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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Spatial Engineering Direct Cooperativity between Binding Sites for Uranium Sequestration

Preorganization is a basic design principle used by nature that allows for synergistic pathways to be expressed. Herein, a full account of the conceptual and experimental development from randomly distributed functionalities to a convergent arrangement that facilitates cooperative binding is given, thus conferring exceptional affinity toward the analyte of interest. The resulting material with chelating groups populated adjacently in a spatially locked manner displays up to two orders of magnitude improvement compared to a random and isolated manner using uranium sequestration as a model application. This adsorbent shows exceptional extraction efficiencies, capable of reducing the uranium concentration from 5 ppm to less than 1 ppb within 10 min, even though the system is permeated with high concentrations of competing ions. The efficiency is further supported by its ability to extract uranium from seawater with an uptake capability of 5.01 mg g -1 , placing it among the highest-capacity seawater uranium extraction materials described to date. The concept presented here uncovers a new paradigm in the design of efficient sorbent materials by manipulating the spatial distribution to amplify the cooperation of functions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2021

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks

Abstract Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

One- and two-qubit gate infidelities due to motional errors in trapped ions and electrons

In this work, we derive analytic formulas that determine the effect of error mechanisms on one- and two-qubit gates in trapped ions and electrons. First, we analyze and derive expressions for the effect of driving field inhomogeneities on one-qubit gate fidelities. Second, we derive expressions for two-qubit gate errors, including static motional frequency shifts, trap anharmonicities, field inhomogeneities, heating, and motional dephasing. We show that, for small errors, each of our expressions for infidelity converges to its respective numerical simulation; this shows that our formulas are sufficient for determining error budgets for high-fidelity gates, obviating numerical simulations in future projects. All of the derivations are general to any internal qubit state, and any mixed state of the ion crystal's motion that is diagonal in the Fock state basis. Our treatment of static motional frequency shifts, trap anharmonicities, heating, and motional dephasing apply to both laser-based and laser-free gates, while our treatment of field inhomogeneities applies to laser-free systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

PayamDiba/CoNSEPT

Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference.

Dibaeinia, Payam↗
DOE OSTI2023

Transcriptome and secretome profiling of sensory neurons reveals sex differences in pathways relevant to insulin sensing and insulin secretion

Sensory neurons in the dorsal root ganglia (DRG) convey somatosensory and metabolic cues to the central nervous system and release substances from stimulated terminal endings in peripheral organs. Sex-biased variations driven by the sex chromosome complement (XX and XY) have been implicated in the sensory–islet crosstalk. However, the molecular underpinnings of these male–female differences are not known. Here, we aim to characterize the molecular repertoire and the secretome profile of the lower thoracic spinal sensory neurons and to identify molecules with sex-biased insulin sensing- and/or insulin secretion-modulating activity that are encoded independently of circulating gonadal sex hormones. We used transcriptomics and proteomics to uncover differentially expressed genes and secreted molecules in lower thoracic T5-12 DRG sensory neurons derived from sexually immature 3-week-old male and female C57BL/6J mice. Comparative transcriptome and proteome analyses revealed differential gene expression and protein secretion in DRG neurons in males and females. Furthermore, the transcriptome analysis identified, among others, higher insulin signaling/sensing capabilities in female DRG neurons; secretome screening uncovered several sex-specific candidate molecules with potential regulatory functions in pancreatic β cells. Together, these data suggest a putative role of sensory interoception of insulin in the DRG–islet crosstalk with implications in sensory feedback loops in the regulation of β-cell activity in a sex-biased manner. Finally, we provide a valuable resource of molecular and secretory targets that can be leveraged for understanding insulin interoception and insulin secretion and inform the development of novel studies/approaches to fathom the role of the sensory–islet axis in the regulation of energy balance in males and females.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Eucalyptus grandis MYB‐Like and RAN‐Like Zinc Finger Proteins Display Dual Roles in Regulating Plant Immunity and Symbiosis Pathways

Plant roots live in constant contact with diverse microbes in the soil. Plant fitness, therefore, relies on signaling pathways that mount an effective immune response against pathogens while fostering mutualistic symbioses. Plant pathways, and specifically immune genes that may act as "switches," discriminating between pathogenic or mutualistic fungi, remain largely unknown. Using Eucalyptus grandis as a model system, we investigate alterations to the root transcriptomic landscape during pre-symbiosis with either the pathogen Armillaria luteobubalina or the mutualistic fungus Pisolithus microcarpus. Comparative analyses identified three strongly counter-regulated genes that may act as immune switches to accommodate or to repress fungal colonization. We characterized two of these, a MYB-like and RAN-like zinc finger protein, using a transgenic approach and demonstrated that they have bifunctional roles in the regulation of cell death and a hypersensitive-like response, depending on the lifestyle of the associated fungus. Using co-expression network analysis, we identified hypothetical pathways correlated to these genes. We functionally validated these predictions using plants with transgenic roots with increased or decreased transcription of these genes, thereby showing the power of co-expression networks as an a priori approach to identify key immune response pathways in plants. Overall, our results demonstrate that prior to physical contact with microbes, MYB-like and RAN-like zinc finger proteins are key regulators of plant immune signaling that respond to fungal signals and enable or repress symbiotic establishment.

mycorrhizal fungi↗
DOE OSTI2022

Connecting Material Properties and Redox Flow Cell Cycling Performance through Zero-Dimensional Models

Improvements in redox flow battery (RFB) performance and durability can be achieved through the development of new active materials, electrolytes, and membranes. While a rich design space exists for emerging materials, complex tradeoffs challenge the articulation of unambiguous target criteria, as the relationships between component selection and cycling performance are multifaceted. Here, we derive zero-dimensional, analytical expressions for mass balances and cell voltages under galvanostatic cycling, enabling direct connections between material/electrolyte properties, cell operating conditions, and resulting performance metrics (e.g., energy efficiency, capacity fade). To demonstrate the utility of this modeling framework, we highlight several considerations for RFB design, including upper bound estimation, active species decay, and membrane/separator conductivity-selectivity tradeoffs. Furthermore, we also discuss modalities for extending this framework to incorporate kinetic losses, distributed ohmic losses, and multiple spatial domains. Importantly, because the mass balances are solved analytically, hundreds of cycles can be simulated in seconds, potentially facilitating detailed parametric sweeps, system optimization, and parameter estimation from cycling experiments. More broadly, this approach provides a means for assessing the impact of cell components that simultaneously influence multiple performance-defining processes, aiding in the elucidation of key descriptors and the identification of favorable materials combinations for specific applications.

25 ENERGY STORAGE↗
DOE OSTI2021

(U) A Theoretical Study of Asay Foil Trajectories (V.02)

We consider the trajectory of a generic Asay foil ejecta momentum diagnostic sensor, for scenarios where ejecta are produced at a planar surface and fly ballistically through a perfect vacuum to the sensor. To do so, we build upon a previously established mathematical formalism derived for the analytic study of stationary sensors (i.e., piezopins). First, we derive the momentum conservation equation for the problem, in a form amenable to accelerating sensors, in terms of a generic ejecta source areal mass function (“source model”). This defines an integro-differential equation (IDE) for the foil trajectory. When ejecta production is instantaneous - as is generally assumed in momentum diagnostic data analyses - the IDE leads to an implicit and easily calculable closed-form solution for the foil trajectory in a perfect system, as long as the ejecta particle velocity distribution is twice-integrable. General properties of the instant-production trajectory solution indicate the existence of a boundary condition the particle velocity distribution must satisfy in order for the analytically predicted foil trajectories to be compatible with certain features commonly observed in foil data. This boundary condition is identical to one derived previously from a consideration of piezopin data. Armed with the analytic solution for instant production, we also consider various techniques used to extract time-dependent cumulative ejecta masses from foil trajectories, and derive an expression for the error imposed by using an approximated equation of motion. This analytic trajectory solution furthermore makes it possible to examine the common practice of presenting inferred cumulative ejecta masses as a function of a normalized implied velocity; we derive conditions under which this methodology is and is not meaningful. We also propose a strategy for extending the instant-production trajectory solution to time-dependent source functions.

42 ENGINEERING↗
DOE OSTI2024

Kubo-Anderson theory of polariton line shape

We apply the Kubo-Anderson stochastic theory of molecular spectral line shapes to the case of polaritons formed in the collective strong-coupling regime. We investigate both the fast and slow limits of the random frequency modulation of the emitter as well as the intermediate regime and show how the interplay between the characteristic timescales of the cavity and the molecular disorder is expressed in the observed polaritons line shapes. The analytical solution obtained for the slow limit is valid for any ratio between the inhomogeneous broadening of the molecules and the Rabi splitting, which is especially relevant for molecular polaritons where these two quantities can be of the same order of magnitude.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Rapid Targeted Quantitation of Protein Overexpression with Direct Infusion Shotgun Proteome Analysis (DISPA-PRM)

While much effort has been placed on comprehensive quantitative proteome analysis, certain applications demand measurement of only a few target proteins from complex systems. Traditional approaches to targeted proteomics rely on nano-liquid chromatography (nLC) and targeted mass spectrometry (MS) methods, e.g., parallel reaction monitoring (PRM). However, the time requirement for nLC can limit throughput of targeted proteomics. To achieve rapid and high-throughput targeted methods, here we show that nLC separations can be eliminated and replaced with direct infusion shotgun proteome analysis (DISPA) using high-field asymmetric waveform ion mobility spectrometry (FAIMS) with PRM. We demonstrate application of DISPA-PRM for rapid targeted quantification of bacterial enzymes utilized in the production of biofuels by monitoring temporal expression in 72 metabolically engineered bacterial cultures in under 2.5 hours, with a measured dynamic range >1200-fold. Here, we conclude that DISPA-PRM presents a valuable innovative tool with results comparable to nLC-MS/MS, enabling fast and rapid detection of targeted proteins in complex mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Functional genetic encoding of sulfotyrosine in mammalian cells

Protein tyrosine O -sulfation (PTS) plays a crucial role in extracellular biomolecular interactions that dictate various cellular processes. It also involves in the development of many human diseases. Regardless of recent progress, our current understanding of PTS is still in its infancy. To promote and facilitate relevant studies, a generally applicable method is needed to enable efficient expression of sulfoproteins with defined sulfation sites in live mammalian cells. Here we report the engineering, in vitro biochemical characterization, structural study, and in vivo functional verification of a tyrosyl-tRNA synthetase mutant for the genetic encoding of sulfotyrosine in mammalian cells. We further apply this chemical biology tool to cell-based studies on the role of a sulfation site in the activation of chemokine receptor CXCR4 by its ligand. Our work will not only facilitate cellular studies of PTS, but also paves the way for economical production of sulfated proteins as therapeutic agents in mammalian systems.

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

Kinetic simulations comparing quasi-parallel and quasi-perpendicular piston-driven collisionless shock dynamics in magnetized laboratory plasmas

Magnetized collisionless shocks are common in astrophysical systems, and scaled versions can be created in laboratory experiments by utilizing laser-driven piston plasmas to create these shocks in a magnetized background plasma. A key parameter for these experiments is the angle θB between the shock propagation direction and the background magnetic field. We performed quasi-1D piston-driven shock simulations to explore shock formation, evolution, and key observables relevant to laboratory experiments for a range of shock angles between θB=90° to θB=30°. Our results show that the spatial and temporal scales of shock formation for all angles considered are similar when expressed in terms of the perpendicular component of the magnetic field. In a steady state, ion and electron temperatures become more isotropic, and the electron-to-ion temperature ratio is higher for smaller θB. At θB=30°, ion heating parallel to the magnetic field becomes dominant, associated with more ions being reflected at one discontinuity and subsequently trapped by the next discontinuity due to shock reformation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates — and to do so with enough efficiency at large scale. In this work, we propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

42 ENGINEERING↗
DOE OSTI2024

Role of physics in physics-informed machine learning

Physical systems are characterized by inherent symmetries, one of which is encapsulated in the units of their parameters and system states. These symmetries enable a lossless order-reduction, e.g., via dimensional analysis based on the Buckingham theorem. Despite the latter's benefits, machine learning (ML) strategies for the discovery of constitutive laws seldom subject experimental and/or numerical data to dimensional analysis. We demonstrate the potential of dimensional analysis to significantly enhance the interpretability and generalizability of ML-discovered secondary laws. Our numerical experiments with creeping fluid flow past solid ellipsoids show how dimensional analysis enable both deep neural networks and sparse regression reproduce old results, e.g., Stokes law for a sphere, and generate new ones, e.g., an expression for an ellipsoid misaligned with the flow direction. Furthermore, our results suggest the need to incorporate other physics-based symmetries and invariances into ML-based techniques for equation discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fractalkine enhances endometrial receptivity and activates iron transport towards trophoblast cells in an in vitro co-culture system of HEC-1A and JEG-3 cells

Highlights: • Fractalkine activates SOX-17 and the NRF2-Keap-1 system in HEC-1A endometrium cells. • Fractalkine triggers IL-6 and IL-1β cytokine secretions contributing endometrium receptivity. • Fractalkine regulates iron homeostasis of endometrium. • Fractalkine contributes to the iron availability of the trophoblast cells. Endometrium receptivity and successful implantation require a complex network of regulatory factors whom production is strictly controlled especially at the implantation window. Many regulators like steroid hormones, prostaglandins, cytokines, extracellular matrix proteins and downstream cell signalling pathways are involved in the process of embryo-endometrium interaction. Our work reveals the effect of fractalkine (FKN), a unique chemokine on progesterone receptor, SOX-17 and NRF2 expressions in HEC-1A endometrial cell line. FKN activates fractalkine receptor signalling and the expression of SOX-17 through progesterone receptor in HEC-1A endometrial cells, and as a consequence it increases endometrial receptivity. Fractalkine also activates the NRF2-Keap-1 signal transduction pathway regulating the IL-6 and IL-1β cytokine productions, which increase endometrial receptivity, as well. The NRF2 transcription factor increases the expression of the iron exporter ferroportin in HEC-1A cells activating iron release towards JEG-3 trophoblast cells. The iron measurements show that iron content of endometrial cells decreases while heme concentration increases at FKN treatment. At the same time, the trophoblast cells show increased iron uptake and total iron content. Based on our results it seems that FKN enhances the establishment of endometrial receptivity and meanwhile it regulates the iron homeostasis of endometrium contributing to the iron availability of the trophoblast cells and the embryo.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

25 ENERGY STORAGE↗