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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A chloroplast protein atlas reveals punctate structures and spatial organization of biosynthetic pathways

Chloroplasts are eukaryotic photosynthetic organelles that drive the global carbon cycle. Despite their importance, our understanding of their protein composition, function, and spatial organization remains limited. Here, we determined the localizations of 1,034 candidate chloroplast proteins using fluorescent protein tagging in the model alga Chlamydomonas reinhardtii. The localizations provide insights into the functions of poorly characterized proteins; identify novel components of nucleoids, plastoglobules, and the pyrenoid; and reveal widespread protein targeting to multiple compartments. We discovered and further characterized cellular organizational features, including eleven chloroplast punctate structures, cytosolic crescent structures, and unexpected spatial distributions of enzymes within the chloroplast. We also used machine learning to predict the localizations of other nuclear-encoded Chlamydomonas proteins. The strains and localization atlas developed here will serve as a resource to accelerate studies of chloroplast architecture and functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Harnessing citizen science to contextualize adaptation mechanism discovery

Species occupying broad geographic regions have evolved multiple mechanisms to regulate phenological characteristics, enabling adaptations to diverse native habitats. By developing computer vision AI to process citizen science observations across native habitats over North America, we uncovered a consistent latitudinal trend of earlier flowering at higher latitudes in warm-season perennial grasses. To explore the underlying mechanisms of adaptation, we conducted common garden experiments with one species (switchgrass) and discovered the opposite latitudinal flowering-time trend. Integration of differential plasticity of GI-Hd1-FTL1 haplotypes of flowering time regulatory genes, haplotype range, and local environmental profiles found that observations from native habitats capture only part of the genotype-environment-phenotype spectrum established in common garden experiments, therefore reconciling the discrepancy. Two mechanisms emerged as key forces shaping current haplotype ranges and influencing future shifts. Our study highlights the power of combining citizen science observations with designed experiments to uncover mechanisms of adaptation across spatiotemporal scales.

FTL1↗
DOE OSTI2026

The silicon citizen naturalist

Smartphone-wielding citizen scientists and an AI called FLORIST are transforming ecology at the continental scale. Here, in this issue of Cell, when Tibbs-Cortes et al. pair the crowdsourced data with controlled genetics, they discover how switchgrass times its flowering to outwit both frost and heat, depending on latitude.

Hudson, Matthew E. [University of Illinois at Urba↗
DOE OSTI2021

Early age hydration and application of blended magnesium potassium phosphate cements for reduced corrosion of reactive metals

Highlights: • Magnesium potassium phosphate cements were studied for nuclear waste encapsulation. • The optimised formulation in this study was determined to be 0.24 w/s ratio. • Different reaction kinetics were discovered for the FA/MKPC and BFS/MKPC binders. • Struvite-K was the only crystalline reaction product observed via in-situ SXPD. • Uranium trials revealed MKPC corrosion was reduced compared to a UK nuclear grout. Magnesium potassium phosphate cements (MKPC) were investigated to determine their efficacy towards retardation of reactive uranium metal corrosion. Optimised low-water content, fly ash (FA) and blast furnace slag (BFS) blended MKPC formulations were developed and their fluidity, hydration behaviour, strength and phase assemblage investigated. In-situ time resolved synchrotron powder X-ray diffraction was used to detail the early age (~60 h) phase assemblage development and hydration kinetics, where the inclusion of BFS was observed to delay the formation of struvite-K by ~14 h compared to FA addition (~2 h). All samples set within this period, suggesting the possible formation of a poorly crystalline binding phase prior to struvite-K crystallisation. Long-term corrosion trials using metallic uranium indicated that MKPC systems are capable of limiting uranium corrosion rates (reduced by half), when compared to a UK nuclear industry grout, which highlights their potential application radioactive waste immobilisation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atomistic thermodynamics and kinetics of dicalcium silicate dissolution

Low-temperature cement manufacturing has garnered academic and industrial attention for its low environmental footprints. However, the sluggish hydration kinetics of the resultant cement affects their early-age strength development. This motivates fundamental studies to unravel the mechanistic picture of the dissolution process and discover science-informed pathways to accelerate hydration. Standard atomistic simulations seldomly exceed a microsecond making them impractical to study slow dissolution processes. We are using rare event sampling techniques, we provide the mechanistic picture of Ca 2+ ion dissolution from a kink site on the dicalcium silicate surface. The Ca 2+ ion dissolution is comprised of two sequential stages: breaking restraints from the kink sites to form a ledge adatom and detaching from the ledge/terrace adatom sites into the solution. The first and second stages feature free energy barriers of ~63 kJ/mol and ~ 29 kJ/mol respectively, making the first stage the rate-limiting step of the entire Ca 2+ dissolution kinetics. Using the reactive flux method, the rate and equilibrium constants for each reaction step are calculated, which yield the Ca 2+ ion activity of ~1.03 × 10 -5 . The diffusion calculations indicate that the surface effects lower the self-diffusion coefficient of Ca 2+ ions at the solid-water interface.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Stable perovskite-fluorite dual-phase composites synthesized by one-pot solid-state reactive sintering for protonic ceramic fuel cells

We report that this work synthesized a series of oxide composites with nominal compositions of BaCe 05 Zr 0.4 Y 0.1 O 3-δ (BCZY)-Ce 0.5 Y 0.5 O 2-δ (YDC) based on the BCZY/YDC molar ratios of 0.5:1, 1:1, 2:1, and 4:1 (BCZY-YDC-0.5–1, BCZY-YDC-1-1, BCZY-YDC-2-1, and BCZY-YDC-4-1) using a one-pot solid state reactive sintering (SSRS) method. The X-ray diffraction (XRD) patterns and refinement proved that the one-pot SSRS at 1450 °C for 12 h could achieve the perovskite-fluorite dual-phase composites (DPCs) with the desired structure compositions. The scanning electron microscopy (SEM) images showed that the two DPCs of BCZY-YDC-1-1 and BCZY-YDC-2-1 formed excellent percolation for perovskite and fluorite phases. The conductivity measurement by electrochemical impedance spectroscopy (EIS) and the transference number measurement by the electromotive force (EMF) test proved that changing the BCZY/YDC molar ratio and operating condition could adjust the DPC's conduction property to make them suitable for electrolytes and electrode scaffolds for protonic ceramic fuel cells (PCFCs). The long-term conductivity testing and the crystal structure analysis after EMF testing under versatile conditions indicated that the DPCs of BCZY-YDC-2-1 and BCZY-YDC-1-1 were durable PCFC component materials. The PCFC button cells with the as-discovered BCZY-YDC-2-1 and BCZY-YDC-1-1 as an electrolyte and an anode scaffold, respectively, and the reported Ba–Ce–Fe–Co–O perovskite-perovskite composite as a cathode showed promising performance under H 2 /air gradient.

08 HYDROGEN↗
DOE OSTI2021

A transcriptional relationship with a natural product disrupts mitochondrial biogenesis

Discovery of new compound leads and oncology targets to treat cancer will require overcoming resistance to traditional therapies such as BRAF and mitogen-activated protein kinase inhibitors (MAPKi). In addition, potent new therapies will benefit from selective localization at the cancer site rather than general cell toxicity which can lead to undesired side effects. This selectivity can be gained by identifying new compound leads that are “prodrugs”, that is they are activated by mechanisms selective to cancer cells leading to strong pharmacological activity (Zhang, 2017). Continuing discovery of anticancer agents that overcome resistance and take advantage of cancer cell mechanisms will be greatly augmented by returning to the potent well of natural products; in fact, an analysis of a 70-year period ending in 2014 showed that nearly 50% of approved anticancer drugs are or are derived from natural products (Newman, 2016). Discovery of new agents with high potency and selectivity will require natural product research focused on discovering biologically active compounds with unique structures and mechanisms of action that utilize target cell functions.

Wright, Aaron T.↗
DOE OSTI2021

Experimental and modeling studies of PR and ND oxalate solubility to high ionic strengths: Insight into actinide(III) oxalates

Actinide oxalates are chemical compounds important to nuclear industry, ranging from actinide separation in waste reprocessing, to production of specialty actinides, and to disposal of high level nuclear waste (HLW) and spent nuclear fuel (SNF). In this study, the solubility constants for Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O by performing solubility experiments in HNO 3 and mixtures of HNO 3 and H 2 C 2 O 4 at 23.0 ± 0.2 °C have been determined. The targeted starting materials, Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O, were successfully synthesized at room temperature using PrCl 3 , NdCl 3 and oxalic acid as the source metrials. Then, we utilized the targeted solubility-controlling phases to conduct solubility measurements. There was no phase change over the entire periods of experiments, demonstrating that Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O were the solubility-controlling phases in our respective experiments. Based on our experimental data, we have developed a thermodynamic model for Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O in the mixtures of HNO 3 and H 2 C 2 O 4 to high ionic strengths. The model for Pr 2 (C 2 O 4 ) 3 •10H 2 O reproduces well the reported experimental data for Pu 2 (C 2 O 4 ) 3 •10H 2 O, which are not utilized for the model development, demonstrating that Pr(III) is an excellent analog for Pu(III). Similarly, the model for Nd 2 (C 2 O 4 ) 3 •10H 2 O reproduces the solubility of Am 2 (C 2 O 4 ) 3 •10H 2 O and Cm 2 (C 2 O 4 ) 3 •10H 2 O. The Pitzer model was used for the calculation of activity coefficients. Based on the published, well established model for dissociation constants for oxalic acid and stability constants for actinide-oxalate complexes [i.e., AmC 2 O 4 + , and Am(C 2 O 4 ) 2 - ] to high ionic strengths, we have obtained the solubility constants (log 10 K 0 ) for the following reactions at 25 °C, Pr 2 (C 2 O 4 ) 3 •10H 2 O ⇌ 2Pr 3+ + 3C 2 O 4 2- + 10H 2 O(l). Nd 2 (C 2 O 4 ) 3 •10H 2 O ⇌ 2Nd 3+ + 3C 2 O 4 2- + 10H 2 O(l). to be -30.82 ± 0.30 (2σ), and - 31.14 ± 0.35 (2σ), respectively. These values for can be directly applied to Pu 2 (C 2 O 4 ) 3 •10H 2 O, Am 2 (C 2 O 4 ) 3 •10H 2 O and Cm 2 (C 2 O 4 ) 3 •10H 2 O. The model established for actinide oxalates by this study provides the needed knowledge with regard to solubilities of actinide/REE oxalates at various ionic strengths, and is expected to find applications in many fields, including the geological disposal of nuclear waste and the mobility of REE under the surface conditions, as Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O can be regarded as the pure Pr and Nd end-members of deveroite, a recently discovered natural REE oxalate with the following stoichiometry, (Ce 1.01 Nd 0.33 La 0.32 Pr 0.11 Y 0.11 Sm 0.01 Pb 0.04 U 0.03 Th 0.01 Ca 0.04 ) 2.01 (C 2 O 4 ) 2.99 •9.99H 2 O. Regarding its importance in the geological disposal of nuclear waste, Am 2 (C 2 O 4 ) 3 •10H 2 O/Pu 2 (C 2 O 4 ) 3 •10H 2 O/Cm 2 (C 2 O 4 ) 3 •10H 2 O can be the source-term phase for actinides, as demonstrated by the instance in the disposal in clay/shale formations. This is exemplified by the stability of Am 2 (C 2 O 4 ) 3 •10H 2 O in comparison with Am(OH) 3 (am), Am(OH) 3 (s) and AmCO 3 (OH)(s) under the relevant geological repository conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Efficient and reversible electron bifurcation with either normal or inverted potentials at the bifurcating cofactor

A longstanding mystery surrounding electron bifurcation is the significance of inverted (or “crossed”) reduction potentials of the two-electron bifurcating cofactor. Using a many-electron open-system kinetic model, we show that reversible and efficient electron bifurcation is possible without inverted reduction potentials at the bifurcating site if the absolute value of the difference between first and second reduction potentials of the bifurcating species is sufficiently large (on the scale of the redox-potential span of the high- and low-potential branches). Surprisingly, the case with strong, normally ordered potentials at the bifurcating cofactor can produce electron bifurcation that is just as effective as the case with strongly inverted potentials. Lastly, this finding amplifies the puzzle surrounding the recruitment of inverted potentials in the few well-characterized bifurcating systems of nature and suggests that electron bifurcating cofactors without strongly inverted potentials may yet be discovered.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Emergent solvation phenomena in non-aqueous electrolytes with multiple anions

As the search for new battery chemistries with higher capacities and more stable supply chains expands, requiring increasingly complex electrolytes with multiple solvents and anions, it is becoming clear that understanding and controlling the working cation solvation structure is key to enabling improved stability and reversibility. In this work, we discover an emergent solvation behavior in multivalent electrolytes containing multiple anions, where bis(trifluoromethane sulfonyl) imide (TFSI⁻) anions that are fully dissociated in isolation form contact ion pairs with Zn 2+ when combined with more strongly coordinating halides. This coordination modifies the electrochemical response, activating additional redox species as the halide association strength weakens (i.e., Cl⁻ > Br⁻ > I⁻) and systematically lowering overpotentials for metal deposition. This work suggests a completely new framework for electrolyte design in which anion chemistry can be used to tune both the bulk speciation and the interfacial solvation structure, enabling profound changes to the electrochemical behavior of the system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Reinforced double-threaded slide-ring networks for accelerated hydrogel discovery and 3D printing

Traditionally, slide-ring gels are stretchable but soft as a result of an elasticity-stretchability trade-off. Herein, we introduce a new approach to breaking this trade-off and creating reinforced slide-ring networks with mobile crosslinkers. Our approach involves the construction of a polyethylene glycol double-threaded γ-cyclodextrin-based pro-slide-ring crosslinker that serves as a modular component for 3D printing and copolymerization. The resulting crystalline-domain-reinforced slide-ring hydrogels, or CrysDoS-gels, exhibit both high elasticity and high stretchability. The modular synthesis allows for high-throughput synthesis of CrysDoS-gels, generating a large amount of data for structure-property analysis. Here, by employing data science techniques, such as machine learning and linear regression, not only were we able to identify which chemical components influence the mechanical properties of CrysDoS-gels, but this analysis also aided in the discovery of better-performing CrysDoS-gels. Finally, we demonstrate the potential application of the newly discovered CrysDoS-gels as sensing devices by 3D printing them as stress sensors with high sensitivity and a broad detection range.

3D-printing↗
DOE OSTI2022

Diverse events have transferred genes for edible seaweed digestion from marine to human gut bacteria

Humans harbor numerous species of colonic bacteria that digest fiber polysaccharides in commonly consumed terrestrial plants. More recently in history, regional populations have consumed edible macroalgae seaweeds containing unique polysaccharides. It remains unclear how extensively gut bacteria have adapted to digest these nutrients. Here, in this work, we show that the ability of gut bacteria to digest seaweed polysaccharides is more pervasive than previously appreciated. Enrichment-cultured Bacteroides harbor previously discovered genes for seaweed degradation, which have mobilized into several members of this genus. Additionally, other examples of marine bacteria-derived genes, and their mobile DNA elements, are involved in gut microbial degradation of seaweed polysaccharides, including genes in gut-resident Firmicutes. Collectively, these results uncover multiple separate events that have mobilized the genes encoding seaweed-degrading-enzymes into gut bacteria. This work further underscores the metabolic plasticity of the human gut microbiome and global exchange of genes in the context of dietary selective pressures.

42 ENGINEERING↗
DOE OSTI2023

High-speed X-ray study of process dynamics caused by surface features during continuous-wave laser polishing

During high-speed X-ray imaging of laser surface polishing experiments of specimens of 316L stainless steel at Argonne National Lab's Advanced Photon Source, it was discovered that the induced keyhole changes shape and dimensions while crossing an engineered surface feature without altering process parameters. It was observed that the post-surface feature keyhole was deeper than that of the pre surface feature keyhole. Here, this work reports on the first in-situ observation of the effect of localized surface geometry on underlying melt pool behavior. This has implications for defect formation mechanisms during laser melting processes that rely on melt pool geometry.

42 ENGINEERING↗
DOE OSTI2020

A physics-informed operator regression framework for extracting data-driven continuum models

The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. Here, we demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.

42 ENGINEERING↗
DOE OSTI2020

A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks

The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification. While there are numerous efforts to develop paradigms and standard procedures to validate models, difficulties may arise due to the sequential, manual, and often biased nature of the commonly adopted calibration and validation processes, thus slowing down data collections, hampering the progress towards discovering new physics, increasing expenses and possibly leading to misinterpretations of the credibility and application ranges of proposed models. This work attempts to introduce concepts from game theory and machine learning techniques to overcome many of these existing difficulties. Here, we introduce an automated meta-modeling game where two competing AI agents systematically generate experimental data to calibrate a given constitutive model and to explore its weakness such that the experiment design and model robustness can be improved through competitions. The two agents automatically search for the Nash equilibrium of the meta-modeling game in an adversarial reinforcement learning framework without human intervention. In particular, a protagonist agent seeks to find the more effective ways to generate data for model calibrations, while an adversary agent tries to find the most devastating test scenarios that expose the weaknesses of the constitutive model calibrated by the protagonist. By capturing all possible design options of the laboratory experiments into a single decision tree, we recast the design of experiments as a game of combinatorial moves that can be resolved through deep reinforcement learning by the two competing players. Our adversarial framework emulates idealized scientific collaborations and competitions among researchers to achieve a better understanding of the application range of the learned material laws and prevent misinterpretations caused by conventional AI-based third-party validation. Numerical examples are given to demonstrate the wide applicability of the proposed meta-modeling game with adversarial attacks on both human-crafted constitutive models and machine learning models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Latent map Gaussian processes for mixed variable metamodeling

Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. Here we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation. Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization.

42 ENGINEERING↗
DOE OSTI2023

Projection pursuit adaptation on polynomial chaos expansions

Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modular machine learning-based elastoplasticity: Generalization in the context of limited data

The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.

42 ENGINEERING↗