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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 559 records · Page 31

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DOE OSTI2020

Methods of preparing single-walled carbon nanotube networks

Methods for determining desired doping conditions for a semiconducting single-walled carbon nanotube (s-SWCNT) are provided. One exemplary method includes doping each of a plurality of s-SWCNT networks under a respective set of doping conditions; determining a thermoelectric (TE) power factor as a function of a fractional bleach of an absorption spectrum for the plurality of s-SWCNT networks doped under the respective sets of doping conditions; and using the function to identify one of the TE power factors within a range of the fractional bleach of the absorption spectrum. The identified TE power factor corresponds to the desired doping conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Methods of preparing single-walled carbon nanotube networks

Avery, Azure Dee↗

DOE OSTI2023

Methods of preparing single-walled carbon nanotube networks

Avery, Azure Dee↗

DOE OSTI2023

Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes

Complex mechanisms regulate the cellular distribution of cholesterol, a critical component of eukaryote membranes involved in regulation of membrane protein functions directly and through the physiochemical properties of membranes. StarD4, a member of the steroidogenic acute regulator-related lipid-transfer (StART) domain (StARD)-containing protein family, is a highly efficient sterol-specific transfer protein involved in cholesterol homeostasis. Its mechanism of cargo loading and release remains unknown despite recent insights into the key role of phosphatidylinositol phosphates in modulating its interactions with target membranes. We have used large-scale atomistic Molecular dynamics (MD) simulations to study how the dynamics of cholesterol bound to the StarD4 protein can affect interaction with target membranes, and cargo delivery. We identify the two major cholesterol (CHL) binding modes in the hydrophobic pocket of StarD4, one near S136&S147 (the Ser-mode), and another closer to the putative release gate located near W171, R92&Y117 (the Trp-mode). We show that conformational changes of StarD4 associated directly with the transition between these binding modes facilitate the opening of the gate. To understand the dynamics of this connection we apply a machine-learning algorithm for the detection of rare events in MD trajectories (RED), which reveals the structural motifs involved in the opening of a front gate and a back corridor in the StarD4 structure occurring together with the spontaneous transition of CHL from the Ser-mode of binding to the Trp-mode. Further analysis of MD trajectory data with the information-theory based NbIT method reveals the allosteric network connecting the CHL binding site to the functionally important structural components of the gate and corridor. Mutations of residues in the allosteric network are shown to affect the performance of the allosteric connection. These findings outline an allosteric mechanism which prepares the CHL-bound StarD4 to release and deliver the cargo when it is bound to the target membrane.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

On the impact of the galaxy window function on cosmological parameter estimation

One important source of systematics in galaxy redshift surveys comes from the estimation of the galaxy window function. Up until now, the impact of the uncertainty in estimating the galaxy window function on parameter inference has not been properly studied. In this paper, we show that the uncertainty and the bias in estimating the galaxy window function will be salient for ongoing and next-generation galaxy surveys using a simulation-based approach. With a specific case study of cross-correlating emission-line galaxies from the DESI Legacy Imaging Surveys and the Planck cosmic microwave background lensing map, we show that neural network-based regression approaches to modelling the window function are superior in comparison to linear regression-based models. Here, we additionally show that the definition of the galaxy overdensity estimator can impact the overall signal-to-noise of observed power spectra. Finally, we show that the additive biases coming from the window functions can significantly bias the modes of the inferred parameters and also degrade their precision. Thus, a careful understanding of the window functions will be essential to conduct cosmological experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Cause identification of electromagnetic transient events using spatiotemporal feature learning

This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with softmax function for the classification. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine autoencoder, and tapered multi-layer perceptron neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the Electromagnetic Transients Program (EMTP) simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation of the Western System Coordinating Council (WSCC) 9-bus system.

42 ENGINEERING↗

DOE OSTI2021

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

electrical and electronic engineering↗

DOE OSTI2025

An ecological framework for microbial metabolites in the ocean ecosystem

The ocean microbe‐metabolite network involves thousands of individual metabolites that encompass a breadth of chemical diversity and biological functions. These microbial metabolites mediate biogeochemical cycles, facilitate ecological relationships, and impact ecosystem health. While analytical advancements have begun to illuminate such roles, a challenge in navigating the deluge of marine metabolomics information is to identify a subset of metabolites that have the greatest ecosystem impact. Here, we present an ecological framework to distill knowledge of fundamental metabolites that underpin marine ecosystems. We borrow terms from macroecology that describe important species, namely “dominant,” “keystone,” and “indicator” species, and apply these designations to metabolites within the ocean microbial metabolome. These selected metabolites may shape marine community structure, function, and health and provide focal points for enhanced study of microbe‐metabolite networks. Applying ecological concepts to marine metabolites provides a path to leverage metabolomics data to better describe and predict marine microbial ecosystems.

microbial metabolites↗

DOE OSTI2022

Physics-informed neural networks for identification of material properties using standing waves

A metallic structure in its initial stage of failure involves plastic deformation or environmental degradation that changes the elastic modulus and density. This work presents the detection of change in wave velocity (a function of elastic modulus and density) as a system identification problem. A physics-informed neural network (PINN) is proposed to solve the system identification problem. The PINN takes the spatial coordinates of scanning locations and time as inputs and provides the displacement and wave velocity as outputs. The governing partial differential equation of standing waves in a rod is incorporated into the neural network as physics in the form of a loss function. The wave velocity vector is randomly initiated. During the training of the network, physics is used to determine and update the wave velocity target vector from the network’s displacement predictions. The measured data, comprising sparse displacement response on the rod structure, are used to train the PINN. The wave velocity at the sparse locations on the rod is learned from the predicted displacements during the training. Using the predictions of the trained network, the response of free vibration or material property variation can be reconstructed at unscanned locations on the structure to obtain high-resolution maps for full-field imaging to detect and localize the changes caused by plastic deformation. The PINN’s sparse scanning and simultaneous prediction capability during training can lead to high scanning and data-processing speeds. This capability yields a nondestructive evaluation system that can predict the presence of degraded material locations as the structural vibrations are scanned and processed in real time.

Rathod, Vivek↗

DOE OSTI2023

Cosmology from random entanglement

We construct entangled microstates of a pair of holographic CFTs whose dual semiclassical description includes big bang-big crunch AdS cosmologies in spaces without boundaries. The cosmology is supported by inhomogeneous heavy matter and it partially purifies the bulk entanglement of two disconnected auxiliary AdS spacetimes. We show that the island formula for the fine grained entropy of one of the CFTs follows from a standard gravitational replica trick calculation. In generic settings, the cosmology is contained in the entanglement wedge of one of the two CFTs. We then investigate properties of the cosmology-to-boundary encoding map, and in particular, its non-isometric character. Restricting our attention to a specific class of states on the cosmology, we provide an explicit, and state-dependent, boundary representation of operators acting on the cosmology. Finally, under genericity assumptions, we argue for a non-isometric to approximately-isometric transition of the cosmology-to-boundary map for “simple” states on the cosmology as a function of the bulk entanglement, with tensor network toy models of our setup as a guide.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Maintaining the structural and functional homeostasis of the plant endoplasmic reticulum

The endoplasmic reticulum (ER) is a ubiquitous organelle that is vital to the life of eukaryotic cells. It synthesizes essential lipids and proteins, and initiates the glycosylation of intracellular and surface proteins. As such, the ER is necessary for cell growth and communication with the external environment. The ER is also a highly dynamic organelle, whose structure is continuously remodeled through an interaction with the cytoskeleton and the action of specialized ER shapers. Recent and significant advances in ER studies have brought to light conserved and unique features underlying the structure and function of this organelle in plant cells. Exciting developments in the understanding of the mechanisms for plant ER structural and functional homeostasis, particularly those that underpin ER network architecture and ER degradation, are presented and discussed in this work.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Importance of Nuclear Quantum Effects on Aqueous Electrolyte Transport under Confinement in Ti 3 C 2 MXenes

Protons display a high chemical activity and strongly affect the charge storage capability in confined interlayer spaces of two-dimensional (2D) materials. As such, an accurate representation of proton dynamics under confinement is important for understanding and predicting charge storage dynamics in these materials. While often ignored in atomistic-scale simulations, nuclear quantum effects (NQEs), e.g., tunneling, can be significant under confinement even at room temperature. Here, using the thermostatted ring polymer molecular dynamics implementation of path integral molecular dynamics (PIMD) in conjunction with the ReaxFF force field, density functional tight binding (DFTB), and NequIP neural network potential simulations, we investigate the role of NQEs on proton and water transport in bulk water and aqueous electrolytes under confinement in Ti 3 C 2 MXenes. Although overall NQEs are relatively small, especially in bulk, we find that they can alter both quantitative values and qualitative trends on both proton transport and water self-diffusion under confinement relative to classical MD predictions. Therefore, our results suggest the need for NQEs to be considered to simulate aqueous systems under confinement for both qualitative and quantitative accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Amine-functionalized porous organic polymers for carbon dioxide capture

Recent developments in CO 2 capture using porous organic polymers (POPs) have received accrescent attention due to their sorbent properties such as high CO 2 uptake capacity and selectivity, tunable chemical structure and permanent porosity. POPs are constructed using two and/or three-dimensional organic monomers (building blocks) linked to each other through covalent bonding, creating high porosity. The pore structure in POPs is exceptionally stable, which leads to their cyclable CO 2 adsorption performance. However, POPs generally suffer from low CO 2 uptake and selectivity due to their interaction with CO 2 in physisorption limits (20–40 kJ mol -1 ). Similar to that in other physisorbents, the CO 2 uptake capacity of POPs further decreases under humid conditions. Pursuant to these limitations, amine functionalization in POPs has resulted in enhanced CO 2 uptake performance with improved CO 2 selectivity over non-polar gases such as N 2 . More importantly, several types of amine-functionalized POPs showed that the CO 2 uptake could remain intact under humid conditions such as in post-combustion flue gas. This review article covers recent developments in amine-functionalized porous organic polymers. Three main categories of amine functionalization, such as direct amine synthesis, amine impregnation and amine grafting, were investigated in detail by considering the effect of amines on the sorbent properties and CO 2 capture performance of POPs. The recent findings in amine-functionalized POPs were investigated including porous polymeric networks (PPNs), covalent organic frameworks (COFs), amine linked POPs, hyper-crosslinked polymers (HCPs), conjugated microporous polymers (CMPs), benzimidazole linked polymers (BILPs), porous aromatic frameworks (PAFs) and polymers of intrinsic microporosity (PIMs).

36 MATERIALS SCIENCE↗

DOE OSTI2025

Formation of U(VI) peroxide nanoclusters from cascade reactions with a persulfate radical initiator

Radiolysis of water in high radiation fields generates a variety of reactive oxygen species that influence the chemical behavior and complexation of hexavalent uranium. This study investigates the behavior of interaction of a uranyl cation (UO 2 2+ (VI)) with a series of free radicals that are formed in situ via activation of the free radical initiator persulphate (S 2 O 8 2− ), which releases both SO 4 ˙ − and ˙OH species in the solution. Electron Paramagnetic Resonance (EPR) and Raman spectroscopy were used to evaluate the presence of the hydroperoxyl radical (HO 2 ˙) and superoxide radicals (O 2 ˙ − ) that are formed within the solution through radical cascade reactions. In addition, a uranyl peroxide cluster solid (NaU 24 ) was crystallized and characterized using single crystal X-ray diffraction (SCXRD), vibrational spectroscopy, and EPR spectroscopy. The presence of the hydroperoxyl radical (HO 2 ˙) and superoxide radicals (O 2 ˙ − ) was also observed in the solid-state compound, but spectroscopic evidence suggests that it was associated with the Na + network and not the cluster itself. Density functional theory (DFT) calculations were also utilized to further confirm the radical species produced and determine the potential stabilization of radicals detected within the cluster and lattice.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Probabilistic Prediction of Geomagnetic Storms and the K p index

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K p prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K p ≥ 5 – ) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Twistronics of Janus transition metal dichalcogenide bilayers

Twisted multilayers of two-dimensional (2D) materials are an increasingly important platform for investigating quantum phases of matter, and in particular, strongly correlated electrons. The moiré pattern introduced by the relative twist between layers creates effective potentials of long wavelength, leading to electron localization. However, in contrast to the abundance of 2D materials, few twisted heterostructures have been studied until now. Here, in this study, we develop a first-principles continuum theory to study the electronic bands introduced by moire patterns of twisted Janus transition metal dichalcogenide (TMD) homo- and heterobilayers. The model includes lattice relaxation, stacking-dependent effective mass, and Rashba spin-orbit coupling. We then perform a high-throughput generation and characterization of DFT-extracted continuum models for more than a hundred possible combinations of materials and stackings. Our model predicts that the moiré physics and emergent symmetries depend on chemical composition, vertical layer orientation, and twist angle, so that the miniband wave functions can form triangular, honeycomb, and kagome networks. Rashba spin-orbit effects, peculiar of these systems, can dominate the moiré bandwidth at small angles. Our work enables detailed investigation of Janus twisted heterostructures, allowing the discovery and control of novel electronic phenomena.

36 MATERIALS SCIENCE↗