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At least 1,621 records · Page 90

Liquid state properties of SEI components in dimethoxyethane

The solid-electrolyte interphase (SEI) layer is a critical constituent of battery technology, which incorporates the use of lithium metals. Since the formation of the SEI is difficult to avoid, the engineering and harnessing of the SEI are absolutely critical to advancing energy storage. One problem is that much fundamental information about SEI properties is lacking due to the difficulty in probing a chemically complex interfacial system. One such property that is currently unknown is the dissolution of the SEI. This process can have significant effects on the stability of the SEI, which is critical to battery performance but is difficult to probe experimentally. Here, we report the use of ab initio computational chemistry simulations to probe the solution state properties of SEI components LiF, Li 2 O, LiOH, and Li 2 CO 3 in order to study their dissolution and other solution-based characteristics. Ab initio molecular dynamics was used to study the solvation structures of the SEI with a combination of radial distribution functions, discrete solvation structure maps, and vibrational density of states, which allows for the determination of free energies. From the change in free energy of dissolution, we determined that LiOH is the most likely component to dissolve in the electrolyte followed by LiF, Li 2 CO 3 , and Li 2 O although none were favored thermodynamically. Furthermore, this indicates that dissolution is not probable, but Li 2 O would make the most stable SEI with regard to dissolution in the electrolyte.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enhanced spectral purity of WSe 2 quantum emitters via conformal organic adlayers

Quantum emitters in solid-state materials are typically embedded in the bulk of their hosts, making their electronic transitions inaccessible to surface modification. In contrast, two-dimensional materials, with their all-surface nature, offer a platform for tuning quantum emitters via chemical functionalization. Because of its semiconducting properties that enable electrical addressability, monolayer WSe 2 is a promising candidate for quantum emission, although the complex interplay between point defects and the localized strain needed to activate quantum emission leads to poor spectral purity. Here, we demonstrate that functionalizing monolayer WSe 2 with conformal adlayers of 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA) improves quantum emission spectral purity. Optical spectroscopy reveals that PTCDA functionalization lowers defect activation energies by 10 meV and induces a 30 nm redshift in quantum emission wavelength, while preserving the bright and dark exciton energies of monolayer WSe 2 . First-principles calculations corroborate these findings, thus providing molecular-level insight into the underlying mechanism of enhanced spectral purity.

Ananth, Riddhi [Northwestern Univ., Evanston, IL (↗

Radar transponder antenna pattern analysis for the space shuttle

In order to improve tracking capability, radar transponder antennas will soon be mounted on the Shuttle solid rocket boosters (SRB). These four antennas, each being identical cavity-backed helices operating at 5.765 GHz, will be mounted near the top of the SRB's, adjacent to the intertank portion of the external tank. The purpose is to calculate the roll-plane pattern (the plane perpendicular to the SRB axes and containing the antennas) in the presence of this complex electromagnetic environment. The large electrical size of this problem mandates an optical (asymptotic) approach. Development of a specific code for this application is beyond the scope of a summer fellowship; thus a general purpose code, the Numerical Electromagnetics Code - Basic Scattering Code, was chosen as the computational tool. This code is based on the modern Geometrical Theory of Diffraction, and allows computation of scattering of bodies composed of canonical problems such as plates and elliptic cylinders. Apertures mounted on a curved surface (the SRB) cannot be accomplished by the code, so an antenna model consisting of wires excited by a method of moments current input was devised that approximated the actual performance of the antennas. The improvised antenna model matched well with measurements taken at the MSFC range. The SRB's, the external tank, and the shuttle nose were modeled as circular cylinders, and the code was able to produce what is thought to be a reasonable roll-plane pattern.

Radcliff, Roger↗

Panel-Stiffener Debonding and Analysis Using a Shell/3D Modeling Technique

A shear loaded, stringer reinforced composite panel is analyzed to evaluate the fidelity of computational fracture mechanics analyses of complex structures. Shear loading causes the panel to buckle. The resulting out-of-plane deformations initiate skin/stringer separation at the location of an embedded defect. The panel and surrounding load fixture were modeled with shell elements. A small section of the stringer foot, web and noodle as well as the panel skin near the delamination front were modeled with a local 3D solid model. Across the width of the stringer foot, the mixed-mode strain energy release rates were calculated using the virtual crack closure technique. A failure index was calculated by correlating the results with a mixed-mode failure criterion of the graphite/epoxy material. The objective was to study the effect of the fidelity of the local 3D finite element model on the computed mixed-mode strain energy release rates and the failure index.

Krueger, Ronald↗

Panel Stiffener Debonding Analysis using a Shell/3D Modeling Technique

A shear loaded, stringer reinforced composite panel is analyzed to evaluate the fidelity of computational fracture mechanics analyses of complex structures. Shear loading causes the panel to buckle. The resulting out -of-plane deformations initiate skin/stringer separation at the location of an embedded defect. The panel and surrounding load fixture were modeled with shell elements. A small section of the stringer foot, web and noodle as well as the panel skin near the delamination front were modeled with a local 3D solid model. Across the width of the stringer fo to, the mixed-mode strain energy release rates were calculated using the virtual crack closure technique. A failure index was calculated by correlating the results with a mixed-mode failure criterion of the graphite/epoxy material. The objective was to study the effect of the fidelity of the local 3D finite element model on the computed mixed-mode strain energy release rates and the failure index.

Krueger, Ronald↗

NASA Space Launch System Completes Key Hot Fire Test and Begins Vehicle Integration

NASA and its commercial and international partners are on the way back to the Moon. Significant progress towards that goal was made in 2021, including on the agency’s Space Launch System (SLS) rocket – a key part of the Artemis initiative. The SLS core stage for the Artemis I mission – the first launch of SLS and an uncrewed Orion capsule – completed its Green Run test series in early 2021 and was successfully mated with the SLS twin solid rocket boosters on the mobile launcher at Kennedy Space Center (KSC). The launch vehicle stage adapter, interim cryogenic propulsion stage, and the Orion stage adapter structural test article and Orion spacecraft mass simulator were also stacked. The flight Orion spacecraft and Orion stage adapter are being prepared for stacking and launch, targeted for late 2021 following a wet dress rehearsal at Launch Complex 39B. SLS hardware for Artemis II, which will be the first flight of crew on Orion and SLS, is also in work. Work progressed on core stage components, booster segments, and other hardware for Artemis III and future missions. This paper will detail the progress made.

John Honeycutt↗

The superior hydrothermal stability of Pd/SSZ-39 in low temperature passive NOx adsorption (PNA) and methane combustion

We successfully synthesized uniform SSZ-39 with an average crystal size of about a micron. Pd (0.7 - 3 wt%) was supported on SSZ-39 with Si/Al ratio ~12. The as-synthesized materials were characterized by FTIR, XRD, Helium Ion Microscopy, HAADF-STEM imaging, 27 Al, 29 Si and H solid state NMR spectroscopic techniques. FTIR studies with CO and NO probe molecules reveal that the 0.7 wt% Pd/SSZ-39 material with Si/Al ~12 has the majority of Pd dispersed atomically as isolated Pd(II) and Pd(II)-OH centers, and thus can be used as a low-temperature passive NOx adsorber. Pd(II)-NO, Pd(II)(OH)(NO) and Pd(II)(CO)(NO) complexes form during PNA in this material. We compare this PNA material directly with the Pd/SSZ-13 system (with Si/Al ratio ~12) and show its superior hydrothermal stability. Remarkably, Pd/SSZ-39 with Si/Al ratio ~12 survives hydrothermal aging up to 815 ºC in 10% H 2 O/Air vapor for 16 hours without significant loss in activity. The SSZ-39 crystal structure remains intact during hydrothermal aging up to 1,000 ºC as we elucidate it with XRD and HAADF-STEM imaging/EDS mapping. However, changes to the framework during such harsh hydrothermal treatment significantly change the NOx release profiles during PNA as evidenced by high-field 27 Al NMR on fresh and aged Pd/SSZ-39 samples as well as PNA performance measurements. Besides PNA application, these hydrothermally very stable materials (3 wt% Pd on SSZ-39 with Si/Al ratio ~12) can be used as a robust methane combustion catalyst under industrially relevant conditions (GHSV~600,000hr -1 ). This catalyst shows minimal deactivation after both harsh hydrothermal aging at 750 and 800 ºC, and prolonged time on stream (105 hrs) at 425 °C. In contrast, both 3wt% Pd/alumina and 3wt% SSZ-13 supported samples lose a significant portion of their activity. Our study opens new avenues to prepare the most hydrothermally stable known Pd/zeolite materials with applications for adsorption and catalytic hydrocarbon combustion under industrially relevant conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effective properties of semitransparent radiative cooling materials with spectrally variable properties

Radiative cooling is a promising solid-state, non-vapor-compression technology for passive refrigeration and air conditioning. Although this phenomenon occurs naturally, achieving a significant amount of cooling to make it a technically and economically viable technology requires highly engineered, spectrally selective radiative surfaces. These characteristics make radiative cooling difficult to estimate, particularly when it is integrated with other systems such as photovoltaic panels or building envelopes. The complexity further increases when the substrate also participates in the radiative cooling (along with the radiative coating). Energy estimation is becoming increasingly critical because of the recent focus on the semitransparent radiative coatings that transmit a variety of colors to enhance the aesthetic appeal of the system. In this work, we propose an iterative method to calculate the effective radiative properties, which provides the same net radiative cooling that would be observed using the spectral properties at both the coating and substrate surfaces. Compared to traditional methods that rely on either computationally expensive full spectral analysis or methods for averaging each radiative surface parameter locally, our proposed method focuses on calculating effective properties that provide the same the net cooling effect as a full spectral analysis by accounting the emissivity, absorptivity, and transmissivity collectively, thereby providing an overall estimation error of less than 0.2%. We believe that this study will be beneficial to the engineering communities that employ complex simulation codes and require lumped solar and thermal radiation related parameters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗

Understanding the Reactivity and Decomposition of a Highly Active Iron Pincer Catalyst for Hydrogenation and Dehydrogenation Reactions

The iron pincer complex ( iPr PNP)Fe(H)(CO) (1, iPr PNP – = N(CH 2 CH 2 PiPr 2 ) 2 - ) is an active (pre)catalyst for many hydrogenation and dehydrogenation reactions. This is in part because 1 can reversibly add H 2 across the iron-amide bond to form ( iPr PN H P)Fe(H) 2 (CO) (2, iPr PN H P = HN(CH 2 CH 2 P i Pr 2 ) 2 ). However, rapid decomposition limits the catalytic performance of 1 and related complexes. We explored the pathways through which catalytic intermediates related to 1 and 2 undergo decomposition. This involved characterizing the unstable and previously unobserved complexes [( iPr PN H P)Fe(H)(CO)(L)] + (5-L; L = THF or N 2 ) and [( iPr PN H P)Fe(H)(H 2 )(CO)] + (8), which are proposed as intermediates when 1 and 2 are used as catalysts. Compound 8 was synthesized through the reaction of ( iPr PN H P)Fe(H)(CO)(PF 6 ) (6) with H 2 , and the solid-state structure was established using both X-ray and neutron diffraction. As part of our studies on understanding the reactivity of 5-L, we determined the thermodynamic hydricity of 2, which is valuable for predicting its reactivity as a hydride donor. Further, it is shown that species such as 5-L decompose to the same inactive species observed in catalysis using 1 and 2, and theoretical calculations suggest that this likely occurs via a bimolecular pathway. To provide support for this hypothesis, we isolated the dimeric species [{( iPr PN H P)Fe(H)(CO)} 2 {μ-CN}] + (11) and [{( iPr PN H P)Fe(H)(CO)} 2 {μ-OC(H)O}] + (12), which show that catalytic intermediates ligated by iPr PN H P can form dimeric species. Our results provide general strategies for improving catalysis using 1 and 2, and we used this information to rationally increase the performance of 1 in formic acid dehydrogenation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Functionalized Porous Polymer Networks as High-Performance PFAS Adsorbents

Toxic per- and polyfluoroalkyl substances (PFAS) are now found in nearly every water source on the planet. Exposure to these molecules can have negative health consequences, but the low concentration of PFAS relative to other solutes in water makes their removal challenging. Adsorbents offer a promising treatment route, but often exhibit low selectivities and removal capacities, as well as slow kinetics. The performance in these metrics can be improved by chemically optimizing PFAS binding sites and maximizing PFAS-adsorbent interactions. To explore how to achieve this, a porous polymer network solid (PPN-6, also known as PAF-1) was postsynthetically modified with various chemical moieties capable of leveraging unique combinations of electrostatic, hydrogen-bonding, hydrophobic, and fluorophilic interactions with PFAS molecules. Batch adsorption experiments and computational studies revealed that electrostatic and hydrogen-bonding interactions drive short-chain PFAS adsorption, while hydrophobic and fluorophilic interactions improve long-chain PFAS adsorption. In complex water matrices, a combination of electrostatic and fluorophilic interactions led to the greatest total PFAS removal. The best-performing material, functionalized with a fluorinated alkylammonium (PPN-6-FNDMB), selectively adsorbs PFAS with high capacity (up to 4.0 mmol/g) and rapid kinetics (equilibrium reached in <30 s). Furthermore, PPN-6-FNDMB outperforms several commercial adsorbents, achieving near-complete removal of 21 different PFAS from a groundwater sample collected at a US Air Force base. The PFAS could subsequently be desorbed from PPN-6-FNDMB, concentrating them by a factor of over 50 times. The recycled PPN-6-FNDMB could then be reused with minimal losses in long-chain PFAS adsorption capacity over four cycles.

Pezoulas, Ethan R↗

High-throughput printing of combinatorial materials from aerosols

The development of new materials and their compositional and microstructural optimization are essential in regard to next-generation technologies such as clean energy and environmental sustainability. However, materials discovery and optimization have been a frustratingly slow process. The Edisonian trial-and-error process is time consuming and resource inefficient, particularly when contrasted with vast materials design spaces 1 . Whereas traditional combinatorial deposition methods can generate material libraries 2,3 , these suffer from limited material options and inability to leverage major breakthroughs in nanomaterial synthesis. Here we report a high-throughput combinatorial printing method capable of fabricating materials with compositional gradients at microscale spatial resolution. In situ mixing and printing in the aerosol phase allows instantaneous tuning of the mixing ratio of a broad range of materials on the fly, which is an important feature unobtainable in conventional multimaterials printing using feedstocks in liquid–liquid or solid–solid phases 4–6 . We demonstrate a variety of high-throughput printing strategies and applications in combinatorial doping, functional grading and chemical reaction, enabling materials exploration of doped chalcogenides and compositionally graded materials with gradient properties. The ability to combine the top-down design freedom of additive manufacturing with bottom-up control over local material compositions promises the development of compositionally complex materials inaccessible via conventional manufacturing approaches.

36 MATERIALS SCIENCE↗

Revisiting the pseudo-supercritical path method: An improved formulation for the alchemical calculation of solid–liquid coexistence

Alchemical free energy calculations via molecular dynamics have been applied to obtain thermodynamic properties related to solid–liquid equilibrium conditions, such as melting points. In recent years, the pseudo-supercritical path (PSCP) method has proved to be an important approach to melting point prediction due to its flexibility and applicability. In the present work, we propose improvements to the PSCP alchemical cycle to make it more compact and efficient through a concerted evaluation of different potential energies. The multistate Bennett acceptance ratio (MBAR) estimator was applied at all stages of the new cycle to provide greater accuracy and uniformity, which is essential concerning uncertainty calculations. In particular, for the multistate expansion stage from solid to liquid, we employed the MBAR estimator with a reduced energy function that allows affine transformations of coordinates. Free energy and mean derivative profiles were calculated at different cycle stages for argon, triazole, propenal, and the ionic liquid 1-ethyl-3-methyl-imidazolium hexafluorophosphate. Comparisons showed a better performance of the proposed method than the original PSCP cycle for systems with higher complexity, especially the ionic liquid. Furthermore, a detailed study of the expansion stage revealed that remapping the centers of mass of the molecules or ions is preferable to remapping the coordinates of each atom, yielding better overlap between adjacent states and improving the accuracy of the methodology.

25 ENERGY STORAGE↗

Comparative Assessment of U-Net-Based Deep Learning Models for Segmenting Microfractures and Pore Spaces in Digital Rocks

Segmentation of high-resolution X-ray microcomputed tomography (µCT) images is crucial in digital rock physics (DRP), affecting the characterization and analysis of microscale phenomena in the porous media. The complexity of geological structures and nonideal scanning conditions pose significant challenges to conventional image segmentation approaches. Motivated by the recent increasing popularity of deep learning (DL) techniques in image processing, this work undertakes a comparative study of DL models, specifically U-Net and its variants, for segmenting multiple targets with distinguished features in digital rocks, including discrete fracture networks (DFNs), pore spaces, and solid rock. Particularly, DFNs have a smaller volumetric fraction over others, bringing in a substantial challenge of imbalanced segmentation. The primary focus is to evaluate the architecture and feature enhancement strategies of various DL models, including U-Net, attention U-Net, residual U-Net, U-Net++, and residual U-Net++. The models were designed as 2.5D, utilizing a central 2D image and its two adjacent upper and lower 2D images as input to provide a pseudo-3D context. In addition, because the ground truth of segmentation was unknown for real-world digital rocks, we created a benchmark data set following the inverse operations of segmentation. The data synthesis started from the label images (i.e., solid rock, pore spaces, and DFNs), followed by simulating partial volume blurring, adding random background noise, and introducing ring artifacts to mimic real raw X-ray µCT images. The data set, which included various rock types (i.e., sandstone and artificial data), scanning resolution, and magnitudes of noise and artifacts, was divided into training and testing data sets with a 90% and 10% ratio, respectively. Moreover, in addition to the conventional pixel-wise evaluation metrics, the physics-based metric of the lattice-Boltzmann method (LBM) simulated permeability provided more comprehensive assessments. The results demonstrated that the residual connections, nested architectures, and redesigned skip connections contribute to the model performance and give the residual U-Net++ the highest accuracy. The improvements were mainly on the boundaries and small targets, especially the DFNs, which dominate the interconnectivity and therefore affect the permeability greatly. This study also rigorously evaluated the efficiency and generalization of each model, demonstrating that the sophisticated architectures achieved excellent practicability and maintained robust performance on completely unseen data, ensuring their suitability for diverse and challenging DRP applications.

58 GEOSCIENCES↗

Implementation of Detailed Polyethylene Pyrolysis Kinetics into CFD Simulations using Machine Learning

Municipal solid waste (MSW) and waste plastics have received significant attention due to the issues of waste generation and storage, as well as their potential as an energy resource. High-density polyethylene (HDPE) makes up a large portion of plastic waste and has been the subject of several conversion studies. However, the mechanisms associated with converting HDPE through pyrolysis and gasification are extensive and complex making them difficult to implement into high-fidelity computational fluid dynamic (CFD) simulations. For this project, a primary pyrolysis mechanism containing 42 unique species and 737 heterogeneous reactions was used to generate kinetic data over a range of operating conditions. A machine learning (ML) model was developed to replicate the results of the detailed pyrolysis mechanism while significantly increasing the computational efficiency. A deep operator network (DeepONet) architecture was adopted to train the model using time steps relevant to CFD simulations. The ML used physics-based loss functions to ensure mass conservation. The ML model has been deployed in simple MFiX CFD simulations, single particle, and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗

Mass balances for a biological life support system simulation model

Design decisions to aid the development of future space based biological life support systems (BLSS) can be made with simulation models. The biochemistry stoichiometry was developed for: (1) protein, carbohydrate, fat, fiber, and lignin production in the edible and inedible parts of plants; (2) food consumption and production of organic solids in urine, feces, and wash water by the humans; and (3) operation of the waste processor. Flux values for all components are derived for a steady state system with wheat as the sole food source. The large scale dynamics of a materially closed (BLSS) computer model is described in a companion paper. An extension of this methodology can explore multifood systems and more complex biochemical dynamics while maintaining whole system closure as a focus.

Volk, Tyler↗

Its a Wind, Its a Wave, Its Two Phenomena in One: Jerry Schubert, Superrotation, and the UV Markings on Venus

In the early 1970's, ground-based astronomers had already discovered that Ultraviolet (UV) cloud markings on Venus reappeared every 4 days. When radar evidence later revealed a 243-day rotation period for the solid planet, planetary scientists were faced with a quandary: Could the Venus atmosphere really move 60 times as fast as the planet below, or were the apparent movements of the UV features just an illusion caused by propagating waves? The former explanation seemed unlikely - a planet that hardly rotates should generate only a very sluggish circulation. The historical impact of Jerry Schubert's moving flame theory was twofold: It was the forerunner of current thermal tide explanations of the cloud-level superrotation, but it was also the first plausible mechanism for explaining a seemingly inconsistent set of observations. In 1974, Mariner 10 acquired UV images of the Venus clouds at unprecedented levels of detail. Although few have noted it, this began the shift of planetary atmospheric research primarily from the domain of astronomy to that of meteorology. Jerry was among the first scientists to apply terrestrial meteorology to the analysis of planetary data. At that time a young UCLA graduate student with plans to do research on mantle convection, but having flunked the solid earth geophysics section of his departmental comprehensive exam, was gently invited by Jerry to switch to atmospheric science. Jerry suggested that the Venus UV features could be revealing both superrotating winds and planetary-scale waves at the same time, and that we could distinguish the two by looking at motions on different spatial scales. This was my first science research lesson - the complexity of real geophysical systems. Over the next couple of years I was inculcated with Jerry's philosophy of a comprehensive, rigorous approach to research, which manifested itself as a scouring of the literature and the UCLA meteorology faculty to learn about every possible type of planetary-scale wave. The resulting identification of large-scale UV features on Venus as the product of Kelvin and Rossby-type wave motions was validated by extensive Pioneer Venus observations a decade later and remains one of the best examples of the use of terrestrial knowledge to understand other planets.

DelGenio, Anthony↗

Hierarchical Composites Patterned via 3D Printed Cellular Fluidics

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.

36 MATERIALS SCIENCE↗