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Results for “High throughput characterization”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Case Study of High-Throughput Drug Screening and Remote Data Collection for SARS-CoV-2 Main Protease by Using Serial Femtosecond X-ray Crystallography

Since early 2020, COVID-19 has grown to affect the lives of billions globally. A worldwide investigation has been ongoing for characterizing the virus and also for finding an effective drug and developing vaccines. As time has been of the essence, a crucial part of this research has been drug repurposing; therefore, confirmation of in silico drug screening studies have been carried out for this purpose. Here we demonstrated the possibility of screening a variety of drugs efficiently by leveraging a high data collection rate of 120 images/second with the new low-noise, high dynamic range ePix10k2M Pixel Array Detector installed at the Macromolecular Femtosecond Crystallography (MFX) instrument at the Linac Coherent Light Source (LCLS). The X-ray Free-Electron Laser (XFEL) is used for remote high-throughput data collection for drug repurposing of the main protease (Mpro) of SARS-CoV-2 at ambient temperature with mitigated X-ray radiation damage. We obtained multiple structures soaked with nine drug candidate molecules in two crystal forms. Although our drug binding attempts failed, we successfully established a high-throughput Serial Femtosecond X-ray crystallographic (SFX) data collection protocol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Custom High-Throughput Optical Mapping Instrument (OMI) for Accelerated Stress Testing of PV Module Materials

The performance and integrity of photovoltaic (PV) modules and materials may be assessed through optical characterizations. We have developed a custom Optical Mapping Instrument (OMI) to improve measurement throughput time, facilitate automated measurements, and obtain spatial information relative to commercial benchtop spectrophotometer instruments. Both the key components and the methods used to improve the measurements are described. The measurement precision for the OMI is benchmarked for hemispherical transmittance relative to a previous round-robin study for commercial spectrophotometers. Example mapping measurements and their implications are given for specimens from other studies, including: (1) the depletion of the UV absorber is revealed for specimens beyond a certain size after standardized artificial ultraviolet (UV) weathering, (2) an optical degradation by internal haze formation is confirmed following Damp Heat (85°C/85% relative humidity) testing, (3) the better uniformity of the wear pattern for falling sand abrasion is used to select the DIN 52348 fixture over the ASTM D968 fixture, and (4) the local (millimeter-scale) heterogeneity of surface contamination in the soiling-prone location of Dubai relative to Kuwait. The results of the UV study have already been used to improve the standardized weathering of encapsulants. The example for Damp Heat testing is given to increase awareness of a degradation believed to be ubiquitously occurring in safety and design-type qualification testing of PV modules.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Impacts of Soil Microbiome Variations on Root Colonization by Fungi and Bacteria and on the Metabolome of Populus tremula × alba

Trees depend on beneficial interactions between roots and soil microbes for their nutrition and protection against stresses. The soil microbiome provides the main reservoir of microbes for root colonization and is subject to natural variations that can affect its composition. It is not clear whether the tree’s root system is able to buffer the natural variations occurring in the soil microbiome to capture a stable and effective microbiome or whether these variations affect its microbiome to impact its physiology. To address this question, we planted cuttings of Gray Poplar (Populus tremula × alba clone 717-1B4) in natural soil taken from a poplar stand under the same tree over two consecutive years and grew them in a greenhouse. We analyzed the soil and root microbiomes by high throughput Illumina MiSeq sequencing of fungal rDNA internal transcribed spacer and bacterial 16S rRNA amplicons and we characterized the root metabolome by gas chromatography-mass spectrometry. Soil and root microbial communities significantly shifted over the 2 years. A modification of the balance between endophytes, saprophytes, and mycorrhizal fungi occurred in the roots as well as a replacement of some dominant operational taxonomic units by others. These modifications were correlated with a significant alteration of the levels of about 10% of primary and secondary metabolites, suggesting that natural fluctuations in soil microbial communities can have a profound impact on tree root metabolism and physiology. Tree roots functioning may thus be indirectly strongly affected by the effects of future extreme climatic variations on the soil microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a “one-pot” process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

09 BIOMASS FUELS↗
DOE OSTI2021

Nutrient Exposure Alters Microbial Composition, Structure, and Mercury Methylating Activity in Periphyton in a Contaminated Watershed

The conversion of mercury (Hg) to monomethylmercury (MMHg) is a critical area of concern in global Hg cycling. Periphyton biofilms may harbor significant amounts of MMHg but little is known about the Hg-methylating potential of the periphyton microbiome. Therefore, we used high-throughput amplicon sequencing of the 16S rRNA gene, ITS2 region, and Hg methylation gene pair (hgcAB) to characterize the archaea/bacteria, fungi, and Hg-methylating microorganisms in periphyton communities grown in a contaminated watershed in East Tennes(see United States). Furthermore, we examined how nutrient amendments (nitrate and/or phosphate) altered periphyton community structure and function. We found that bacterial/archaeal richness in experimental conditions decreased in summer and increased in autumn relative to control treatments, while fungal diversity generally increased in summer and decreased in autumn relative to control treatments. Interestingly, the Hg-methylating communities were dominated by Proteobacteria followed by Candidatus Atribacteria across both seasons. Surprisingly, Hg methylation potential correlated with numerous bacterial families that do not contain hgcAB, suggesting that the overall microbiome structure of periphyton communities influences rates of Hg transformation within these microbial mats. To further explore these complex community interactions, we performed a microbial network analysis and found that the nitrate-amended treatment resulted in the highest number of hub taxa that also corresponded with enhanced Hg methylation potential. This work provides insight into community interactions within the periphyton microbiome that may contribute to Hg cycling and will inform future research that will focus on establishing mixed microbial consortia to uncover mechanisms driving shifts in Hg cycling within periphyton habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

LIvermore SEM image Tools

Scanning Electron Microscopy (SEM) images provide a variety of structural and morphological information for the characterization of the nanomaterials. This code offers automatic recognition and quantitative analysis of SEM images in a high-throughput manner using computer vision and machine learning techniques. The main function of this application is to extract particle size and morphology information of overlapping nanoparticles and core-shell nanostructures in a user friendly interface. The code is written in C++ with QT environment, and has been tested on MacOSX.

KIM, HYOJIN↗
DOE OSTI2024

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design

Background: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. Results: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass (R 2 = 0.82) and root surface area (R 2 = 0.8). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. Conclusion: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?

Classical force fields are essential for computer simulations of proteins and are typically parameterized to reproduce secondary and tertiary structure of isolated proteins. However, while protein-protein interactions are ubiquitous in nature, they are not considered in parameterization efforts and are far less understood than isolated proteins. A better characterization of intermolecular interactions is widely recognized as a key to revolutionizing drug and therapeutic developments with high-throughput computational screening. Urgently needed is a critical assessment of the performance of modern protein force fields against first-principles electronic structure methods and experiments. In a daring step toward this goal, we here describe a comparison of peptide folding dynamics as predicted by a molecular mechanics force field on the one hand and by an approximate electronic structure quantum mechanical (QM) method based on density-functional tight-binding (DFTB) on the other. We further compare the dynamics from straightforward DFTB simulations with a near-linear scaling version of DFTB for massively parallel computation based on the fragment molecular orbital (FMO-DFTB) method. We illustrate differences between the phenomenology of the folding dynamics from these three methods for a small model peptide, as well as charge polarization and dynamic fluctuations, point out possible correlations and implications for force field developers, and discuss the lessons learned that might become applicable to future predictive high-throughput computer screening for personalized neoantigen cancer therapy.

Irle, Stephan↗
DOE OSTI2023

Mass Spectrometry-Based High-Throughput Quantification of Bioproducts in Liquid Culture

To meet the ever-increasing need for high-throughput screening in metabolic engineering, information-rich, fast screening methods are needed. Mass spectrometry (MS) provides an efficient and general approach for metabolite screening and offers the capability of characterizing a broad range of analytes in a label-free manner, but often requires a range of sample clean-up and extraction steps. Liquid extraction surface analysis (LESA) coupled MS is an image-guided MS surface analysis approach that directly samples and introduces metabolites from a surface to MS. Here, we combined the advantages of LESA–MS and an acoustic liquid handler with stable isotope-labeled internal standards. This approach provides absolute quantitation of target chemicals from liquid culture-dried droplets and enables high-throughput quantitative screening for microbial metabolites. In this paper, LESA–MS was successfully applied to quantify several different metabolites (itaconic acid, triacetic acid lactone, and palmitic acid) from different yeast strains in different mediums, demonstrating its versatility, accuracy, and efficiency across a range of microbial engineering applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Thermal scanning probe and laser lithography for patterning nanowire based quantum devices

Semiconductor nanowire (NW) quantum devices offer a promising path for the pursuit and investigation of topologically-protected quantum states, and superconducting and spin-based qubits that can be controlled using electric fields. Theoretical investigations into the impact of disorder on the attainment of dependable topological states in semiconducting nanowires with large spin–orbit coupling and g-factor highlight the critical need for improvements in both growth processes and nanofabrication techniques. In this work, we used a hybrid lithography tool for both the high-resolution thermal scanning probe lithography and high-throughput direct laser writing of quantum devices based on thin InSb nanowires with contact spacing of 200 nm. Electrical characterization demonstrates quasi-ballistic transport. The methodology outlined in this study has the potential to reduce the impact of disorder caused by fabrication processes in quantum devices based on 1D semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of a Laser Ultrasonics-based Approach for Rapid Screening of High Entropy Alloys

This project utilized a laser ultrasonic technique to systematically study temperature-induced evolution of material properties in a set of interrelated binary alloys and a high entropy alloy (HEA) fabricated using arc-melting and spark plasma sintering processes. This technique involved the use of a nanosecond duration, high-intensity pulsed laser to thermo-elastically generate ultrasonic waves that propagate in the bulk of the metal alloy. Sub-nanometer-scale displacements associated with the propagating bulk ultrasonic waves were detected along the epicenter on the opposite surface of the sample using a 1 GHz bandwidth photorefractive interferometer. Phase transformations and microstructural changes were inferred from the temperature-dependent trends in the bulk acoustic velocities and features in the ultrasonic epicentral waveforms measured in the binary alloys. These inferences were then correlated with electron/optical microscopy observations and predictions using calculations of phase diagrams (CALPHAD). The results showed that the laser-generated ultrasonic pulses were strongly influenced by changes in material microstructure and could accurately track thermally driven phase transformations and detect the presence of microscale heterogeneities (grain boundaries, dendritic structures, etc.) in the set of binary alloy samples. The measurement approach was then applied to a quinary HEA sample for estimating phase transition temperature and determining microstructural heterogeneity. The rapid, non-contact and non-destructive ultrasonic testing approach demonstrated here is amenable to high throughput combinatorial investigations that can be applied to graded composition HEAs produced using advanced manufacturing methods. When paired with atomistic simulations and CALPHAD modeling, this approach can overcome the bottlenecks faced by current material characterization methods in efficiently screening the vast discovery space of HEAs that spans over a hundred million unique quinary alloy compositions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments

Non-negative matrix factorization (NMF) is an appealing class of methods for performing unsupervised learning on streaming spectral data, particularly in time-sensitive applications such as in situ characterization of materials. These methods seek to decompose a dataset into a small number of components and weights that can compactly represent the underlying signal while effectively reconstructing the observations with minimal error. However, canonical NMF methods have no underlying requirement that the reconstruction uses components or weights that are representative of the true physical processes. In this work, we demonstrate how constraining a subset of the NMF weights or components as rigid priors, provided as known or assumed values, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch-based method for efficiently applying constrained NMF and demonstrate its application to several synthetic examples. Our implementation allows an expert researcher-in-the-loop to provide and dynamically adjust the constraints during a live experiment involving streaming spectral data. Such interactive priors allow researchers to specify known or identified independent components, as well as functional expectations about the mixing or transitions between the components. We further demonstrate the application of this method to measured synchrotron x-ray total scattering data from in situ beamline experiments. In such a context, constrained NMF can result in a more interpretive and scientifically relevant decomposition than canonical NMF or other decomposition techniques. As a result, the details of the method are provided, along with general guidance for employing constrained NMF in the extraction of critical information and insights during time-sensitive experimental applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-throughput functional variant screens via in vivo production of single-stranded DNA

Significance We report a methodology for the pooled construction of mutants bearing precise genomic sequence variations and multiplex phenotypic characterization of these mutants using next-generation sequencing (NGS). Unlike existing techniques depending on CRISPR-Cas–directed genomic breaks for genome editing, this strategy instead uses single-stranded DNA produced by a retron element for recombineering. This enables libraries of millions of elements to be constructed and offers relaxed design constraints which permit natural DNA or random variation to be used as inputs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Wild Isolates of Neurospora crassa Reveal Three Conidiophore Architectural Phenotypes

The vegetative life cycle in the model filamentous fungus, Neurospora crassa, relies on the development of conidiophores to produce new spores. Environmental, temporal, and genetic components of conidiophore development have been well characterized; however, little is known about their morphological variation. We explored conidiophore architectural variation in a natural population using a wild population collection of 21 strains from Louisiana, United States of America (USA). Our work reveals three novel architectural phenotypes, Wild Type, Bulky, and Wrap, and shows their maintenance throughout the duration of conidiophore development. Furthermore, we present a novel image-classifier using a convolutional neural network specifically developed to assign conidiophore architectural phenotypes in a high-throughput manner. To estimate an inheritance model for this discrete complex trait, crosses between strains of each phenotype were conducted, and conidiophores of subsequent progeny were characterized using the trained classifier. Our model suggests that conidiophore architecture is controlled by at least two genes and has a heritability of 0.23. Additionally, we quantified the number of conidia produced by each conidiophore type and their dispersion distance, suggesting that conidiophore architectural phenotype may impact N. crassa colonization capacity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Cut site preference allows influenza A virus PA-X to discriminate between host and viral mRNAs

Many viruses block host gene expression to take over the infected cell. This process, termed host shutoff, is thought to promote viral replication by preventing antiviral responses and redirecting cellular resources to viral processes. Several viruses from divergent families accomplish host shutoff through RNA degradation by endoribonucleases. However, viruses also need to ensure expression of their own genes. The influenza A virus endoribonuclease PA-X solves this problem by sparing viral mRNAs and some host RNAs necessary for viral replication. Here, to understand how PA-X distinguishes between RNAs, we characterized PA-X cut sites transcriptome-wide using 5' rapid amplification of complementary DNA ends coupled to high-throughput sequencing. This analysis, along with RNA structure predictions and validation experiments using reporters, shows that PA-Xs from multiple influenza strains preferentially cleave RNAs at GCUG tetramers in hairpin loops. Importantly, GCUG tetramers are enriched in the human but not the influenza transcriptome. Moreover, optimal PA-X cut sites inserted in the influenza A virus genome are quickly selected against during viral replication in cells. This finding suggests that PA-X evolved these cleavage characteristics to preferentially target host over viral mRNAs in a manner reminiscent of cellular self versus non-self discrimination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multifunctional fiber-reinforced composites for passive sensing and energy harvesting with enhanced mechanical performance

Fiber-reinforced polymer composites (FRPCs) are valid alternatives to metals, especially in the aerospace industry, for their superior strength-to-weight ratio. FRPCs are generally manufactured by stacking layers of fibers and infusing them with epoxy-based adhesives to result in laminates. Such layer-by-layer structure often ensues a poor interlaminar property of the composites. Additionally, FRPCs can sustain complex damage modes because of their inherent anisotropic characteristics. For example, delamination can occur due to low-velocity impact at a subsurface level, which can result in premature catastrophic failures if undetected. Thus, structural monitoring is crucial to identify such damage. Point-based sensors (e.g., strain gauges, accelerometers, among many) can be embedded in the FRPCs for structural monitoring. However, their electrical power demand often embroils their usage. Therefore, the main objective of this research is to design and implement multifunctional composites that can simultaneously perform passive sensing and energy harvesting while exhibiting better mechanical performance. Previous research efforts have demonstrated that a continuous feed-through deposition of functional materials on fiber surfaces simultaneously enhanced the mechanical and sensing properties of the FRPCs. This study explores a similar approach to encode passive sensing and energy harvesting properties in the FRPCs by integrating ferroelectric microparticles on the fiber surfaces. Upon ensuring their superior interlaminar shear strength, sensing and energy harvesting properties were characterized through experimental studies. The outcome is a multifunctional composite fabricated by coating the fibers with functional microparticles through a high throughput, scalable, and low-cost approach that enables passive sensing, energy harvesting, and improved mechanical performance.

Gupta, Sumit↗
DOE OSTI2021

Friction testing and torque sensing systems

The present invention relates, in part, to systems for characterizing force (e.g., friction, wear, and/or torque). In one embodiment, the system allows for wear testing of samples in a high throughput manner. In another embodiment, the system allows for torque sensing in a non-contact manner.

Nation, Brendan L.↗