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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 235 records · Page 13

Characterization of cellular oxidative stress response by stoichiometric redox proteomics

The thiol redox proteome refers to all proteins whose cysteine thiols are subjected to various redox-dependent posttranslational modifications (PTMs) including S-glutathionylation (SSG), S-nitrosylation (SNO), S-sulfenylation (SOH), and S-sulfhydration (SSH). These modifications can impact various aspects of protein function, including activity, binding, conformation, localization, and interactions with other molecules. It is highly desirable to have robust redox proteomics methods that can provide global, site-specific, and stoichiometric quantification of redox PTMs in order to elucidate the complex redox landscape and identify novel redox regulators in signaling and regulation. Mass spectrometry-based redox proteomics has emerged as the primary platform for enabling the broad characterization of thiol PTMs in cells and tissues. Herein we review recent advances in MS-based redox proteomics approaches for quantitative profiling of redox PTMs in cells and tissues at physiological or oxidative stress conditions and highlight some recent applications using these enabling techniques. Considering the relative maturity of available methods, a primary emphasis will be on two types of modifications: 1) total oxidation (i.e., all reversible thiol modifications), the level of which represents the overall redox state, and 2) S-glutathionylation, a major form of reversible thiol oxidation. We also discuss the significance of stoichiometric measurements of thiol PTMs as well as future perspectives towards a better understanding of cellular redox regulatory networks in cells and tissues.

59 BASIC BIOLOGICAL SCIENCES↗

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

36 MATERIALS SCIENCE↗

Spontaneous dynamical disordering of borophenes in MgB2 and related metal borides

Abstract Layered boron compounds have attracted significant interest in applications from energy storage to electronic materials to device applications, owing in part to a diversity of surface properties tied to specific arrangements of boron atoms. Here we report the energy landscape for surface atomic configurations of MgB 2 by combining first-principles calculations, global optimization, material synthesis and characterization. We demonstrate that contrary to previous assumptions, multiple disordered reconstructions are thermodynamically preferred and kinetically accessible within exposed B surfaces in MgB 2 and other layered metal diborides at low boron chemical potentials. Such a dynamic environment and intrinsic disordering of the B surface atoms present new opportunities to realize a diverse set of 2D boron structures. We validated the predicted surface disorder by characterizing exfoliated boron-terminated MgB 2 nanosheets. We further discuss application-relevant implications, with a particular view towards understanding the impact of boron surface heterogeneity on hydrogen storage performance.

36 MATERIALS SCIENCE↗

An Interdisciplinary View of Interfaces: Perspectives Regarding Emergent Phase Formation

A perspective on emergent phase formation is presented using an interdisciplinary approach gained by working at the “interface” between diverse application areas, including solid oxide fuel cells (SOFCs) and ionic membrane systems, solid state lithium batteries, and ceramics for nuclear waste immobilization. The grain boundary interfacial characteristics of model single-phase materials in these application areas, including (i) CeO2, (ii) Li7La3Zr2O12 (LLZO), and (iii) hollandite of the form BaxCsyGa2x+yTi8-2x-yO16, as well as the potential for emergent phase formation in composite systems, are discussed. The potential physical properties resulting from emergent phase structure and distribution are discussed, including an overview of existing three-dimensional (3D) imaging techniques recently used for characterization. Finally, an approach for thermodynamic characterization of emergent phases based on melt solution calorimetry is outlined, which may be used to predict the energy landscape including phase formation and stability of complex multiphase systems.

25 ENERGY STORAGE↗

Guarded Hot Cylinder Apparatus for Characterization of Thermal Insulation Systems and Materials at Liquid Hydrogen Temperatures

As interest in liquid hydrogen (LH2) continues to grow within the energy and mobility sectors, so does the demand for testing capabilities at deep cryogenics temperatures. However, cost-, complexity-, and safety-related challenges associated with handling LH2 effectively limit the landscape of possible options. As an alternative, LH2 temperatures can be accessed via a helium-based cryogenic refrigerator, or “cryocooler”. Recently, NASA and its partners CB&I and Shell began the development of a cryocooler-based calorimeter to characterize the thermal performance of insulations and other materials down to 20 K. Deemed the Guarded Hot Cylinder (GHC), the apparatus utilizes a small vacuum chamber in conjunction with a GM cryocooler and trim heater to control the cold boundary temperature. A sealed, cylindrical copper cup bolts to the cryocooler and houses the material specimen, with an internal, cylindrical test heater assembly to maintain the warm boundary. The steady-state heat load, traveling radially through the specimen, is measured via the electrical input power to the test heater and then used to evaluate the material’s absolute thermal performance. Initial checkout and validation of the GHC using a common bulk-fill insulation material showed close agreement with published data from standardized LN2 boiloff calorimetry testing. The instrument is now considered a lab standard, with the goal of incorporating it into the ASTM C1774 standard in the future, and it is in continuous use, examining insulation materials for next-generation LH2 applications.

Swanger, Adam (ORCID:000000021487592X)↗

Persistent Protein Motions in a Rugged Energy Landscape Revealed by Normal Mode Ensemble Analysis

Proteins are allosteric machines that couple motions at distinct, often distant, sites to control biological function. Low-frequency structural vibrations are a mechanism of this long-distance connection and are often used computationally to predict correlations, but experimentally identifying the vibrations associated with specific motions has proved challenging. Spectroscopy is an ideal tool to explore these excitations, but measurements have been largely unable to identify important frequency bands. The result is at odds with some previous calculations and raises the question what methods could successfully characterize protein structural vibrations. Here we show the lack of spectral structure arises in part from the variations in protein structure as the protein samples the energy landscape. However, by averaging over the energy landscape as sampled using an aggregate 18.5 μs of all-atom molecular dynamics simulation of hen egg white lysozyme and normal-mode analyses, we find vibrations with large overlap with functional displacements are surprisingly concentrated in narrow frequency bands. These bands are not apparent in either the ensemble averaged vibrational density of states or isotropic absorption. However, in the case of the ensemble averaged anisotropic absorption, there is persistent spectral structure and overlap between this structure and the functional displacement frequency bands. We systematically lay out heuristics for calculating the spectra robustly, including the need for statistical sampling of the protein and inclusion of adequate water in the spectral calculation. The results show the congested spectrum of these complex molecules obscures important frequency bands associated with function and reveal a method to overcome this congestion by combining structurally sensitive spectroscopy with robust normal mode ensemble analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Development of a scalar-based geometric parameterization approach for the crystal structure landscape of dithienylethene-based crystalline solids

Dithienylethenes (DTEs) are a promising class of organic photoswitches that can be used to create crystalline solids with properties controlled by light. However, the ability of DTEs to adopt multiple conformations, only one of which is photoactive, complicates the rational design of these materials. Herein, the synthesis and structural characterization of 19 crystalline solids containing a single DTE molecule are described. A novel D – D analysis of the molecular geometries obtained from rotational potential energy surface calculations and the ensemble of experimental structures were used to construct a crystal landscape for DTE. Of the 19 crystal structures, 17 contained photoinactive DTE rotamers and only 2 were photoactive. These results highlight the challenges associated with the design of these materials. Overall, the D – D analysis described herein provides rapid, effective and intuitive means of linking the molecular structure to photoactivity that could be applied more broadly to afford a general strategy for producing photoactive diarylethene-based crystalline solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A combined experimental and computational study of the mechanism of fructose dehydration to 5-hydroxymethylfurfural in dimethylsulfoxide using Amberlyst 70, PO 4 3- /niobic acid, or sulfuric acid catalysts

We report on a combined experimental and theoretical study of the acid catalyzed dehydration of d-fructose in dimethylsulfoxide (DMSO) using; Amberlyst 70, PO 4 3- /niobic acid, and sulfuric acid as catalysts. The reaction has been studied and intermediates characterized using; 13C, 1H, and 17O NMR, and high resolution electrospray ionization mass spectrometry (HR ESI–MS). High level G4MP2 theory calculations are used to understand the thermodynamic landscape for the reaction mechanism in DMSO. We have experimentally identified two key intermediates in the dehydration of fructose to form HMF that were also identified, using theory, as local minima on the potential surface for reaction. A third intermediate, a species capable of undergoing keto–enol tautomerism, was also experimentally detected. However, it was not possible to experimentally distinguish between the keto and the enol forms. These data with different catalysts are consistent with common intermediates along the reaction pathway from fructose to HMF in DMSO. The role of oxygen in producing acidic species in reactions carried out in DMSO in presence of air is also discussed.

59 BASIC BIOLOGICAL SCIENCES↗

Mechanisms of Biomolecular Self-Assembly Investigated Through In Situ Observations of Structures and Dynamics

Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This report will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mechanisms of Biomolecular Self–Assembly Investigated Through In Situ Observations of Structures and Dynamics

Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This review will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. Furthermore, the objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Challenges and Opportunities in the Enzymatic Recycling of Nylons

Enzymatic depolymerization has the potential to contribute to nylon waste recycling. However, the implementation of a viable industrial process still lags far behind progress in enzymatic polyester recycling. Here we review the current biocatalytic nylon recycling landscape, highlighting biochemical, structural, and materials science barriers that currently limit depolymerization extents. We detail efforts to identify, engineer, and characterize new nylon depolymerases, where currently even the best biocatalysts rarely exceed ~1 wt% conversion of solid polymer to products. Based on our analyses, we suggest that limited substrate accessibility due to substrate crystallinity and hydrogen bonding, rather than intrinsic enzyme inefficiency or instability, is the primary bottleneck to enhanced depolymerization. Guided by successes in polyester enzymatic recycling, we outline a research roadmap combining nylonase engineering with polymer pretreatment, chemo-enzymatic cascades, and process analyses to accelerate development of viable enzymatic nylon recycling processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exploring a Nearby Habitable World...Orbiting an M-Dwarf Star

Topics include: the landscape of extrasolar planets and detection techniques, direct and indirect detection methods, summary of the known exoplanets, exploiting transits to characterize super earth atmospheres, how to characterize exoplanet atmospheres, and emitted or reflected spectra of hot Jupiters.

Deming, Drake↗

Elucidating the Origins of High Capacity in Iron-Based Conversion Materials: Benefit of Complementary Advanced Characterization toward Mechanistic Understanding

Lithium-ion batteries are recognized as an important electrochemical energy storage technology due to their superior volumetric and gravimetric energy densities. Graphite is widely used as the negative electrode, and its adoption enabled much of the modern portable electronics technology landscape. However, developing markets, such as electric vehicles and grid-scale storage, have increased demands, including higher energy content and a diverse materials supply chain. Alternatives that provide the opportunity to increase capacity and address supply chain concerns are of interest. Understanding the fundamental mechanisms that govern battery function is crucial to driving further improvements in the field. Advanced characterization techniques, such as those enabled by synchrotron light sources and high-resolution electron microscopes, that can uncover these mechanisms have become a necessity for elucidating structural evolution upon electrochemical conversion at the nano- to mesoscales. Performing these experiments with relevant electrochemistry using in situ and operando experiments imparts the ability to identify critical reaction pathways and capture intermediate (dis)charge products not discernible by traditional experiments.

36 MATERIALS SCIENCE↗

Universality Classes of Interaction Structures for NK Fitness Landscapes

Kauffman’s NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of k≤L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on the NK model suggested that most of its properties are robust with regard to the choice of neighborhoods, recent work has revealed an important and sometimes counter-intuitive influence of the interaction structure on the properties of NK fitness landscapes. Here we review these developments and present new results concerning the number of local fitness maxima and the statistics of selectively accessible (that is, fitness-monotonic) mutational pathways. In particular, we develop a unified framework for computing the exponential growth rate of the expected number of local fitness maxima as a function of L, and identify two different universality classes of interaction structures that display different asymptotics of this quantity for large k. Moreover, we show that the probability that the fitness landscape can be traversed along an accessible path decreases exponentially in L for a large class of interaction structures that we characterize as locally bounded. Finally, we discuss the impact of the NK interaction structures on the dynamics of evolution using adaptive walk models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Hybrid machine learning/physics-based approach for predicting oxide glass-forming ability

Predicting the liquid compositions that will vitrify at experimentally accessible quench rates remains one of the grand challenges in the field of condensed matter physics. This glass-forming ability can be quantified as the critical quench rate needed to suppress crystallization. Knowledge of this critical quench rate also informs which glass composition could be used for new applications. There have been several physical and empirical models presented in the literature to predict the critical quench rate/glass forming ability. These models range from those theoretically derived to those quantified only through experimental characterization. In this work, we instead propose a new method to calculate the critical quench rate using the recently developed toy landscape model combined with machine learning. The toy landscape model accesses the underlying physics that control the vitrification behavior by directly simulating the liquid thermodynamics and kinetics. In conclusion, the results are discussed in terms of industrial impact, physical insights, and how the glass science community can develop improved predictions of glass-forming ability.

Crystallization↗

Combining Theory and Experiment to Map the Atomic-Level Structure–Energy Pathways of Adsorbate-Mediated Phase Changes in a Cooperatively Flexible Metal–Organic Framework

An important subclass of metal–organic frameworks (MOFs) exhibits cooperative flexibility, wherein individual crystallites undergo global structural phase changes in response to external stimuli. Where cooperative flexibility results in reversible changes between crystalline states of distinct accessible porosity, these frameworks can exhibit rare yet desirable behaviors that cannot be explained by local dynamics alone. Yet, the chemical and structural origins of cooperative flexibility and how frameworks undergo these reversible phase changes at the atomic level remain poorly understood. Deliberate design for specific applications is therefore exceedingly difficult, and there is great impetus to develop a fundamental understanding of this phenomenon. Here, an effective and widely accessible computational approach is developed, which is designed to provide microscopic resolution via direct comparison to experimental data along the desorption-guided pathway. The strategy is applied to explain the desorption-induced phase change in an experimentally well-characterized framework, CdIF-13 (sod-Cd(benzimidazolate)2), where experiment alone was unable to resolve the atomistically detailed phase change landscape. Our findings reveal that the cooperative phase change pathways are adsorbate dependent with thermodynamics of intermediate structural states dictated by a nuanced interplay of ligand orientation, skeletal symmetry, and modes of surface adsorption. The results reveal that this isotropically flexible framework is “chaperoned” through a complex energy landscape by specific adsorbates, revealed by the reported computational approach with atomic-level insight and validated by experimentally determined structures. Thus, this work facilitates both understanding and future design of flexible materials for applications in gas storage, transport, delivery, and separation technologies.

03 NATURAL GAS↗

DVMDOSTEM v0.8.3: a terrestrial ecosystem model designed to represent arctic, boreal and permafrost ecosystem dynamics

The impacts of climate change on natural ecosystems are the result of complex physical and ecological processes operating and interacting at a variety of spatio-temporal scales, that can be represented in process-based ecosystem models. DVMDOSTEM is an advanced process-based terrestrial ecosystem model (TEM) designed to study ecosystem responses to climate changes and disturbances. It has a particular focus on permafrost regions (i.e. regions characterized by soils that stay partially frozen all year round for at least two consecutive years), encompassing boreal, arctic, and alpine landscapes. The model couples two previous versions of the Terrestrial Ecosystem Model (TEM) (McGuire et al., 1992): DVMTEM that includes a dynamic vegetation module (DVM) (E. S. Euskirchen et al., 2009), and DOSTEM that includes a dynamic organic soil module (DOS) (H. Genet et al., 2013; Yi et al., 2010). DVMDOSTEM simulates processes at yearly and monthly scales, with some physical processes operating at an even finer temporal resolution. Its versatility allows for site-specific to regional simulations, making it valuable for predicting shifts in permafrost, vegetation, and carbon (C) and nitrogen (N) dynamics. While DVMDOSTEM has been described in the methods sections of many manuscripts, this paper is the first stand alone description of DVMDOSTEM, independent of a particular scientific investigation.

Carman, Tobey B. [Univ. of Alaska, Fairbanks, AK (↗

Illuminating the pathways to carbon liberation: a systems approach to characterizing the consequential unknowns of carbon transformation and loss from thawing permafrost peatlands (Final Report)

The IsoGenie3 Project delivered new systems-level insights into carbon cycling in thawing permafrost landscapes, with an emphasis on methane and carbon dioxide emissions. From >200 samples from the site collected over a decade, co-analyzed for geochemistry and microbiology, the team recovered ~1,500 assembled microbial genomes and ~1,900 viral population genomes, revealing appreciable genetic novelty - from a new highly abundant bacterial phylum, to novel methane consumers and their activities, to rampant viral novelty. IsoGenie3 linked these organisms to carbon compound transformations (which define the cycling of organic matter in soils, and the loss of the greenhouse gases carbon dioxide and methane), and saw that the microbes at each stage of permafrost thaw had different genetic potential to degrade categories of compounds, expressed that genetic potential differently, and actually transformed carbon compounds into greenhouse gases in different ways. IsoGenie 3 identified that some of the thaw-stage differences were due to plant-microbiome relationships; the plant species across the thaw gradient contributed different carbon compounds into the soil, and hosted distinct microbiota (differing among parts of plants as well as species). Lastly, microbes in the saturated post-thaw conditions appeared likely to contribute to the mobilization and toxification of mercury released during thaw. In parallel with ongoing field sampling and analysis, hypotheses arising from field observations were tested via lab incubation experiments. When communities are taken out of their native habitats, they behave differently, and the team first rigorously quantified the magnitude of this effect on microbiome composition and functional capacity, organic matter composition, and gas production; overall the main system processes were maintained in the lab incubations under the conditions tested. Further, the microbial data could inform geochemical reaction network models of those processes. Then, the team ran experiments with additions of compounds, varying temperature, and “live” vs. “dead” peat (the latter having been gamma irradiated, with a few additional variants to control for methodological artifacts). From these, we (a) determined the importance of plant-derived soluble phenolic compounds in bogs’ extraordinary recalcitrance of organic matter, and carbon gas emissions skewed to carbon dioxide; (b) proposed an abiotic ‘tanning’ mechanism, which could contribute to Sphagnum’s inhibitory effect on anaerobic decomposition through alteration of N availability. IsoGenie3 illuminated longer-term and landscape-scale interactions of permafrost thaw and carbon cycling, advancing knowledge of the drivers of methane dynamics not only across in the permafrost-associated peatland (where hydrology and plant communities dictate microbiomes) but also their interconnected lakes (where sediment carbon quality and resident microbiota are determined by position within lake, and lake features). By leveraging observations of site methane dynamics extending well before this project, the team was able to construct a 44-year portrait of the interplay of permafrost thaw, hydrology, vegetation dynamics, and carbon gas emissions, and the doubling of the fully-thawed fens over this time. From the detailed study of this focal site, IsoGenie3 also aimed to improve model representation of these kinds of sites and processes. To improve predictions of methane transformations, we incorporated acetate and isotope dynamics into the ‘DNDC’ biogeochemistry model. In addition, recovered genomes were grouped into ‘functional groups’, i.e. the genomes that perform a specific function of interest, then used to parameterize maximum growth rate and optimum growth temperature (via signatures in their sequence composition) for the BioCrunch model. The BioCrunch model was then in turn used to test the impact of increasing functional resolution of the microbes, on the carbon gas emissions. Lastly for modeling, the ecosys model was parameterized from the microbial and other data, and used to evaluate drivers of e.g. change in methane emissions. Finally, this project also led to the development of a range of new methods and tools, a new metric of organic matter decomposability, as well as a graph-database solution to multidisciplinary data storage and querying. This project’s ongoing analyses at our focal site also contributed to broader advancements in understanding elements of genetic plasticity and methane metabolism, climate change microbiology and community assembly, global peatland geochemistry and Arctic lakes’ roles in climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗