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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

FY24 Advanced Computing HPC Annual Report for allocation "vtocei"

This project aims to elucidate the mechanisms underlying the formation of the cathodeelectrolyte interphase (CEI) and its impact on the performance of Li-ion batteries during electrochemical cycling. Our approach integrates advanced multiscale modeling with multimodal characterization to offer a comprehensive understanding of CEI dynamics. We employ a validated multiscale modeling framework to analyze microstructure-dependent transport properties, alongside joint theory-experiment protocols for detailed resolution of interfacial chemistry via spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of Flow-Enhanced Electrochemical Sensor Testing and Deployments for MSRs

This report serves as the deliverable for Milestone M3RS-23AN0401061 that is part of Work Package RS-23AN040106 (Flow Enhanced Sensors for MSRs – ANL). The goal of this milestone was to determine performance of the flow enhanced electrochemical sensor (FEES) and modular flow instrumentation testbed (MFIT) in safeguards relevant scenarios. Flow enhanced electrochemical sensors are a type of electroanalytical sensor that has been developed at Argonne National Laboratory to be installed directly into MSR flow conduits to make measurements of the salt composition. These sensors represent a significant improvement in capabilities compared to earlier electroanalytical sensors that instead can only be operated in quiescent conditions. Previous work has focused on testing of the FEES in flowing conditions provided by the MFIT to assess the accuracy and precision of the sensor measurements. To further improve this capability, in FY23 we undertook a campaign of safeguards relevant scenarios in molten salt containing a range of uranium chloride concentrations (0 to 3 wt%). All the testing carried out in FY23 was aided by a control system designed to automatically actuate flow conditions and collect data. This new automation system is estimated to have increased experimental throughput by a factor of four and enabled testing in a variety of complex conditions. The advancements in throughput and repeatability led to improved quantification of actinide concentrations using the in-flow sensors, with a reduction of the mean absolute relative error from 5.6% in FY22 to 3.1% in FY23. In addition to safeguards scenarios run in the MFIT, FY23 work included deployment of a FEES at a partner institution where it will be tested in a flowing salt loop. The FEES was successfully integrated into that loop and is being tested prior to loop startup. In FY23, work also continued on the smaller flow system that we have named the mini-MFIT. This smaller system is capable of rapid prototyping of new sensor designs prior to installation in the larger MFIT radiological flow system. Work was carried out to test this new system in non-radiological molten salts in a separate glovebox. This work is helping us to enhance the accuracy of our salt monitoring capabilities through the integration of multiple types of sensors. The high degree of accuracy required by 10 CFR 74 represents a significant challenge, and further design evolution and integration of the sensors into multimodal sensing frameworks will be needed to further push the measurement accuracy to the needed level.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Assessment of Flow-Enhanced Sensors for Actinide Quantification in MSRs

This report serves as the deliverable for Milestone M2RS-22AN0401062 that is part of Work Package RS-22AN040106 (Flow Enhanced Sensors for MSRs – ANL). The goal of this milestone is to assess the capabilities of flow-enhanced electrochemical sensors (FEES) to quantify actinides in molten salt reactors. Flow enhanced electrochemical sensors are a type of electroanalytical sensor that has been developed at Argonne National Laboratory to be installed directly into MSR flow conduits in order to make measurements of the salt composition. These sensors represent a significant improvement in capabilities compared to earlier electroanalytical sensors that instead can only be operated in quiescent conditions. To support this work, Argonne constructed a molten salt flow system to serve as a testbed for the development of safeguards and process monitoring-relevant sensors in challenging molten salt flow conditions. Through extensive flow testing using UCl3-bearing salts, Argonne has been able to show that the FEES are able to make good measurements of salt composition. Concentration measurements with a mean absolute error of 0.09 wt% have been achieved for representative fuel salt mixtures across a range of UCl3 loadings. Repeatability was also good, with the relative standard deviation from repeated measurements being less than 1.0%. These sensors have also been shown to be able to measure the flow rate of the salt within the flow system’s transfer line. Although the accuracy of the sensors has been good, the sensors have thus far not been able to achieve performance good enough to satisfy the nuclear material accountancy requirements in 10 CFR 74. To do so, accuracy levels of 0.1% must be achieved; the FEES when operated in Argonne’s flow system have so far only been able to achieve relative errors of approximately 5.0%. Much of the error that has been observed at this stage is due to challenges in maintaining a stable salt composition in the molten salt flow system, and it is not believed that there are any inherent flaws in the sensor design. Nonetheless, the high degree of accuracy required by 10 CFR 74 represents a significant challenge, and further design evolution and integration of the sensors into multimodal sensing frameworks will likely be needed to push the measurement accuracy to the needed level.

Moore, Colin E.↗
NASA NTRS1988

Development and flight test experiences with a flight-crucial digital control system

Engineers and scientists in the advanced fighter technology integration (AFTI) F-16 program investigated the integration of emerging technologies into an advanced fighter aircraft. AFTI's three major technologies included: flight-crucial digital control, decoupled aircraft flight control, and integration of avionics, flight control, and pilot displays. In addition to investigating improvements in fighter performance, researchers studied the generic problems confronting the designers of highly integrated flight-crucial digital control. An overview is provided of both the advantages and problems of integration digital control systems. Also, an examination of the specification, design, qualification, and flight test life-cycle phase is provided. An overview is given of the fault-tolerant design, multimoded decoupled flight control laws, and integrated avionics design. The approach to qualifying the software and system designs is discussed, and the effects of design choices on system qualification are highlighted.

Mackall, Dale A.↗
DOE OSTI2023

In situ spin coater for multimodal grazing incidence x-ray scattering studies

We present herein a custom-made, in situ, multimodal spin coater system with an integrated heating stage that can be programmed with spinning and heating recipes and that is coupled with synchrotron-based, grazing-incidence wide- and small-angle x-ray scattering. The spin coating system features an adaptable experimental chamber, with the ability to house multiple ancillary probes such as photoluminescence and visible optical cameras, to allow for true multimodal characterization and correlated data analysis. This system enables monitoring of structural evolutions such as perovskite crystallization and polymer self-assembly across a broad length scale (2 Å–150 nm) with millisecond temporal resolution throughout a complete thin film fabrication process. The use of this spin coating system allows scientists to gain a deeper understanding of temporal processes of a material system, to develop ideal conditions for thin film manufacturing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

An Efficient Storage-Driven Machine Learning Model for Performance in the Era of Multimodal Scientific Data

Scientific workflows are increasingly relying on machine learning (ML), simulation, and hybrid techniques to predict, understand, and optimize the behavior of complex experiments. High-performance computing has greatly improved researchers’ ability to acquire diverse data modalities in these workflows. Recent studies suggest that the performance of machine learning models can be improved by integrating data from various sources. Unfortunately, these workloads pose unprecedent pressure on the network storage to meet the demands associated with accessing these multimodal data. To mitigate the impact of intensive IO, we propose a solution that utilizes a multi-tier High-Performance Computing (HPC) distributed storage and data processing framework, placing computation where the data resides for better performance. By adopting this project, the scientific community will gain new opportunities to explore multimodal storage-driven possibilities, integrating multiple scientific data sources with advanced streaming frameworks. Additionally, our framework effectively utilizes computing resources and bridges the gaps identified by HPC experts. Our proposed approach tackles scalability and persistence challenges by leveraging native persistency, which has posed difficulties in traditional approaches. Furthermore, we seek to enhance fault-tolerance and load-balance of computations by leveraging real-time streaming in diverse scientific computing environments, thereby propelling advanced scientific computing research into the next generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Potential Adoption and Benefits of Co-Optimized Multimode Engines and Fuels for U.S. Light-Duty Vehicles

Exploring a diverse portfolio of technologies for decarbonization is crucial to understanding the potential impacts of different technological solutions and their associated environmental implications. Using high-octane, high-sensitivity biofuel blends in co-optimized multimode engines can increase engine efficiency and reduce vehicle emissions. Here, the multimode engine research focuses on the benefits of light-duty vehicle engines, which can operate in multiple modes depending on the vehicle's load. Low-temperature combustion can improve efficiency and reduce emissions (such as those from oxides of nitrogen and particulate matter) during low-load operation, while spark ignition performance is maintained in high-load operation. These advanced engines can be optimized to run on blends of biobased fuels. This analysis models scenarios for potential market adoption of co-optimized multimode vehicles fueled by three different bioblendstocks: ethanol, isopropanol, and isobutanol. An integrated modeling approach is used to forecast the energy and environmental impacts of the deployment of co-optimized multimode vehicles and fuels in the light-duty sector over the 2020-to-2050 time horizon. The multidisciplinary approach combines vehicle sales modeling, system dynamics modeling of the biorefining industry, and life cycle assessment to estimate the emissions and energy benefits. The models consider market forces such as consumer preferences for vehicle attributes, biofuel supply and demand dynamics subject to biorefinery capacity build-out and bioresource constraints, and forecasted changes to the U.S. bulk energy system over time. Market adoption of co-optimized vehicles is evaluated across a wide parameter space for incremental vehicle cost and engine efficiency improvement. This analysis reveals that the deployment of co-optimized multimode fuels and vehicles results in up to a 5% reduction in annual sector-wide life cycle greenhouse gas (GHG) emissions by 2050, relative to a business-as-usual scenario, but is also indicates environmental trade-offs, such as higher life cycle water-use. Emission benefits could potentially increase beyond 2050, as the new technologies penetrate the market and gain a foothold. Results also show that, under certain circumstances, vehicles with engines co-optimized for use with high-octane, high-sensitivity biofuel blends can be cost-competitive with conventional gasoline, while reducing GHG emissions. Our modeling results indicate that co-optimized multimode fuels and engines can be strategically leveraged in tandem with electrification to decarbonize the light-duty sector. Co-optimized vehicles could play a role in the early years of the time horizon, while electric vehicles (EVs) could become more competitive in the later years, highlighting the complementary benefits of these technologies for GHG reductions.

Oke, Doris↗
DOE OSTI2026

A multimodal large language model for materials science

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

Tang, Yingheng [Lawrence Berkeley National Laborat↗
DOE OSTI2024

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells via RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrates high measurement precision with deep proteomic and transcriptomic profiling of single-cells. We apply nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing insights into cell cycle regulation. We extend nanoSPLITS to primary cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types and their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Accordingly, we establish nanoSPLITS as a multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of biological systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. While tools for co-profiling single-cell genomes, transcriptomes, and epigenomes are available, accessing proteomes in parallel is more challenging. We developed nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells using RNA sequencing and mass spectrometry-based proteomics, respectively. nanoSPLITS can precisely quantify over 5000 genes, 2000 proteins, and 140 phosphopeptides per single cell and identify candidate cell markers from these modalities. By exploring Cdk1-mediated cell cycle arrest, we demonstrate how nanoSPLITS single-cell multiomics can provide comprehensive cellular characterization with insights into covarying protein/gene clusters, unique phosphorylation events, and mitotic pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unveiling the Structure and Dynamics of Water Confined in Colloidal Boehmite Suspensions

Thin fluid layers confined between nanoparticles play an important role in several natural and industrial systems, including radioactive wastes stored in tanks at the U.S. Department of Energy’s Hanford site. Multimodal neutron and computational approaches have been integrated to examine the properties of one, two, and four layers of water (H 2 O or D 2 O) on nanoparticulate, hydrous, or deuterated boehmite (γ-AlOOH or γ-AlOOD). Exposure of deuterated boehmite to H 2 O at 90 °C yielded rapid H/D isotopic exchange likely driven by a Grotthus-like proton-hopping mechanism. The in-plane and out-of-plane vibrations of the structural hydroxyls involved in this exchange were observed for both the hydrated and deuterated boehmite. These observations were confirmed by molecular dynamics simulations that also showed that single water molecules on the (010) surface bond to the structure via four bonds: two from hydrogens (deuteriums) on the water to surrounding oxygens and two from the water’s oxygen to surrounding OD/OH. Bulk water-like properties began to appear once four monolayers of water had been added, but steric crowding limited water diffusion rates once two monolayers had been added. The super-Arrhenius temperature dependence observed at four monolayers indicated glass-like behavior in a well-formed hydrogen-bonding network. Such a network is not sufficiently developed, however, when the surface water coverage is less than four layers. In conclusion, the unique nature of these layers can provide critical information for understanding forces between particles in proximity, and resultant effects on suspension rheology.

Anovitz, Lawrence M. [Oak Ridge National Laborator↗
NASA NTRS1992

Reconfigurable optical interconnection network for multimode optical fiber sensor arrays

A single-source, single-detector architecture has been developed to implement a reconfigurable optical interconnection network multimode optical fiber sensor arrays. The network was realized by integrating LiNbO3 electrooptic (EO) gratings working at the Raman Na regime and a massive fan-out waveguide hologram (WH) working at the Bragg regime onto a multimode glass waveguide. The glass waveguide utilized the whole substrate as a guiding medium. A 1-to-59 massive waveguide fan-out was demonstrated using a WH operating at 514 nm. Measured diffraction efficiency of 59 percent was experimentally confirmed. Reconfigurability of the interconnection was carried out by generating an EO grating through an externally applied electric field. Unlike conventional single-mode integrated optical devices, the guided mode demonstrated has an azimuthal symmetry in mode profile which is the same as that of a fiber mode.

Chen, R. T.↗
DOE OSTI2023

Single-cell multimodal imaging uncovers energy conversion pathways in biohybrids

Microbe–semiconductor biohybrids, which integrate microbial enzymatic synthesis with the light-harvesting capabilities of inorganic semiconductors, have emerged as promising solar-to-chemical conversion systems. Improving the electron transport at the nano–bio interface and inside cells is important for boosting conversion efficiencies, yet the underlying mechanism is challenging to study by bulk measurements owing to the heterogeneities of both constituents. Here we develop a generalizable, quantitative multimodal microscopy platform that combines multi-channel optical imaging and photocurrent mapping to probe such biohybrids down to single- to sub-cell/particle levels. We uncover and differentiate the critical roles of different hydrogenases in the lithoautotrophic bacterium Ralstonia eutropha for bioplastic formation, discover this bacterium’s surprisingly large nanoampere-level electron-uptake capability, and dissect the cross-membrane electron-transport pathways. Furthermore, this imaging platform, and the associated analytical framework, can uncover electron-transport mechanisms in various types of biohybrid, and potentially offers a means to use and engineer R. eutropha for efficient chemical production coupled with photocatalytic materials.

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

Possibility of an integrated transmission electron microscope: enabling complex in-situ experiments

Abstract Multimodal in-situ experiments are the wave of the future, as this approach will permit multispectral data collection and analysis during real-time nanoscale observation. In contrast, the evolution of technique development in the electron microscopy field has generally trended toward specialization and subsequent bifurcation into more and more niche instruments, creating a challenge for reintegration and backward compatibility for in-situ experiments on state-of-the-art microscopes. We do not believe this to be a requirement in the field; therefore, we propose an adaptive instrument that is designed to allow nearly simultaneous collection of data from aberration-corrected transmission electron microscopy (TEM), probe-corrected scanning transmission electron microscopy, ultrafast TEM, and dynamic TEM with a flexible in-situ testing chamber, where the entire instrument can be modified as future technologies are developed. The value would be to obtain a holistic understanding of the underlying physics and chemistry of the process-structure–property relationships in materials exposed to controlled extreme environments. Such a tool would permit the ability to explore, in-situ, the active reaction mechanisms in a controlled manner emulating those of real-world applications with nanometer and nanosecond resolution. If such a powerful tool is developed, it has the potential to revolutionize our materials understanding of nanoscale mechanisms and transients. Graphical Abstract

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗
DOE OSTI2024

Mode-multiplexed photonic integrated vector dot-product core from inverse design

Photonic computing has the potential to harness the full degrees of freedom (DOFs) of the light field, including the wavelength, spatial mode, spatial location, phase quadrature, and polarization, to achieve a higher level of computing parallelism and scalability than digital electronic processors. While multiplexing using the wavelength and other DOFs can be readily integrated on silicon photonics platforms with compact footprints, conventional mode-division multiplexed (MDM) photonic designs occupy areas exceeding tens to hundreds of microns for a few spatial modes, significantly limiting their scalability. Here, we utilize inverse design to demonstrate an ultracompact photonic computing core that calculates vector dot products based on MDM coherent mixing. Our dot-product core integrates the functionalities of two-mode multiplexers and one multimode coherent mixer within a nominal footprint of 5 μm x 3 μm . We have experimentally demonstrated computing examples on the fabricated dot-product core, including complex number multiplication and motion estimation using optical flow. The compact dot-product core design enables large-scale on-chip integration in a parallel photonic computing primitive cluster for high-throughput scientific computing and computer vision tasks.

97 MATHEMATICS AND COMPUTING↗