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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 OSTI2020

Improved Value of the Gasoline and Fuel Oil Co-Product Fractions Generated by the PNNL/LanzaTech Alcohol-to-Jet Process (Final Report)

The BETO-funded PNNL collaboration with LanzaTech, Inc. utilizes patented technology to address the need for low aromatic jet fuel blendstocks. The PNNL/LanzaTech alcohol-to-jet (ATJ) process converts ethanol from LanzaTech’s syngas fermentation process to jet-range isoparaffins. The aim of this Cooperative Research Development Agreement (CRADA) project was to improve the commercial viability of the LanzaTech/ PNNL ATJ process by developing two new co-product options. Two Focus Areas (FA) were explored: FA1) Increasing the RON of the lighter-than-jet gasoline fraction above 98; and FA2) Creating a synthetic lubricant base oil from the heavier-than-jet fraction. For FA 1, multiple processing approaches were evaluated, and RONs ranging from 96-100 were obtained, each comprising different processing costs. The most cost effective approach identified was a single-step process and yielded a liquid product of 97. Thus, additional development is required to obtain a RON > 98. We will continue work on this focus area with separate Direct Funding Opportunity (DFO) funding within the DOE-BETO Co-Optima Consortium (expected to begin Q2-FY21). Experimental work will focus on i) increasing single pass conversion to > 50%, and ii) obtaining a > 98 RON product. Technoeconomic analysis performed in this project suggests cost competitiveness provided these technical targets can be met. For FA2, multiple approaches were evaluated for increasing the viscosity index (VI) of the heavier-than-jet fraction to at least 120 in order to meet ASTM specifications for a Group III Base Oil (ASTM D6074). All other ASTM specifications are already met. A viscosity index of 115 was obtained, and with minimal undesirable cracking products. We believe a VI of 115 is about the highest possible from a catalytic approach, per review of the patent literature. In order to further increase the VI to > 120 we believe additives and/or liquid-liquid extraction is required. TEA performed earlier in the project suggests cost competitiveness with market prices assuming cracking byproducts are kept < 10 wt.%. If TEA updated with this additional processing still projects economic feasibility next steps could be taken in a subsequent effort.

02 PETROLEUM↗
DOE OSTI2025

Machine learning for reactor power monitoring with limited labeled data

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in a transfer learning paradigm. Twenty-three supervised models were trained on labeled sequences of magnetic field and acceleration data from each of the target sites. Self-learning and transfer learning methods were applied to the top performing models to assess their classification performance with increasing amounts of labeled data. While reactor power level classification was achieved with a Matthews Correlation Coefficient of up to 0.739 ± 0.003 and 0.622 ± 0.009 with only 400 sequences per power state for the large research reactor and TRIGA target sites, respectively, self-learning and transfer learning leveraging source site data did not improve target classification performance. These findings suggest that alternative methods, such as higher sensitivity sensors, digital twins, or the use of physics-informed models, are required to enable high-performance classification in machine learning approaches to reactor monitoring with a dearth of target ground truth.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A non-intrusive optical (NIO) approach to characterize heliostats in utility-scale power tower plants: Methodology and in-situ validation

No reliable in-situ optical characterization tool is available that can efficiently and accurately measure various optical errors of individual heliostats in a utility-scale power-tower plant. Here, an innovative non-intrusive optical (NIO) approach is proposed to measure mirror surface slope error, mirror facet canting error, and heliostat tracking error based on reflection images using the natural target—the tower—in a heliostat field. The NIO approach adopts various techniques in photogrammetry, reflectometry, and geometrical optics to first determine the camera position and the actual tower position with respect to the heliostat. Then, Snell’s law is applied to find the surface normal at the reflection point or line of a reflection image and derive the surface slope error, mirror facet canting error, and heliostat tracking error accordingly. In addition, an in-situ validation is performed to demonstrate the feasibility of the NIO approach. Analysis with multiple sets of reflection images on one single heliostat of interest demonstrates that the NIO method can achieve a measurement uncertainty of 0.22 mrad for mirror facet slope error (root-mean-square) at the given test conditions. The sensitivity of measurement uncertainty to various parameters will be presented in a separate work. In the future, state-of-the-art unmanned aerial systems (UAS) are planned to perform the efficient image collection with a goal to complete the survey across a utility-scale field of over 10,000 heliostats within one or a few days depending on the number of UASs. The development of the NIO approach is expected to fill the critical gap for successful solar-field operation and maintenance, which is necessary for the global growth of the solar power-tower technology. The NIO approach can also be applied to any relevant industry requiring high-precision mirror surface shape characterization.

14 SOLAR ENERGY↗
DOE OSTI2020

Bench Scale Testing of Next Generation Hollow Fiber Membrane Modules

This project seeks to develop a next generation membrane material (PI-2) for application in Air Liquide’s (AL) hybrid process. Combining the cold membrane operation with an integrated CO 2 Compression and Purification Unit (CPU) significantly reduces the overall cost of CO 2 capture. The proposed work will accelerate the membrane bundle fabrication, scale-up and performance validation of this new material. The proposed novel membrane development, combined with the results of the previous Cold Membrane Project, will provide the best opportunity for AL to meet the long term development targets of the DOE with respect to timing, cost, and performance for CO 2 capture. Our previous field test program (DE-FE0013163) showed promising results for the hybrid process utilizing the existing commercial AL membrane bundles (PI-1 material). The project work has also shown the potential for significant improvements through initial laboratory tests with the novel material (PI-2). The initial PI-2 results show a step-change in membrane permeance. This will enable further reduction in the cost of CO 2 capture by reducing the number of membrane modules and associated equipment in the system. In order to capture this value, however, the new material needs to be validated by field testing of large bundles, representative of commercial production. Lastly, a comprehensive evaluation of novel hybrid processes and costs needs to be completed to ensure optimal use of this improved material performance. The current project seeks to develop this next generation membrane to TRL 5 (approximately 600 - 1,000 Nm3/h flue gas, 350 - 600 scfm, 0.2 - 0.3 MWe equivalent). Air Liquide manufacturing methodology will be applied to fabricate prototype (4”) membrane modules from PI-2 material. Once the manufacturing methods prove reliable with the new material, and performance is demonstrated in line with expectation, 6” commercial scale bundles will be fabricated. Recognizing that a low cost membrane solution by itself may not meet DOE’s LCOE target for CO 2 capture, we will also perform simulation studies of novel hybrid processes in combination with the next generation membrane technology. This approach has the best potential for exceeding the DOE targets. The best evaluation of the new commercial units will be performance testing under real flue gas at the NCCC. Finally, a rigorous TEA, which incorporates the novel process studies and field performance, will be completed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A targeted opsonization platform for programming innate immunity against rapidly evolving novel viruses.

Recent work has shown that artificial opsonins stimulate the targeted destruction of bacteria by phagocyte immune cells. Artificial opsonization has the potential to direct the innate immune system to target novel antigens, potentially even viral pathogens. Furthermore, the engagement of innate immunity presents a potential solution for the spread of pandemics in a scenario when a vaccine is unavailable or ineffective. Funded by the LDRD late start bioscience pandemic response program, we tested whether artificial opsonins can be developed to target viral pathogens using phage MS2 and a SARS-CoV-2 surrogate. To direct opsonization against these viruses we purified antibody derived viral targeting motifs and attempted the same chemical conjugation strategies that produced bacterial targeting artificial opsonins. However, the viral targeting motifs proved challenging to conjugate using these methods, frequently resulting in precipitation and loss of product. Future studies may be successful with this approach if a smaller and more soluble viral-targeting peptide could be used.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Modeling Needs for High Power Target

The next generation of high power targets will use more complex geometries, novel materials, and new concepts (like flowing granular materials); however, the current numerical approaches will not be sufficient to converge towards a reliable target design that satisfies the physical requirements. We will discuss what can be improved in the next 10 years in target modeling to support high power (MW class) targets.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Measuring Economy-Wide Circularity of the United States: An Input-Output Model in Mass Units

The ideal of creating closed material cycles by transforming the way we make, use and repurpose goods has become known as the circular economy. Besides its visionary appeal, material efficiency strategies central to circular economy can allow us to meet decarbonization goals that are otherwise out of reach. Production of basic materials such as cement, iron and steel and petrochemicals is one of the largest drivers of greenhouse gas emissions. Measuring circularity, and understanding its relationship with other sustainability metrics, however, is still difficult due to lack of data in mass units covering the entire economic system. Input-output (I-O) tables were originally created as a means of tracking the monetary flows that represent exchanges of goods and services within an economy, but have found additional uses in life cycle assessment and material flow accounts. In the traditional approach of environmentally extended I-O tables, emissions and energy data augment monetary flows. This approach can lead to price effects that distort physical quantities. Also, circular economy strategies and their goals relate to mass, not monetary flows. Therefore, it is best to simulate them using physical quantities. With these issues in mind, we have developed I-O tables in mass units to measure the flow of goods in the U.S. economy. This allows us to better measure how circular the economy really is, and how policy changes to affect this circularity may also affect decarbonization goals. Improving knowledge of these linkages could allow manufacturers to make changes in their production to achieve their sustainability and decarbonization targets. Our tool also provides a standard approach and public repository for data in physical units, making such data more available, useful, and meaningful. Following an established set of material flow metrics used by the European Union, we have used our tool to calculate material footprints over time differentiated by oil and gas versus other extractive industries. This approach also shows the relative trade balances of the U.S. in materials. We have developed a case study for the iron and steel sector, showing how different decarbonization scenarios affect not only economy-wide greenhouse gas emissions, but also total material use.

circular economy↗
DOE OSTI2023

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)—also known as multiple reaction monitoring (MRM)—and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

mathematical modeling↗
DOE OSTI2025

Simulating regional workforce impacts of decarbonizing integrated steelmaking

Global efforts to mitigate climate change are increasing pressure on heavy manufacturing industries to decarbonize production. The iron and steel industry is responsible for 7% of CO 2 emissions globally (2% in the United States) and is often a major employer in the regions where iron and steel is produced. Understanding the future prospects for workers in regions with high CO 2 emitting industries—including impacts of phasing out or evolving such industries—will be critical for informing regional economic and clean energy strategies. We simulate the impact of an “in-place” transition that replaces today’s integrated production with direct reduced iron (DRI) used in electric arc furnaces (EAFs), using Southwest Pennsylvania as an application of our generalizable approach. Our results suggest that the integrated steelmaking workforce today has the skills, knowledge, and abilities (SKAs) to fill over 95% of all jobs required by DRI/EAF facilities, but the number of jobs is only 25% of those at integrated plants. We also find that some occupational groups have greater general transferability into the broader job market, while other groups, such as production workers, are ill-equipped today based on current SKAs to transition out of the iron and steel industry. Our methodology further suggests factors that limit transitions: Around 85% of occupations are more limited by missing skills, while 15% are more limited by insufficient wages. These results may help to improve the design of social policy and the targeting of retraining programs, while the simulation approach can be readily adapted for other regions and industries.

decarbonization↗
DOE OSTI2020

Characterization of Carbon Sequestration Targets of the Mid-Atlantic Coastal Plain and Adjacent Offshore Region

Lower to mid-Cretaceous age rock formations were identified on the onshore Mid-Atlantic U.S. Coastal Plain and offshore northern Baltimore Canyon Trough (BCT) that show great potential as reservoirs for carbon sequestration. These targets are constrained using a sequence stratigraphic approach by integration of multi-channel seismic, geophysical well-log, core, and biostratigraphic datasets. In the onshore coastal plain of New Jersey, Delaware, and Maryland, the Lower Cretaceous Waste Gate Formation and mid-Cretaceous Potomac Formation/Group were divided into three major sequences (Waste Gate-Potomac Unit I, Potomac Unit II, and Potomac Unit III). The analysis concluded that Waste Gate-Potomac Unit I sequence is most suitable for carbon storage, as it has thick, correlatable sands in New Jersey and Maryland that are well confined in New Jersey, and could store potentially large volumes of CO2 (~ 8-34 Gt). In the offshore, we evaluate the mid-Cretaceous Logan Canyon Formation which we divide into three sequences (LC3, LC2, and LC1). All three sequences show potential for large volumes of CO2 storage (~5.9 Gt on the Great Stone Dome (GSD); preliminary volume estimates in the MRCSP ATLAS are currently being updated by MAOCSRAP). The Logan Canyon Sands are an excellent target for carbon storage in the offshore Baltimore Canyon Trough; they are thick, correlatable sands with high permeabilities and porosities, and are confined by the thick Dawson Canyon Shale. Sands are particularly thick, porous, and very permeable in wells on the GSD, which provides a great structural trap, and as such is a world-class target for carbon sequestration.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Correlation functions from tensor network influence functionals: The case of the spin-boson model

We investigate the application of matrix product state (MPS) representations of the influence functionals (IFs) for the calculation of real-time equilibrium correlation functions in open quantum systems. Focusing specifically on the unbiased spin-boson model, we explore the use of IF-MPSs for complex time propagation, as well as IF-MPSs for constructing correlation functions in the steady state. We examine three different IF approaches: one based on the Kadanoff–Baym contour targeting correlation functions at all times, one based on a complex contour targeting the correlation function at a single time, and a steady state formulation, which avoids imaginary or complex times, while providing access to correlation functions at all times. We show that within the IF language, the steady state formulation provides a powerful approach to evaluate equilibrium correlation functions.

Chemistry↗
DOE OSTI2020

Unconventional Catalytic Approaches to Ammonia Synthesis

Ammonia is a critically important industrial chemical and is largely responsible for sustaining the growing global population. To provide ammonia to underdeveloped regions and/or regions far from industrial production hubs, modular systems have been targeted and often involve unconventional production methodologies. These novel approaches for ammonia production can tap renewable resources at smaller scales located at the point of use, while decreasing the CO 2 footprint. Plasma-assisted catalysis and electrochemical ammonia synthesis have promise owing to their atmospheric pressure and low-temperature operation conditions and the ability to construct units at scales desired for modularization. Fundamental and applied studies are underway to assess these processes, although many unknowns remain. In this review, we discuss recent developments and opportunities for unconventional ammonia synthesis with a focus on plasma-stimulated systems.

42 ENGINEERING↗
DOE OSTI2025

Modeling Co2 Flow Through Faulted/Fractured Reservoirs Using Tedfm in Corner-Point Grids

Interest in underground CO2 storage has increased significantly over the last decade, driven by growing concern about global warming and rising levels of greenhouse gases in the atmosphere. Given that CO2 accounts for 80% of these greenhouse gases, carbon capture, utilization, and storage (CCUS) is considered one of the most direct approaches to achieving the net-zero carbon target. Although CO2 storage in deep saline aquifers and depleted gas reservoirs has been studied extensively, most studies use commercial simulators that model faults/fractures by simply modifying the transmissibility in the direction perpendicular to the fault surfaces. This work shows that this simplistic approach ignores the accelerated flow in the directions parallel to the fault plane, leading to significantly higher leakage along the fault surface. To accurately model CO2 flow in faulted reservoirs, we present the first transient embedded discrete-fracture model for corner-point grids (tEDFM-CPG). By comparing the tEDFM-CPG results with high-resolution reference solutions, we show that this approach is accurate and efficient at predicting CO2 flow in faulted/fractured reservoirs. In conclusion, this work presents the use of mixed reality (MR) to efficiently observe CO2 gas migration in the interior of these corner-point grid systems.

02 PETROLEUM↗
DOE OSTI2025

ACCELERATED DEPLOYMENT OF NOVEL MATERIALS BASED ON RELIABILITY INTEGRITY MANAGEMENT USING CUMULATIVE DAMAGE MODELING

There is currently no widely agreed, detailed general method for licensing a novel plant incorporating novel materials (or materials being deployed in novel environments); in many such situations, there are no directly applicable engineering code cases for decision-makers (including regulators) to rely on. This paper discusses a framework for solving this problem that is based on the Reliability and Integrity Management (RIM) approach delineated in ASME BPVC Section XI Division 2. NRC Regulatory Guide 1.246, Rev. 0, endorses, with conditions, the subject portion of the 2019 ASME Code. The proposed framework is meant to support development of a licensing case by addressing certain remaining technical challenges. The framework discussed here is compatible with the Licensing Modernization Project, but applying it in a specific case will call for advances in the state of practice, if not the state of the art. The RIM approach calls for applicants to (a) allocate reliability targets to plant structures, systems, and components (SSCs), (b) show that they are able to relate the currently observed physical condition of each SSC in the program to its failure probability well enough to determine whether the target reliability allocations are being satisfied, allowing for uncertainty related to the novelty of the materials/designs/operating environments, and (c) be able to demonstrate that the proposed program of surveillances will reliably detect unacceptable degradation of an SSC before SSC failure occurs. A modeling approach potentially applicable to item (b), based on cumulative damage modeling rather than failure rates, is briefly illustrated.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Exploring Classification of Topological Priors With Machine Learning for Feature Extraction

In many scientific endeavors, increasingly abstract representations of data allow for new interpretive methodologies and conceptualization of phenomena. For example, moving from raw imaged pixels to segmented and reconstructed objects allows researchers new insights and means to direct their studies toward relevant areas. Thus, the development of new and improved methods for segmentation remains an active area of research. With advances in machine learning and neural networks, scientists have been focused on employing deep neural networks such as U-Net to obtain pixel-level segmentations, namely, defining associations between pixels and corresponding/referent objects and gathering those objects afterward. Topological analysis, such as the use of the Morse-Smale complex to encode regions of uniform gradient flow behavior, offers an alternative approach: first, create geometric priors, and then apply machine learning to classify. This approach is empirically motivated since phenomena of interest often appear as subsets of topological priors in many applications. Using topological elements not only reduces the learning space but also introduces the ability to use learnable geometries and connectivity to aid the classification of the segmentation target. Here, in this article, we describe an approach to creating learnable topological elements, explore the application of ML techniques to classification tasks in a number of areas, and demonstrate this approach as a viable alternative to pixel-level classification, with similar accuracy, improved execution time, and requiring marginal training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Transformation and gene editing in the bioenergy grass Miscanthus

Miscanthus, a C4 member of Poaceae, is a promising perennial crop for bioenergy, renewable bioproducts, and carbon sequestration. Species of interest include nothospecies M. x giganteus and its parental species M. sacchariflorus and M. sinensis. Use of biotechnology-based procedures to genetically improve Miscanthus, to date, have only included plant transformation procedures for introduction of exogenous genes into the host genome at random, non-targeted sites. We developed gene editing procedures for Miscanthus using CRISPR/Cas9 that enabled the mutation of a specific (targeted) endogenous gene to knock out its function. Classified as paleo-allopolyploids (duplicated ancient Sorghum-like DNA plus chromosome fusion event), design of guide RNAs (gRNAs) for Miscanthus needed to target both homeologs and their alleles to account for functional redundancy. Prior research in Zea mays demonstrated that editing the lemon white1 (lw1) gene, involved in chlorophyll and carotenoid biosynthesis, via CRISPR/Cas9 yielded pale green/yellow, striped or white leaf phenotypes making lw1 a promising target for visual confirmation of editing in other species. Using sequence information from both Miscanthus and sorghum, orthologs of maize lw1 were identified; a multi-step screening approach was used to select three gRNAs that could target homeologs of lw1. Embryogenic calli of M. sacchariflorus, M. sinensis and M. x giganteus were transformed via particle bombardment (biolistics) or Agrobacterium tumefaciens introducing the Cas9 gene and three gRNAs to edit lw1. Leaves on edited Miscanthus plants displayed the same phenotypes noted in maize. Sanger sequencing confirmed editing; deletions in lw1 ranged from 1 to 26 bp in length, and one deletion (433 bp) encompassed two target sites. Confocal microscopy verified lack of autofluorescence (chlorophyll) in edited leaves/sectors. We developed procedures for gene editing via CRISPR/Cas9 in Miscanthus and, to the best of our knowledge, are the first to do so. This included five genotypes representing three Miscanthus species. Designed gRNAs targeted all copies of lw1 (homeologous copies and their alleles); results also confirmed lw1 made a good editing target in species other than Z. mays. The ability to target specific loci to enable endogenous gene editing presents a new avenue for genetic improvement of this important biomass crop.

09 BIOMASS FUELS↗
DOE OSTI2023

Evaluation of 134 Ce/ 134 La as a PET Imaging Theranostic Pair for 225 Ac α-Radiotherapeutics

225 Ac-targeted α-radiotherapy is a promising approach to treating malignancies, including prostate cancer. However, α-emitting isotopes are difficult to image because of low administered activities and a low fraction of suitable γ-emissions. The in vivo generator 134 Ce/ 134 La has been proposed as a potential PET imaging surrogate for the therapeutic nuclides 225 Ac and 227 Th. In this report, we detail efficient radiolabeling methods using the 225 Ac-chelators DOTA and MACROPA. These methods were applied to radiolabeling of prostate cancer imaging agents, including PSMA-617 and MACROPA-PEG 4 -YS5, for evaluation of their in vivo pharmacokinetic characteristics and comparison to the corresponding 225 Ac analogs. Methods: Radiolabeling was performed by mixing DOTA/MACROPA chelates with 134 Ce/ 134 La in NH 4 OAc, pH 8.0, at room temperature, and radiochemical yields were monitored by radio–thin-layer chromatography. In vivo biodistributions of 134 Ce-DOTA/MACROPA.NH 2 complexes were assayed through dynamic small-animal PET/CT imaging and ex vivo biodistribution studies over 1 h in healthy C57BL/6 mice, compared with free 134 CeCl 3 . In vivo, preclinical imaging of 134 Ce-PSMA-617 and 134 Ce-MACROPA-PEG 4 -YS5 was performed on 22Rv1 tumor–bearing male nu/nu-mice. Ex vivo biodistribution was performed for 134 Ce/ 225 Ac-MACROPA-PEG 4 -YS5 conjugates. Results: 134 Ce-MACROPA.NH 2 demonstrated near-quantitative labeling with 1:1 ligand-to-metal ratios at room temperature, whereas a 10:1 ligand-to-metal ratio and elevated temperatures were required for DOTA. Rapid urinary excretion and low liver and bone uptake were seen for 134 Ce/ 225 Ac-DOTA/MACROPA. NH 2 conjugates in comparison to free 134 CeCl 3 confirmed high in vivo stability. An interesting observation during the radiolabeling of tumor-targeting vectors PSMA-617 and MACROPA-PEG 4 -YS5—that the daughter 134 La was expelled from the chelate after the decay of parent 134 Ce—was confirmed through radio–thin-layer chromatography and reverse-phase high-performance liquid chromatography. Both conjugates, 134 Ce-PSMA-617 and 134 Ce-MACROPA-PEG 4 -YS5, displayed tumor uptake in 22Rv1 tumor–bearing mice. The ex vivo biodistribution of 134 Ce-MACROPA.NH 2 , 134 Ce-DOTA and 134 Ce-MACROPA-PEG 4 -YS5 corroborated well with the respective 225 Ac-conjugates. Conclusion: These results demonstrate the PET imaging potential for 134 Ce/ 134 La-labeled small-molecule and antibody agents. The similar 225 Ac and 134 Ce/ 134 La-chemical and pharmacokinetic characteristics suggest that the 134 Ce/ 134 La pair may act as a PET imaging surrogate for 225 Ac-based radioligand therapies.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization

Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redoxactive materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the- art machine learning ( ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. Furthermore, we anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗