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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,117 records · Page 62

From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape

Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.

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Autonomous design of new chemical reactions using a variational autoencoder

Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set.

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Multiscale modeling and nonlinear model predictive control for flue gas desulfurization

The primary source of sulfur dioxide (SO 2 ) emissions is flue gas from fossil fuels-based power plants. SO 2 emissions are known to not only cause health issues, but also have an adverse effect on the environment in various ways. Several Flue Gas Desulfurization (FGD) technologies have been incorporated in power plants. The most popular technology is Wet FGD, where a limestone slurry is used to absorb SO 2 from the flue gas. A detailed droplet scale model describing the instantaneous and finite rate chemistry is developed. The ill-posed Differential-Algebraic Equation (DAE) droplet model is reformulated to a well-posed index-1 DAE through index reduction. The droplet model is integrated with the bulk phase by incorporating gas-liquid mass transfer, and an oxidation reactor model to simulate the dynamic operation of the counter-current spray scrubber. As a result, the model has a well-conditioned Jacobian and overcomes the modeling challenges of previous works and enables numerical solution without requiring carefully selected initialization or specialized solution procedures. The model is successfully validated using power plant measurements, and nonlinear model predictive control (NMPC) studies are demonstrated to optimize recycle stream flowrates to minimize pumping costs.

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Solution strategies and heat transfer calculations for three-dimensional configurations at hypersonic speeds

A procedure which reduces the memory requirements for computing the viscous flow over a modified Orbiter geometry at a hypersonic flight condition is presented. The Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) code which incorporates a thermochemical nonequilibrium chemistry model, a finite rate catalytic wall boundary condition and wall temperature distribution based on radiation equilibrium is used in this study. In addition, the effect of choice of 'min mod' function, eigenvalue limiter and grid density on surface heating is investigated. The surface heating from a flowfield calculation at Mach number 22, altitude of 230,000 ft and 40 deg angle of attack is compared with flight data from three Orbiter flights.

Weilmuenster, K. J.↗

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature’s own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and “smart” architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Furthermore, continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

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Comparison of vapour pressure osmometry, freezing point osmometry and direct membrane osmometry for determining the osmotic pressure of concentrated solutions

Osmometry is the analytical method of measuring osmotic pressure ($π$) and related colligative properties. Accurately measuring $π$ is essential for applications that use osmosis, including drug delivery systems and water filtration methods such as forward osmosis. The three most common forms of directly or indirectly measuring $π$ are vapour pressure osmometry (VPO), freezing point osmometry (FPO), and direct membrane osmometry (DMO). In this report, the performance of VPO, FPO, and DMO are compared for various aqueous solutions, including small molecule and polymer solutes, and potentially more difficult samples such as concentrated, carbonated, or viscous solutions. Furthermore, potential problems with each of the three methods are examined. Good practices can avoid inaccuracies in DMO due to external concentration polarization and solute diffusion through the membrane. Similarly, it is important to be aware of and avoid weaknesses in VPO and FPO in order to obtain reliable results. Even with care, however, the three methods still give different values of $π$ for concentrated aqueous solutions.

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Indirect electrochemical method for high accuracy quantification of protein adsorption on gold surfaces

We demonstrate the use of copper underpotential deposition (UPD) for measurement of the electrochemically active surface area of a protein-modified gold electrode. The kinetics of adsorption and the subsequent structural rearrangement of β-casein, as a model protein, on the surface of a gold electrode have been followed over time. The protein-free surface area was determined by measuring the charge derived from UPD copper deposition on the non-blocked surface area at different stages of adsorption. The behaviour of β-casein adsorption as a function of surface roughness and the microstructure of the gold electrode, as well as the concentration of the protein solution, are studied using this method.

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Covalent Triazine Framework-Derived Membranes: Engineered Sol–Gel Construction and Gas Separation Application

Covalent triazine frameworks (CTFs) represent one of the most extensively studied organic networks characterized by graphitic π-conjugated structures linked by aza-fused rings, possessing unique features such as compositions of light elements (e.g., C, H, and N), porous architectures abundant heteroatom involvement, and extensively conjugated structures. In addition, the textural and chemical structures of CTFs could be engineered via synthesis control to accommodate diverse applications. CTF materials with notable characteristics, including plentiful (ultra-)micropores, high surface areas, and the presence of CO 2 -philic functional groups involving nitrogen (N), oxygen (O), and fluorine (F), hold great promise as potential candidates for anthropogenic CO 2 capture and sequestration (CCS) applications. However, the conventional high-temperature involved ionothermal procedures and the solution-based coupling pathway only afforded CTF materials in powder form, which is difficult to be processed toward membrane formation. Successful fabrication of CTF-derived membranes will rely on the development of alternative polymerization approaches as well as structural engineering to afford membrane architectures with controllable porosity distribution and active interaction sites with CO 2 benefiting the CO 2 separation procedure. In this Account, a demonstration of the latest progress in the development of CTF-derived membranes was provided. The CTF membranes were mainly synthesized via a superacid (e.g., CF 3 SO 3 H)-promoted sol–gel approach involving the polymerization of aromatic nitrile monomers. The formation of the triazine unit through the trimerization of cyano groups served as the cross-linkers, resulting in the creation of π-conjugated networks alongside the arenes present in the starting materials. The aromatic nitrile monomers with rigid and sterically hindered structures were required to afford CTF membranes with nanoporous architectures. The acidity of the superacid and reactivity of the aromatic monomers played critical roles in the polymerization efficiency. The monomer diversity and synthesis tunability endowed the introduction of CO 2 -philic functionalities (e.g., pyrazole and fluorine) within the CTF skeletons, and integration of ionic moieties was achieved by adopting FSO 3 H with stronger acidity as the catalyst and aromatic nitrile monomers with pyrazine structures. To ensure the successful construction of fluorinated CTF membranes, it is important to avoid any fluorines on the ortho-position of the cyano groups on the benzene ring. Through control over the monomers and reaction conditions, flexible, transparent, and insoluble CTF membranes could be fabricated. The sol–gel method could be further expanded to membrane fabrication through acetyl-to-benzene transformation through synthesis control. The mild oxidation-exfoliation-filtration method was also demonstrated to fabricate substrate-supported CTF membranes. The as-afforded membranes are well characterized to determine the structural features and provide information to study the structure-performance relationship. Here, the application of CTF membranes in CO 2 separation was summarized, focusing on the approaches being developed to enhance CO 2 uptake and separation performance. In addition to utilizing the pristine CTF membranes for gas separation, functionalized carbon molecular sieve membranes could be obtained from the pyrolysis of thermally stable CTF membrane precursors toward efficient CO 2 separation, benefiting from the abundant ultramicropores being created during the pyrolysis/decomposition procedure and involvement of CO 2 -philic functionalities such as fluorine and nitrogen-containing moieties. Based on these achievements, unsolved issues in CTF membrane-related fabrication and applications, including the potential solution approaches, have been proposed to advance the application of CTF membranes.

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Resolving Heterogeneous Dynamics of Excess Protons in Aqueous Solution with Rate Theory

Rate theories have found great utility across the chemical sciences by providing a physically transparent way to analyze dynamical processes. Here we demonstrate the benefits of using transition state theory and Marcus theory to study the rate of proton transfer in HCl solutions. By using long ab initio molecular dynamics simulations we show that good agreement is obtained between these two different formulations of rate theory and how they can be used to study the pathways and life-time of proton transfer in aqueous solution. Since both rate theory formulations utilize identical sets of molecular data, this provides a self-consistent theoretical picture of the rates of aqueous phase proton transfer. Specifically, we isolate and quantify the rates of proton transfer, ion-pair dissociation, and solvent exchange in concentrated HCl solutions. Our analysis predicts a concentration dependence to both proton transfer and ion-pairing. Moreover, our estimate of the life-time for the Zundel species is 0.8 ps and 1.3 ps for 2M and 8M HCl, respectively. In this paper, we demonstrate that concentration effects, can indeed be quantified through the combination of state-of-the-art simulation and theory and provides a picture of the important correlations between the cation (hydronium) and the counter-ion in acid solutions.

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Gating ion and fluid transport with chiral solvent

Nonlinear spectroscopy and electrokinetic measurements reveal that the electrochemical properties of a solid–liquid interface in salt solutions in propylene carbonate are dependent on the solvent chirality.

Chemistry↗

Improving Performance of III-V Solar Cells Grown on Spalled Germanium with Ex Situ Substrate Planarization

Controlled spalling allows removal of devices and provides an opportunity for cost reduction through substrate reuse. However, the fracture-based process can leave behind morphological surface features, notably river lines, that can disrupt epitaxial growth and degrade device performance. We investigate the viability of various wet etch chemistries to planarize river lines to ensure high-quality device growth and performance without mechanical repolishing, and so maintain a route towards cost-effective reuse. Etching in a HF: HNO 3 :CH 3 COOH solution effectively planarizes river lines and produces a surface that yields devices with equivalent performance to those grown on epi-ready Ge wafer surfaces. Further studies will focus on optimizing etch composition, temperature, and time to minimize material removal while maintaining a suitable surface for high-quality epitaxy.

germanium↗

Two-stage formation-energy correction (NbZr, TaZr, VZr)

This bundle contains the scripts, the raw and corrected per-structure data, and the manuscript plots for the NbZr / TaZr / VZr BCC binary formation energies and the associated RMSDs. Why a two-stage correction is necessary: The "raw" formation energy of every relaxed VASP configuration is computed in the usual way, FE_raw(c) = E_alloy(c) - sum_i x_i * E_pure_i , where E_pure_i are the per-atom total energies of the pure-element reference structures (Nb, Ta, V, Zr in the same BCC supercell, with identical INCAR / KPOINTS / PAW choices). With perfectly consistent reference runs the raw FE should vanish at the two pure-element endpoints (x = 0 and x = 1) by construction. In practice this does not hold for two reasons that are present in our dataset: 1. Reference-energy inconsistency (composition-dependent bias). Even with identical input parameters, the pure-element runs (stored in `corrected_DFT_pure_element_runs/`) differ slightly from the values that would be implied by the alloy runs at near-pure compositions (a few meV/atom). This bias is approximately linear in concentration, because the residual error in E_pure_Nb (or E_pure_Ta / E_pure_V) propagates into FE_raw(c) as (1 - x) * dE_pure_1, and the corresponding error in E_pure_Zr propagates as x * dE_pure_2. Left uncorrected, this produces a non-physical "tilt" of FE_raw(x) and shifts the entire FE-vs-x cloud away from zero at the endpoints. 2. Endpoint anchoring against the audited true endpoints. The strict endpoint values (FE_x0_meVatom, FE_x1_meVatom in `corrected_fe_strict_endpoints_20260518/strict_endpoint_check_20260518.csv`) were re-derived from an independent cross-check of the pure-element runs. After stage 1 removes the linear bias, the near-pure compositions in the alloy dataset still extrapolate to values that differ slightly from these audited endpoints — because stage 1 is fit from a few near-end alloy bins, not from the audited pure-element references themselves. The README.txt file discusses how these issues are addressed by the two-stage correction, and describes folder layout, pipeline summary, and how to re-run.

36 MATERIALS SCIENCE↗

Improving Gas Reactor Design with Complex Non-Standard Reaction Mechanisms in a Reactive Flow Model

Fluorinated gases are critical to a number of high-technology industries including semiconductor manufacturing and related processes such as flat panel display, LED, and photovoltaic manufacturing. These fluorinated gases have very high global warming potentials (GWPs) and, unfortunately are chemically very stable, in some cases lasting for thousands of years in the atmosphere. For environmental reasons, it is imperative that the release of these gases be minimized, but existing methods to destroy them requires a significant amount of energy. The goal of this project was to develop a more energy efficient method of destroying these high GWP gases using computational fluid dynamics, but the computational requirements are significant. In this project, we bring together resources available at the national labs, including high-performance computing hardware and modern computational methods, in order to explore an industrially important problem with key environmental and economic impact. In this project, we examined the state-of-the-art models for the physical processes involved, and made subtle changes to arrive at alternative formulations considerably better suited for computation and study. We examined several representative scenarios and developed a set of analyses techniques and tools that could be used to explore the systems in much greater detail. This exploratory project has demonstrated to ALZETA that HPC can indeed provide a unique view into their complex engineering problem - one that is simply inaccessible by any other means. The tools allow us to explore the details of the chemical process, and allow one to construct "what-if" scenarios to address shortcomings of the physical device in its current operating modes. However, as with many sources of completely new information, many new questions are illuminated with this capability. It will take considerable time, effort and experience before we learn to probe the solutions, pose operating scenarios and confirm modifications to the device that lead to real engineering advances.

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Polymers in Deep Eutectic Solvents

The project investigated the behavior of polymers in ionic liquids such as deep eutectic solvents using an array of techniques. These included the development of atomistic force fields, coarse graining these force fields to the united atom level, large scale molecular dynamics (MD) simulations using new thermostat algorithms, and machine learning (ML) methods for the phase behavior. The project demonstrated the feasibility and accuracy of first principles force fields for ionic liquids, deep-eutectic solvents, and urea-water mixtures. A novel hierarchical coarse graining method was then used to develop accurate and efficient united-atom models, using which microsecond simulations were performed for polymers in ionic liquids. These simulations were in quantitative agreement with experiment, thus resolving previous controversies. Methods were also developed to obtain the potential of mean force between complex ions in solution. Finally, supervised ML methods were developed for the phase behavior of polymers in ionic liquids.

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Magnetic Nanoparticle Extraction of Lithium from Produced Waters - CRADA 483 (Final Report)

The demand for lithium in the energy production industry is expected to increase sharply, development of simple and cost-effective techniques for lithium production and recovery from various lithium sources is essential. In this project, core/shell magnetic nanoparticles were successfully designed to selectively extract lithium from aqueous lithium sources as an extension of Pacific Northwest National Laboratory’s magnetic nanofluid extraction technology. The core/shell magnetic nanoparticles are composed of manganese oxide-based lithium ion sieve shells, which allow selective lithium uptake from brines with multiple coexisting ions, over iron oxide cores, which can respond to external magnetic fields for effective recovery and reuse of adsorbents from a liquid. The synthesized lithium ion-sieves and core/shell magnetic nanoparticles were characterized using several techniques to reveal their crystallinity and morphology. The lithium uptake properties of the lithium ion-sieves and core/shell magnetic nanoparticles were evaluated in terms of lithium adsorption capacity, removal percentage, selectivity, and cycling performance in simulated and natural brines. Magnetic properties of the core/shell magnetic nanoparticles were tested by measuring magnetic saturation, and magnetic response of colloidal solutions containing the core/shell magnetic nanoparticles was tested with permanent magnets.

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