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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using matrix-assisted laser desorption/ionization and tandem mass spectrometry (MALDI-MS/MS), and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived “motifs” to inform a machine-learning algorithm toward conformation prediction. Here, this multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.

data-driven analysis↗
DOE OSTI2025

Quantitative multiplexed analysis of gene and protein expression patterns in Yarrowia lipolytica

In this report, we present coordinated observations of protein and mRNA transcript counts at the single-cell level in the oleaginous yeast model Yarrowia lipolytica. The transcription factor Xbp1p regulates entry into a quiescent state, representing a shift of resources to sequestration of nutrients rather than cell division. We observed the responses of wild-type and Δxbp1 cells to protein (by fluorescence) and transcript quantification and localization at both single-cell and population-averaged levels. Data were collected via single-molecule fluorescence in situ hybridization (smFISH) and qPCR under nitrogen depletion, a condition that drives lipid accumulation. These techniques reveal a complex and heterogeneous population of Xbp1p dynamics and downstream regulation. Our findings highlight the need for single-cell resolution analyses to describe cellular dynamics and regulatory processes.

Yarrowia lipolytica↗
DOE OSTI2020

Patterns in extracellular enzyme activity and microbial diversity in deep-sea Mediterranean sediments

Deep-sea sediments are populated by diverse microbial communities that derive their nutritional requirements from the degradation of organic matter. Extracellular hydrolytic enzymes play a key role in the survival of microbes by enabling them to access and degrade complex organic compounds that are found in seafloor sediments. Despite their importance, extracellular enzymatic activity is poorly characterized at water depths greater than a few hundred meters where physical properties, such as pressure and temperature, create a unique environment for influencing enzyme behavior. In this work, we investigated microbial communities and enzyme activities in surface sediment collected at four sampling stations in the central Mediterranean Sea at water depths ranging from 800 to 2200 m. Fluorometric assays revealed that extracellular hydrolytic activity varied according to substrate type and water depth which suggests that the distributions of these enzymes within this basin are not homogenous. Furthermore, enzyme activities indicated substantial demand for phosphomonoesters and proteins, with measurable but much lower demand for polysaccharides. Barcoded amplicon sequencing of bacterial and archaeal SSU genes revealed that microbial communities varied across sampling stations and some groups displayed water-depth related trends. Our results demonstrate that heterotrophic capabilities of microbes in deep-sea Mediterranean sediments can differ substantially even within the same region.

16S rRNA↗
DOE OSTI2022

How to see hidden patterns in metamaterials with interpretable machine learning

Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usually relied on black box deep neural networks, whose reasoning processes are opaque and require enormous datasets that are expensive to obtain. Here, in this work, we develop two novel machine learning approaches to metamaterials discovery that have neither of these disadvantages. These approaches, called shape-frequency features and unit-cell templates, can discover 2D metamaterials with user-specified frequency band gaps. Our approaches provide logical rule-based conditions on metamaterial unit-cells that allow for interpretable reasoning processes, and generalize well across design spaces of different resolutions. The templates also provide design flexibility where users can almost freely design the fine resolution features of a unit-cell without affecting the user’s desired band gap.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Species- and element-specific patterns of metal flux from contaminated wetlands versus metals shed with exuviae in emerging dragonflies

Dragonfly adults and their aquatic immature stages are important parts of food webs and provide a link between aquatic and terrestrial components. During emergence, contaminants can be exported into terrestrial food webs as immature adults fly away or be shed with their exuviae and remain in the wetland. Our previous work established metals accumulating in dragonfly nymphs throughout a contaminated constructed wetland designed to regulate pH and sequester trace metals from an industrial effluent line. In this paper we evaluated the concentration and mass of metals leaving the wetland in flying emergents versus remaining in the wetland with the shed exuviae in 10 species of dragonflies belonging to 8 genera. Nine elements (Cu, Zn, Cd, Mn, V, Mg, Fe, Al, Pb) were evaluated that include essential and nonessential elements as well as trace and major metals. Metal concentrations in the emergent body and exuviae can differ by orders of magnitude. Aluminum, Fe, Mn, and Pb were largely shed in the exuviae. Vanadium and Cd were more variable among species but also tended to be shed with the exuviae. In contrast, Cu, Zn, and Mg showed a higher tendency to leave the wetland with an emerging dragonfly. Metals shed in dragonfly exuviae can moderate the transport of metals from contaminated wetlands. Taxonomic- and metal-specific variability in daily metal flux from the wetland depended upon concentration accumulated, individual body mass, and number of individuals emerging, with each factor's relative importance often differing among species. This illustrates the importance of evaluating the mass of metals in an individual and not only concentrations. Furthermore, differences in numbers of each species emerging will magnify differences in individual metal flux when calculating community metal flux. A better understanding of the variability of metal accumulation in nymphs/larvae and metal shedding during metamorphosis among both metals and species is needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Detecting technological maturity from bibliometric patterns

We report the capability to identify emergent technologies based upon easily accessed open-source indicators, such as publications, is important for decision-makers in industry and government. The scientific contribution of this work is the proposition of a machine learning approach to the detection of the maturity of emerging technologies based on publication counts. Time-series of publication counts have universal features that distinguish emerging and growing technologies. We train an artificial neural network classifier, a supervised machine learning algorithm, upon these features to predict the maturity (emergent vs. growth) of an arbitrary technology. With a training set comprised of 22 technologies we obtain a classification accuracy ranging from 58.3% to 100% with an average accuracy of 84.6% for six test technologies. To enhance classifier performance, we augmented the training corpus with synthetic time-series technology life cycle curves, formed by calculating weighted averages of curves in the original training set. Training the classifier on the synthetic data set resulted in improved accuracy, ranging from 83.3% to 100% with an average accuracy of 90.4% for the test technologies. The performance of our classifier exceeds that of competing machine learning approaches in the literature, which report an average classification accuracy of only 85.7% at maximum. Moreover, in contrast to current methods our approach does not require subject matter expertise to generate training labels, and it can be automated and scaled.

97 MATHEMATICS AND COMPUTING↗