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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Biophysical Perspective on Enzyme Catalysis

According to the article, even after a century of investigation, our understanding of how enzymes work remains far from complete. In particular, several factors that enable enzymes to achieve high catalytic efficiencies remain only poorly understood. A number of theories have been developed, which propose or reaffirm that enzymes work as structural scaffolds, serving to bring together and properly orient the participants so that the reaction can proceed; therefore, leading to enzymes being viewed as only passive participants in the catalyzed reaction. A growing body of evidence shows that enzymes are not rigid structures but are constantly undergoing a wide range of internal motions and conformational fluctuations. In this Perspective, on the basis of studies from our group, we discuss the emerging biophysical model of enzyme catalysis that provides a detailed understanding of the interconnection among internal protein motions, conformational substates, enzyme mechanisms, and the catalytic efficiency of enzymes. For a number of enzymes, networks of conserved residues that extend from the surface of the enzyme all the way to the active site have been discovered. These networks are hypothesized to serve as pathways of energy transfer that enables thermodynamical coupling of the surrounding solvent with enzyme catalysis and play a role in promoting enzyme function. Additionally, the role of enzyme structure and electrostatic effects has been well acknowledged for quite some time. Collectively, the recent knowledge gained about enzyme mechanisms suggests that the conventional paradigm of enzyme structure encoding function is incomplete and needs to be extended to structure encodes dynamics, and together these enzyme features encode function including catalytic rate acceleration.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Harnessing Fermentation May Enhance the Performance of Biological Sulfate-Reducing Bioreactors

Biological sulfate reduction (BSR) represents a promising strategy for bioremediation of sulfate-rich waste streams, yet the impact of metabolic interactions on performance is largely unexplored. Here, genome-resolved metagenomics was used to characterize 17 microbial communities in reactors treating synthetic sulfate-contaminated solutions. Reactors were supplemented with lactate or acetate and a small amount of fermentable substrate. Of the 163 genomes representing all the abundant bacteria, 130 encode 321 NiFe and FeFe hydrogenases and all genomes of the 22 sulfate-reducing microorganisms (SRM) encode genes for H 2 uptake. We observed lactate oxidation solely in the first packed bed reactor zone, with propionate and acetate oxidation in the middle and predominantly acetate oxidation in the effluent zone. The energetics of these reactions are very different, yet sulfate reduction kinetics were unaffected by the type of electron donor available. We hypothesize that the comparable rates, despite the typically slow growth of SRM on acetate, are a result of the consumption of H 2 generated by fermentation. This is supported by the sustained performance of a predominantly acetate-supplemented stirred tank reactor dominated by diverse fermentative bacteria encoding FeFe hydrogenase genes and SRM capable of acetate and hydrogen consumption and CO 2 assimilation. Thus, addition of fermentable substrates to stimulate syntrophic relationships may improve the performance of BSR reactors supplemented with inexpensive acetate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Programming temporal shapeshifting

Shapeshifting enables a wide range of engineering and biomedical applications, but until now transformations have required external triggers. This prerequisite limits viability in closed or inert systems and puts forward the challenge of developing materials with intrinsically encoded shape evolution. Herein we demonstrate programmable shape-memory materials that perform a sequence of encoded actuations under constant environment conditions without using an external trigger. We employ dual network hydrogels: in the first network, covalent crosslinks are introduced for elastic energy storage, and in the second one, temporary hydrogen-bonds regulate the energy release rate. Through strain-induced and time-dependent reorganization of the reversible hydrogen-bonds, this dual network allows for encoding both the rate and pathway of shape transformations on timescales from seconds to hours. In conclusion, this generic mechanism for programming trigger-free shapeshifting opens new ways to design autonomous actuators, drug-release systems and active implants.

42 ENGINEERING↗
DOE OSTI2021

Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane

Microbial metabolisms and interactions that facilitate subsurface conversions of recalcitrant carbon to methane are poorly understood. We deployed an in situ enrichment device in a subsurface coal seam in the Powder River Basin (PRB), USA, and used BONCAT-FACS-Metagenomics to identify translationally active populations involved in methane generation from a variety of coal-derived aromatic hydrocarbons. From the active fraction, high-quality metagenome-assembled genomes (MAGs) were recovered for the acetoclastic methanogen, Methanothrix paradoxum, and a novel member of the Chlorobi with the potential to generate acetate via the Pta-Ack pathway. Members of the Bacteroides and Geobacter also encoded Pta-Ack and together, all four populations had the putative ability to degrade ethylbenzene, phenylphosphate, phenylethanol, toluene, xylene, and phenol. Metabolic reconstructions, gene analyses, and environmental parameters also indicated that redox fluctuations likely promote facultative energy metabolisms in the coal seam. The active "Chlorobi PRB" MAG encoded enzymes for fermentation, nitrate reduction, and multiple oxygenases with varying binding affinities for oxygen. "M. paradoxum PRB" encoded an extradiol dioxygenase for aerobic phenylacetate degradation, which was also present in previously published Methanothrix genomes. Finaly, these observations outline underlying processes for bio-methane from subbituminous coal by translationally active populations and demonstrate activity-based metagenomics as a powerful strategy in next generation physiology to understand ecologically relevant microbial populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy

Abstract Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN (Atomistic Line Graph Neural Network) encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2019

High-dimensional optical quantum logic in large operational spaces

The probabilistic nature of single-photon sources and photon–photon interactions encourages encoding as much quantum information as possible in every photon for the purpose of photonic quantum information processing. Here, by encoding high-dimensional units of information (qudits) in time and frequency degrees of freedom using on-chip sources, we report deterministic two-qudit gates in a single photon with fidelities exceeding 0.90 in the computational basis. Constructing a two-qudit modulo SUM gate, we generate and measure a single-photon state with nonseparability between time and frequency qudits. We then employ this SUM operation on two frequency-bin entangled photons—each carrying two 32-dimensional qudits—to realize a four-party high-dimensional Greenberger–Horne–Zeilinger state, occupying a Hilbert space equivalent to that of 20 qubits. Although high-dimensional coding alone is ultimately not scalable for universal quantum computing, our design shows the potential of deterministic optical quantum operations in large encoding spaces for practical and compact quantum information processing protocols.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantum repeaters based on concatenated bosonic and discrete-variable quantum codes

Abstract We propose an architecture of quantum-error-correction-based quantum repeaters that combines techniques used in discrete- and continuous-variable quantum information. Specifically, we propose to encode the transmitted qubits in a concatenated code consisting of two levels. On the first level we use a continuous-variable GKP code encoding the qubit in a single bosonic mode. On the second level we use a small discrete-variable code. Such an architecture has two important features. Firstly, errors on each of the two levels are corrected in repeaters of two different types. This enables for achieving performance needed in practical scenarios with a reduced cost with respect to an architecture for which all repeaters are the same. Secondly, the use of continuous-variable GKP code on the lower level generates additional analog information which enhances the error-correcting capabilities of the second-level code such that long-distance communication becomes possible with encodings consisting of only four or seven optical modes.

Physics↗
DOE OSTI2025

Quantum frequency resampling

In signal processing, resampling algorithms can modify the number of resources encoding a collection of data points. Downsampling reduces the cost of storage and communication, while upsampling interpolates new data from limited one, e.g., when resizing a digital image. We present a toolset of quantum algorithms to resample data encoded in the probabilities of a quantum register, using the quantum Fourier transform to adjust the number of high-frequency encoding qubits. We discuss advantage over classical resampling algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Schrödinger cat states of a nuclear spin qudit in silicon

High-dimensional quantum systems are a valuable resource for quantum information processing. They can be used to encode error-correctable logical qubits, which has been demonstrated using continuous-variable states in microwave cavities or the motional modes of trapped ions. For example, high-dimensional systems can be used to realize ‘Schrödinger cat’ states, which are superpositions of widely displaced coherent states that can be used to illustrate quantum effects at large scales. Recent proposals have suggested encoding qubits in high-spin atomic nuclei, which are finite-dimensional systems that can host hardware-efficient versions of continuous-variable codes. Here, in this study, we demonstrate the creation and manipulation of Schrödinger cat states using the spin-7/2 nucleus of an antimony atom embedded in a silicon nanoelectronic device. We use a multi-frequency control scheme to produce spin rotations that preserve the symmetry of the qudit, and we constitute logical Pauli operations for qubits encoded in the Schrödinger cat states. Our work demonstrates the ability to prepare and control non-classical resource states, which is a prerequisite for applications in quantum information processing and quantum error correction, using our scalable, manufacturable semiconductor platform.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Expanded encyclopaedias of DNA elements in the human and mouse genomes

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An expanded registry of candidate cis -regulatory elements

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, the ENCODE consortium mapped biochemical signals across hundreds of cell types and tissues and integrated these data to develop a registry containing 0.9 million human and 300,000 mouse candidate cis-regulatory elements (cCREs) annotated with potential functions. Here we have expanded the registry to include 2.37 million human and 967,000 mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays such as STARR-seq, massively parallel reporter assay, CRISPR perturbation and transgenic mouse assays have profiled more than 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer and silencer roles in different cellular contexts. Integrating the registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by the identification of KLF1 as a novel causal gene for red blood cell traits. This expanded registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

Moore, Jill E. [Univ. of Massachusetts, Worchester↗
DOE OSTI2019

Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network

Materials and devices age with time. Material aging and degradation has important implications for lifetime performance of materials and systems. While consensus exists that materials should be studied and designed for degradation, materials inspection during operation is typically performed manually by technicians. The manual inspection makes studies prone to errors and uncertainties due to human subjectivity. In this work, we focus on automating the process of degradation mechanism detection through the use of a fully convolutional deep neural network architecture (F-CNN). We demonstrate that F-CNN architecture allows for automated inspection of cracks in polymer backsheets from photovoltaic (PV) modules. The developed F-CNN architecture enabled an end-to-end semantic inspection of the PV module backsheets by applying a contracting path of convolutional blocks (encoders) followed by an expansive path of decoding blocks (decoders). First, the hierarchy of contextual features is learned from the input images by encoders. Next, these features are reconstructed to the pixel-level prediction of the input by decoders. The structure of the encoder and the decoder networks are thoroughly investigated for the multi-class pixel-level degradation type prediction for PV module backsheets. The developed F-CNN framework is validated by reporting degradation type prediction accuracy for the pixel level prediction at the level of 92.8%.

14 SOLAR ENERGY↗
DOE OSTI2023

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Genome sequence and characterization of a novel Pseudomonas putida phage, MiCath

Abstract Pseudomonads are ubiquitous bacteria with importance in medicine, soil, agriculture, and biomanufacturing. We report a novel Pseudomonas putida phage, MiCath, which is the first known phage infecting P. putida S12, a strain increasingly used as a synthetic biology chassis. MiCath was isolated from garden soil under a tomato plant using P. putida S12 as a host and was also found to infect four other P. putida strains. MiCath has a ~ 61 kbp double-stranded DNA genome which encodes 97 predicted open reading frames (ORFs); functions could only be predicted for 48 ORFs using comparative genomics. Functions include structural phage proteins, other common phage proteins (e.g., terminase), a queuosine gene cassette, a cas4 exonuclease, and an endosialidase. Restriction digestion analysis suggests the queuosine gene cassette encodes a pathway capable of modification of guanine residues. When compared to other phage genomes, MiCath shares at most 74% nucleotide identity over 2% of the genome with any sequenced phage. Overall, MiCath is a novel phage with no close relatives, encoding many unique gene products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Characterization of key triacylglycerol biosynthesis processes in rhodococci

In this study, oleaginous microorganisms have considerable potential for biofuel and commodity chemical production. Under nitrogen-limitation, Rhodococcus jostii RHA1 grown on benzoate, an analog of lignin depolymerization products, accumulated triacylglycerols (TAGs) to 55% of its dry weight during transition to stationary phase, with the predominant fatty acids being C16:0 and C17:0. Transcriptomic analyses of RHA1 grown under conditions of N-limitation and N-excess revealed 1,826 dysregulated genes. Genes whose transcripts were more abundant under N-limitation included those involved in ammonium assimilation, benzoate catabolism, fatty acid biosynthesis and the methylmalonyl-CoA pathway. Of the 16 atf genes potentially encoding diacylglycerol O-acyltransferases, atf8 transcripts were the most abundant during N-limitation (~50-fold more abundant than during N-excess). Consistent with Atf8 being a physiological determinant of TAG accumulation, a Δ atf8 mutant accumulated 70% less TAG than wild-type RHA1 while atf8 overexpression increased TAG accumulation 20%. Genes encoding type-2 phosphatidic acid phosphatases were not significantly expressed. By contrast, three genes potentially encoding phosphatases of the haloacid dehalogenase superfamily and that cluster with, or are fused with other Kennedy pathway genes were dysregulated. Overall, these findings advance our understanding of TAG metabolism in mycolic acid-containing bacteria and provide a framework to engineer strains for increased TAG production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

A single-shot, multiwavelength electro-optic data-acquisition system for inertial confinement fusion applications (invited)

Electro-optic data-acquisition systems encode the output from voltage-history diagnostics onto optical signals. The optical signals can propagate long distances over fiber-optic links without degrading the bandwidth of the encoded signal while protecting the recording electronics from overvoltage damage. The sinusoidal response and tolerance to high-input voltages of the Mach-Zehnder modulator used for the encoding leads to the additional advantage of a high dynamic range and a reduced need for manually swapping attenuators. We have demonstrated a single-shot, electro-optic data-acquisition system with a 600:1 dynamic range. This system provides optical isolation and a bandwidth of 6 GHz. The prototype system uses multiple optical wavelengths to allow for the multiplexing of up to eight signals onto one photodetector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Development and operation of a high-throughput accurate-wavelength lens-based spectrometer

A high-throughput spectrometer for the 400–820 nm wavelength range has been developed for charge exchange recombination spectroscopy or general spectroscopy. A large 2160 mm{sup −1} grating is matched with fast f/1.8 200 mm lenses, which provide stigmatic imaging. A precision optical encoder measures the grating angle with an accuracy ≤0.075 arc sec. A high quantum efficiency low-etaloning CCD detector allows operation at longer wavelengths. A patch panel allows input fibers to interface with interchangeable fiber holders that attach to a kinematic mount at the entrance slit. Computer-controlled hardware allows automated control of wavelength, timing, f-number, automated data collection, and wavelength calibration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗