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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Materials genome innovation for computational software (magics) center

Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.

2D Materials↗
DOE OSTI2022

Octane Requirements of Lean Mixed-Mode Combustion in a Direct-Injection Spark-Ignition Engine

Here, this study investigates the octane requirements of a hybrid flame propagation and controlled autoignition mode referred to as mixed-mode combustion (MMC), which allows for strong control over combustion parameters via a spark-initiated deflagration phase. Due to the throughput limitations associated with both experiments and 3-D computational fluid dynamics calculations, a hybrid 0-D and 1-D modeling methodology was developed, supported by experimental validation data. This modeling approach relied on 1-D, two-zone engine simulations to predict bulk in-cylinder thermodynamic conditions over a range of engine speeds, compression ratios, intake pressures, trapped residual levels, fueling rates, and spark timings. Those predictions were then transferred to a 0-D chemical kinetic model, which was used to evaluate the autoignition behavior of fuels when subjected to temperature–pressure trajectories of interest. Finally, the predicted autoignition phasings were screened relative to the progress of the modeled deflagration-based combustion in order to determine if an operating condition was feasible or infeasible due to knock or stability limits. The combined modeling and experimental results reveal that MMC has an octane requirement similar to modern stoichiometric spark-ignition engines in that fuels with high research octane number (RON) and high octane sensitivity (S) enable higher loads. Experimental trends with varying RON and S were well predicted by the model for 1000 and 1400 rpm, confirming its utility in identifying the compatibility of a fuel’s autoignition behavior with an engine configuration and operating strategy. However, the model was not effective in predicting (nor designed to predict) operability limits due to cycle-to-cycle variations, which experimentally inhibited operation of some fuels at 2000 rpm. Putting the operable limits and efficiency from MMC in the context of a state-of-the-art engine, the MMC showed superior efficiencies over the range investigated, demonstrating the potential to further improve fuel economy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Impact of fission yield covariance matrices on decay heat uncertainty quantification with the DARWIN2 package

Although fission yields are strongly correlated, correlations between them are usually not taken into account when performing decay heat uncertainty calculations with the CEA DARWIN2 package due to the lack of reference covariance matrices associated to the JEFF-3.1.1 evaluation. However, covariance matrices for {sup 235}U and {sup 239}Pu thermal fission yields have been produced recently by the subgroup 37 of OECD/NEA Working Party on International Nuclear Data Evaluation Cooperation (WPEC). The study presented in this paper evaluates the effect of those covariances on the decay heat uncertainty calculation, both on fission burst experiments and on integral decay heat measurements that are part of the DARWIN2 experimental validation database. Although some differences are observed, partly due to the variety of models used to produce the matrices, the propagation of fission yield covariances always leads to a reduction of the decay heat uncertainty for cooling time above 10 seconds, indicating that not taking them into account is a conservative hypothesis from a safety point of view. Nevertheless, the strong impact of those covariances on the decay heat uncertainty also highlights the need of documented and consistent fission yield uncertainties and correlation data. On top of that, all the covariance matrices used for this study represent the correlation between the physical model parameters, but none of them includes the experimental correlations. An evaluation of the experimental correlations would also be of strong interest for decay heat uncertainty calculations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Silicon photonic chip for 16-channel wavelength division (de-)multiplexing in the O-band

We experimentally demonstrate a silicon photonic chip-scale 16-channel wavelength division multiplexer (WDM) operating in the O-band. The silicon photonic chip consists of a common-input bus waveguide integrated with a sequence of 16 spectral add-drop filters implemented by 4-port contra-directional Bragg couplers and resonant cladding modulated perturbations. The combination of these features reduces the spectral bandwidth of the filters and improves the crosstalk. An apodization of the cladding modulated perturbations between the bus and the add/drop waveguides is used to optimize the strength of the coupling coefficient in the propagation direction to reduce the intra-channel crosstalk on adjacent channels. The fabricated chip was validated experimentally with a measured intra-channel crosstalk of ∼−18.9 dB for a channel spacing of 2.6 nm. The multiplexer/demultiplexer chip was also experimentally tested with a 10 Gbps data waveform. The resulting eye-pattern indicates that this approach is suitable for datacenter WDM-based interconnects in the O-band with large aggregate bandwidths.

Davis, Jordan A. (ORCID:0000000343483295)↗
DOE OSTI2023

Integration of seismic-pressure-petrophysics inversion of continuous active-seismic monitoring data for monitoring and quantifying CO 2 plume (Final Report)

The overall objective of this project is to develop and validate an integrated package of joint seismic-pressure-petrophysics inversion (jSPPI) of continuous active-source seismic monitoring dataset capable of providing real-time monitoring of CO 2 plume during geologic carbon sequestration (GCS). The three specific developments include: (a) the methodologies for fast seismic full waveform inversion of continuous active source seismic monitoring, (CASSM) datasets for simultaneously estimating velocity and attenuation, and with data assimilation; (b) joint Bayesian petrophysical inversion of seismic models and pressure data for providing and updating CO 2 saturation models; (c) the methods using multiple datasets including (Crainfield and Frio-II borehole) synthetic, laboratory, and field CASSM datasets. The outcomes of jSPPI include (a) a workflow for processing CASSM data, (b) Bayesian inversion algorithms using CASSM data and pressure response data, and (c) integration with data assimilation algorithms for continuously updating site-specific models used for prediction and reservoir management. The validation of joint FWI will be conducted using synthetic models based on the Cranfield and Frio experiments as well as field CASSM datasets collected as part of the Frio-II pilot injection. To quantify and map the mass and distribution of CO 2 (saturation), we will jointly invert velocity and attenuation measurements from the FWI with a Bayesian approach using a rock physics model for attenuation (e.g., White’s attenuation model with two selected patch sizes (White, 1976; Dutta and Seriff, 1979)). The Bayesian inversion will be applied to each time step in the CASSM survey in an updating scheme, which integrates with an ensemble of reservoir simulations at each step. A more complete experimental validation dataset will be collected as part of a mesoscale (2-3 m) gas-CO 2 injection experiment utilizing a higher frequency version of the CASSM system developed for laboratory studies; the integrated inversion will be demonstrated using this dataset which will provide both a dense geometry as well as more precise secondary confirmation measurements (e.g. saturation) typically not available in the field. The resulting real-time map of CO 2 saturation is able to provide a deeper scientific understanding of the complex, time-varying dynamics of subsurface fluid flow migration path as well as the rapid detection of CO 2 leakage hazards.

25 ENERGY STORAGE↗
DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

EF-Hand Battle Royale: Hetero-ion Complexation in Lanmodulin

The lanmodulin (LanM) protein has emerged as an effective means for rare earth element (REE) extraction and separation from complex feedstocks without the use of organic solvents. Whereas the binding of LanM to individual REEs has been well characterized, little is known about the thermodynamics of mixed metal binding complexes (i.e., heterogeneous ion complexes), which limits the ability to accurately predict separation performance for a given metal ion mixture. In this paper, we employ the law of mass action to establish a theory of perfect cooperativity for LanM-REE complexation at the two highest-affinity binding sites. The theory is then used to derive an equation that explains the nonintuitive REE binding behavior of LanM, where separation factors for binary pairs of ions vary widely based on the ratio of ions in the aqueous phase, a phenomenon that is distinct from single-ion-binding chemical chelators. We then experimentally validate this theory and perform the first quantitative characterization of LanM complexation with heterogeneous ion pairs using resin-immobilized LanM. Importantly, the resulting homogeneous and heterogeneous constants enable accurate prediction of the equilibrium state of LanM in the presence of mixtures of up to 10 REEs, confirming that the perfect cooperativity model is an accurate mechanistic description of REE complexation by LanM. We further employ the model to simulate separation performance over a range of homogeneous and heterogeneous binding constants, revealing important insights into how mixed binding differentially impacts REE separations based on the relative positioning of the ion pairs within the lanthanide series. In addition to informing REE separation process optimization, these results provide mathematical and experimental insight into competition dynamics in other ubiquitous and medically relevant, cooperative binding proteins, such as calmodulin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Shock compression of vanadium at extremes: Theory and experiment

Here, the equation of state (EOS) and shock compression of bulk vanadium were investigated using canonical ab initio molecular dynamic simulations, with experimental validation to 865 GPa from shock data collected at Sandia's Z Pulsed Power Facility. In simulations the phase space was sampled along isotherms ranging from 3000 K to 50 000 K, for densities between ρ = 3 and 15 g / cm 3 , with a focus on the liquid regime and the body-centered-cubic phase in the vicinity of the melting limit. The principal Hugoniot predicted from first principles is overall consistent with shock data, while it showed that current multiphase SESAME-type EOS for vanadium needed revision in the liquid regime. A more accurate SESAME EOS was developed using constraints from experiments and simulations. This work emphasizes the need to use a combined theoretical and experimental approach to develop high-fidelity EOS models for extreme conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Effect of NO on DME-Methanol HCCI Combustion Using a Reduced Chemical Kinetics Mechanism

Methanol is an attractive fuel for the maritime sector due to its wide availability. Its direct use as a fuel, however, is accompanied by challenges such as high latent heat of vaporization and low cetane number. A potential solution to overcome the ignition properties of methanol could be through on-board generation of dimethyl ether (DME) via catalytic dehydration of methanol. The resulting mixture from dehydration can be mixed in with the intake air to generate a homogenous charge compression ignition (HCCI) preburn for subsequent direct injection (DI) mixing controlled compression ignition (MCCI) of neat methanol. Within that context, complementary experimental work found that the influence of combustion residuals on the heat release rate (HRR) was significant, specifically for residual NO. This finding motivated the present computational and kinetic evaluation of the effects of NO on the low (LTHR) and high (HTHR) temperature heat release rates. The strong influence of small quantities of NO on the combustion process of a DME/methanol/H2O mixture (low catalyst or reactor efficiency) necessitated a kinetics-based investigation into this phenomenon. A mechanism sourced from the existing literature with NO had 172 species and 1375 reactions, making it computationally expensive for use. Hence, a mechanism reduction effort was implemented, and a rate constant (k) tuning effort based on sensitivity analysis was needed to validate experimental results using a zero-dimensional engine model in Cantera. The reduced mechanism was able to successfully capture the negligible influence of NO addition on DME HCCI combustion, whereas an advancement in LTHR and HTHR for a DME/methanol/H2O mixture was kinetically confirmed. Reaction pathway analysis showed that addition of NO chemically counteracted the OH sink created by alcohols like methanol, increasing the effectiveness of DME ignition.

Tyrewala, Daanish [ORNL] (ORCID:0000000208599324)↗
DOE OSTI2022

A spatial beam property analyzer based on dispersive crystal diffraction for low-emittance X-ray light sources

Abstract The advent of low-emittance synchrotron X-ray sources and free-electron lasers urges the development of novel diagnostic techniques for measuring and monitoring the spatial source properties, especially the source sizes. This work introduces an X-ray beam property analyzer based on a multi-crystal diffraction geometry, including a crystal-based monochromator and a Laue crystal in a dispersive setting to the monochromator. By measuring the flat beam and the transmitted beam profiles, the system can provide a simultaneous high-sensitivity characterization of the source size, divergence, position, and angle in the diffraction plane of the multi-crystal system. Detailed theoretical modeling predicts the system’s feasibility as a versatile characterization tool for monitoring the X-ray source and beam properties. The experimental validation was conducted at a bending magnet beamline at the Swiss Light Source by varying the machine parameters. A measurement sensitivity of less than 10% of a source size of around 12 µm is demonstrated. The proposed system offers a compact setup with simple X-ray optics and can also be utilized for monitoring the electron source.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A standardized quantitative analysis strategy for stable isotope probing metagenomics

ABSTRACT Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg , an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, “who is eating what?” and “who is active?” within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism’s taxonomic identity and genome composition while providing quantitative estimates of the microorganism’s isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Microchannel-based Membrane-less Extraction of Li from Unconventional Lithium Sources & the Separation of REE

This final report provides an overview of the Project's entire duration, covering July 1, 2021 to December 31, 2023. It primarily focuses on the achievements, technological developments, and unique challenges the team faced while working on separating and extracting Lithium from produced waters. The project's primary aim was to create an integrated, high-throughput, membrane-less, and modular microfluidic platform that could extract Lithium from unconventional sources. We have successfully met all goals and milestones envisioned in the SOPO document. The most critical primary milestones, including the Go-No-Go milestone (refer to the Gantt chart in the Appendices), were successfully accomplished. We demonstrated phase separation (>90%) and extraction (>85%) performance in the MPSE using synthetic, and representative produced water composition feed at 50 ml/min total flow through MPSE 36. We have also performed a parametric study of the MPSE operations, beyond the scope of SOPO, exploring operating conditions of current and broader interest. The extended investigation of operational parameters is concurrent with our efforts to seek further development of the MPSE technology beyond the scope of the Project. Along these lines of development, we have made efforts to be responsive to DOE calls for technological developments of other types of resources (beyond PW) for the recovery of Critical Materials and higher TRL development (beyond TRL 4). During the work on this Project, we developed and implemented three innovative technical approaches that emerged from our efforts to successfully meet the Project milestones. The innovative & original technical approaches developed and implemented in this Project are now the contributions to process engineering that could be clearly credited to the Project. First, Convergent Design Approach is a comprehensive feedforward & feedback loop of four design phases: i) design for functionality, ii) design for manufacturing, iii) design for sustainability, and iv) design for market. Next was Process Intensification. A major aim of this Project was to create an innovative phase separation & extraction microscale-based technology for Li separation – thus the words microchannel-based in the Project title. A microscale-based technology is intrinsically in the center of the Process Intensification domain as defined by its unique principles. Therefore, Process Intensification was implicitly envisioned in the Project’s SOPO. Lastly, Time Scale Analysis is a novel tool for discovering the needs and directions of Process Intensification implementations in any process technology. This Project is fully credited for developing and implementing the three novel technical approaches mentioned above. These are general contributions to process engineering that emerged from this Project. Beyond the original SOPO scope, the OSU-U.Pitt research group utilized a Convergent Design methodology, integrating first-principles mathematical modeling with experimental validation on the Minimum Development Vehicle. By creating these Digital Twins, the team rapidly assessed manufacturing iterations to support TEA analysis. This framework further enabled the development of advanced Surface Modification Techniques, where hydrophobic and oleophobic coating strategies were optimized via Digital Twin tools and validated through rigorous 100-hour longevity testing. TEA Analysis: The closing efforts of this Project were focused on the TEA analysis. TEA analysis had two primary functions: i) enabling critical assessments of design variations withing 10 the Concurrent Design Approach, thus enabling evolution of the MPSE design to reach faster- better-cheaper alternatives; and ii) to create a bridge between the accomplishments of this Project and future projects of higher TRL, beyond TRL 6 level. It is important to note that the TEA model created in the Project stirred the technological solutions for the recovery of critical materials toward a vision of a very profitable modular plant that has unique zero-waste water discharge signature. More importantly, thanks to our experimental performance data and conservative assumptions, the TEA model predicts minimal technological and investment risks. Low cost of a modular unit of a nominal capacity of [1000 tons of Li 2 CO 3 /year] positions the MPSE based technology within the reach of community investors, thus offering a paradigm shift in the development of critical technologies. The project successfully navigated two primary challenges: solvent selection and manufacturing adaptation. Restricted by the SOPO to existing literature for lithium recovery, the team identified a critical need for a "material excellence program" to develop next-generation solvents, eventually concluding with a preliminary investigation into promising Ionic Liquids (ILs). Simultaneously, COVID-19 supply chain disruptions forced a pivot from traditional manufacturing to advanced additive methods at ATAMI-OSU. By transitioning from stainless steel to 3D-printed polymer substrates, the team achieved a transformative three-order-of- magnitude reduction in manufacturing costs and compressed prototyping timelines from several months to just two days. The MPSE technology offers significant energy, environmental, and economic advantages by overcoming the traditional bottlenecks of phase-separation hardware and contactor size. Unlike conventional mixer-settlers or membrane-based systems, MPSE operates without moving parts or fouling-prone membranes, achieving robust performance even with challenging, viscous, or particulate-heavy feeds. Key performance metrics include an energy intensity reduction of 5–50x (3–40 kJ/m 3 ) compared to incumbent technologies and a dramatic reduction of processing time to under 60 seconds, which drastically reduces the physical plant footprint. These technical efficiencies translate into superior economic outcomes; for a 100 t/year Li 2 CO 3 facility, implementing MPSE is projected to nearly halve contactor CAPEX (from $\$$6.08M to $\$$3.01M) and significantly increase the project's Net Present Value (NPV), derisking new investment and enabling distributed critical-mineral processing configurations. The commercialization of MPSE technology is being spearheaded by Vigsur Dynamics Inc., which has adopted a structured, parallel approach to technical and business development since its formation in January 2026. Following extensive customer discovery and engagement with the Oregon State University accelerator, Vigsur Dynamics is working to establish a business model that transitions from pilot demonstrations to modular hardware sales, ultimately aiming for a "build-own-operate" service strategy. Current technical milestones—including 100 hours of continuous operation, superior energy efficiency, and successful 6-unit modular scale-up— provide a foundation for this transition. Backed by ongoing IP licensing and a growing network of industrial and venture advisors, the company is actively de-risking the platform to replace conventional mixer-settler systems in the critical minerals market.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Passive Energy-Saving Solutions for Clothes Dryers: A Modeling and Experimental Study for Improved Efficiency and Affordability

Dryers are integral appliances in modern households, yet their significant power consumption remains a critical challenge for reducing energy bills and upgrades. This work presents the outcomes of a comprehensive investigation aimed at lowering the operating energy costs of the dryer through modeling and experimental approaches. Specifically, the objectives of this research are twofold: (1) to reduce energy consumption without negatively impacting drying performance or time; and (2) to ensure affordability by developing retrofittable solutions characterized by low cost and a quick payback period. These strategies promise universal applicability to all dryer categories, encompassing both gas and electric models, by tackling core issues such as unnecessary heat loss and excess energy supply, both prevalent across dryer designs. Our methodology combines robust modeling frameworks and experimental validation to target energy efficiency improvements through three primary pathways: (1) effective heat loss management using ultralow-cost insulation materials tailored for dryer systems; (2) heat management across the drying cycle, facilitated by passive heat transfer mechanisms, and (3) optimization of heat supply to the load to ensure precise energy delivery, minimizing waste. These innovations are designed to seamlessly integrate with existing dryer configurations, providing a scalable and retrofittable solution that ensures affordability without requiring substantial redesigns or expensive components. Using these methods, the study demonstrates improvement opportunities in dryer energy consumption while maintaining the desired performance. The modeling component utilizes computational simulations to evaluate the thermal and energy performance of these innovations under varied operational conditions, providing foundational insights for experimental design. By addressing critical areas such as heat loss, energy recovery, and supply optimization, this work proposes impactful solutions for lowering the energy costs in domestic clothes dryers. The ultralow-cost, retrofittable nature of the proposed strategies ensures widespread adoption potential across diverse dryer categories, making significant strides toward affordable household energy practices.

Cheekatamarla, Praveen [ORNL] (ORCID:0000000248827↗
DOE OSTI2023

Computational modeling and validation of a modified Marple cascade impactor

Oak Ridge National Laboratory (ORNL) is investigating respirable aerosol fractions from failed high-burnup (HBU) commercial spent nuclear fuel (CSNF) rods (Montgomery et al., 2022). The work aims to characterize the source term of radioactive aerosols that could result from mechanical failure and handling of failed fuel rods during storage and/or transportation. The experiment involves capturing and characterizing aerosols from a four-point bending experiment of fueled rod segments at ORNL’s Irradiated Fuels Examination Laboratory (IFEL). For this, a Marple cascade impactor (Series 290) with the ability to collect particles up to aerodynamic equivalent diameters of ~15 μm was tested for feasibility. The commercially available Marple cascade impactor design was modified for the experiment, where the cascade inlet was replaced with a 3D printed inlet nozzle. Collection efficiency curves were then generated with the modified impactor, and the cascade’s performance was tested using ISO dust capture experiments followed by SEM analysis of the collection and computational modeling. The current work highlights the performance testing and experimental validation of this modified Marple Impactor for spent nuclear fuel aerosol collection.

Kumar, Vineet↗
DOE OSTI2022

Approaching PetaVolts per Meter Plasmonics Using Structured Semiconductors

A newly uncovered class of plasmons in the strongly excited limit opens access to unprecedented Petavolts per meter electromagnetic fields with wide-ranging, transformative impact. Unlike conventional plasmons, such plasmons are constituted by non-perturbative, large-amplitude oscillations of the ultradense, delocalized free electron Fermi gas inherent in conductive media. Here structured semiconductors doped to have an appropriate conduction electron density are introduced to tune the properties of the Fermi gas for matched excitation of large-amplitude plasmons using readily available electron beams which enables immediate experimental validation. Specifically, an electrostatic, surface “crunch-in” plasmon is collisionlessly excited by the beam launched inside a tube. Strong excitation due to matching results in relativistic oscillations of the electron gas and unravels unique phenomena. Relativistically induced ballistic electron transport comes about due to relativistic multifold increase in the mean free path and also leads to unconventional heat deposition beyond Ohm’s law. This explains the absence of observed damage or solid-plasma formation in past experiments on conductive samples interacting with electron bunches shorter than 10-13 seconds. Furthermore, relativistic momentum leads to copious tunneling of electron gas across the surface, which then crunches inside the tube. Relativistic effects along with large, localized electron density variations underlying these modes necessitate kinetic approach to theoretical and computational modeling. Kinetic model presented here demonstrates experimental viability of observing tens of gigavolts per meter plasmonic fields excited by matching readily available electron beams to plasmons in semiconductors with 1018cm-3 free electron density, and paves the way for Petavolts per meter plasmonics.

42 ENGINEERING↗
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Project DarkStar: Vision for LLNL in 2030

DarkStar was a Strategic Initiative (FY2021-FY2024) to investigate applications of Artificial Intelligence (AI) and Machine Learning (ML) to scientific problems of complex hydrodynamics, shockwave physics and energetic materials. The research focused on physics and engineering design as a process that can be tremendously accelerated through merging AI with advanced physics simulation on exascale-class platforms, and to experimentally validate this revolutionary new approach through dynamic materials campaigns. A central thread of scientific inquiry was in the application of AI to enable human understanding of how to control hydrodynamic instability (which has impacts to areas such as inertial confinement fusion) via engineering features and time-dependent sources. Motivated by an unfinished line of research started by Dr. Johnny von Neumann, AI-enabled simulation approaches were developed that allowed DarkStar researchers to uncover several ground-breaking discoveries regarding hydrodynamic instability, including how to completely suppress Richtmyer-Meshkov instability (RMI). These S&T discoveries, along with other advances, have shown the way for an entirely new approach to time-dependent problems known as inverse design – the idea that complex systems can be developed directly from a final state that is to be achieved and resolve the initial design via satisfying several constraints simultaneously via AI/ML. Through experimental campaigns conducted across a wide range of facilities in the NNSA complex (the High Explosive Application Facility at LLNL, the Dynamic Compression Sector/Advanced Photon Source at Argonne National Lab, and Special Technologies Laboratory at MSTS) the radical new AI/ML approach to engineering complex material dynamics was verified, establishing a new field of study within the realm of shock physics. As advanced manufacturing capabilities continue to develop, the great importance of inverse design as a means to apply that technology effectively for NNSA missions will feature prominently over this decade. DarkStar has positioned NNSA as a world-leader in this newly emerging cross-disciplinary area of AI methods for advanced physics simulation and pioneered multiple novel approaches that have enabled the broader scientific community. By allowing us to see past the horizon, to 2030 and beyond, DarkStar has illuminated the vast potential of AI/ML to impact a wide range of new national security missions and, consequently, multiple areas of further research have already emerged across the NNSA and DOD complex.

42 ENGINEERING↗
DOE OSTI2025

Screening and Development of Sacrificial Cathode Additives for Lithium‐Ion Batteries

Abstract This work presents a computational screening approach to identify Li‐rich transition‐metal oxide sacrificial cathode additives and provides experimental validation of antifluorite‐structured Li 6 MnO 4 as a potential candidate. Initial attempts to synthesize this compound result in low purity (≤40% by weight) owing to close thermodynamic competition with Li 2 O and MnO at low temperature. However, it is shown that a much higher purity of 85% by weight can be achieved by combining Li excess with rapid cooling from high temperature, which effectively stabilizes the Li 6 MnO 4 phase. The synthesized product delivers a high irreversible Li release capacity that exceeds 700 mAh g −1 by utilizing combined Mn oxidation (Mn 2+/3+ and Mn 3+/4+ ) and O oxidation. These results demonstrate that Li 6 MnO 4 may therefore be useful as a potential sacrificial cathode additive in Li‐ion batteries and motivate further investigation of other structurally‐related compounds. While attempts were made to synthesize two additional compounds among computationally screened candidates, it was not successful to experimentally realize the two candidates. The difficulty of experimental realization of the newly predicted materials remains a challenge and it is suggested that more efforts need to be devoted to developing computational techniques to precisely predict synthesizability and propose potential synthetic routes of the predicted materials.

Kim, Haegyeom↗