Results for “High throughput screening”

NASA NTRS2012

On-chip polarimetry for high-throughput screening of nanoliter and smaller sample volumes

A polarimetry technique for measuring optical activity that is particularly suited for high throughput screening employs a chip or substrate (22) having one or more microfluidic channels (26) formed therein. A polarized laser beam (14) is directed onto optically active samples that are disposed in the channels. The incident laser beam interacts with the optically active molecules in the sample, which slightly alter the polarization of the laser beam as it passes multiple times through the sample. Interference fringe patterns (28) are generated by the interaction of the laser beam with the sample and the channel walls. A photodetector (34) is positioned to receive the interference fringe patterns and generate an output signal that is input to a computer or other analyzer (38) for analyzing the signal and determining the rotation of plane polarized light by optically active material in the channel from polarization rotation calculations.

Bornhop, Darryl J.↗

NASA NTRSDate not supplied

High-Throughput Screening of Li Solid-State Electrolytes With Bond Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗

NASA NTRS2008

Informatics and High Throughput Screening of Thermophysical Properties

The combination of computer-aided experiments with computational modeling enables a new class of powerful tools for materials research. A non-contact method for measuring density, thermal expansion, and creep of undercooled and high-temperature materials has been developed, using electrostatic levitation and optical diagnostics, including digital video. These experiments were designed to take advantage of the large volume of data (many gigabytes/experiment, terabytes/campaign) to gain additional information about the samples. For example, using sub-pixel interpolation to measure about 1000 vectors per image of the sample's surface allows the density of an axisymmetric sample to be determined to an accuracy of about 200 ppm (0.02%). A similar analysis applied to the surface shape of a rapidly rotating sample is combined with finite element modeling to determine the stress-dependence of creep in the sample in a single test. Details of the methods for both the computer-aided experiments and computational models will be discussed.

Hyers, Robert W.↗

NASA NTRSDate not supplied

High throughput screen of small molecules as potential countermeasures to galactic cosmic radiation induced cellular dysfunction

Explore the source record for details and available documents.

M. Weiss↗

NASA NTRSDate not supplied

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery↗

NASA NTRSDate not supplied

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery↗

NASA NTRSDate not supplied

Screening of Li-Based Solid Electrolytes Using Bond-Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗

NASA NTRS2003

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

Although mRNA amplification is necessary for microarray analyses from limited amounts of cells and tissues, the accuracy of transcription profiles following amplification has not been well characterized. We tested the fidelity of differential gene expression following linear amplification by T7-mediated transcription in a well-established in vitro model of cytokine [tumor necrosis factor alpha (TNFalpha)]-stimulated human endothelial cells using filter arrays of 13,824 human cDNAs. Transcriptional profiles generated from amplified antisense RNA (aRNA) (from 100 ng total RNA, approximately 1 ng mRNA) were compared with profiles generated from unamplified RNA originating from the same homogeneous pool. Amplification accurately identified TNFalpha-induced differential expression in 94% of the genes detected using unamplified samples. Furthermore, an additional 1,150 genes were identified as putatively differentially expressed using amplified RNA which remained undetected using unamplified RNA. Of genes sampled from this set, 67% were validated by quantitative real-time PCR as truly differentially expressed. Thus, in addition to demonstrating fidelity in gene expression relative to unamplified samples, linear amplification results in improved sensitivity of detection and enhances the discovery potential of high-throughput screening by microarrays.

Evaluation Studies↗

NASA NTRS2006

Protein Crystallization Using Room Temperature Ionic Fluids

The ionic liquids (ILs) 1-butyl-3-methylimidizolium chloride (C4mim-C1), 1-butyl-3- methylimidizolium diethyleneglycol monomethylethersulfate ([C4mim]DEMGS), and 1-butyl-1 -methylpyrollidinium dihydrogenphosphate ([p1,4]dhp) were tested for their effects on the crystallization of the proteins canavalin, beta-lactoglobulin B, xylanase, and glucose isomerase, using a standard high throughput screen. The crystallization experiments were set up with the ILs added to the protein solutions at 0.2 and 0.4 M final concentrations. Crystallization droplets were set up at three proteixprecipitant ratios (1:1, 2:1, and 4:l), which served to progressively dilute the effects of the screen components while increasing the equilibrium protein and IL concentrations. Crystals were obtained for all four proteins at a number of conditions where they were not obtained from the IL-free control experiment. Over half of the protein-IL combinations tested had more successful outcomes than negative, where the IL-free crystallization was better than the corresponding IL-containing outcome, relative to the control. One of the most common causes of a negative outcome was solubilization of the protein by the IL, resulting in a clear drop. In one instance, we were able to use the IL-induced solubilizing to obtain beta-lactoglobulin B crystals from conditions that gave precipitated protein in the absence of IL. The results suggest that it may be feasible to develop ILs specifically for the task of macromolecule crystallization.

Pusey, Marc L.↗

NASA NTRS2009

Catalysts for Efficient Production of Carbon Nanotubes

Several metal alloys have shown promise as improved catalysts for catalytic thermal decomposition of hydrocarbon gases to produce carbon nanotubes (CNTs). Heretofore almost every experiment on the production of carbon nanotubes by this method has involved the use of iron, nickel, or cobalt as the catalyst. However, the catalytic-conversion efficiencies of these metals have been observed to be limited. The identification of better catalysts is part of a continuing program to develop means of mass production of high-quality carbon nanotubes at costs lower than those achieved thus far (as much as $100/g for purified multi-wall CNTs or $1,000/g for single-wall CNTs in year 2002). The main effort thus far in this program has been the design and implementation of a process tailored specifically for high-throughput screening of alloys for catalyzing the growth of CNTs. The process includes an integral combination of (1) formulation of libraries of catalysts, (2) synthesis of CNTs from decomposition of ethylene on powders of the alloys in a pyrolytic chemical-vapor-decomposition reactor, and (3) scanning- electron-microscope screening of the CNTs thus synthesized to evaluate the catalytic efficiencies of the alloys. Information gained in this process is put into a database and analyzed to identify promising alloy compositions, which are to be subjected to further evaluation in a subsequent round of testing. Some of these alloys have been found to catalyze the formation of carbon nano tubes from ethylene at temperatures as low as 350 to 400 C. In contrast, the temperatures typically required for prior catalysts range from 550 to 750 C.

Sun, Ted X.↗

NASA NTRS2005

Improved Charge-Transfer Fluorescent Dyes

Improved charge-transfer fluorescent dyes have been developed for use as molecular probes. These dyes are based on benzofuran nuclei with attached phenyl groups substituted with, variously, electron donors, electron acceptors, or combinations of donors and acceptors. Optionally, these dyes could be incorporated as parts of polymer backbones or as pendant groups or attached to certain surfaces via self-assembly-based methods. These dyes exhibit high fluorescence quantum yields -- ranging from 0.2 to 0.98, depending upon solvents and chemical structures. The wavelengths, quantum yields, intensities, and lifetimes of the fluorescence emitted by these dyes vary with (and, hence, can be used as indicators of) the polarities of solvents in which they are dissolved: In solvents of increasing polarity, fluorescence spectra shift to longer wavelengths, fluorescence quantum yields decrease, and fluorescence lifetimes increase. The wavelengths, quantum yields, intensities, and lifetimes are also expected to be sensitive to viscosities and/or glass-transition temperatures. Some chemical species -- especially amines, amino acids, and metal ions -- quench the fluorescence of these dyes, with consequent reductions in intensities, quantum yields, and lifetimes. As a result, the dyes can be used to detect these species. Another useful characteristic of these dyes is a capability for both two-photon and one-photon absorption. Typically, these dyes absorb single photons in the ultraviolet region of the spectrum (wavelengths < 400 nm) and emit photons in the long-wavelength ultraviolet, visible, and, when dissolved in some solvents, near-infrared regions. In addition, these dyes can be excited by two-photon absorption at near-infrared wavelengths (600 to 800 nm) to produce fluorescence spectra identical to those obtained in response to excitation by single photons at half the corresponding wavelengths (300 to 400 nm). While many prior fluorescent dyes exhibit high quantum yields, solvent-polarity- dependent fluorescence behavior, susceptibility to quenching by certain chemical species, and/or two-photon fluorescence, none of them has the combination of all of these attributes. Because the present dyes do have all of these attributes, they have potential utility as molecular probes in a variety of applications. Examples include (1) monitoring curing and deterioration of polymers; (2) monitoring protein expression; (3) high-throughput screening of drugs; (4) monitoring such chemical species as glucose, amines, amino acids, and metal ions; and (5) photodynamic therapy of cancers and other diseases.

Meador, Michael↗

NASA NTRS2004

Rapid Chemometric Filtering of Spectral Data

A method of rapid, programmable filtering of spectral transmittance, reflectance, or fluorescence data to measure the concentrations of chemical species has been proposed. By programmable is meant that a variety of spectral analyses can readily be performed and modified in software, firmware, and/or electronic hardware, without need to change optical filters or other optical hardware of the associated spectrometers. The method is intended to enable real-time identification of single or multiple target chemical species in applications that involve high-throughput screening of multiple samples. Examples of such applications include (but are not limited to) combinatorial chemistry, flow cytometry, bead assays, testing drugs, remote sensing, and identification of targets. The basic concept of the proposed method is to perform real-time crosscorrelations of a measured spectrum with one or more analytical function(s) of wavelength that could be, for example, the known spectra of target species. Assuming that measured spectral intensities are proportional to concentrations of target species plus background spectral intensities, then after subtraction of background levels, it should be possible to determine target species concentrations from cross-correlation values. Of course, the problem of determining the concentrations is more complex when spectra of different species overlap, but the problem can be solved by use of multiple analytical functions in combination with computational techniques that have been developed previously for analyses of this type. The method is applicable to the design and operation of a spectrometer in which spectrally dispersed light is measured by means of an active-pixel sensor (APS) array. The row or column dimension of such an array is generally chosen to be aligned along the spectral-dispersion dimension, so that each pixel intercepts light in a narrow spectral band centered on a wavelength that is a known function of the pixel position. The proposed method admits of two hardware implementations for computing cross-correlations in real time.

Beaman, Gregory↗

NASA NTRS2020

Computational Design of Eutectic Molten Salt Mixtures: What Can Thermodynamic Models Do?

In the search for efficient energy storage battery technologies, designing stable electrolytes has been a long-standing challenge. Electrolytes based on molten salt eutectics are known for their stability with minimum parasitic reactions when compared to their widely used organic counterparts. However, the operating temperatures of these molten salt electrolyte-based batteries are dictated by the melting point of the eutectic mixtures. Design and high throughput screening of low melting temperature eutectic molten salt mixtures have been hindered by the lack of computational models. In this work, we develop thermodynamic models to predict the eutectic points of several molten salt mixtures. The framework of the COSMO-SAC model is used for the predictions and is compared with experimental data and other thermodynamic approaches. Rapid thermodynamics-based approaches, as shown in this study, can accelerate the discovery of new materials, complementing experimental techniques.

Ashwin Ravichandran↗

NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗

NASA NTRSDate not supplied

Designing Molten Salt Eutectics: A Combined Thermodynamic Modeling and Machine Learning Approach

Designing stable electrolytes with target properties is an important challenge in realizing next generation energy storage devices. Molten salt eutectics-based electrolytes are known for their stability with minimal parasitic reactions when compared to traditional organic electrolytes and are an attractive option for different battery chemistries. The operating temperature of the molten salt batteries depends on the melting temperature of the eutectic and hence there is a necessity to discover novel low melting temperature molten salt eutectic mixtures for energy storage applications. In this work we develop a high throughput computational screening approach for molten salt mixtures using thermodynamic modeling and machine learning (ML). COSMO-SAC model and ML approaches were independently developed based on the existing experimental data and these models were further used to predict the eutectic melting temperature and composition of several new binary, ternary, and quaternary mixtures. We show that combining ML and thermodynamic modeling strategies is effective in exploring the vast design space of molten salt mixtures.

Thermodynamics↗

NASA NTRSDate not supplied

Designing Molten Salt Eutectics

Designing stable electrolytes with target properties is an important challenge in realizing next generation energy storage devices. Molten salt eutectics-based electrolytes are known for their stability with minimal parasitic reactions when compared to traditional organic electrolytes and are an attractive option for different battery chemistries. The operating temperature of the molten salt batteries depends on the melting temperature of the eutectic and hence there is a necessity to discover novel low melting temperature molten salt eutectic mixtures for energy storage applications. In this work we develop a high throughput computational screening approach for molten salt mixtures using thermodynamic modeling and machine learning (ML). COSMO-SAC model and ML approaches were independently developed based on the existing experimental data and these models were further used to predict the eutectic melting temperature and composition of several new binary, ternary, and quaternary mixtures. We show that combining ML and thermodynamic modeling strategies is effective in exploring the vast design space of molten salt mixtures.

Ashwin Ravichandran↗

NASA NTRS2010

Screening and Crystallization Plates for Manual and High-throughput Protein Crystal Growth

In one embodiment, a crystallization and screening plate comprises a plurality of cells open at a top and a bottom, a frame that defines the cells in the plate, and at least two films. The first film seals a top of the plate and the second film seals a bottom of the plate. At least one of the films is patterned to strongly pin the contact lines of drops dispensed onto it, fixing their position and shape. The present invention also includes methods and other devices for manual and high-throughput protein crystal growth.

Thorne, Robert E.↗

NASA NTRSDate not supplied

Neurovascular Outcomes of Ionizing Radiation in Human Blood-Brain Barrier Models

One of the main health risks in human deep space exploration is central nervous system (CNS) damage by ionizing radiation due to exposure to galactic cosmic rays (GCRs). In animal models, irradiation with simulated GCRs or their components has been shown to cause neurodegeneration and neuroinflammation associated with cognitive and behavioral dysfunction. The extent of CNS damage is partially mediated by the blood-brain barrier (BBB), which regulates the interaction between CNS and systemic responses to stressors in the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal death, neuroinflammation and oxidative stress. However, studies on BBB and astrocyte functions in regulating CNS responses to ionizing radiation have been limited, especially in human tissue/organ analogs. Therefore, we developed a high throughput 3D human neurovascular system model, based on commercially available Mimetas OrganoPlates seeded by primary human cells, to investigate the neurovascular responses to simulated deep space radiation. Using this system, we have demonstrated that 600MeV/n 56Fe irradiation leads to cellular damage and increased blood-brain barrier permeability via dysfunction of brain cells called astrocytes, which appear to be the weakest link and therefore a highly suitable countermeasure target to reduce the impact of space radiation on the blood-brain barrier. We anticipate that our results form merely the first step in ongoing development of organ models, including their adaptation for personalized risk assessment, high throughput approaches to countermeasure screening and validation, and eventual payload adaptation.

organ-on-a-chip↗