molecular weight compounds (average MW of 131 Da) against PYCR1 using an enzyme activity assay. Hit compounds were validated with X‐ray crystallography and kinetic assays to determine affinity parameters. The library was counter‐screened against human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3 and proline dehydrogenase (PRODH) to assess specificity/promiscuity. Twelve PYCR1 and one PRODH inhibitor crystal structures were determined. Three compounds inhibit PYCR1 with competitive inhibition parameter of 100 μM or lower. Among these, (S)‐tetrahydro‐2H‐pyran‐2‐carboxylic acid (70 μM) has higher affinity than the current best tool compoundN‐formyl‐l‐proline, is 30 times more specific for PYCR1 over human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3, and negligibly inhibits PRODH. Structure‐affinity relationships suggest that hydrogen bonding of the heteroatom of this compound is important for binding to PYCR1. The structures of PYCR1 and PRODH complexed with 1‐hydroxyethane‐1‐sulfonate demonstrate that the sulfonate group is a suitable replacement for the carboxylate anchor. This result suggests that the exploration of carboxylic acid isosteres may be a promising strategy for discovering new classes of PYCR1 and PRODH inhibitors. The structure of PYCR1 complexed withl‐pipecolate and NADH supports the hypothesis that PYCR1 has an alternative function in lysine metabolism.

Biochemistry & Molecular Biology↗
DOE OSTI2017

Bulk viscous corrections to screening and damping in QCD at high temperatures

Non-equilibrium corrections to the distribution functions of quarks and gluons in a hot and dense QCD medium modify the \hard thermal loops" (HTL). The HTLs determine the retarded, advanced, and symmetric (time-ordered) propagators for gluons with soft momenta as well as the Debye screening and Landau damping mass scales. Here, we compute such corrections to a thermal as well as to a non-thermal fixed point. The screening and damping mass scales are sensitive to the bulk pressure and hence to (pseudo-) critical dynamical scaling of the bulk viscosity in the vicinity of a second-order critical point. This could be reected in the properties of quarkonium bound states in the deconfined phase and in the dynamics of soft gluon fields.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Screening for anticancer properties of thiazolidinedione compounds in a galactose media metastatic breast cancer cell model

The metastatic tumors of breast cancer lead to the mortality of patients in part due to the failure of chemotherapeutics to reach the brain and emergence of drug resistance. With the increase in drug resistance, we focused on targeting mitochondrial metabolism, which was recently found to be a viable and novel drug target in breast cancer. Here we screened several ligands for anticancer activity in a phenotypic screen using the MTT dye as a marker for cell proliferation. MCF-7 breast cancer cells were treated with compounds in the presence of galactose to ensure mitochondrial interaction by the compounds. The lead compound of our group, NL-1, a thiazolidinedione (TZD), was found to inhibit cell proliferation with an IC{sub 50} of 7.1 μM, whereas its unsaturated derivative CI-987 was more potent with a IC{sub 50} of 3.3 μM. The TZDs were affected by substitution on both the aromatic ring and the TZD warhead. Taken together, these compounds can serve as lead structures for mitochondrial targeted drug discovery programs in breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

High-throughput screening of environmental polysaccharide-degrading bacteria using biomass containment and complex insoluble substrates

Abstract Carbohydrate degradation by microbes plays an important role in global nutrient cycling, human nutrition, and biotechnological applications. Studies that focus on the degradation of complex recalcitrant polysaccharides are challenging because of the insolubility of these substrates as found in their natural contexts. Specifically, current methods to examine carbohydrate-based biomass degradation using bacterial strains or purified enzymes are not compatible with high-throughput screening using complex insoluble materials. In this report, we developed a small 3D printed filter device that fits inside a microplate well that allows for the free movement of bacterial cells, media, and enzymes while containing insoluble biomass. These devices do not interfere with standard microplate readers and can be used for both short- (24–48 h) and long-duration (> 100 h) experiments using complex insoluble substrates. These devices were used to quantitatively screen in a high-throughput manner environmental isolates for their ability to grow using lignocellulose or rice grains as a sole nutrient source. Additionally, we determined that the microplate-based containment devices are compatible with existing enzymatic assays to measure activity against insoluble biomass. Overall, these microplate containment devices provide a platform to study the degradation of complex insoluble materials in a high-throughput manner and have the potential to help uncover ecologically important aspects of bacterial metabolism as well as to accelerate biotechnological innovation.

3D printing↗
DOE OSTI2025

Sample screening of uranium ore concentrates using portable spectrophotometers: investigating the correlation between visible colors and chemical signatures

A research collaboration between the Japan Atomic Energy Agency and the Department of Energy’s National Nuclear Security Administration examined nuclear forensic signatures and analytical methods for tracing the origins of uranium ore concentrates (UOCs). Here, this study focuses on utilizing portable spectrophotometers capable of reflectance measurements in the visible light spectrum as a potential rapid screening tool for nuclear forensics analysis. Unlike laboratory-based near-infrared spectroscopy or digital image analysis, this research investigated the potential to correlate visible color measurements with key nuclear forensics signatures using seven types of UOC samples with known origins and three UOC certified reference materials. Results demonstrated that distinct color groups, quantified using CIELAB values, correlated with major uranium compounds. Furthermore, the findings indicated that trace elements can influence the UOC colors, providing additional insights into material characteristics. Although this approach requires further validation across a broader range of UOC species, this study demonstrated that simple colorimetric analysis using visible spectrophotometry, which does not require complex sample preparation or data processing, can serve as a practical and novel rapid tool for preliminary screening and attribution in nuclear forensics investigations.

and nuclear chemistry↗
DOE OSTI2018

Novel stable HBV producing cell line systems for expression and screening antiviral inhibitor of hepatitis B virus in human hepatoma cell line

Highlights: • Novel HBV clones with high replication activities were identified from the sera of patients with chronic HBV. • Novel stable cell lines showed HBV replication activities and were susceptible to several antiviral inhibitors. • Novel stable cell lines are valuable tools for analyzing viral phenotypes in vitro and for screening antiviral agents. Chronic hepatitis B virus (HBV) infection is currently a major public health burden. Therefore, there is an urgent need for the development of novel antiviral inhibitors. The stable HBV-producing cell lines of genotype D are widely used to investigate the HBV life cycle and to evaluate antiviral agents. However, stable HBV-producing cell lines of different genotypes do not exist. To construct more convenient and efficient novel cell systems, stable cell lines of genotypes A, B, and C were established using a full-length HBV genome sequence isolated from chronic HBV patients in human hepatoma HepG2 cells. Novel HBV clones were identified and stable HBV-producing cell lines derived from these clones were constructed. HBV replication activities demonstrated time-dependent expression, and the novel cell lines were susceptible to several antiviral inhibitors with no cytotoxicity. Furthermore, infectious viruses were produced from these cell lines. In conclusion, we have established novel stable HBV-producing cell line systems of genotypes A, B, and C. These systems can provide valuable tools for screening antiviral agents and analyzing viral phenotypes in vitro.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Reprint of: A chemical screen identifies trifluoperazine as an inhibitor of glioblastoma growth

Highlights: • Screening of GBM cells uncovers 45 active dopaminergic ligands and 8 were validated. • Ligand selectivity suggests dopamine-mediated cell viability regulation in GBM. • The DRD2 antagonist trifluoperazine inhibits cell viability and growth of GBM cells. • DR expression profile of GBM cells alter the susceptibility to trifluoperazine. Glioblastoma (GBM) is regarded as the most common malignant brain tumor but treatment options are limited. Thus, there is an unmet clinical need for compounds and corresponding targets that could inhibit GBM growth. We screened a library of 80 dopaminergic ligands with the aim of identifying compounds capable of inhibiting GBM cell line proliferation and survival. Out of 45 active compounds, 8 were further validated. We found that the dopamine receptor D2 antagonist trifluoperazine 2HCl inhibits growth and proliferation of GBM cells in a dose dependent manner. Trifluoperazine’s inhibition of GBM cells is cell line dependent and correlates with variations in dopamine receptor expression profile. We conclude that components of the dopamine receptor signaling pathways are potential targets for pharmacological interventions of GBM growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A natural compound, aristoyagonine, is identified as a potent bromodomain inhibitor by mid-throughput screening

Bromodomain-containing protein 4 (Brd4) is known to play a key role in tumorigenesis. It binds acetylated histones to regulate the expression of numerous genes. Because of the importance of brd4 in tumorigenesis, much research has been undertaken to develop brd4 inhibitors with therapeutic potential. As a result, various scaffolds for bromodomain inhibitors have been identified. To discover new scaffolds, we performed mid-throughput screening using two different enzyme assays, alpha-screen and ELISA. We found a novel bromodomain inhibitor with a unique scaffold, aristoyagonine. This natural compound showed inhibitory activity in vitro and tumor growth inhibition in a Ty82-xenograft mouse model. In addition, we tested Brd4 inhibitors in gastric cancer cell lines, and found that aristoyagonine exerted cytotoxicity not only in I-BET-762-sensitive cancer cells, but also in I-BET-762-resistant cancer cells. This is the first paper to describe a natural compound as a Brd4 bromodomain inhibitor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Computational catalyst screening: Scaling, bond-order and catalysis

Here, the design of new and better heterogeneous catalysts needed to accommodate the growing demand for energy from renewable sources is an important challenge for coming generations. Most surface catalyzed processes involve a large number of complex reaction networks and the energetics ultimately defines the turn-over-frequency and the selectivity of the process. In order not to get lost in the large quantities of data, simplification schemes that still contain the key elements of the reaction are required. Adsorption and transition state scaling relations constitutes such a scheme that not only maps the reaction relevant information in terms of few parameters but also provides an efficient way of screening for new materials in a continuous multi-dimensional energy space. As with all relations they impose certain restrictions on what can be achieved and in this paper, I show why these limitations exist and how we can change the behavior through an energy-resolved approach that still maintains the screening capabilities needed in computational catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The use of gas chromatography – high resolution mass spectrometry for suspect screening and non-targeted analysis of per- and polyfluoroalkyl substances

This study is a workflow development for the analysis, identification, and categorization of per- and polyfluoroalkyl substances (PFAS) using gas chromatography-high resolution mass spectrometry (GC-HRMS) with non-targeted analysis (NTA) and suspect screening techniques. The behavior of various PFAS in a GC-HRMS was studied with regards to retention indices, ionization susceptibility, fragmentation patterns, etc. A custom PFAS database was constructed from 141 diverse PFAS. The database contains mass spectra from electron ionization (EI) mode, as well as MS and MS/MS spectra from positive and negative chemical ionization (PCI and NCI, respectively) modes. Common fragments of PFAS were identified across a diverse set of 141 PFAS analyzed. A workflow for suspect screening of PFAS and partially fluorinated products of incomplete combustion/destruction (PICs/PIDs) was developed which utilized both the custom PFAS database and external databases. PFAS and other fluorinated compounds were identified in both a challenge sample (designed to test the identification workflow) and incineration samples suspected to contain PFAS and fluorinated PICs/PIDs. The challenge sample resulted in a 100% true positive rate (TPR) for PFAS which were present in the custom PFAS database. In conclusion, several fluorinated species were tentatively identified in the incineration samples using the developed workflow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Biomarkers of Tuberculosis Severity and Treatment Effect: A Directed Screen of 70 Host Markers in a Randomized Clinical Trial

More efficacious treatment regimens are needed for tuberculosis, however, drug development is impeded by a lack of reliable biomarkers of disease severity and of treatment effect. In this study, we conducted a directed screen of host biomarkers in participants enrolled in a tuberculosis clinical trial to address this need. Serum samples from 319 protocol-correct, culture-confirmed pulmonary tuberculosis patients treated under direct observation as part of an international, phase 2 trial were screened for 70 markers of infection, inflammation, and metabolism. Biomarker assays were specifically developed for this study and quantified using a novel, multiplexed electrochemiluminescence assay. We evaluated the association of biomarkers with baseline characteristics, as well as with detailed microbiologic data, using Bonferroni-adjusted, linear regression models. Across numerous analyses, seven proteins, SAA1, PCT, IL-1, IL-6, CRP, PTX-3 and MMP-8, showed recurring strong associations with markers of baseline disease severity, smear grade and cavitation; were strongly modulated by tuberculosis treatment; and had responses that were greater for patients who culture-converted at 8 weeks. With treatment, all proteins decreased, except for osteocalcin, MCP-1 and MCP-4, which significantly increased. Several previously reported putative tuberculosis-associated biomarkers (HOMX1, neopterin, and cathelicidin) were not significantly associated with treatment response. In conclusion, across a geographically diverse and large population of tuberculosis patients enrolled in a clinical trial, several previously reported putative biomarkers were not significantly associated with treatment response, however, seven proteins had recurring strong associations with baseline radiographic and microbiologic measures of disease severity, as well as with early treatment response, deserving additional study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

High-Throughput Screening of Solid-State Li-Ion Conductors Using Lattice-Dynamics Descriptors

Low lithium-ion migration barriers have recently been associated with low average vibrational frequencies or phonon band centers, further helping identify descriptors for superionic conduction. To further explore this correlation, here we present the computational screening of ~14,000 Li-containing compounds in the Materials Project database using a descriptor based on lattice dynamics reported recently to identify new promising Li-ion conductors. An efficient computational approach was optimized to compute the average vibrational frequency or phonon band center of ~1,200 compounds obtained after pre-screening based on structural stability, band gap, and their composition. Combining a low computed Li phonon band center with large computed electrochemical stability window and structural stability, 18 compounds were predicted to be promising Li-ion conductors, one of which, Li 3 ErCl 6 , has been synthesized and exhibits a reasonably high room-temperature conductivity of 0.05–0.3 mS/cm, which shows the promise of Li-ion conductor discovery based on lattice dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A Quick Screening Approach for Design of Multi-Principle Element Alloy with Solid Solution Phase

Multi-principle element alloys (MPEAs) provide an innovative way to design materials with potential applications in extreme conditions, such as high-dose irradiation in nuclear reactors, high temperatures in aeronautic and aerospace engineering, etc. However, the composition design and optimization of MPEAs are challenging due to the vast options and complexity which make traditional theory and methods insufficient or even invalid. To effectively design MPEAs, a two-parameter approach is developed based on newly proposed enthalpy and entropy-enthalpy competition parameters. This approach can not only be used to classify conventional steels, high-entropy steels (HESs), high-entropy alloys (HEAs) and amorphous alloys, but also to predict the optimal composition of MPEAs with stable solid solution (SS) phases. The optimal compositions of three sets of Fe-Mn-Cr-Co-Ni alloys are quickly screened from millions of options to form HESs or HEAs with stable SS phases. This quick screening approach can also be applied to design other alloys and facilitate the research and development of MPEAs possessing the targeted properties and economic acceptability.

Zheng, Mingjie↗
DOE OSTI2025

CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

Here, we present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications.

CoRE MOF database↗