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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Theory of Ion-Mediated Segmental Localization, Activated Structural Relaxation, and the Glass Transition in Polymerized Ionic Liquids

Here we combine polymer integral equation theory for structural correlations with a force-level theory for activated segmental relaxation to construct a microscopic theory for how ions modify the onset of segmental localization, the polymer alpha relaxation time, and glass transition temperature, T g , in dry polymerized ionic liquids (PolyILs). Our most important findings concerning the behavior of T g are (i) a weak dependence on anion–cation Coulomb attraction strength due to strong decoupling of ion and segmental dynamics, (ii) strong reduction with increasing mobile ion size due to both plasticization effects and weakening of ion-modified monomer caging, and (iii) increase with backbone rigidity and intrachain dynamic cooperativity. We quantitatively estimate T g variations as ion size and polymer static and dynamic rigidity are widely varied. The experimental data of 19 PolyILs are organized into a specific pattern consistent with the theoretical predictions with glass transition temperatures varying over a very wide range (~250 K) as observed experimentally. Based on the theoretical results, suggestions are made concerning the search for high conductivity Li- or Na-based PolyILs based on further lowering of T g via new polymer synthesis. Calculations of the alpha time as a function of packing fraction reveal the dynamic fragility increases for larger mobile ions, a more flexible backbone, and/or a smaller degree of intrachain dynamic cooperativity, as a consequence of the increasing importance of collective elasticity in determining the activation barrier. An analysis of transient segmental localization and a crossover to activated dynamics reveals similar, but not identical, trends as predicted at the laboratory vitrification timescale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗
DOE OSTI2023

Engineering Robust Metallic Zero-Mode States in Olympicene Graphene Nanoribbons

Metallic graphene nanoribbons (GNRs) represent a critical component in the toolbox of low-dimensional functional materials technology serving as 1D interconnects capable of both electronic and quantum information transport. The structural constraints imposed by on-surface bottom-up GNR synthesis protocols along with the limited control over orientation and sequence of asymmetric monomer building blocks during the radical step-growth polymerization have plagued the design and assembly of metallic GNRs. Here, we report the regioregular synthesis of GNRs hosting robust metallic states by embedding a symmetric zero-mode (ZM) superlattice along the backbone of a GNR. Tight-binding electronic structure models predict a strong nearest-neighbor electron hopping interaction between adjacent ZM states, resulting in a dispersive metallic band. First-principles density functional theory-local density approximation calculations confirm this prediction, and the robust, metallic ZM band of olympicene GNRs is experimentally corroborated by scanning tunneling spectroscopy.

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

Structural and Dynamical Design Principles for Iron-Sulfur Clusters in Sequence Defined Polymers

This integrated synthetic and computational effort uses sequence defined polymers such as peptoids, and triazines, capable of mimicking a flexible protein environment to control the positioning of side chains around redox cofactors. This strategy will allow modulation of redox properties through both direct and indirect mechanisms. To this end the aim is defined to test the underlying hypothesis: that principles and critical functionalities governing the binding and tuning of naturally occurring electron carrier co-factors can be identified and used to predictably design and synthesize maquettes based on non-natural polymers resulting in new modular biomimetic scaffolds for Electron Transfer Materials. This will be achieved through design and synthesis of ligands that can stabilize iron-sulfur clusters and tune the redox potential based on sequence.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Metamaterials as a Platform for the Development of Novel Materials for Energy Applications

To explore the fundamental properties of metamaterials (MMs) / metasurfaces and their potential for control of energy at the sub‐wavelength scale in support of the mission of the Department of Energy and the office of Basic Energy Sciences. Electromagnetic metamaterials provide a platform for the discovery and design of new materials with novel structures, functions, and properties. The PI proposes to advance the knowledge base of these materials through fundamental investigations of the experimental and theoretical properties of metamaterials for the discovery, prediction and design of new materials with novel structures, functions, and properties. The proposed research activities emphasize a complete basic research program including the conceptual / computational design, fabrication / synthesis of the materials, and the characterization and analysis of their electromagnetic properties. The proposed project explores the fundamental properties of metamaterials / metasurfaces and their potential for energy applications. There are three main topics which will be investigated: 1) Dispersion engineering with metamaterials and metasurfaces, 2) Epsilon near zero metamaterial absorbers and emitters, and 3) All dielectric metamaterials. The program implements a complete basic research program consisting of theory / design, modeling, characterization, and analysis, in order to fully characterize metamaterials and metasurfaces, while at the same time minimizing iterations necessary to achieve the proposal goals.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simulation and Optimization of Volatile Fatty Acid Upgrading Strategies for Sustainable Transportation Fuel Production

The multicomponent nature of and variability of biomass makes chemical upgrading via a single process stream to a single end use infeasible for most feedstocks. A more promising approach is to identify upgrading strategies (encompassing bioprocessing, catalysis, and separations) which valorize varied biomass fractions to distinct products to which each is best suited. A robust procedure to carry out this goal should incorporate comparison of various upgrading procedures as well as suitability of products to a slate of identified end uses. We applied this strategy to one biomass-derived feedstock, volatile fatty acids (VFAs) derived from wet waste arrested anaerobic digestion, by developing a computer program, VFA Upgrading to Liquid Transportation fUels Refinery Estimation (VULTURE) which evaluates VFA catalytic upgrading to liquid transportation fuels. VULTURE considers multiple separations, catalysis (ketonization, hydrogenation), and fuel application options, generating hundreds of candidate scenarios for a given VFA stream, then selects several promising strategies that optimize bio-content of products with properties best suited for target fuel types. We find that VFAs are upgraded most efficiently when separate light alcohol (C3-6) and heavy hydrocarbon or alcohol (C7-13) fractions are targeted to create gasoline and heavy-duty (diesel or jet) fuels or fuel blends. Surrogate property testing of VULTURE-recommended fuels reveals that most predictive models employed are robust, while rigorous process simulation shows that the simple unit operation assumptions used in VULTURE are largely accurate, especially for heavy-duty fuel synthesis. Techno-economic and life-cycle analyses show that VFA-derived fuels are profitable and have dramatically (=57%) lower carbon intensities than fossil analogs.

BIOMASS FUELS↗
DOE OSTI2025

Self-Driving Microscopy for AI/ML-Enabled Physics Discovery and Materials Optimization

Materials are the bedrock of economy and foundation for all real-world technologies. The viability of space travel, grid energy storage, solar to fuels conversion, methane removal, and photovoltaic energy solutions hinge on the discovery and optimization of novel materials and rapid scaling toward manufacturing. The last 20 years have seen an exponential growth in the theoretical predictive capability for crystalline materials and small molecules. However, it is only in the last five years that we have seen the rapid expansion of high-throughput synthesis enabled by laboratory robotics and microfluidics, as well as a resurgence of combinatorial synthesis (Abolhasani and Kumacheva 2023; Epps and Abolhasani 2021; Jiang et al. 2022; Rajan 2008; Soldatov et al. 2021; Szymanski et al. 2023). Combinatorial synthesis, microfluidics, and ultimately dip-pen megalibraries have demonstrated the ability to “write” multicomponent nanomaterials at high throughput scale, generating millions of material examples in the 3D, 4D, and 5D composition spaces (Chen et al. 2016, 2019; Jibril et al. 2022).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Defining Lipidomic Responses to Coronavirus Infection

Highly pathogenic human coronavirus infection can cause a severe atypical, rapid onset pneumonia with a mortality rate of up to 10% for severe acute respiratory syndrome coronavirus 1 (SARS-CoV 1), 35% for Middle East respiratory syndrome coronavirus (MERS-CoV), or 1% for severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2 causative agent of COVID 19). While medical countermeasures successfully controlled the worldwide SARS-CoV epidemic, the MERS-CoV epidemic is still ongoing and continues to be a concern for travelers in the Middle East and the multi-year SARS-CoV 2 pandemic underscore the importance of defining biomarkers that are diagnostic and/or predictive of severe disease outcomes for respiratory viruses. Systems biology approaches provide global snapshots of infection induced changes in host cells/tissues and provide extremely rich datasets for understanding host pathogen interactions. Metabolites, especially lipids, are critical for viral replication but less is understood about infection induced changes to lipids due to limits in lipid species detection and identification. To characterize how individual lipid species and proteins with lipid associated functions contribute to highly pathogenic human coronavirus replication and disease severity, existing datasets were probed to determine cell type specific lipid responses to MERS-CoV infection and verification studies were performed to determine if modification of the host lipid signature (by inhibiting enzymatic functions that produce specific lipid species) can perturb CoV replication in human lung cells. MERS-CoV infects human lung epithelial, endothelial and fibroblast cells. All three cell types were infected with MERS-CoV and samples collected to analyze lipids, proteins, metabolites, and transcripts from 12 to 48 hours post infection. Time matched mock-infected cells were collected in parallel for each cell type. Following sample and statistical analysis, functional enrichment and bioinformatic analysis was performed to identify differentially expressed lipids and proteins. Two lipid species were found to be significantly upregulated following MERS-CoV infection, ceramides, and triacylglycerol both of which are indicative of cells undergoing apoptotic cell death. In contrast, sphingomyelins (lipid molecules that can serve as a precursor for one pathway for ceramide synthesis) had significantly decreased expression in MERS-CoV infected cells. An inhibitor of acid sphingomyelinase (that converts sphingomyelin to ceramides and phosphorylcholine) reduced MERS-CoV replication suggesting that production of ceramides is key for successful viral replication and transmission. Acyl-CoA-synthetase 3 (ACSL3), the only protein whose function is lipid associated and had increased differential expression in our dataset, regulates the synthesis of triacylglycerol (increased expression). Inhibitors that directly block ACSL3 (Triacsin C) but not steps later in the triacylglycerol synthesis pathway (Etomoxir) inhibit MERS-CoV replication suggesting that triacylglycerol production and/or ACSL3 activity is also key for viral replication. ACSL3 expression is also upregulated in lung cancer cells and is predicted to promote continued cell viability which would also enhance viral replication. The differentially expressed lipid and lipid-associated protein species identified in our studies suggest that MERS-CoV infection is activating cellular death pathways to limit the number of cells that become infected but also stimulating the production of lipid-associated enzymes that can prolong host cell viability and the amount of time progeny virions can be produced and released. As the inhibitors that worked against MERS-CoV infection were also efficacious against SARS-CoV 2 infection, countermeasures that target these host pathways may provide novel ways to block highly pathogenic human coronavirus infection and/or prevent severe disease outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Coupling Metabolic Source Isotopic Pair Labeling and Genome Wide Association for Metabolite and Gene Annotation in Plants (Final Technical Report)

In this project, we applied our labeling pipeline to Arabidopsis and sorghum by feeding tissues with isotopically labeled versions of commercially available amino acids to identify all metabolite features that incorporate the label. In sorghum, we fed five accessions, sampled across the diversity of sorghum, to identify the precursor-of-origin for metabolites that vary between accessions as well as those that may be missing from a single reference genotype. This provided us with precursor-of-origin annotation for thousands of unknown metabolites. We then used GWA to map genes responsible for the synthesis of precursor-of-origin classified metabolites. For sorghum leaf and root ducible metabolites, we performed untargeted metabolomics on leaf and root tissues from 300 diverse genotyped sorghum inbred lines. The amino acid precursor-of-origin metabolite library were then used to identify the corresponding metabolites in the GWA data sets and to identify novel gene-metabolite associations. Finally, we utilized existing and newly generated sequenced EMS mutants of sorghum to validate the predicted gene-metabolite relationships that our labelling analysis identified. In parallel, we conducted similar feeding experiments in Arabidopsis to categorize metabolites based on precursor-of-origin, identify those that vary across our existing Arabidopsis metabolite GWA dataset, and identify genes required for the synthesis of each metabolite. To provide an independent test of gene annotation and pathway involvement, we tested the GWA gene-metabolite associations in Arabidopsis by analyzing the metabolic phenotypes of gene knockouts. Genes of particular interest from both sorghum and Arabidopsis were studied in detail by directly measuring the activity of the corresponding enzymes following heterologous expression. In summary, this work classified as-yet-unknown amino acid-derived metabolites and identified genes involved in their production generated through “omics” technologies. This information was used to validate gene function and identify new metabolism in Arabidopsis and sorghum.

09 BIOMASS FUELS↗
DOE OSTI2021

Epitaxial Stabilization and Oxygen Evolution Reaction Activity of Metastable Columbite Iridium Oxide

Non-rutile polymorphs of binary iridium oxide such as columbite IrO 2 (α-IrO 2 ) are promising candidates for highly active acid-stable oxygen evolution reaction (OER) catalysts, yet their synthesis has been challenging due to the dominant thermodynamic stability of rutile IrO 2 (R-IrO 2 ). Here, we report the growth of α-IrO 2 via epitaxial thin films using pulsed laser deposition. We observe that, in competition with R-IrO 2 (100), the films can be optimized to be predominantly (100)-oriented α-IrO 2 . Surprisingly, the activity of α-IrO 2 shows a large discrepancy of ~0.2 V in the overpotential compared to its predicted activity, which is resolved via theoretical calculations to be a crystal orientation effect. This demonstrates that the electrocatalytic activity can be significantly varied upon crystal orientation, a parameter that is difficult to control in conventional polycrystalline systems but accessible in epitaxial thin films. In total, this study demonstrates epitaxial thin film growth as a powerful technique, which can overcome large energetic instabilities on the order of ~300 meV to stabilize metastable material structures inaccessible by bulk synthesis. As a result, this provides unique opportunities to effectively identify the atomic structure of active catalysts by combining investigations of metastable materials with theoretical predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Forest regeneration within Earth system models: current process representations and ways forward

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Here, our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Determination of positive anode sheath in anodic carbon arc for synthesis of nanomaterials

In the atmospheric pressure anodic carbon arc, ablation of the anode serves as a feedstock of carbon for production of nanomaterials. It is known that the ablation of the graphite anode in this arc can have two distinctive modes with low and high ablation rates. The transition between these modes is governed by the power deposition at the arc attachment to the anode and depends on the gap between the anode and the cathode electrodes. Probe measurements combined with optical emission spectroscopy are used to analyze the voltage drop between the arc electrodes. These measurements corroborated previous predictions of a positive anode sheath (i.e. electron attracting sheath) in this arc, which appears in both low and high ablation modes. However, the positive anode sheath was determined to be ~3–8 V, significantly larger than ~0.5 V predicted by previous models. Thus, there are apparently other physical mechanisms not considered by these models that force the anode sheath to be electron attracting in both ablation regimes. Another key result is a relatively low electron temperature (~0.6 eV) obtained from OES using a collisional radiative model. This result partially explains a higher arc voltage (~20 V) required to sustain the arc current of 50–70 A than predicted by existing simulations of this discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Metal-Confined Synthesis of ZnS 2 Monolayer Catalysts for Dinitrogen Electroreduction

Atomically thin two-dimensional (2D) materials are of great significance in catalytic, energy storage, and electronic devices. So far, such materials are limited to several categories with layered structures. Herein, we predict an unusual atomically thin ZnS2 monolayer and exploit a metal-confined chemical vapor deposition strategy for the successful preparation of such an unprecedented 2D material, where a sulfide monolayer was first grown on metal nanosheets to form a sulfide/metal/sulfide sandwich structure, and then, the metal core was etched out to obtain freestanding sulfide monolayers. As shown in this work, the ZnS 2 monolayer possesses unique electron-deficient properties related to the atomic structure and exhibits high catalytic activity for dinitrogen electroreduction. Our work will enable creating 2D TMD materials that were previously inexistent or inaccessible.

2D Materials↗
DOE OSTI2023

Fluorine-Substituted Lithium Chloride Solid Electrolytes for High-Voltage All-Solid-State Lithium-Ion Batteries

Lithium ternary halides are promising solid electrolytes, owing to their high ionic conductivity and reasonably high oxidative and chemical stability. Recently, fluorine substitution in Li 3 MCl 6 has been suggested as a promising approach for further enhancing oxidation stability. Accordingly, this study outlines a material design strategy for F-substituted Li 3 MCl 6 through systematic theoretical analyses. Calculations reveal that the mixing limit of F in Li 3 MCl 6–x F x is in the range of 0.5–1.5, and the resulting Li 3 MCl 6–x F x phases can retain ionic conductivity above 1 mS/cm up to x = 1.0. Additionally, the calculations also predict that the formation of F-containing passivating phases could increase the oxidation potential for Li3MCl5F to ~6.3 V. The proposed material design strategy is validated through the synthesis of Li 3 YCl 5 F, which is confirmed to show both high ionic conductivity and enhanced oxidation stability. The design guidelines presented herein can accelerate the potential use of halide-based electrolyte chemistries in high-voltage all-solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage

The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.

Li, He↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NOx during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

02 PETROLEUM↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NO x during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

42 ENGINEERING↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗