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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 289 records · Page 16

Spectroscopic Investigations of Molecular Symmetry Breakdown

This project addressed precise measurements of the spectra of molecules to address fundamental questions in chemical physics in the realm of nuclear spin symmetries and their breakdown in molecules, and parity non-conservation caused by the weak nuclear force. Nuclear spin symmetry is intimately connected to parity in symmetrical molecules and plays a central role in the physical and chemical properties of symmetric molecules, their collisional relaxation, and the establishment of thermodynamic equilibrium in an ensemble. Such effects are present in every molecular system or process, but their small size means they are often only directly observable under special conditions. However, their measurement is of fundamental, and often practical importance, and strongly relates to the themes of coherence in light and matter in the recent Basic Energy Sciences Advisory Committee (BESAC) report “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science”. We begin by addressing lifetime broadening of spectroscopic lines caused by self-collisions in acetylene, where efficient resonant rotational energy transfer between a pair of molecules separated in energy by rotational quantum number ΔJ = ± 2, can result in a different pressure broadening for ortho- and para- nuclear spin state molecules. This work was prompted by a controversial report [Iwakuni et al. Phys. Rev Letts. 117 (2016) 143902] of the observation of such an effect in the v 1 +v 3 combination band transition of acetylene discussed in the original proposal. After delays caused by the COVID-19 pandemic, we have completed these measurements and a paper was published in the Journal of Chemical Physics describing the work. The measurements pointed out that the use of an approximate model in the analysis of the original work was the cause of the erroneous conclusion that ortho and para acetylene molecules had substantially different collisional lifetime broadening. In the process, much improved broadening measurements have been made that will permit more reliable modeling of the effects in the future. Subsequently, we have completed a new set of measurements of self- and nitrogen-collisional broadening at a series of low and intermediate temperatures to characterize the temperature dependence of the effects in the molecule. This work is currently being analyzed and will be the subject of a paper to be published in the relatively near future. In addition, some very preliminary measurements of CH 4 as described in the proposal, have been completed. If facilities and time permit, these will continue as an undergraduate research project.

74 ATOMIC AND MOLECULAR PHYSICS↗

Experimental Characterization of Hygrothermal Aging: Competition Between Thermo-Oxidation and Hydrolysis Phenomena

Abstract In this work, we have presented a comparison between effects of accelerated thermo-oxidative, hydrolytic and hygrothermal aging on the mechanical and chemical properties of a polyurethane based (PUB) adhesive. The adhesive is a flexible adhesive which is extensively used in automobile industry as a glass sealant. During service life it gets exposed to the hygrothermal environment and the integrity of matrix interface is most vulnerable. This study focuses on weighing and comparing effects of different aging environments on material behavior; and it shows that hygrothermal aging environment results as a competition between two sub-aging phenomena i.e., thermo-oxidation and hydrolytic aging. Samples were exposed to 0%RH, 80%RH and submerged condition in distilled water. Uni-axial tensile tests, and cross-link density analysis by swelling test were carried out on as-received and aged samples. The aging process was conducted at three different temperatures (60°C, 80°C and 95°C) and for four different exposure durations of 1, 10, 30, and 90 days. This work confirms that the effect of hygrothermal aging is a result of competition between thermo-oxidation and hydrolytic aging environments. The total mechanical and environmental damage incurred by specimens is a collaborative effect of all exposure environments involved i.e., aging time, temperature, oxygen, water and humidity. The results are in agreement with the chemical test outcomes as well.

Shaafaey, Mamoon↗

Correlative X-ray micro-nanotomography with scanning electron microscopy at the Advanced Light Source

Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.

58 GEOSCIENCES↗

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗

Comparing the fluorination of Pb+ and Po+: insights into p-block relativistic effects for superheavy elements

It is expected that the chemical properties of the heaviest of the superheavy elements (SHEs, = 113 – 118) do not align with what is suggested by their current positions on the periodic table. Specifically, the onset of significant relativistic effects, including increased spin–orbit splitting of the p-orbitals may lead to enhanced stability of low-oxidation states. Notably, it is predicted that flerovium ( = 114) may exhibit pseudo-noble gas behavior from a electron configuration that acts as an ‘inert pair’. Even though it is expected that the – splitting becomes pronounced toward the end of the p-block’s sixth row, there is currently limited experimental evidence to confirm its extent or impact. Here, the production of gas-phase lead ( = 82) and polonium ( = 84) fluoride cations (PbF and PoF ) were compared to elucidate differences in accessible oxidation states. The PoF and PbF species were produced and identified with the FIONA spectrometer at the Lawrence Berkeley National Laboratory 88-Inch Cylctron Facility. Polonium showed notably different fluorination chemistry than lead, producing PoF and PoF as primary products compared to lead’s PbF and PbF . The distribution of PoF products observed offer insights as to the role of spin–orbit splitting for polonium. Similar studies of superheavy elements would elucidate the accessibility of their low-oxidation states as well as to inform chemical predictions for eighth-row elements not yet discovered.

Pore, Jennifer L [Nuclear Science Division, Lawren↗

Mesoscale structural gradients in human tooth enamel

The outstanding mechanical and chemical properties of dental enamel emerge from its complex hierarchical architecture. An accurate, detailed multiscale model of the structure and composition of enamel is important for understanding lesion formation in tooth decay (dental caries), enamel development (amelogenesis) and associated pathologies (e.g., amelogenesis imperfecta or molar hypomineralization), and minimally invasive dentistry. Although features at length scales smaller than 100 nm (individual crystallites) and greater than 50 µm (multiple rods) are well understood, competing field of view and sampling considerations have hindered exploration of mesoscale features, i.e., at the level of single enamel rods and the interrod enamel (1 to 10 µm). Here, we combine synchrotron X-ray diffraction at submicrometer resolution, analysis of crystallite orientation distribution, and unsupervised machine learning to show that crystallographic parameters differ between rod head and rod tail/interrod enamel. This variation strongly suggests that crystallites in different microarchitectural domains also differ in their composition. Thus, we use a dilute linear model to predict the concentrations of minority ions in hydroxylapatite (Mg 2+ and CO 3 2- /Na + ) that plausibly explain the observed lattice parameter variations. While differences within samples are highly significant and of similar magnitude, absolute values and the sign of the effect for some crystallographic parameters show interindividual variation that warrants further investigation. In conclusion, by revealing additional complexity at the rod/interrod level of human enamel and leaving open the possibility of modulation across larger length scales, these results inform future investigations into mechanisms governing amelogenesis and introduce another feature to consider when modeling the mechanical and chemical performance of enamel.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Observing Chemical and Morphological Changes in a Cu@TiO x Core@Shell Catalyst: Impact of Reversible Metal-Oxide Interactions on CO 2 Activation and Hydrogenation

A combination of several in-situ techniques (XRD, XAS, AP-XPS, E-TEM) was used to explore links between the structural and chemical properties of a Cu@TiO x catalyst under CO 2 hydrogenation conditions. The active phase of the catalyst involved an inverse oxide/metal configuration, but the initial core@shell motif was disrupted during the pre-treatment in H 2 . As a consequence of strong metal-support interactions, the titania shell cracked and Cu particles migrated from the core to on top of the oxide with the simultaneous formation of a Cu-Ti-O x phase. The generated Cu particles had a diameter of 20-40 nm and were decorated by small clusters of TiO x (< 5 nm in size). Results of in-situ XAS and XRD and images of E-TEM showed a very dynamic system, where the inverse oxide/metal configuration promoted the reactivity of the system towards CO 2 and H 2 . At room temperature, CO 2 oxidized the Cu nanoparticles (CO 2,gas → CO gas + O oxide ) inducing a redistribution of the TiO x clusters and big modifications in catalyst surface morphology. The generated oxide overlayer disappeared at elevated temperatures (> 180 °C) upon exposure to H 2 , producing a transient surface that was very active for the reverse water-gas shift reaction (CO 2 + H 2 → CO + H 2 O) but was not stable at 250 °C. When oxidation and reduction occurred at the same time, under a mixture of CO 2 and H 2 , the surface structure evolved toward a dynamic equilibrium that strongly depended on the temperature. Neither CO 2 nor H 2 can be considered as passive reactants. In the Cu@TiO x system, morphological changes were linked to variations in the composition of metal-oxide interfaces which were reversible with temperature or chemical environment and affected the catalytic activity of the system. Finally, the present study illustrates the dynamic nature of phenomena associated with the trapping and conversion of CO 2 .

36 MATERIALS SCIENCE↗

Enabling Multireference Calculations on Multimetallic Systems with Graphic Processing Units

Modeling multimetallic systems efficiently enables faster prediction of desirable chemical properties and the design of new materials. This work describes an initial implementation for performing multireference wave function method localized active-space self-consistent field (LASSCF) calculations through the use of multiple graphics processing units (GPUs) to accelerate time-to-solution. Density fitting is leveraged to reduce memory requirements, and we demonstrate the ability to fully utilize multi-GPU compute nodes. Performance improvements of 5–10x in total application runtime were observed in LASSCF calculations for multimetallic catalyst systems up to 1200 AOs and an active space of (22e,40o) using up to four NVIDIA A100 GPUs. Furthermore, written with performance portability in mind, a comparable performance is also observed in early runs on the Aurora exascale system using Intel Max Series GPUs.

Algorithms↗

Impact of Actinide Complexation on Ligand Radiolysis

Although the actinides boast many unique physical and chemical properties, their inherent susceptibility to radioactive decay are what make them truly interesting elements to study. The absorption of ionizing radiation from actinide decay leads to the formation of a variety of transient and steady-state radicals, ions, and molecular radiolysis products that can lead to significant changes in the surrounding environment, and ultimately dictate steady-state actinide redox distributions and the longevity of molecules designed for actinide complexation. Radiolysis of the latter leads to complexant destruction and the concomitant formation of degradation products that can complicate actinide studies and processes. However, the radiation chemistry of most actinide complexants have been studied in the absence of the actinides they were designed to complex, which can lead to inaccurate conclusions on longevity and degradation product distributions, as metal ion complexation has been historically shown to influence a ligand’s radiolytic behavior. Consequently, bridging this knowledge gap is important for actinide science. Here, I will discuss the impact of actinide complexation on the steady-state and time-resolved radiation-induced reactivity of a variety of complexants, including, tributyl phosphate (TBP) and N,N,N',N'-tetraoctyl diglycolamide (TODGA) .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model

Soot produced by detonation or combustion events exhibits different chemical properties depending on the fuel, device construction, and environmental conditions in which the event occurs. These properties can be useful for defining relevant signatures for probabilistically identifying the different types of events that occurred, based on the soot that is produced from these events. However, it is rare to observe samples of soot from a detonation or combustion that are not contaminated by outside particles. In this paper, we present a method for resolving mixtures of soot to determine the contributions of sources that may be present in samples of recovered soot. We use Latent Dirichlet Allocation to describe the generative process for a sample of recovered soot, and use Variational Bayesian Inference to learn about the parameters associated with the generative model. We demonstrate the utility of this method by considering real samples of mixtures of soot under various frameworks to show that the model is able to identify the different components present in a sample of soot as well as their mixing proportions.

54 ENVIRONMENTAL SCIENCES↗

Improved loss functions for machine-learned atomic potentials

Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Predicting 3D moisture sorption behavior of materials from 1D investigations

Abstract Moisture in materials can be a source of future outgassing and exacerbate unwanted changes in physical and chemical properties. Here, we investigate the effect of sample size and shape on the moisture transport phenomena through a combined experimental and modeling approach. Several different materials varying in size and shape were investigated over a wide range of relative humidities (0–90%) and temperatures ( $$30{-}70^{\,\circ } \hbox {C}$$ 30 - 70 ∘ C ) using gravimetric type dynamic vapor sorption (DVS). A dynamic triple-mode sorption model, developed previously, was employed to describe the experimental results with good success; the model includes absorption, adsorption, pooling (clustering) of species, and molecular diffusion. Here we show that the full triple-mode sorption model is robust enough to predict the dynamic uptake and outgassing of 3-dimensional (3D) samples using parameters derived from quasi-1D samples. This successful demonstration on three different materials (filled polydimethylsiloxane (PDMS), unfilled PDMS, and ceramic inorganic composite) illustrates that the model is robust at describing the scale-independent physics and chemistry of moisture sorption and diffusion materials. This work demonstrates that while sorption mechanisms manifest in testing of all sample sizes, some of these mechanisms were so subtle that they were overlooked in our initial modeling and assessment, illustrating the importance of multi-scale experiments in the development of robust predictive capabilities. Our study also outlines the challenges and viable solutions for global optimization of a multi-parameter model. The ability to quantify moisture sorption and diffusion, independent of scale, using 1D lab-scale experiments enables prediction of long-term bulk materials behavior in real applications.

36 MATERIALS SCIENCE↗

Role of Zeolite Structural Properties toward Iodine Capture: A Head-to-head Evaluation of Framework Type and Chemical Composition

This study evaluated zeolite-based sorbents for iodine gas [I2(g)] capture. Based on the framework structures and porosities, five zeolites, including two faujasite (FAU), one ZSM-5 (MFI), one mesoMFI, one ZSM-22 (TON), as well as two mesoporous materials, were evaluated for I2(g) capture at room temperature and 150 °C in an iodine-saturated environment. From these preliminary studies, the three best-performing zeolites were ion-exchanged with Ag+ and evaluated for I2(g) capture under similar conditions. Energy-dispersive X-ray spectroscopy data suggest that Ag-FAU frameworks were the materials with the highest capacity for I2(g) in this study, showing ~3× higher adsorption compared to Ag-mordenite (Ag-MOR) at room temperature, but X-ray diffraction measurements show that the faujasite structure collapsed during the adsorption studies because of dealumination. The Ag-MFI zeolites are decent sorbents in real-life applications, showing both good sorption capacities and higher stability. In-depth analyses and characterizations, including synchrotron X-ray absorption spectroscopy, revealed the influence of structural and chemical properties of zeolites on the performance for iodine adsorption from the gas phase.

aluminosilicate zeolites, iodine, porosity, acidit↗

Crossover Effects of Transition‐Metal Ions on Lithium‐Metal Anode in Localized High Concentration Electrolytes

Abstract The stability of the solid–electrolyte interphase (SEI) is critical to the cycle life of lithium‐metal batteries (LMBs). While the crossover effect of transition‐metal ions from cathode to anode is extensively studied in lithium‐ion batteries with graphite anodes, its impact on LMBs remains largely unexplored. Herein, this study investigates the electrochemical and chemical properties of SEI layers formed on lithium‐metal anodes in localized high‐concentration electrolytes (LHCEs) containing dissolved transition‐metal ions (Ni 2+ , Mn 2+ , and Co 2+ ). It is demonstrated that transition‐metal ions in LHCEs reduce the coulombic efficiency (CE) and significantly degrade the cycle life of LMBs. Time‐of‐flight secondary‐ion mass spectrometry (ToF‐SIMS) reveals that SEI structures differ depending on the dissolved TM ion, with Mn 2+ and Co 2+ inducing severe destabilization, and Ni 2+ exhibiting a less severe impact. These findings underscore the detrimental effects of transition‐metal crossover effects in LMB systems.

Guo, Zezhou [Materials Science and Engineering Pro↗

Charging Reactions Promoted by Geometrically Necessary Dislocations in Battery Materials Revealed by In Situ Single-Particle Synchrotron Measurements

Crystallographic defects exist in many redox active energy materials, e.g., battery and catalyst materials, which significantly alter their chemical properties for energy storage and conversion. However, there is lack of quantitative understanding of the interrelationship between crystallographic defects and redox reactions. Herein, crystallographic defects, such as geometrically necessary dislocations, are reported to influence the redox reactions in battery particles through single-particle, multimodal, and in situ synchrotron measurements. Through Laue X-ray microdiffraction, many crystallographic defects are spatially identified and statistically quantified from a large quantity of diffraction patterns in many layered oxide particles, including geometrically necessary dislocations, tilt boundaries, and mixed defects. The in situ and ex situ measurements, combining microdiffraction and X-ray spectroscopy imaging, reveal that LiCoO 2 particles with a higher concentration of geometrically necessary dislocations provide deeper charging reactions, indicating that dislocations may facilitate redox reactions in layered oxides during initial charging. The present study illustrates that a precise control of crystallographic defects and their distribution can potentially promote and homogenize redox reactions in battery materials.

25 ENERGY STORAGE↗

Fluoride-Catalyzed Siloxane Exchange as a Robust Dynamic Chemistry for High-Performance Vitrimers

Sustainable development of new technologies requires materials having advanced physical and chemical properties while maintaining reprocessability and recyclability. Vitrimers are designed for this purpose; however, their dynamic covalent chemistries often have drawbacks or are limited to specialized polymers. Here, fluoride-catalyzed siloxane exchange is reported as an exceptionally robust chemistry for scalable production of high-performance vitrimers through industrial processing of commodity polymers such as poly(methyl methacrylate), polyethylene, and polypropylene. The vitrimers show improved resistance to creep, heat, oxidation, and hydrolysis, while maintaining excellent melt flow for processing and recycling. Furthermore, the siloxane exchange between different vitrimers during mechanical blending results in self-compatibilized blends without any compatibilizers. In conclusion, this offers a general, scalable method for producing sustainable high-performance vitrimers and a new strategy for recycling mixed plastic wastes.

36 MATERIALS SCIENCE↗

Tailoring the Coordination Micro‐Environment in Nanotraps for Efficient Platinum/Palladium Separation

Abstract Recovering platinum group metals from secondary resources is crucial to meet the growing demand for high‐tech applications. Various techniques are explored, and adsorption using porous materials has emerged as a promising technology due to its efficient performance and environmental beingness. However, the challenge lies in effectively recovering and separating individual platinum group metals (PGMs) given their similar chemical properties. Herein, a breakthrough approach is presented by sophisticatedly tailoring the coordination micro‐environment in a series of aminopyridine‐based porous organic polymers, which enables the creation of platinum‐specific nanotraps for efficient separation of binary PGMs (platinum/palladium). The newly synthesized POP‐o2NH 2 ‐Py demonstrates record uptakes and selectivity toward platinum over palladium, with the amino groups adjacent to the pyridine moieties being vital in improving platinum binding performance. Further breakthrough experiments underline its remarkable ability to separate platinum and palladium. Spectroscopic analysis reveals that POP‐o2NH 2 ‐Py offers a more favorable coordination fashion to platinum ions compared to palladium ions owing to the greater interaction between N and Pt 4+ and stronger intramolecular hydrogen bonding between the amino groups and four coordinating chlorines at platinum. These findings underscore the importance of fine‐tuning the coordination micro‐environment of nanotraps through subtle modifications that can greatly enhance the selectivity toward the desired metal ions.

Chemistry↗