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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of an All-Aqueous Thermally Regenerative Redox Flow Battery to Support Fossil Fuel Assets

Low-temperature thermal energy, a largely untapped resource, holds significant promise for large-scale electrical power generation globally. Various stationary sources, including industrial entities and thermal power plants, emit considerable low-temperature heat that currently remains unutilized. This energy is often overlooked because its low temperature renders it unsuitable for efficient power generation using conventional methods. However, current research is exploring diverse technologies capable of converting low-temperature heat into grid-scale power, aiming to enhance grid efficiency, further decarbonization initiatives, and facilitate a shift toward more decentralized power systems. One such innovative technology is the thermally regenerative battery (TRB), noted for its high power and energy densities compared to similar technologies, positioning it as a potential game-changer in power generation. TRBs integrate two scalable and well-established unit operations: a redox flow battery and a distillation column. This integration suggests that once an effective TRB chemistry is established, the pathway to commercialization could be expedited. The copper-based thermally regenerative ammonia battery (Cu aq -TRAB) stands out as the first TRB that circumvents electrodeposition/dissolution reactions, stabilizing Cu(I) and Cu(II) within the electrolyte and maintaining stability of all electroactive species in an aqueous phase. This stabilization has led to improvements in coulombic efficiency, open circuit potential, and copper solubility, thereby enhancing power density, energy density, and overall energy efficiency. Preliminary tests were conducted to determine the effects of various electrolyte species on the performance metrics of the battery, both theoretically and experimentally. These tests revealed that the solubility of copper in the Cu aq -TRAB electrolyte was constrained by the Cu(I)-NH 3 complex. Adjusting the background electrolyte to 5 M NH4Br and the ligand concentration to 4 M NH 3 enabled the copper concentration to reach a maximum of 0.6 M. This modification led to an estimated theoretical maximum energy density of 9.5 Wh L -1 for the Cu aq -TRAB. Additionally, full cell testing indicated a tradeoff between peak power and energy density with varying copper and ammonia concentrations. Increasing the applied current density during discharge linearly raised the average power output, with a minimal reduction in energy density due to a balance between higher ohmic overpotential and reduced time for undesirable ammonia crossover. Furthermore, a comprehensive numerical sensitivity analysis of the complete Cu aq -TRAB system was performed. This analysis aimed to assess how the battery and the distillation column responded to changes in system input parameters, providing insights into optimal research directions for enhancing system performance. The analysis revealed that at room temperature, battery power was significantly more sensitive to ohmic losses than to mass transfer, with reaction rates having minimal impact. This trend continued even at higher temperatures. Also, the thermal energy required for ammonia separation was studied, showing that increased temperatures generally reduced energy requirements, except in low-pressure scenarios above 65 °C. An investigation into membrane performance in the Cu aq -TRAB was undertaken, given the significant impact of ammonia transport control and ohmic losses on system performance. Various membranes were evaluated to identify key performance metrics. Among the tested membranes, Selemion CMVN exhibited the highest performance, with a peak power density of 84 mW cm -2 and average values of 26 ± 6.8 mW cm -2 for power density and 2.9 Wh L -1 for energy density at an applied current density of 50 mA cm -2 . An economic assessment indicated a levelized cost of storage at $410 per MWh under optimal conditions, highlighting the commercial potential of the Cu aq -TRAB when utilizing cost-effective, readily available materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Nano-enhanced solid-state hydrogen storage: Balancing discovery and pragmatism for future energy solutions

Nanomaterials have revolutionized the battery industry by enhancing energy storage capacities and charging speeds, and their application in hydrogen (H 2 ) storage likewise holds strong potential, though with distinct challenges and mechanisms. H 2 is a crucial future zero-carbon energy vector given its high gravimetric energy density, which far exceeds that of liquid hydrocarbons. However, its low volumetric energy density in gaseous form currently requires storage under high pressure or at low temperature. This review critically examines the current and prospective landscapes of solid-state H 2 storage technologies, with a focus on pragmatic integration of advanced materials such as metal-organic frameworks (MOFs), magnesium-based hybrids, and novel sorbents into future energy networks. These materials, enhanced by nanotechnology, could significantly improve the efficiency and capacity of H 2 storage systems by optimizing H 2 adsorption at the nanoscale and improving the kinetics of H 2 uptake and release. We discuss various H 2 storage mechanisms—physisorption, chemisorption, and the Kubas interaction—analyzing their impact on the energy efficiency and scalability of storage solutions. The review also addresses the potential of “smart MOFs”, single-atom catalyst-doped metal hydrides, MXenes and entropy-driven alloys to enhance the performance and broaden the application range of H 2 storage systems, stressing the need for innovative materials and system integration to satisfy future energy demands. High-throughput screening, combined with machine learning algorithms, is noted as a promising approach to identify patterns and predict the behavior of novel materials under various conditions, significantly reducing the time and cost associated with experimental trials. In closing, we discuss the increasing involvement of various companies in solid-state H 2 storage, particularly in prototype vehicles, from a techno-economic perspective. In conclusion, this forward-looking perspective underscores the necessity for ongoing material innovation and system optimization to meet the stringent energy demands and ambitious sustainability targets increasingly in demand.

25 ENERGY STORAGE↗
DOE OSTI2021

Data Mining of Polymer Phase Transitions upon Temperature Changes by Small and Wide-Angle X-ray Scattering Combined with Raman Spectroscopy

The complex physical transformations of polymers upon external thermodynamic changes are related to the molecular length of the polymer and its associated multifaceted energetic balance. The understanding of subtle transitions or multistep phase transformation requires real-time phenomenological studies using a multi-technique approach that covers several length-scales and chemical states. A combination of X-ray scattering techniques with Raman spectroscopy and Differential Scanning Calorimetry was conducted to correlate the structural changes from the conformational chain to the polymer crystal and mesoscale organization. Current research applications and the experimental combination of Raman spectroscopy with simultaneous SAXS/WAXS measurements coupled to a DSC is discussed. In particular, we show that in order to obtain the maximum benefit from simultaneously obtained high-quality data sets from different techniques, one should look beyond traditional analysis techniques and instead apply multivariate analysis. Data mining strategies can be applied to develop methods to control polymer processing in an industrial context. Crystallization studies of a PVDF blend with a fluoroelastomer, known to feature complex phase transitions, were used to validate the combined approach and further analyzed by MVA.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effects of Tethered Polymers on Dynamics of Nanoparticles in Unentangled Polymer Melts

Polymer-tethered nanoparticles (NPs) are commonly added to a polymer matrix to improve the material properties. Critical to the fabrication and processing of such composites is the mobility of the tethered NPs. Here, we study the motion of tethered NPs in unentangled polymer melts using molecular dynamics simulations, which offer a precise control of the grafted chain length N g and the number z of grafted chains per particle. As Ng increases, there is a crossover from particle-dominated to tethered-chain-dominated terminal diffusion of NPs with the same z. The mean squared displacement of loosely tethered NPs in the case of tethered-chain-dominated terminal diffusion exhibits two subdiffusive regimes at intermediate time scales for small z. The first one at shorter time scales arises from the dynamical coupling of the particle and matrix chains, while the one at longer time scales is due to the participation of the particle in the dynamics of the tethered chains. The friction of loosely grafted chains in unentangled melts scales linearly with the total number of monomers in the chains, as the friction of individual monomers is additive in the absence of hydrodynamic coupling. As more chains are grafted to a particle, hydrodynamic interactions between grafted chains emerge. As a result, there is a nondraining layer of hydrodynamically coupled chain segments surrounding the bare particle. Outside the nondraining layer is a free-draining layer of grafted chain segments with no hydrodynamic coupling. The boundary of the two layers is the stick surface where the shear stress due to the relative melt flow is balanced by the friction between the grafted and melt chains in the interpenetration layer. The stick surface is located further away from the bare surface of the particle with higher grafting density.

42 ENGINEERING↗
DOE OSTI2026

Enhancing Value-Added CO Production from CO 2 Hydrogenation by Tailoring the Ru-CeO 2 Interface on MgO

Catalytic CO 2 hydrogenation presents a promising route for converting CO 2 into valuable products, contributing to the mitigation of net CO 2 emissions. Supported Ru catalysts have recently gained considerable attention due to their tunability for 100% CO selectivity via the reverse water-gas shift pathway, effectively suppressing the competing methanation route. However, despite achieving full CO selectivity, the overall CO yield remains limited by low CO 2 conversion, necessitating further improvement. In this work, CeO 2 was introduced to modify a Ru/MgO single-atom catalyst for CO 2 hydrogenation. The resulting Ru-CeO 2 /MgO catalyst, featuring abundant Ru-CeO 2 interfacial sites, exhibited a favorable balance of CO 2 conversion and CO selectivity, delivering the highest CO yield (32.5% at 500 °C), which is 9.0 and 1.8 times higher than that on Ru/MgO (3.6%) and Ru/CeO 2 (18.4%), respectively. Although the CO selectivity was slightly compromised due to enhanced CO binding at Ru-CeO 2 interfacial sites, H 2 was more efficiently activated at these interfaces and readily reacted with CO 2 adsorbed on CeO 2 -MgO surfaces, thereby boosting the CO 2 hydrogenation activity and CO yield. This study underscores the critical role of Ru-metal oxide interface engineering in improving CO yield and advancing the rational design of highly efficient Ru catalysts for CO production from CO 2 hydrogenation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A two-phase substrate model for enzymatic hydrolysis of lignocellulose: application to batch and continuous reactors

Abstract Background Enzymatic hydrolysis continues to have a significant projected production cost for the biological conversion of biomass to fuels and chemicals, motivating research into improved enzyme and reactor technologies in order to reduce enzyme usage and equipment costs. However, technology development is stymied by a lack of accurate and computationally accessible enzymatic-hydrolysis reaction models. Enzymatic deconstruction of cellulosic materials is an exceedingly complex physico-chemical process. Models which elucidate specific mechanisms of deconstruction are often too computationally intensive to be accessible in process or multi-physics simulations, and empirical models are often too inflexible to be effectively applied outside of their batch contexts. In this paper, we employ a phenomenological modeling approach to represent rate slowdown due to substrate structure (implemented as two substrate phases) and feedback inhibition, and apply the model to a continuous reactor system. Results A phenomenological model was developed in order to predict glucose and solids concentrations in batch and continuous enzymatic-hydrolysis reactors from which liquor is continuously removed by ultrafiltration. A series of batch experiments were performed, varying initial conditions (solids, enzyme, and sugar concentrations), and best-fit model parameters were determined using constrained nonlinear least-squares methods. The model achieved a good fit for overall sugar yield and insoluble solids concentration, as well as for the reduced rate of sugar production over time. Additionally, without refitting model coefficients, good quantitative agreement was observed between results from continuous enzymatic-hydrolysis experiments and model predictions. Finally, the sensitivity of the model to its parameters is explored and discussed. Conclusions Although the phenomena represented by the model correspond to behaviors that emerge from clusters of mechanisms, and hence a set of model coefficients are unique to the substrate and the enzyme system, the model is efficient to solve and may be applied to novel reactor schema and implemented in computational fluid dynamics (CFD) simulations. Hence, this modeling approach finds the right balance between model complexity and computational efficiency. These capabilities have broad application to reactor design, scale-up, and process optimization.

09 BIOMASS FUELS↗
DOE OSTI2022

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Plating and Stripping of Lithium Metal Stabilized by a Block Copolymer Electrolyte: Local Current Density Measurement and Modeling

Here, plating and stripping of lithium protrusions in lithium metal symmetric cells containing a solid block copolymer electrolyte was studied as a function of time in 3D using time-resolved X-ray tomography. These measurements enabled determination of the spatial variation in current densities at the plating and stripping electrodes. The initial interelectrode distance was 27 μm. Correlation functions were calculated to reveal the relationships between current densities at the two electrodes and local electrolyte thickness. Current densities at opposing electrode locations during protrusion growth is uncorrelated until the local interelectrode distance decreases to less than 6 μm, just before the cell shorts. Mass balance was used to determine the area from which lithium ions that form a protrusion were stripped. Computational modeling of the plating and stripping process reveals the interplay between electrochemical and mechanical driving forces and their effect on nonuniform current distribution. Model predictions were compared with experiments without resorting to any adjustable parameters. The computed correlation functions were in qualitative agreement with experiments. Finally, the model was used to calculate contour plots of electrochemical potential within the electrolyte, shedding light on how geometry, salt concentration, interelectrode distance, and mechanical stress influence local rates of electrochemical reaction.

36 MATERIALS SCIENCE↗
DOE OSTI2026

040226_AnnularFuel_FuelPerformance_LRSv2

This slide deck summarizes a comparative fuel performance assessment of four annular fuel concepts for pressurized water reactor applications against a reference 17×17 fuel design. Overall, annular fuel geometry improves several key thermal-mechanical performance metrics, with the large-annulus configuration providing the most favorable balance of benefits. Relative to the reference design, annular fuel reduces peak fuel temperature by lowering thermal resistance through a thinner fuel pellet and smaller heat-conduction path. At the same time, the reduced fuel volume raises local burnup and correspondingly increases fission gas release. Despite this, rod internal pressure at end of life decreases because the annular geometry provides greater internal void volume. Annular designs also tend to delay fuel-cladding gap closure, although this advantage diminishes in thin-gap configurations where the smaller initial gap accelerates closure. Similarly, hoop stress and hoop strain are generally reduced for annular fuel, but both increase as the initial gap becomes smaller, indicating a potential cladding integrity concern for thin-gap designs. Corrosion performance shows no meaningful variation among the concepts considered. Taken together, the results indicate that annular fuel can offer significant performance advantages in a PWR environment, with large-annulus designs emerging as the strongest candidate while thin-gap geometries introduce more challenging mechanical margins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Biologically Cooling the Planet: A BRISK Approach that Directly Connects the Ground to Deep Space

Climate change is a grave threat to our ability to thrive and prosper on Earth. Accumulation of greenhouse gases in the atmosphere stifle the Earth’s ability to cool itself, which leads to a rise of the average temperature – Global warming. The planetary cooling is exclusively radiative – thermal radiation escapes into cold outer space via a flux of photons at the low-infrared range. The penetration of these photons through Earth’s atmosphere is dictated by atmospheric transparency, which, serendipitously possesses a window centered around the peak of the Plank spectrum for a black body at about room temperature – the average temperature of Earth. This window spanning about 8 --13 μm wavelengths allows most of the photons in this range to escape into the Cosmos carrying our planet’s heat away. The efficiency of this outer heat flux – and thus planet’s cooling ability – can be controlled via emissivity – a function of wavelength which multiplies the fundamental Plank spectrum of a black body at a given temperature. In the presence of non-trivial atmospheric transparency it can be shown that radiative cooling can be enhanced with properly “designed” emissivity function. For the last 100 years, or so, people have been looking into ways to engineer materials to take advantage of such enhancement for various cooling applications, from making ice to cool clothes, and substantial progress has been achieved, including cooling in direct sunlight. The beauty if this approach is that it is entirely passive – no energy input is required. The objective of this project was to investigate whether biological systems possess such advanced cooling capacity, developed via millennia of evolution, to enhance plant’s adaptation to a hot environment, to increase cooling capability, and possibly modulate spectral emissivities on short or long time scales. We wanted to know if BRISK (Biological Radiative Sky Cooling) occurs and to what extent. It is unknown whether biological systems can perform this "spectral redistribution." It is reasonable to expect, however, that they might: in particular, desert plants under high heat stress and arid conditions might evolve this mechanism in order to maintain a tolerable internal temperature. Our intended approach was twofold: extensive literature search and supporting experimental investigations on select plants. We obtained the answers from the former, while the latter proved to be more complicated to perform with required accuracy in the timespan/budget of a small seedling project. After one year of research, we concluded that among the plant species studied there seem to be no evidence of BRISK, but it’s not the end of the story. Our planet’s biosphere is immense, and the evolution is continuing. With our preliminary results and a better understanding of what to look for further discoveries await, considering the accelerated pace of infrared imaging and spectroscopy technology development, from microscopic to satellite-based studies of plants and canopies. In the end, Earth’s energy balance is ultimately tied to radiative cooling so that further investigation into this problem is directly tied to solving the Climate Change challenge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Effects of PLA/PHB Blend Ratio and Wood Flour Loading on the Melt Rheology and Thermomechanical Properties of Biobased Polymer Composites

The development of sustainable, biobased polymer composites is crucial for reducing supply chain dependence on fossil fuels. A major challenge lies in understanding and predicting the processability of these sustainable material alternatives, which directly impacts large-scale manufacturing. Here, this study systematically investigates the effect of poly(lactic acid) (PLA)/polyhydroxybutyrate (PHB) blend ratios and wood flour (WF) loadings on the thermal, rheological, and mechanical properties of the composites. Composites were prepared via melt compounding and characterized by using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), oscillatory shear melt rheology, tensile testing, and scanning electron microscopy. Results showed that increasing the PHB content lowered the glass transition temperature, while higher PHB and WF loadings decreased thermal stability. Complex viscosity decreased with increasing PHB in 0 and 10% WF, whereas it increased in 20% and 30% WF for PHB-rich blends (80/20 and 70/30 PLA/PHB) due to stronger filler interactions with flexible PHB chains. At 30% WF, PHB-containing composites also showed elastic dominance with G′ higher than G″. This behavior was confirmed by van Gurp–Palmen plots, where the phase angle decreased with increasing PHB and WF loadings. WF addition also shifted Cole–Cole plots away from semicircular terminal relaxation, while time–temperature superposition validity was maintained, confirming that polymer chain dynamics continue to dominate melt behavior. Young’s modulus increased with WF loading, while tensile strength and toughness decreased due to weaker interfacial adhesion between the matrix and the filler. Interestingly, 10% WF systems containing PHB exhibited the highest toughness among all of the filled systems, indicating synergistic reinforcement from WF fillers and PHB-induced ductility. Overall, melt rheology effectively captured the internal structure and stiffness of the composites as the formulation changed, reinforcing the concept that the PLA/PHB ratio and WF content can be tuned to adjust melt elasticity and balance mechanical property trade-offs for targeted applications.

melt rheology↗
DOE OSTI2021

Internal Conversion between Bright (1 1 B $^+_u$) and Dark (21 A $^-_g$) States in s- trans -Butadiene and s- trans -Hexatriene

Internal conversion (IC) between the two lowest singlet excited states, 1 1 B$_u^+$ and 2 1 A$_g^–$, of s-trans-butadiene and s-trans-hexatriene is investigated using a series of single- and multi- reference wave function and density functional theory (DFT) methodologies. Three independent types of the equation-of-motion coupled-cluster (EOMCC) theory capable of providing an accurate and balanced description of one- as well as two-electron transitions, abbreviated as δ-CR-EOMCC(2,3), DIP-EOMCC(4h2p){N o }, and DEA-EOMCC(4p2h){N u } or DEA-EOMCC(3p1h,4p2h){N u }, consistently predict that the 1 1 B$_u^+$/2 1 A$_g^–$ crossing in both molecules occurs along the bond length alternation coordinate. However, the analogous 1 1 B$_u^+$ and 2 1 A$_g^–$ potentials obtained with some multireference approaches, such as CASSCF and MRCIS(D), as well as with the linear-response formulation of time-dependent DFT (TDDFT), do not cross. Hence, caution needs to be exercised when studying the low-lying singlet excited states of polyenes with conventional multiconfigurational methods and TDDFT. The multistate many-body perturbation theory methods, such as XMCQDPT 2 , do correctly reproduce the curve crossing. Among the simplest and least expensive computational methodologies, the DFT approaches that incorporate the contributions of doubly excited configurations, abbreviated as MRSF (mixed reference spin-flip) TDDFT and SSR( 4,4 ), accurately reproduce our best EOMCC results. This is highly promising for nonadiabatic molecular dynamics simulations in larger systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Economic NMPC for a Reversible Solid Oxide Cell

Reversible solid oxide fuel cells (rSOCs) offer the flexibility to operate in tandem with the electric grid by switching between fuel cell and electrolysis modes based on real-time electricity prices. However, their complex, tightly coupled dynamic behavior poses significant challenges in determining optimal operating strategies. In this work, we present an economic nonlinear model predictive control (E-NMPC) framework to optimize the operation of rSOCs. The proposed E-NMPC is applied to a detailed rSOC flowsheet model that includes a utility scale rSOC module as well as balance-of-plant equipment necessary for thermal management. Our results demonstrate that in fuel cell mode, the E-NMPC strategy reduces hydrogen consumption compared to conventional set-point tracking NMPC, while maintaining the same level of electricity output. Also, in electrolysis mode, the E-NMPC yields a marginal improvement in hydrogen production. In addition, we explore the integration of a battery with the rSOC system to enhance flexibility in meeting electricity production and consumption targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Solar Photovoltaic (PV) Manufacturing Expansions in the United States, 2017-2019: Motives, Challenges, Opportunities, and Policy Context

This report analyzes U.S. PV manufacturing announcements from 2017 through July-2019 and explores the potential impact of tariffs on the competitiveness of U.S. PV module manufacturing. First, the fraction of announcements that resulted in established facilities in the time frame specified is analyzed, as well as firm nationality and the existence of any other U.S. facilities for the firm. The prevalence of different PV product types among the announcements was also analyzed, as well as the type of facilities selected for development (greenfield, existing PV factories, or repurposed buildings). The cost to build these facilities was also analyzed in the context of their capacity, the value of incentives received by state and local governments, and the number of jobs created. Case studies of Auxin Solar, First Solar, Hanwha Q Cells, JinkoSolar, LG, Silfab, Solaria, and SunPower were conducted and highlight five factors influencing firms’ decisions to expand their U.S. PV production capacity. These included: 1) proximity to demand, including supply contracts and demand for domestic content 2) tariffs and other incentives. The Section 201 tariffs on PV module imports were often described helpful but tariffs on cell imports (Section 201) or materials (Section 301, Section 232) were often described as counterproductive since U.S. supply chains do not exist for many necessary goods. The corporate tax decrease and local incentives were often mentioned as helpful. 3) the ability to move quickly in order to maximize the benefit of the Section 201 tariffs. This affected decisions to select localities with faster permitting or moving into existing buildings. 4) competition with scale, where competing with Asian PV imports or avoiding imports of material from China was challenging due to the larger scale of Asian manufacturers. Additionally, smaller firms expressed concerns that larger firms may not need their U.S. facilities to profitable. 5) access to capital, which was a particular challenge for smaller firms. Several U.S. tariffs affect the economics of PV cells, modules, and balance-of-module (BOM) materials, some of which are applicable to a long list of countries and others applicable only to China or Taiwan. We analyze how the tariffs interact to affect the prices of c-Si modules sourced under various assumptions about domestic and imported components and assembly. We estimate module prices under a range of baseline scenarios featuring U.S.-assembled modules with Southeast Asian cells (not from China or Taiwan). When all BOM components are sourced from the United States, baseline module prices are $0.37/W (below the 2.5-GW Section 201 cell cap) to $0.42/W (above the 2.5-GW cell cap). If all BOM components are sourced from China, the module price in this baseline U.S.-assembly scenario can reach up to $0.60/W. Imported module prices are lower than or within the range of the U.S.-assembled baseline prices under some conditions. Importing bifacial modules from Southeast Asia is the lowest-priced alternative, at $0.35/W, because bifacial modules are excluded from the Section 201 tariffs. Importing Southeast Asia modules that contain U.S. cells results in a price of $0.38/W, also owing to a Section 201 exclusion. Importing Southeast Asian modules that have Southeast Asian cells yields a price of $0.44/W, and importing Chinese modules that have U.S. cells yields a price of $0.58/W. These results suggest that U.S. tariff policy in 2019 may encourage U.S. assembly of mono-facial c-Si modules if the 2.5-GW cell import cap is not reached and Chinese BOM imports are negligible. However, some imported module options may offer the lowest prices if the 2.5-GW cell import cap is reached and/or U.S. module manufacturers must rely on significant Chinese BOM content. The analysis shows the 2.5-GW cell cap may be exceeded in 2019 and following years. It is also likely that U.S. module manufacturers will need to use at least some Chinese BOM content, since U.S. module manufacturers have stated that U.S. supply chains do not exist (or do not exist at a sufficient scale) for most BOM components, which makes them at least partially reliant on Chinese BOM and thus subject to Section 301 tariffs.

14 SOLAR ENERGY↗
DOE OSTI2020

Stochastic Analysis for Long Term Capital Structures, Systems, and Components Refurbishment and Replacement

As commercial Nuclear Power Plants (NPPs) pursue extended plant operation in the form of Second License Renewal (SLR), opportunities exist for these plants to provide capital investments to ensure long-term safe and economic performance. At the current time, several utilities have announced an intention to pursue extended operation for one or more of their NPPs via SLR . The goal of this research is to develop a risk-informed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions for NPP operations. Since the capital investments are influenced by various factors, such as markets, safety and regulatory, the decision-making process of NPP operations should take into account relevant factors for balancing risks, costs and profits. The traditional method of capital budgeting is based on the priority list of candidate projects using economic measures such as benefit-investment ratio, net present value (NPV) and internal rate of return. In the literatures, the problem of capital budgeting or the variant can be represented by an appropriate knapsack problem. The knapsack approach to capital budgeting takes as input as investment, along with the cost and profit of each project. The objective of capital budgeting is to find the combination of the binary decisions for every investment such that the overall profit is as large as possible. The output is a collection of projects to be carried out, and we refer this selected collection of projects as a project portfolio. One limitation of traditional optimization models for capital budgeting is that they do not account for risk/uncertainty in profit and cost streams associated with individual projects, they do not account for risk in resource availability in future years [1,2,3]. Projects can incur cost over-runs, especially when projects are large, performed infrequently, and when there is risk regarding technical viability, external contractors, and/or suppliers of requisite parts and materials. Occasionally, projects are performed ahead of schedule and with cost savings. Planned budgets for capital improvements can be cut and key personnel may be lost. Or, there may be surprise windfalls in budgets for maintenance activities due to decreased costs for “unplanned” maintenance. In these cases, how should we resolve capital budgeting when we have risk forecasts for costs, profits and budgets? One approach we proposed in this summary is to re-solve the optimization models based on assumed statistical distributions of given parameters. If these distributions were not available, a two-stage stochastic optimization approach can be used to provide priority lists to decision-makers to support better risk-informed decisions [4, 5]. In this summary, we will only focus on the first approach.

42 ENGINEERING↗
DOE OSTI2024

Correlation of Band Bending and Ionic Losses in 1.68 eV Wide Band Gap Perovskite Solar Cells

Abstract Perovskite solar cells (PSCs) are promising for high‐efficiency tandem applications, but their long‐term stability, particularly due to ion migration, remains a challenge. Despite progress in stabilizing PSCs, they still fall short compared to mature technologies like silicon. This study explores how different piperazinium salt treatments using iodide, chloride, tosylate, and bistriflimide anions affect the energetics, carrier dynamics, and stability of 1.68 eV bandgap PSCs. Chloride‐based treatments achieved the highest power conversion efficiency (21.5%) and open‐circuit voltage (1.28 V), correlating with stronger band bending and n‐type character at the surface. At the same time, they showed reduced long‐term stability due to increased ionic losses. Tosylate‐treated devices offered the best balance, retaining 96.4% efficiency after 1000 h (ISOS‐LC‐1I). These findings suggest that targeted surface treatments can enhance both efficiency and stability in PSCs.

14 SOLAR ENERGY↗
DOE OSTI2023

Critical Roles of Chalcogenide Anion on Strengthening Stability of Ni 2 Mo 6 Te 8 for Almost Exclusive Electrocatalysts Nitrate to Ammonia Conversion

Electrochemical hydrogenation of nitrate to ammonia using renewable electricity is a promising route for sustainability but lacks catalysts that can deliver balanced selectivity, activity, and durability. For this work, a new family of noble metal-free and high-performing Chevrel phase Ni 2 Mo 6 T 8 (T = S, Se, and Te) catalysts that have similar structural and textural properties and differ presumably only in chalcogenide anion is systematically studied. The side-by-side comparisons allow the uncovering of the critical roles of chalcogenide anions in impacting kinetic activities and long-term durability. The incorporation of anions with larger size and smaller electronegativity from sulfide to selenide and telluride invokes stronger inhibition of the otherwise competing hydrogen evolution reaction (HER) and steers the hydrogenation toward the selective formation of ammonia, thus improving both Faradic selectivity and the turnover frequency to high levels of 99.4% and 21.5 s –1 , respectively, on the Ni 2 Mo 6 Te 8 catalyst. More significantly, the bulkier anion in the Ni 2 Mo 6 T 8 catalyst kinetically inhibited the intercalation of electrolyte cations, a major degradation mechanism in the catalyst family examined here and delivered several times improved durability. Therefore, this study introduces novel active motifs for selective nitrate reduction and provides insights into the catalyst degradation mechanism and practical ways to improve durability.

36 MATERIALS SCIENCE↗