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DOE OSTI2023

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4–40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Parsed synthesis of pyocyanin via co-culture enables context-dependent intercellular redox communication

Abstract Background Microbial co-cultures and consortia are of interest in cell-based molecular production and even as “smart” therapeutics in that one can take advantage of division of labor and specialization to expand both the range of available functions and mechanisms for control. The development of tools that enable coordination and modulation of consortia will be crucial for future application of multi-population cultures. In particular, these systems would benefit from an expanded toolset that enables orthogonal inter-strain communication. Results We created a co-culture for the synthesis of a redox-active phenazine signaling molecule, pyocyanin (PYO), by dividing its synthesis into the generation of its intermediate, phenazine carboxylic acid (PCA) from the first strain, followed by consumption of PCA and generation of PYO in a second strain. Interestingly, both PCA and PYO can be used to actuate gene expression in cells engineered with the soxRS oxidative stress regulon, although importantly this signaling activity was found to depend on growth media. That is, like other signaling motifs in bacterial systems, the signaling activity is context dependent. We then used this co-culture’s phenazine signals in a tri-culture to modulate gene expression and production of three model products: quorum sensing molecule autoinducer-1 and two fluorescent marker proteins, eGFP and DsRed. We also showed how these redox-based signals could be intermingled with other quorum-sensing (QS) signals which are more commonly used in synthetic biology, to control complex behaviors. To provide control over product synthesis in the tri-cultures, we also showed how a QS-induced growth control module could guide metabolic flux in one population and at the same time guide overall tri-culture function. Specifically, we showed that phenazine signal recognition, enabled through the oxidative stress response regulon soxRS, was dependent on media composition such that signal propagation within our parsed synthetic system could guide different desired outcomes based on the prevailing environment. In doing so, we expanded the range of signaling molecules available for coordination and the modes by which they can be utilized to influence overall function of a multi-population culture. Conclusions Our results show that redox-based signaling can be intermingled with other quorum sensing signaling in ways that enable user-defined control of microbial consortia yielding various outcomes defined by culture medium. Further, we demonstrated the utility of our previously designed growth control module in influencing signal propagation and metabolic activity is unimpeded by orthogonal redox-based signaling. By exploring novel multi-modal strategies for guiding communication and consortia outcome, the concepts introduced here may prove to be useful for coordination of multiple populations within complex microbial systems.

Chun, Kayla↗

DOE OSTI2022

WBG-Enabled Current-Source Inverters for Integrated PM Motor Drives

This project takes advantage of the special capabilities of wide-bandgap (WBG) power semiconductor devices to develop innovative power electronics in the form of new current-source inverters (CSIs) that offer promising advantages over the dominant voltage-source inverter (VSI) topology. These CSIs, in turn, are extremely well-suited for integration into the housings of permanent magnet synchronous machines (PMSMs) to form integrated motor drives (IMDs). These new IMDs offer great promise for achieving major energy savings in a wide variety of applications that benefit from adjustable-speed control, including air conditioners, laundry appliances, industrial pumps/compressors, electric vehicles, and aerospace drives. WBG devices play a critical enabling role in these new IMDs because of their transformative features including much higher switching frequencies, lower losses, and compatibility with high operating temperatures. When incorporated into new CSI-based IMDs with PM machines, these WBG switches open the door to achieving major increases in power density, drive system efficiency, and fault tolerance, as well as substantial reductions in electromagnetic interference (EMI), manufacturing cost, and temperature-induced failures. The higher operating temperature capability of WBG devices compared to conventional silicon power devices is very appealing in IMD applications because the power electronics is mounted in close proximity to the motors which typically operate at temperatures well above the maximum limits of today’s silicon-based power electronics. This project has succeeded in designing, building, and testing multiple prototype versions of this CSI-IMD that have overcome many technical challenges in order to demonstrate the impressive performance improvements that can be achieved by the WBG-enabled CSI-IMD. The preliminary demo and bench-top versions of the current-source inverter developed during the first two years of the project were critical to laying the technical foundations for the 3 kW prototype CSI-IMD unit that was successfully built and tested during the third year. This prototype CSI-IMD unit was designed to fit within the housing envelope of the original permanent magnet (PM) synchronous machine in order to meet the demanding power density requirements that were set at the beginning of the project. All of the remaining performance objectives set for the prototype CSI-IMD unit including efficiency and electromagnetic interference (EMI) were also met. The last 18 months of the project were devoted to developing further enhancements of the WBG-enabled CSI-IMD technology that better prepare it for commercial production. More specifically, an upgraded version of the prototype CSI-IMD unit was developed that moved the power electronics into the same machine housing chamber as the motor, substantially raising the thermal demands on the power electronics. Tests with five different combinations of motor enclosure types and air cooling configurations were evaluated. Importantly, this work confirmed that the power electronics can deliver its full rated power and still operate well within its maximum temperature limits even for worst-case conditions when the housing is “totally-enclosed” without any openings for air to enter or exit the enclosure, and no blower/fan is provided to blow air over the outside surface of the enclosure. Reaching the full performance and energy-savings potential of this disruptive CSI-IMD motor drive technology is highly consistent with ARPA-E’s stated mission to “enhance the economic and energy security of the United States” while also supporting its commitment to “ensure that the U.S. maintains a technological lead in developing and deploying advanced energy technologies”. Follow-on projects are under way to explore the scalability of WBG-enabled CSI-IMD technology to 100 kW (peak) electric vehicle traction drives and fault-tolerant modular motor drives for future electrified aircraft propulsion applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗

DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Technology Enablers for Advanced Gasoline Compression Ignition Engines

To meet current emission regulations and increasingly demanding global fleet CO2 standards on fuel economy and future trends towards life cycle GHG emissions, advanced combustion engines remain significant in the passenger vehicle sector to achieve high efficiency and low emissions over the full operating range. Lean burn gasoline compression ignition (GCI) technology has shown to have the most potential in reaching these goals, although it faces challenges in the operating range. In this study, an advanced GCI engine is considered with the capability to operate under two combustion modes, namely low temperature combustion (LTC) and GCI. They are enabled with the use of two Hyundai in-house developed technologies; an advanced valve control mechanism known as continuously variable valve duration (CVVD) and a high-pressure gasoline injection system. At low load, the engine utilizes dual CVVD and dual CVVT (continuously variable valve timing) mechanisms for both intake and exhaust valvetrains to enable NVO (negative valve overlap) to trap hot residuals. The hot residuals enable low load auto-ignition, and the phasing control of that auto-ignition is achieved by varying the degrees of NVO, and in turn, the amount of hot residuals. This is done in real time with respect to the changes in engine loads or operating conditions, significantly improving cylinder-phasing control in LTC mode, and subsequently is able to realize a fuel economy gain and reduced emission benefits. Early direct injection during NVO or intake stroke is used to form homogenous or quasi-homogenous air-fuel mixture for LTC. At mid to high loads, late direct injection of gasoline or GCI mode is employed to take advantage of the high volatility and high resistance to auto-ignition of gasoline fuel, so that diesel-engine like high combustion efficiency can be achieved with lower soot emissions for the same NOx level as diesel combustion. In this paper, the key control and fueling technologies that enable this multiple mode combustion are introduced, and the resultant analysis on engine test and CFD simulation at LTC and GCI operating conditions are presented in detail.

Gasoline compression ignition, low temperature com↗

DOE OSTI2024

Digital Twin Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derate while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Digital-Twin-Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Coaxial Nanowire Electrodes Enable Exceptional Fuel Cell Durability

Polymer-electrolyte-membrane fuel cells (PEMFCs) hold great promise for applications in clean energy conversion, but cost and durability continue to limit commercialization. This work presents a new class of catalyst/electrode architecture that does not rely on Pt particles or carbon supports, eliminating the primary degradation mechanisms in conventional electrodes, and thereby enabling transformative durability improvements. The coaxial nanowire electrode (CANE) architecture consists of an array of vertically aligned nanowires, each comprising an ionomer core encapsulated by a nanoscale Pt film. This unique design eliminates the triple-phase boundary and replaces it with two double-phase boundaries, increasing Pt utilization. It also eliminates the need for carbon support and ionomer binder, enabling improved durability and faster mass transport. Fuel cell membrane electrode assemblies based on CANEs demonstrate extraordinary durability in accelerated stress tests (ASTs), with only 2% and 5% loss in performance after 5000 support AST cycles and 30000 catalysts AST cycles, respectively. The high-power density and extremely high durability provided by CANEs can enable a paradigm shift from random electrodes based on unstable platinum nanoparticles dispersed on carbon to ordered electrodes based on durable Pt nanofilms, facilitating rapid deployment of fuel cells in transportation and other clean energy applications.

25 ENERGY STORAGE↗

DOE OSTI2025

Abnormal Slow Phonon Dynamics Toward Prolonging Excited States Dynamics Enabled by Crystalline‐Assembling Donor–Acceptor Molecules

Phonon dynamics are a critical factor to control the optical properties of excited states in light-emitting materials. Here, we report an extremely slow relaxation of photoexcited lattice vibrations enabled by assembling the donor-acceptor (D–A) molecules [2-(9,9-dimethylacridin-10(9H)-yl)-9,9-dimethyl-9H-thioxanthene 10,10-dioxide], namely AC molecules, into dipolar crystal. By using photoexcitation-modulated Raman spectroscopy, we find that the crystalline-lattice vibrations monitored by Raman-scattering laser beam of 785 nm demonstrate an un-usual slow relaxation in the time scale of seconds after ceasing photoexcitation beam of 343 nm in such dipolar crystal. This presents extremely slow phonon dynamics enabled by crystalline-assembling the D–A molecules into a dipolar crystal. Simultaneously, the photoluminescence (PL) exhibits a prolonged behavior, lasting 10 ms after ceasing photoexcitation in dipolar AC crystal. This phenomenon provides an experimental hypothesis that the slow phonon dynamics function as an important mechanism to unusually prolong excited states dynamics upon crystalline-assembling the D–A molecules into dipolar crystal. Here, this hypothesis can be verified by directly suppressing the phonon dynamics through freezing D–A molecular liquid into dipolar crystalline solid at 77 K to largely prolong the PL to 1 s- after removing photoexcitation. Clearly, crystalline-assembling D–A molecules provide the necessary conditions to enable slow phonon dynamics toward prolonging excited states dynamics.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Nanocrystal‐Enabled Perovskite Heterojunctions in Photovoltaic Applications and Beyond

Abstract Heterojunctions are used to tailor the properties of semiconductors in optoelectronic devices, yet for emerging devices composed of metal halide perovskites, fabricating perovskite/perovskite heterojunctions has proved challenging due to solvent incompatibilities and rapid homogenization due to ion migration. Recent studies have demonstrated various strategies for using perovskite nanocrystals as a component to fabricate perovskite/perovskite heterojunctions, either with a perovskite thin film or a second nanocrystal layer. Heterojunctions such as these can impart many advantages of both bulk and nanocrystalline perovskite morphologies. This perspective focuses on recent developments of solution‐processed perovskite heterojunctions for solar cells and novel optoelectronic devices, in particular, highlighting the demonstrated and potential advantages of nanocrystal‐enabled fabrication strategies. A central tenet of this perspective is that the synthesis and dispersion of perovskite nanocrystals in non‐polar organic solvents offers a key processing advantage over traditional perovskite precursor solutions in polar solvents since the former allows for layer‐by‐layer deposition without dissolving an underlying perovskite film or crystal. This processing advantage, coupled with nanocrystal size control and ligand chemistry, enables perovskite heterojunctions with highly tunable optical and electrical properties. Such heterojunctions may enable disruptive technological advances in broad classes of devices such as solar cells, photodetectors, sensors, and (in)coherent photon sources with tunable polarization.

14 SOLAR ENERGY↗

DOE OSTI2020

Solvation Rule for Solid‐Electrolyte Interphase Enabler in Lithium‐Metal Batteries

Abstract Despite the exceptionally high energy density of lithium metal anodes, the practical application of lithium‐metal batteries (LMBs) is still impeded by the instability of the interphase between the lithium metal and the electrolyte. To formulate a functional electrolyte system that can stabilize the lithium‐metal anode, the solvation behavior of the solvent molecules must be understood because the electrochemical properties of a solvent can be heavily influenced by its solvation status. We unambiguously demonstrated the solvation rule for the solid‐electrolyte interphase (SEI) enabler in an electrolyte system. In this study, fluoroethylene carbonate was used as the SEI enabler due to its ability to form a robust SEI on the lithium metal surface, allowing relatively stable LMB cycling. The results revealed that the solvation number of fluoroethylene carbonate must be ≥1 to ensure the formation of a stable SEI in which the sacrificial reduction of the SEI enabler subsequently leads to the stable cycling of LMBs.

Su, Chi‐Cheung↗

DOE OSTI2020

Solvation Rule for Solid-Electrolyte Interphase Enabler in Lithium-Metal Batteries

Despite the exceptionally high energy density of lithium metal anodes, the practical application of lithium-metal batteries (LMBs) is still impeded by the instability of the interphase between the lithium metal and the electrolyte. To formulate a functional electrolyte system that can stabilize the lithium-metal anode, the solvation behavior of the solvent molecules must be understood because the electrochemical properties of a solvent can be heavily influenced by its solvation status. Here, we unambiguously demonstrated the solvation rule for the solid-electrolyte interphase (SEI) enabler in an electrolyte system. In this study, fluoroethylene carbonate was used as the SEI enabler due to its ability to form a robust SEI on the lithium metal surface, allowing relatively stable LMB cycling. The results revealed that the solvation number of fluoroethylene carbonate must be ≥ 1 to ensure the formation of a stable SEI in which the sacrificial reduction of the SEI enabler subsequently leads to the stable cycling of LMBs.

25 ENERGY STORAGE↗

DOE OSTI2023

Temporally Stable Supramolecular Polymeric Salts Enabling High-Performance 3D All-Aromatic Polyimide Lattices

Vat photopolymerization (VP) Additive Manufacturing (AM), in which UV light is selectively applied to cure photo-active polymers into complex geometries with micron-scale resolution, has a limited selection of aliphatic thermoset materials that exhibit relatively poor thermal performance. Ring-opening dianhydrides with acrylate-containing nucleophiles yielded diacrylate ester-dicarboxylic acids that enabled photo-active polyimide (PI) precursors, termed polysalts, upon neutralization with an aromatic diamine in solution. In situ FTIR spectroscopy coupled with a solution and photo-rheological measurements revealed a previously unknown time-dependent instability of 4,4'-oxydianiline (ODA) polysalts due to an aza-Michael addition. Replacement of the electron-donating ether-containing diamine with an electron withdrawing sulfone-containing monomer, e.g., 4,4'-diaminodiphenyl sulfone (DDS), prohibited the aza-Michael addition of the aromatic amine to the activated acrylate double bond. Novel DDS polysalt photocurable solutions are similarly analyzed and validated long-term stability, which enabled reproducible printing of polyimide organogel intermediates. Subsequent VP AM afforded 3-dimensional (3D) structures of intricate complexity and excellent surface finish, as demonstrated with scanning electron microscopy. In addition, the novel PMDA-HEA/DDS solution enabled the production of the first beam latticed architecture comprised of all-aromatic polyimide. Finally, the versatility of a polysalt platform for multi-material printing is further demonstrated by printing parts with alternating polysalt compositions.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A New Electrode Paradigm Enabled by Co-Axial Electrospinning

A conventional electrode in a polymer electrolyte membrane fuel cell (PEMFC) often imposes competing requirements on the ionomer, which must enable efficient proton conduction while allowing sufficient oxygen transport. To address these material-level requirements, a co-axial electrospun electrode comprising of a pristine ionomer core and a multifunctional platinum-carbon-ionomer shell is introduced. By decoupling proton and oxygen transport pathways, this architecture enables independent tailoring of core and shell chemistries and their interfaces. The core-shell structure of the electrospun fibers is confirmed by microscopy, while electrochemical and transport properties are quantified using electrochemical impedance spectroscopy and limiting-current diagnostics. The co-axial fibers in their current version meet or exceed the performance of conventional ultrasonically sprayed electrode at relative humidities above 75%, enabled by improvements in non-Fickian diffusion processes. This work establishes co-axial electrospun electrodes as a versatile platform for advanced PEMFC electrodes and a wide range of electrochemical applications, including CO2 electrolysis, water electrolysis, and critical mineral separations.

08 HYDROGEN↗

DOE OSTI2024

Predictive binder jet additive manufacturing enabled by clean burn-off binder design

Binder jet additive manufacturing (BJAM) presents an avenue for advanced manufacturing of various high-value materials due to high deposition rates, scalability, and geometric flexibility. However, conventional organic binders in BJAM introduce residual carbon upon pyrolysis, often leading to imprecise alloy composition in the final sintered part. The undesirable residual carbon from binder burn-off limits the application of BJAM for high-performance alloys due to their high sensitivity to carbon addition. In this study, we have designed poly(vinylpyrrolidone-co-vinyl acetate) (PVP-VAc) as a clean burn-off binder for BJAM, where excess oxygen groups in VAc enable cleaner burn-off and reduce residual carbon retention. Compared to a widely used commercial binder, the optimized PVP-VAc binder reduced residual carbon retention by over 90% in H13 tool steel. The significant reduction in residual carbon enables predictable printing and subsequent sintering of complex H13 tool steel geometries, an alloy known to have substantial challenges around distortion due to the sintering window shifting from carbon addition. The design of a clean burnout binder provides a major path forward for BJAM by enabling new AM designs and applications for composition-sensitive high-performance alloys, such as nickel-based superalloys, titanium alloys, and high alloyed steels.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Enabling residential heating decarbonization through hydronic low-temperature thermal distribution using forced-air assistive devices

Space heating represents approximately one-tenth of the United States’ energy use and has a breadth of potential for emission reduction. An element of space heating, hydronic heat distribution methods, use water supply temperatures up to 82.2 °C (180 °F). However, this operating temperature can be incompatible with high-efficiency heat generation systems, which typically provide heating temperatures of up to 60 °C (140 °F). In this work, a low-cost retrofit solution is developed using experimentally validated computational tools by incorporating an airflow distributor that preferentially directs the airflow from a fan to enhance heat transfer over the finned-tube heat exchanger found in conventional baseboards. The same heat output of traditional baseboard heat distribution systems operating at higher water supply temperatures (71.1 – 82.2 °C) can also be achieved at lower temperatures (≤60 °C). Specifically, results indicate that the technology can produce more than a 46.7% improvement in the heat transfer output at temperatures as low as 60 °C (140 °F), effectively matching the same output range achieved by 71.1 – 82.2 °C (160–180 °F) water supply temperatures. An important benefit of this solution is that it utilizes the existing infrastructure as opposed to requiring the replacement of the entire distribution system. Additionally, this technology enables existing building infrastructure to be coupled with newer, high-efficiency heating systems such as condensing boilers, solar-thermal systems, and geothermal/air-to-water heat pumps that may produce lower water supply temperatures (60 °C). A geospatially resolved techno-economic and environmental analysis is completed and presented to further understand the equivalent carbon footprint of enabling higher efficiency heat generation systems with the improved efficiency heat distribution system disclosed. Using 2021 grid-average emission factors, an emission reduction of up to a 67.5% decrease (2658 kgCO 2 /yr) for a single-family home, depending on state and climate region, could be realized by replacing a traditional natural gas-fired boiler with an air-to-water heat pump coupled to the same high-temperature heat distribution system along with the low-cost retrofit solution. A complete CO 2 emission reduction of 6688 kgCO 2 /yr, depending on state and climate region, could be realized if all electricity is further renewably sourced. Thus, this study provides a possible pathway towards enabling the reduction of operational emissions in space heating.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗