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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An Architectural Survey of the U12G Tunnel Historic District, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), in conjunction with the National Nuclear Security Administration Nevada Field Office (NNSA/NFO), proposes to demolish six buildings and three storage areas located at the U12g Tunnel portal area in Area 12 of the Nevada National Security Site (NNSS). The buildings are 12-358 (Signal Vault); 12-201800 (Storage Quonset Hut); 12-202555 (Walker Shack); 12-868 (Pipe Assembly); 12-B100933 (Electrical Shop); 12-B100944 (Conference Room); and Storage Area 1; Storage Area 2; and Storage Area 3. The buildings and storage areas were selected for demolition as part of the DOE’s Real Property Efficiency Plan to reduce the footprint of unused and non-operational facilities on the NNSS. They are all vacant and have no proposed uses for current or upcoming NNSS missions. Demolition activities constitute an undertaking subject to review under Section 106 of the National Historic Preservation Act (NHPA) (54 United States Code [USC] § 306101) and its implementing regulations, 36 Code of Federal Regulations (CFR) Part 800. Identification efforts began with resources proposed for demolition in federal Fiscal Year (FY) 23. Four buildings were proposed to be demolished in FY23 (12-358, 12-868, 12-201800, and 12-202555). These buildings and the U12g Tunnel Historic District (SHPO No. D444) were recorded in Identification, Evaluation, and Finding of Adverse Effect for the Proposed Demolition of Five Buildings in Area 12, Nevada National Security Site, Nye County, Nevada (Menocal et al. 2023). Identification efforts indicated three buildings (12-358, 12-201800, and 12-868) supported nuclear testing in the U12g Tunnel. The fourth building post-dated the use of U12g Tunnel for nuclear testing activities. The report recommended that three of the four buildings (12-358, 12-201800, 12-868) and the U12g Tunnel Historic District may be eligible for the National Register of Historic Places (NRHP). The report also found that the undertaking would have an adverse effect on the three buildings and on the historic district. The Nevada State Historic Preservation Office (SHPO) concurred with the report’s findings (Reed 2023). The U12g Tunnel was determined eligible as a historic district under the Secretary of the Interior’s (SOI) Significance Criterion A, at the local level, in the context of the Cold War as an underground testing environment for the development of nuclear weapons and to assess the effects of a nuclear explosion on materials and equipment with a period of significance from 1959 to 1971. It was also determined eligible under Significance Criterion C for embodying the distinctive characters of a horizontal tunnel complex used for nuclear testing and as a significant and distinguishable entity. The three buildings were determined to be contributing elements of the district. The undertaking was expanded with the addition of two buildings and three storage areas proposed to be demolished and located within U12g Tunnel Historic District in FY24. These five resources (12-B100933, 12-B100944, and Storage Areas 1, 2, and 3) were recorded in Supplemental Identification, Evaluation, and Finding of Effect for Additional Proposed Demolition at U12g Tunnel, Area 12, Nevada national Security Site, Nye County, Nevada (Brannan et al. 2024). Identification efforts indicated that the two buildings and Storage Area 1 supported nuclear testing in the U12g Tunnel. Storage Area 1 and Storage Area 2 post-dated the nuclear testing activities at U12g Tunnel and were not recommended as contributing elements to the district. The report also found that the undertaking would have an adverse effect on the newly identified buildings and one storage area and on the historic district. The SHPO concurred that the expanded undertaking would result in adverse effects to historic properties (Reed 2025). To resolve these adverse effects, NNSA/NFO, in consultation with the SHPO, is following standard mitigation as stipulated in the 2024 Programmatic Agreement DE-GM58-22NA25554 Among the U.S. Department of Energy and the Nevada State Historic Preservation Officer and the Advisory Council on Historic Preservation Concerning the Protection of Historic Properties on the Nevada National Security Site, Nye County, Nevada (hereafter referred to as the NNSS PA). The standard mitigation measures are outlined in Appendix D of the NNSS PA. As such, this architectural survey has been prepared in accordance with Appendix D of the NNSS PA and follows the report format outlined in Appendix F. It includes a historic context that describes the district’s origin, history, and support functions, its significance in the context of nuclear testing on the NNSS, and identifies contributing and non-contributing elements within the district. The report is accompanied by Architectural Resource Assessment (ARA) forms for individual resources and a Historic District Resource Assessment (HDRA) for the U12g Tunnel Historic District. In total, this architectural report identified 32 primary resources within the district boundary. Six of the primary resources were previously identified as contributing elements. An additional 17 resources are recommended as contributing elements to the district for a total of 23 contributing elements. The other nine resources identified are recommended as non-contributing elements to the district.

12-201800↗
DOE OSTI2024

Development & Experimental Validation of a Generalized Resistance-Capacitance Model for Numerical Simulation of Phase-Change Material Embedded Heat Exchangers

Latent heat thermal energy storage (LHTES) using phase change material (PCM) has attracted increased attention as a viable solution for overcoming the mismatch between energy supply and demand for renewable energy-based systems. PCM-embedded heat exchangers (PCM-HX) have the potential to significantly improve thermal performance due to high storage capacity and low temperature variation during the phase change process. Most models for simulating LHTES heat transfer use Computational Fluid Dynamics (CFD) simulations, which have high computational costs resulting from considering the complex and time-dependent physics relevant to PCM-HXs. In this paper, a Generalized Resistance Capacitance-based Model (GRCM) was developed to predict the thermal performance of arbitrary PCM-HXs in a computationally efficient manner without compromising modeling accuracy. The GRCM is exercised for three case studies: (i) verification for a single-slabbed finned PCM-HX, (ii) verification and validation for a copper foam/paraffin composite PCM-HX, and (iii) validation for a straight tube annular finned PCM-HX. The copper foam PCM-HX uses an electric heater at the top of HX, while the other two configurations utilize water as heat transfer fluid. For the single-slabbed finned PCM-HX melting case, the mean deviation in average PCM temperature predicted by the GRCM compared to the CFD model was between 0.56 – 0.73 K, with maximum temperature deviation of 2.68 K. For the HTF outlet temperature, the validation results showed that GRCM prediction matches very well with experimental data, with mean temperature deviation of 0.24 K during melting case, while for solidification case was 0.34 K. These results showcase the GRCM’s capability for accurately reproducing the thermal characteristics of PCM-HXs with considerably lower computational effort.

42 ENGINEERING↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗
DOE OSTI2026

From machine learning to chemical insight: Darwinian chance and the stability of charge carriers in flow batteries

In chemistry, rationalizing and predicting reaction behavior in complex environments is challenging, but this knowledge is required for practical applications and materials advancement. Here, we focus on one such example that uses organic molecules as redox-active materials in electrochemical energy storage. In flow batteries, these molecules (also known as redoxmers) serve as charge carriers, and exceptional chemical stability in all states of charge is required for long-term use. Here, we show how machine learning combined with chemist's knowledge can be used to reveal patterns in the reactivity of charged redoxmers by providing mechanistically tractable clues.

25 ENERGY STORAGE↗
DOE OSTI2025

Pathways for Sustainable Reaction Kinetics in Li-CO2 Batteries

Lithium-carbon dioxide (Li-CO2) batteries hold great promise for high-energy-density storage applications. However, advancing this technology as a sustainable alternative to Li-ion systems requires a deeper understanding of the underlying reaction mechanisms, which remain elusive. A key challenge stems from the added complexity introduced by the presence of oxygen (O2) in CO2 environment. In this study, we employed a stable Cu3(VBi)0.5Se4 mid-entropy catalyst and conducted a comprehensive investigation to uncover the underlying reaction mechanisms in Li-CO2 batteries under varying CO2/O2 ratios. Under pure CO2 conditions, the battery exhibits excellent rechargeability, sustaining up to 1200 cycles. However, at high current densities, the discharge potential drops significantly (below 2.0 V), primarily due to sluggish reaction kinetics caused by solid carbon formation. Interestingly, introducing O2 mitigates this limitation, leading to a 58% increase of the discharge potential (from 1.7 V to 2.7 V) at the high current density of 0.8 mA/cm2, signifying a substantial boost in energy output. Our results reveal that the reactions follow distinct pathways, shifting from surface- to solution-based mechanism, and may even exhibit coexistence of both mechanisms, depending on the CO2/O2 ratio. These findings offer new insights for designing high-performance and sustainable Li-gas batteries utilizing CO2 and O2 mixtures.

Ngo, Anh [University of illinois Chicago]↗
DOE OSTI2024

Investigation of Key Electronic States in Layered Mixed Chalcogenides With a d 0 Transition Metal as Li-Ion Cathodes

Lithium-rich transition metal chalcogenides are witnessing a revival as candidates for Li-ion cathode materials, spurred by the boost in their capacities from transcending conventional redox processes based on cationic states and tapping into additional chalcogenide states. A particularly striking case is Li 2 TiS 3-y Se y , which features a d 0 metal. While the end members are expectedly inactive, substantial capacities are measured when both Se and S are present. Using X-ray absorption spectroscopy, it is shown that the electronic structure of Li 2 TiS 3-y Se y is not a simple combination of the end members. The data confirm previous hypotheses that, in Li 2 TiS 2.4 Se 0.6 , this behavior is underpinned by concurrent and reversible redox of only S and Se, and identify key electronic states. Moreover, wavelet transforms of the extended X-ray absorption fine structure provide direct evidence of the formation of short Se–Se units upon charging. The study uncovers the underpinnings of this intriguing reactivity and highlights the richness of redox chemistry in complex solids.

25 ENERGY STORAGE↗
DOE OSTI2025

Seismic response of vertical dry storage casks under three-dimensional earthquake motions

Ensuring the long-term seismic safety of dry storage casks (DSCs) is becoming increasingly critical as these systems evolve from temporary to de facto permanent repositories for spent nuclear fuels. Traditional seismic soil–structure interaction (SSI) assessment methods use one-dimensional deconvolution or simplified boundary conditions to model incident waves. Although computationally appealing, simplifying assumptions may alter the seismic risk by neglecting the full complexity of three-dimensional (3D) wave propagation effects. To address this challenge, this paper introduces a novel high-fidelity computational framework that leverages the Domain Reduction Method (DRM) with perfectly matched layers (PML) to accurately transfer complex, 3D seismic wavefields from regional-scale fault-rupture simulations into local-scale finite element models of DSCs. Using broadband, physics-based ground motions from a generic M w 7.0 strike-slip event, both single-cask and multi-cask configurations were investigated under near- and far-field conditions. Emphasis is placed on capturing complex SSI, spatial variability in the ground motion, and nonlinear phenomena such as cask rocking and sliding. Numerical results demonstrate that near-field conditions, where forward directivity and fling-step effects dominate, lead to significantly higher DSC rocking and sliding. Far-field cases, by contrast, generally exhibit modest responses. Incorporating SSI tends to amplify or alter DSC response spectra and introduce response variability, which underscores the need for site-specific evaluations and robust modeling approaches to ensure the seismic integrity of DSCs in interim spent fuel storage installations.

Das, Tonmoy↗
DOE OSTI2024

Enabling fast discharge of Li-ion batteries via electrolyte formulations for urban air mobility applications

High-power discharge requirements are critical for lithium-ion batteries (LIBs) used in electric Vertical Takeoff and Landing (eVTOL) vehicles that are increasingly considered in urban mobility. This investigation places a particular emphasis on understanding the impact of electrolytes on discharge processes and rate capability. We aim to compare the discharge behavior of LiBs using a conventional electrolyte (Gen 2: 1.2 M LiPF 6 in EC:EMC) and the dual salt LiTFSI-LiBOB-based electrolyte. Here we carefully examine the profiles of charging and discharging, the behavior during extended cycles, impedance spectroscopy results, and the characteristics of the electrode surface. Our research findings demonstrate the complex relationship between the composition of electrolytes and the specific high-power discharge requirements of electric vertical takeoff and landing (eVTOL) systems. This research highlights the importance of customizing electrolyte compositions to optimize energy storage density while simultaneously enabling higher power extraction to enhance performance in short-range electric aviation.

25 ENERGY STORAGE↗
DOE OSTI2024

Identifying point defects and ordering in the high-entropy layered oxide Li 1.5 MO 3-δ (M=Mn, Al, Fe, Co, Ni) for energy storage applications

High-entropy layered oxides (HELOs) represent a very promising class of next-generation battery cathodes, combining the well-studied properties of layered cathode materials such as LiCoO 2 with the chemical tunability and stability of high-entropy materials. HELO materials often form particles with complex defects and domain structures, complicating accurate characterization of structure and cation mixing. Understanding disorder and order in HELO materials is necessary for understanding their performance and utility as cathodes. Here we demonstrate the characterization of the HELO Li 1.5 MO 3-δ (M = Mn, Al, Fe, Co, Ni), wherein X-ray powder diffraction, transmission electron microscopy imaging, and electron diffraction patterns are analyzed to reveal the presence of ordering in the HELO, with imaging and diffraction simulation employed to compare experimental results to atomic modeling. Finally, without rigorous characterization at the atomic scale, important features such as defect ordering can be easily overlooked and therefore remain unconsidered when interpreting experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2025

ITreeForeCast: An integrated modeling software to simulate tree level growth and forest carbon storage

Healthy trees in forest act as a natural carbon sink, capturing carbon. As they grow, they store carbon in their trunks, leaves and roots. Not all trees store carbon at the same rate, or in the same quantities, as it depends on a variety of biophysical and climatic factors. Furthermore, although carbon estimation in trees can be complex, the precision of estimates is tightly linked to trees growth, both in diameter and height. However, the simulation of carbon uptake by forest and forest growth has each been modeled separately, and independently at differing levels of detail and spatial resolution. In this paper, we introduce ITreeForeCast, a simulation model combining the two types of modeling on a unified platform, enabling the investigation of impacts of management strategies on carbon sequestration and wood products. ITreeForeCast is a user-extendable framework that offers new opportunities to model, simulate, and visualize the dynamics of individual trees in a forest, simulate management strategies over time, and carbon uptake.

09 - BIOMASS FUELS↗
DOE OSTI2024

Era of entropy: Synthesis, structure, properties, and applications of high-entropy materials

The field of high-entropy materials (HEMs) has emerged as a dynamic area of scientific exploration, driven by the exceptional properties arising from their compositional complexity. Encompassing both high-entropy alloys (HEAs) and high-entropy ceramics (HECs), these materials have garnered significant attention across diverse research domains. From investigations into phase evolution and mechanical characteristics to studies of ionic, electronic, and magnetic behaviors, HEMs demonstrate remarkable potential for a wide array of applications. These range from catalysis and tribology to energy storage and superconductivity. Fundamental research has shed light on crucial phenomena such as configurational entropy, lattice distortion, and sluggish diffusion. These discoveries are paving the way for materials design strategies that enable new functional tunability and resistance to application-specific harsh environments. This burgeoning field promises to revolutionize material design and performance across numerous technological sectors. Here, this special collection between Applied Physics Letters and the Journal of Applied Physics provides a timely overview of the latest research in this area. It highlights the growing interest in understanding the impact of high compositional complexity on conventional structure–process–property–performance relationships in HEMs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Inaugural Symposium of the Institute for Sustainable Energy and Environment, Virginia Commonwealth University

The Inaugural Symposium of the Institute for Sustainable Energy and Environment (ISEE) was held in Virginia Commonwealth University, Richmond, Virginia during April 26 – 28, 2023. The symposium addressed issues and challenges in energy and environment and highlighted the use of clean renewable energy to mitigate the adverse effects of fossil fuels and the greenhouse gases it produces. These issues are complex as the involve educating the public that the climate change is real, it is caused by the use of fossil fuels, it requires scientists and engineers to understand the fundamental science in the production and storage of renewable energy, it requires industries to manufacture and distribute the equipment necessary for commercial application of these technologies, it requires policy makers to put together policies and guidelines to make the transition from fossil fuels to renewable energies efficient, and it requires social scientists and educators to address environmental justice issues and create a workforce to sustain the transition. The symposium considered all the above issues with a diverse list of speakers from the federal and state government, industrial partners, and experts from science and technology to social sciences and public policy. Speakers from the White House Office of Science and Technology Policy, Department of Energy, universities, and non-governmental agencies discussed the challenges and solutions in Plenary as well as Panel sessions. The talks were recorded with permission from the speakers and the panelists and were put in the ISEE website. The participants included students, postdoctoral fellows, and professors from Virginia Commonwealth University and local area Historically Black College and Universities (HBCU) such as Virginia State University and Norfolk State University. The $5000 fund awarded by the Department of Energy were used to support the participation of students, postdoctoral fellows, and faculty from the HBCU institutions. The technical program the list of participants is provided later in the document.

08 HYDROGEN↗
DOE OSTI2025

Enhanced Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS) for On-Site Biomedical and Environmental Monitoring

The convergence of microfluidic and electrochemical biosensor technologies offers significant potential for rapid, in-field diagnostics in biomedical and environmental applications. Traditional systems face challenges in cost, scalability, and operational complexity, especially in remote settings. Addressing these issues, laser-induced graphene microfluidic integrated sensors (LIGMIS) are presented as an innovative platform that integrates microfluidics and electrochemical sensors both comprised of laser-induced graphene. This study advances the LIGMIS concept by resolving issues of uneven fluid transport, increased hydrophobicity during storage, and sensor biofunctionalization challenges. Key innovations include Y-shaped reservoirs for consistent fluid flow, hydrophilic polyethyleneimine coatings to maintain wettability, and separable microfluidic and electrochemical components enabling isolated electrode nanoparticle metallization and biofunctionalization. Multiplexed electrochemical detection of the neonicotinoid imidacloprid and nitrate ions in environmental water samples yields detection limits of 707 nm and 10 -5.4 m with wide sensing ranges of 5–100 µm and 10 -5 –10 -1 m, respectively. Similarly, uric acid and calcium ions are detected in saliva, demonstrating detection limits of 217 nm and 10 -5.3 m with sensing ranges of 10–50 µm, and 10 -5 –10 -2.5 m, respectively. Overall, this biosensing demonstrates the capability of the LIGMIS platform for multiplexed detection in biologically complex solutions, with applications in environmental water quality monitoring and oral cancer screening.

environmental monitoring↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
DOE OSTI2026

Review of Ultrasonic Methods for Monitoring, Damage Detection, and Processing of Lithium-Ion Batteries Throughout Their Life Cycle

Lithium-ion batteries (LIBs) are the leading technology used in consumer electronics, electric vehicles, and grid-level electrochemical energy storage applications. The ever-increasing use of LIBs has highlighted a gap in understanding of their behavior throughout their life cycle. Current monitoring systems rely on electrical and sometimes temperature measurements to assess the internal state which limits information about complex electrochemical processes. In response, ultrasonic testing (UT) has shown promise for non-invasive assessment due to its ease of use and sensitivity to mechanical changes which are correlated with electrochemical changes within the battery. We summarize the research in UT methods applied to LIBs throughout their life cycle. We also discuss physics-based and data-driven modeling approaches used to interpret ultrasonic signals in the context of LIBs, with an emphasis on the existing challenge of establishing rigorous links between electrochemical behavior and elastic and poroelastic wave physics to gain insight regarding physical changes in the LIB that can be directly measured using UT. Finally, we discuss the challenges of implementing UT across the LIB life cycle and identify opportunities for further research. This review aims to provide helpful guidance to researchers and practitioners of UT in the growing field of UT for electrochemical battery systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
DOE OSTI2026

Programmable hydrogels by combining persistent and transient dynamic bonds

Out-of-equilibrium chemistry is currently being applied to polymer systems to mimic the autonomous behavior of biological materials. In this study, hydrogels with self-healing properties were developed that combine persistent crosslinks from dynamic metal-ligand coordination with transient crosslinks from dynamic anhydride bonds. Polymers containing terpyridine ligands and carboxylic acid groups were synthesized and crosslinked with divalent metal ions (Fe 2+ , Ni 2+ , Co 2+ , Zn 2+ , Cu 2+ ). The coordination bonds from terpyridine–metal coordination impart persistent stability, while transient anhydrides formed on treatment with 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC) temporarily increase crosslink density. Distinct behaviors are observed depending on the choice of metal, with Ni 2+ forming robust, stable networks; Zn 2+ creating moderately dynamic gels; and Cu 2+ yielding highly dynamic, soft materials. Treatment with EDC increased storage moduli significantly, with transient effects lasting up to 280 min depending on the metal ion. Self-healing experiments demonstrated independent contributions from metal coordination and transient anhydrides, enabling recovery of stress and strain under varying conditions. Additionally, complex and reversible 2D stiffness patterns were generated by spatially controlled EDC treatment of Zn 2+ and Cu 2+ hydrogel films, demonstrating programmability and reusability.

Rajawasam, Chamoni W. H. [Miami University, Oxford↗
DOE OSTI2024

Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Lithium-sulfur (Li-S) batteries involve complex solid-liquid-solid phase transformations during both discharging and charging processes, where cathode materials, formulation, and structure play a crucial role. Here, a design of experiments (DoE) methodology and an empirical model are developed to systematically explore the interactions and trade-offs among cathode factors and process variables, and to obtain generalizable effects estimates for the multivariate system. Compared to the conventional one-factor-at-a-time (OFAT) approach, this work demonstrates advantages in both efficiency and accuracy by allowing the data to guide future research and decisions. Further, an optimized cathode formulation and processing parameters are predicted and validated experimentally, achieving over 1000 mAh g -1 in discharge capacity and improved cycling under practical lean electrolyte (4 µL mg -1 S) and high S-loading cathodes (>4 mg cm -2 ) conditions. The optimized cathode was scaled up and assembled into Li-S pouch cells, achieving 316 Wh kg -1 in cell-level energy, proving that the comprehensive and rigorous framework for optimizing complex systems with DoE leads to improved performance in a practical pouch cell system.

25 ENERGY STORAGE↗