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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Exploring the Origin of Anionic Redox Activity in Super Li-Rich Iron Oxide-Based High-Energy-Density Cathode Materials

Super alkali-rich materials (alkali:transition metal ratio of ≥2:1), such as Li 5 FeO 4 , exhibit the potential to realize anionic redox upon deep delithiation. Li 5 FeO 4 was shown to undergo reversible cycling between Li 4 FeO 3.5 and Li 3 FeO 3.5 with combined cation/anion redox, a remarkable capacity of 189 mAh/g, and no O 2 release. However, the impact of phase transformations on the reaction thermodynamics and the correlation between the structural changes and reaction reversibility remain unclear. In this study, we use first-principles calculations to examine the delithiation and (re)lithiation reactions of Li 5 FeO 4 . We show that the experimentally observed charge and discharge processes go through non-equilibrium pathways. Upon delithiation, the compound undergoes a phase transformation from Li 5 FeO 4 , with tetrahedrally coordinated (T d ) Fe ions, to a delithiated disordered rocksalt structure, with octahedral (O h ) Fe ions. Fe-ion migration has an asymmetric kinetic barrier that makes T d → O h migration facile, whereas the reverse process has a much larger barrier, explaining the difficulties in reaction reversibility. We further elucidate the transition metal and O redox sequences during the charge cycle and identify the complex electrochemistry associated with the dual participation of cationic redox (Fe 3+ /Fe 4+ ) and anionic redox (O 2– /O – /O o ). Armed with this knowledge, we conduct high-throughput screening of known alkali-rich transition metal oxides by evaluating their potential to enable reversible anionic redox, with multiple candidates proposed for further experimental trials. Furthermore, our work provides a useful guide for the further development of super alkali-rich anionic-redox-active electrodes for high-energy-density batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Formation of active sites on transition metals through reaction-driven migration of surface atoms

Adopting low-index single-crystal surfaces as models for metal nanoparticle catalysts has been questioned by the experimental findings of adsorbate-induced formation of subnanometer clusters on several single-crystal surfaces. Here we used density functional theory calculations to elucidate the conditions that lead to cluster formation and show how adatom formation energies enable efficient screening of the conditions required for adsorbate-induced cluster formation. We studied a combination of eight face-centered cubic transition metals and 18 common surface intermediates and identified systems relevant to catalytic reactions, such as carbon monoxide (CO) oxidation and ammonia (NH 3 ) oxidation. We used kinetic Monte Carlo simulations to elucidate the CO-induced cluster formation process on a copper surface. Scanning tunneling microscopy of CO on a nickel (111) surface that contains steps and dislocations points to the structure sensitivity of this phenomenon. Metal-metal bond breaking that leads to the evolution of catalyst structures under realistic reaction conditions occurs much more broadly than previously thought.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of Carbon Molecular Sieves Hollow Fiber Membranes based on Polybenzimidazole Doped with Polyprotic Acids with Superior H 2 /CO 2 Separation Properties (Final Report)

The goal of this project was to develop a highly efficient membrane-based process to capture CO 2 from coal-derived syngas with 95% CO 2 purity, achieving the cost of electricity (COE) 30% below the baseline capture approaches (i.e., Selexol process) when coupled with the advancement in other areas of the power generation facility. Our core approach is based on high-permeance hollow fiber membranes (HFMs) with superior H 2 /CO 2 separation properties at the syngas process conditions, which can then be further utilized to design membrane reactors for process intensification of H 2 production and purification and CO 2 capture. Three organizations with complementary skills collaborated to achieve the goal, including the University at Buffalo (UB), Los Alamos National Laboratory (LANL), and Trimeric Corporation (Trimeric). We formulated logical steps to bring the membrane technology from Technology Readiness Level (TRL) 3 (Experimental proof of concept) to TRL 4 (Laboratory scale validation in relevant environment). During the budget period (BP) 1, we screened various polymeric materials and identified polybenzimidazole doped with inorganic polyprotic acids as the desirable platform. The acid doping increases the H 2 /CO 2 selectivity, and the sequential carbonization increases H 2 permeability while retaining the high selectivity. By manipulating the acid type and doping level and the carbonization temperature, we demonstrated advanced carbon molecular sieving (CMS) materials with H 2 permeability of above 200 Barrer (1 Barrer = 10 -10 cm 3 (STP) cm cm -2 s -2 cmHg -1 ) and H 2 /CO 2 selectivity of above 40 at 200-300°C with simulated syngas containing CO and water vapor. For example, the PBI-(H 3 PO 4 ) 0.11 carbonized at 700 °C exhibits H 2 permeability of 200 Barrer and H 2 /CO 2 selectivity of 60 at 200 °C, and H 2 permeability of 240 Barrer and H 2 /CO 2 selectivity of 54 at 225 °C, which meets the targeted properties and surpasses Robeson’s upper bound. During the BP2, we focused on the conversion of the advanced CMS materials to stable HFMs. Membranes with H 2 permeance of 1,090 GPU (1 GPU = 10 -6 cm 3 (STP) cm -2 s -2 cmHg -1 ) and H 2 /CO 2 selectivity of 57 at 300 °C were successfully fabricated. The effects of temperature, gas compositions, pressure, and time on the separation properties were systematically investigated. Pencil modules were continuously evaluated for 219 h (dry pure gas) and 669 h (dry simulated syngas) and showed initial decline in permeance and increased H 2 /CO 2 selectivity over time, ultimately achieving a steady state stable value, indicating that the ageing phenomena in the nanoporous structures of the membranes during the test. Membrane reactors were fabricated based on the CMS membranes and evaluated for water-gas shift (WGS) reaction. The use of membranes slightly improves the conversion of the CO. However, parametric tests of the membrane reactors at various temperatures and flow rates need to be conducted, as well as the membranes with improved separation performance. We performed a sensitivity analysis on the impact of H 2 /CO 2 selectivity on the COE based on a hybrid process of a membrane unit and cryogenic unit developed by Membrane Technology and Research, Inc. (MTR). Three H 2 /CO 2 selectivity (40, 60, and 15) cases were developed and compared with the baseline capture process (Case B5B) provided by the DOE report. Increasing the membrane H 2 /CO 2 selectivity reduces COE, but the rate of the decrease of COE also diminishes. The COE values for H 2 /CO 2 selectivities of 40 and 60 were nearly the same. As the H 2 /CO 2 selectivity increases, the inert recycling decreases, leading to smaller equipment, less auxiliary power requirements, and less heating, cooling, and refrigeration duty. The refrigeration system used to liquefy the CO 2 is the most expensive piece of equipment and consumes the most electricity within the CO 2 capture process. Increasing the CO 2 concentration in the recycle stream would improve the economics of the process by reducing the refrigeration duty requirement of the unit and also allow for higher liquefaction temperatures. The high H 2 -selective membrane developed by our team may be applicable in other separation processes where lower pressure H 2 retains value. Typically, hydrogen retains its pressure when it is separated from syngas components. Residual components may be used as low-quality fuel and then vented to the atmosphere. Applications might include control of H 2 /CO ratios or mitigation of the water gas shift reaction by CO 2 recycling to the feed of a gasifier or steam methane reformer. To summarize, we have developed industrial HFMs with the best H 2 /CO 2 separation performance reported in the literature. The membranes demonstrate stability with simulated syngas and show great potential for membrane reactors for WGS reactions, lowering the cost of blue H 2 production.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site 300 (S300): S300 Roadway Improvements - 817 Complex Soil Sampling and Analysis Plan, Addendum 1 (June 2023)

The purpose of this addendum is to amend and update the S300 Roadway Improvements – 817 Complex [project] Soil Sampling and Analysis Plan (SAP) dated May 2023. Field activities to implement the SAP were completed in May 2023 except for SB-17. SB-17 is therefore included in this addendum. In addition, this addendum amends the SAP by covering an expanded scope of work and project limits. The project limits have been expanded to include areas around Building 818. Sampling at the expanded project area is addressed in this addendum by two additional borings for geotechnical and environmental analyses. The project area consists of the access gate and existing access roads for the Process Area complex. Excavation depths are anticipated to be no deeper than 5 feet from current grade. None of the proposed disturbance areas are within known areas of contamination or mapped controlled excavation area (LLNL 2008). Therefore, this addendum proposes a sampling spacing in the vicinity of Building 818 of about 172 feet (Tier 2) in accordance with the Soil Screening and Management Plan (LLNL 2022). The information contained in this addendum supersedes the May 2023 SAP. However, this addendum only addresses elements of the May 2023 SAP that were modified. Elements of the May 2023 SAP that have not been modified are relevant and will remain in effect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Scalable Upcycling of Spent Lithium-Ion Battery Anodic Graphite to Electronic-Grade Graphene

Recycling processes for lithium-ion batteries (LIBs) are imperative to support the sustainable growth of global energy storage systems. This study introduces a scalable method for the upcycling of spent graphite anodes from LIBs to produce electronic-grade graphene nanoplatelets. In addition to comprehensive materials characterization, the electronic quality of the upcycled graphene is demonstrated by formulating it into a screen printing ink that achieves high-resolution patterning and thin-film electrical conductivity exceeding 104 S m−1. This screen printing ink is also used to print planar micro-supercapacitors with exceptional areal capacitance (1.78 mF cm−2), areal energy density (0.247 µWh cm−2), and cycling stability (> 10 000 cycles). Life cycle assessment (LCA) and techno-economic analysis (TEA) highlight the environmental benefits and cost reductions attainable through upcycling of graphite from LIBs. By capturing economic value from spent LIBs, this work fosters a sustainable battery supply chain and provides an abundant and geographically distributed raw material for electronic-grade graphene.

energy storage↗
DOE OSTI2023

Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Automated workflow for non-empirical Wannier-localized optimal tuning of range-separated hybrid functionals

Here, we introduce an automated workflow for generating non-empirical Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functionals. WOT-SRSH functionals have been shown to yield highly accurate fundamental band gaps, band structures, and optical spectra for bulk and 2D semiconductors and insulators. Our workflow automatically and efficiently determines the WOT-SRSH functional parameters for a given crystal structure and composition, approximately enforcing the correct screened long-range Coulomb interaction and an ionization potential ansatz. In contrast to previous manual tuning approaches, our tuning procedure relies on a new search algorithm that only requires a few hybrid functional calculations with minimal user input. We demonstrate our workflow on 23 previously studied semiconductors and insulators, reporting the same high level of accuracy. By automating the tuning process and improving its computational efficiency, the approach outlined here enables applications of the WOT-SRSH functional to compute spectroscopic and optoelectronic properties for a wide range of materials.

Gant, Stephen E. [University of California, Berkel↗
DOE OSTI2025

Efficient screening of rare large pit anomalies on polished surfaces using a minimalist sampling scheme

Lawrence Livermore National Laboratory (LLNL) has made significant strides in generating clean energy through its inertial confinement fusion (ICF) experiments. These experiments rely on high-density carbon (HDC) coated shells to encapsulate the fusion fuel. The success of these experiments is heavily dependent on the surface quality of these shells, as even minor imperfections, such as deep pits, can negatively impact fusion yield. Ensuring the required smoothness involves an extensive surface-finishing process that spans approximately 20 stages, making it both time-intensive and resource-demanding. A critical challenge in this process is the need for high-resolution scans to detect rare deep pits, which can be costly and impractical if performed on every shell. This highlights the necessity of developing more efficient scanning methods to optimize time and cost without compromising accuracy. To address these challenges, we introduce a novel approach that employs the multivariate Dvoretzky–Kiefer–Wolfowitz (DKW) inequality to provide a probabilistic upper bound on the error in estimating pit distribution characteristics via a Kernel Density Estimator (KDE). This error bound enables efficient and reliable estimation of pit distribution characteristics at a specified statistical confidence level using a minimal number of surface scans. The integrated DKW-KDE approach was validated through surface-finishing experiments across two batches of HDC-coated shells, demonstrating consistent and robust performance across multiple stages of the surface-finishing experiments. The validation studies suggest that the integrated DKW-KDE approach achieves comparable accuracy in estimating the risk of deleterious large pits with six scans, thus conserving time and resources. Further evaluations show that performance remains consistent across batches and over multiple polishing stages. In conclusion, based on these findings, one can leverage the minimal-scan insights to strategically improve the bottleneck inspection process, thus enhancing the productivity and quality of shell polishing and similar challenging manufacturing processes.

Inertial confinement fusion↗
DOE OSTI2021

High-throughput virtual laboratory for drug discovery using massive datasets

Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350× mean speedup over the CPU version (50× speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 50× speedup for the full pipeline reduces an initial 43 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Thermophilic site-specific recombination system for rapid insertion of heterologous DNA into the Clostridium thermocellum chromosome

Clostridium thermocellum is an anaerobic thermophile capable of producing ethanol and other commodity chemicals from lignocellulosic biomass. The insertion of heterologous DNA into the C. thermocellum chromosome is currently achieved via a time-consuming homologous recombination process, where a single stable insertion can take 2–4 weeks or more to construct. In this work, we developed a thermostable version of the Serine recombinase Assisted Genome Engineering (tSAGE) approach for gene insertion in C. thermocellum utilizing a site-specific recombinase from Geobacillus sp. Y412MC61, enabling quick and easy insertion of DNA into the chromosome for accelerated genetic tool screening and heterologous gene expression. Using tSAGE, chromosomal insertion of plasmid DNA occurred at a maximum transformation efficiency of 5 × 10 3 CFU/µg, which is comparable to the transformation efficiency of a replicating control plasmid in C. thermocellum. Using tSAGE, we chromosomally integrated and characterized 17 reporter genes, 15 homologous and 31 heterologous constitutive promoters of varying strengths, 4 inducible promoters, and 5 riboswitches in C. thermocellum. We also determined that a 6–7 nucleotide gap between the ribosome binding site (RBS) and the start codon is optimal for high expression by employing a library of superfolder green fluorescent protein expression constructs driven by our strongest tested promoter (P clo1313_1194 ) with different distances between the RBS and start codon. The tools developed here will aid in accelerating C. thermocellum strain engineering for producing sustainable fuels and chemicals directly from plant biomass.

Biofuels↗
DOE OSTI2020

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture

We screen a database of more than 69 000 hypothetical covalent organic frameworks (COFs) for carbon capture using parasitic energy as a metric. To compute CO 2 –framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; more than 70 are better performers than the best experimental COFs and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance to guide the development of improved carbon capture materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploring New Diamond Surfaces with Precision Chemistry and Quantum Spectroscopy

Point defects in diamond known as color centers are a promising platform for quantum sensing. As atom-like systems, they can exhibit excellent spin coherence and can be manipulated with light. As solid-state defects, they can be produced at high densities and be incorporated into scalable devices. Diamond is a uniquely excellent host; it has a large band gap, can be synthesized with sub-ppb impurity concentrations, and can be isotopically purified to eliminate magnetic noise from nuclear spins. Specifically, the nitrogen vacancy (NV) center has been demonstrated to be a highly sensitive, non-invasive magnetic probe capable of resolving the magnetic field of a single electron spin with nanometer spatial resolution. However, the development of NV centers and other color centers in diamond as a quantum platform crucially relies on developing methods to functionalize and process diamond surfaces. Diamond is the hardest material, making polishing and processing challenging, and diamond surfaces are inert and sterically hindered, making them notoriously difficult to chemically terminate and functionalize. Our approach is to discover new surface chemistries for diamond by combining multimodal surface spectroscopy with NV-based quantum spectroscopy to carefully characterize the diamond surface. We have developed a suite of techniques for characterizing the diamond surface using rapid tools for reaction screening and detailed surface spectroscopy tools for definitive identification of the surface bonding.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Standard Analytical Methods for Pyrolysis Bio-Oils

There has been significant recent interest in the production of renewable fuels and chemicals from biomass and waste feedstocks. Pyrolysis pathways produce a liquid bio-oil product, which must be processed further, or upgraded, to yield fuel or chemical products. Bio-oils are very complex and often unstable samples, and research and development on upgrading processes needs reliable analytical information. In particular, chemical characterization techniques are needed to quantify both functional groups and individual compounds present in bio-oils. Reliable analytics are also needed to enable the bioenergy industry, as industrial facilities often have different analytical needs and capabilities than research facilities. In this presentation, we will discuss the development of a suite of standard analytical methods for pyrolysis bio-oils. Analytical methods to be discussed include: Determination of Carbon, Hydrogen, Nitrogen, and Oxygen in bio-oils; Accelerated Aging of Fast Pyrolysis Bio-oil using Carbonyl Titration; Determination of Water Content in Bio-oils by Volumetric Karl Fischer Titration; Determination of Carbon Functional Groups; Elemental Analysis of Bio-oils by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) - Na, K, Mg, Ca, S, P, and Fe; Determination of Phenolic Groups in Bio-oils using Revised Folin-Ciocalteu Methods: Single Cuvette and Plate Reader; Corrosivity of Bio-oils: Screening Test using Metal Leaching; Determination of Biogenic Content by 14C Measurement using Liquid Scintillation Counter. These new analytical methods are publicly available as Laboratory Analytical Procedures (https://www.nrel.gov/bioenergy/bio-oil-analysis.html), along with previously developed standard methods: GC-MS, Acid Titration, Carbonyl Titration, and 31P NMR. Additionally, the development of diffusion ordered NMR for characterization of bio-oil molecular weight will be discussed. Collectively, this suite of analytical methods represents the most comprehensive set of standard methods available for pyrolysis bio-oils. These standard methods are commonly used by the bioenergy community, and provide reliable information that enables research, scaleup, and industrial processing of biomass to produce renewable fuels and chemicals.

analytical↗
DOE OSTI2025

A Computational Workflow of Elucidating Viral Impact on Mediating Microbial Response to In-situ Experimental Warming: Bridging microbial modeling to carbon and mineral modeling

Viruses are abundant in soils and shape microbial communities in ways that can potentially influence ecosystem processes, yet their contributions to carbon cycling and mineral transformations remain poorly understood. Here we present a multi-phase framework that links virus-host interactions to soil biogeochemistry by combining ecological simulations, genome- and community-scale metabolic modeling, and statistical and machine-learning analyses. We first calibrated microbial abundance profiles under explicit infection scenarios to capture how viral pressure alters community structure, then explored alternative interaction strategies, including kill-the-winner, piggyback-the-winner, and mixed lytic-lysogenic modes, through forward simulations. These ecological shifts were translated into metabolic consequences using exchange fluxes summarized into biologically meaningful categories, while integrated statistical and machine-learning screens elevated subtle but consistent signals. Application of this framework revealed that viral infections shift the balance between organic and inorganic fluxes, redirecting metabolism from diffuse organic transformations toward inorganic pools such as protons and CO 2 , directly linking viral regulation to respiration and soil carbon balance. The roll-up analysis also isolated perturbations in critical mineral ions, including magnesium, manganese, zinc, and copper, which serve as essential enzymatic cofactors. In piggyback-the-winner scenarios, uptake of these ions was strongly suppressed. Contrasting viral strategies produced distinct community structures and metabolic outcomes, from broad suppression under kill-the-winner dynamics to dramatic redistributions under high-lytic and high-gain lysogenic regimes that collapsed vulnerable microbial populations while promoting opportunists. Together, these results provide a tractable path to trace viral perturbations from host abundance shifts to metabolic flux adjustments and ecosystem-scale processes, offering a practical way to include viruses in earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A statistical approach to screening isotopic signatures in monitoring for underground nuclear explosions

The ability to differentiate between atmospheric radionuclide signatures from underground nuclear explosions (UNEs) and signals from other sources, such as medical isotope-production facilities and nuclear reactors, can be critical to the detection and monitoring of unannounced, low-yield nuclear events. Signatures having anomalously high amplitudes, compared to background levels, remain the best indicator in screening for a UNE. However, isotopic composition can further validate a suspected UNE signature, but separation from any atmospheric background composition is first necessary. To date, evaluating the challenges of performing this separation has typically involved comparing an observed background with a highly idealized deterministic model of radioxenon signature production by a UNE that does not consider the influence of post-detonation chemical/physical processes in the detonation cavity or the subsequent gas transport mechanisms that can also affect the isotopic composition of the detected gas signature. In addition, purely deterministic models, as previously employed, overlook the uncertainty inherent in estimating critical parameters characterizing the UNE and its detonation environment. In this paper, we create detailed, multi-parameter models of radionuclide evolution using the widely accepted England and Rider post-detonation radionuclide decay-chain network coupled to detailed models simulating physical production and transport processes affecting the gas signature. Because these models are governed by uncertain parameters including barometric fluctuations, realistic ranges of variation for each of the parameters influencing isotopic composition are then defined. A Latin-Hypercube sampling approach is used to obtain a random distribution of isotopic production and gas transport results associated with a given value of each parameter. We apply these results to background histories of two stations, one providing 4-isotope background measurements and the other providing two-isotope measurements associated with the 2013 DPRK announced UNE.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

High-salinity brine desalination with amine-based temperature swing solvent extraction: A molecular dynamics study

Solvent extraction desalination (SED) is a promising technique for efficiently treating high salinity brines. However, only a few studies in the literature have investigated the influence of the molecular characteristics of the organic solvents on the emergent performance of the separation process. Thus, in this work we investigate the influence of the molecular structure of four different secondary amines on the desalination process of a concentrated aqueous solution of NaCl. Molecular dynamics simulations and free energy calculations are used to obtain the thermodynamic and structural properties of the pure solvent bulk phase, as well as a homogeneous solvent-water bulk mixture. Furthermore, solvent-water and solvent-brine interfacial systems are used to explicitly analyze the water partitioning behavior in the relevant temperature range for SED. The water solubilization in these secondary amines is found to be an exothermic process. Additionally, although the hydrogen-bonding interaction between the water and solvent molecules are beneficial, this interaction is not necessarily the dominant factor for ranking the water solubility performance of the different solvents. Finally, the excess volume of mixing of the bulk solvent-water mixture, as well as the electrostatic surface features of the solvents are also found to provide important clues for screening solvent behavior.

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

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗