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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cyclic moisture reactivation of calcium sorbents for long duration thermochemical energy storage

The transition to a flexible and reliable energy infrastructure, using electro-thermal energy generation technologies such as geothermal, concentrated solar power, and nuclear, usually demands simultaneous advancement of thermal energy storage (TES) to support on-demand electricity generation and industrial applications while mitigating the inherent intermittency of renewable energy sources and power outages from direct energy generation. Among TES technologies, thermochemical energy storage (TCES) based on calcium looping emerges as a compelling high-power energy storage candidate due to its high reaction enthalpy, compatibility with elevated operating temperatures, and abundance of low-cost materials. However, the long-term durability of calcium-based sorbents for TCES is hindered by surface sintering and particle aggregation, leading to performance degradation over repeated thermal cycles. This study explores a moisture hydration-based strategy to regenerate a degraded calcium sorbent and mitigate performance degradation for long duration TCES. The addition of moisture transforms calcium oxide into calcium hydroxide and produces intercalation water layers, associated with a regenerated surface area and reduced calcium oxide crystallite size. Both these effects are beneficial in restoring the sorbents' reactivity for carbonization. Additionally, an optimized hydration-assisted reactivation protocol balances the recovered energy storage capacity with heating penalty required for moisture removal from hydrated samples, resulting in an enhanced energy storage capacity up to 176% compared to benchmark sorbents that undergo cycling without reactivation after 60 cycles. In conclusion, these results highlight the potential of hydration-assisted reactivation to enhance the long-term performance of TCES, providing an effective pathway to advancing electro-thermal storage technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Modeling supercritical CO 2 flow and mineralization in reactive host rocks with PFLOTRAN v7.0

Understanding the flow and reactivity of CO 2 injected into geological reservoirs is important for many subsurface applications including secure geologic carbon storage (GCS), critical mineral extraction, enhanced geothermal systems (EGS), and enhanced oil recovery (EOR). Traditionally, subsurface CO 2 injection for GCS applications has focused on geologic formations with favorable subsurface configurations for CO 2 migration and trapping through non-reactive mechanisms such as structural, solubility, and petrophysical trapping. Recently, CO 2 -reactive rocks such as mafic and ultramafic basalts have been investigated for their potential to react with injected CO 2 in situ to simultaneously dissolve host rock minerals and mineralize CO 2 as carbonates. Engineering rapid CO 2 mineralization in the subsurface is attractive because of the increased density of stored CO 2 , the additional safety factors associated with solidification, and the potential to extract valuable critical minerals. Here we present recent developments in the parallel flow and reactive transport simulator PFLOTRAN to model coupled CO 2 -brine flow and reactive transport for a wide range of injection and production applications involving reactive CO 2 -brine systems. These developments are based on the well established and trusted CO 2 flow capabilities in the STOMP-CO 2 simulator. New capabilities added to PFLOTRAN include new CO 2 -brine equations of state with optional thermal coupling, several new constitutive relationships like capillary pressure smoothing and scanning path hysteresis, a fully implicit well model, and native linkage with PFLOTRAN's well-established reactive transport libraries. A series of benchmarks between PFLOTRAN and STOMP-CO 2 verify the newly developed CO 2 -brine flow capabilities. Demonstrations of coupled CO 2 -brine flow modeling and reactive transport show how CO 2 mineralization can be engineered in reactive host rocks. Finally, an example use case involving copper leaching by CO 2 and critical mineral extraction is presented to showcase the strengths of this new implementation. Several limitations still remain, including limited availability of field data to parameterize models. Future work should constrain the evolution of mineral surface area during mineralization and the temperature and/or pH dependence of geochemical reactions for specific systems of interest.

Critical Minerals↗
DOE OSTI2025

Mo Atom Rearrangement Drives Layer-Dependent Reactivity in Two-Dimensional MoS 2

Two-dimensional (2D) materials offer a valuable platform for manipulating and studying chemical reactions at the atomic level, owing to the ease of controlling their microscopic structure at the nanometer scale. While extensive research has been conducted on the structure-dependent chemical activity of 2D materials, the influence of structural transformation during the reaction has remained largely unexplored. In this work, we report the layer-dependent chemical reactivity of MoS 2 during a nitridation atomic substitution reaction and attribute it to the rearrangement of Mo atoms. Our results show that the chemical reactivity of MoS 2 decreases as the number of layers is reduced in the few-layer regime. In particular, monolayer MoS 2 exhibits significantly lower reactivity compared with its few-layer and multilayer counterparts. Atomic-resolution transmission electron microscopy (TEM) reveals that MoN nanonetworks form as reaction products from monolayer and bilayer MoS 2 , with the continuity of the MoN crystals increasing with layer number, consistent with the local conductivity mapping data. The layer-dependent reactivity is attributed to the relative stability of the hypothetically formed MoN phase, which retains the number of Mo atomic layers present in the precursor. Specifically, the low chemical reactivity of monolayer MoS 2 is attributed to the high energy cost associated with Mo atom diffusion and migration necessary to form multilayer Mo lattices in the thermodynamically stable MoN phase. In conclusion, this study underscores the critical role of lattice rearrangement in governing chemical reactivity and highlights the potential of 2D materials as versatile platforms for advancing the understanding of materials chemistry at the atomic scale.

Chemical reactivity↗
DOE OSTI2025

Coupled geomechanical investigation of depletion-induced fault reactivation

Fault reactivation during subsurface fluid production pose significant challenges to safe and sustainable resource extraction. Here, this study presents a three-dimensional coupled geomechanical framework to investigate the processes driving fault reactivation, capturing the interactions between reservoir dynamics and geomechanical responses. Verification against theoretical estimations based on linear poroelasticity confirms the model's capacity in representing reservoir background stress responses. However, the study reveals that relying solely on background stress states can underestimate or overestimate fault reactivation potential, emphasizing the importance of including localized stress perturbations such as differential compaction and stress redistribution. Applied to a fault (M1) inspired by the geological characteristics of the Groningen field, the model shows slip initiation at 2965 m depth with 16.0 MPa depletion, aligning with field observations where seismicity occurred at approximately 3 km depth after 15.8 MPa depletion. Parametric studies reveal: (1) inelastic reservoir compaction delays fault reactivation and mitigates fault slip by reducing stress concentration, (2) higher intermediate in-situ stress magnitudes decrease the Coulomb Failure Stress (CFS) increase rate and reduce fault slip, (3) larger fault offsets amplify shear stress near the offset zone, promoting earlier reactivation and longer rupture propagation, and (4) fault permeability significantly influences pressure diffusion, with low-permeability faults leading to sharper stress changes and earlier fault destabilization. These insights highlight the critical role of geological and mechanical parameters in fault reactivation and provide a predictive framework for mitigating induced seismicity risks.

Coupled geomechanical modeling↗
DOE OSTI2025

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗
DOE OSTI2025

Structural features of xylan dictate reactivity and functionalization potential for bio-based materials

Plant-based materials have the potential to replace some petroleum-based products, offering compostability and biodegradability as critical advantages. Xylan-rich biomass sources are gaining recognition due to their abundance and underutilization in current industrial applications. Research of potential xylan applications has been complicated by the complex and heterogeneous structure that varies for different xylan feedstocks. Acylation is a broadly used reaction in functionalization of polysaccharides at an industrial scale. However, the efficiency of this reaction varies with the xylan source. To optimize xylan valorization, a systematic understanding of structure–reactivity relationships is essential. This study explores, characterizes, and compares various xylan feedstocks in the acylation process. Xylan feedstocks were analyzed for their chemical composition, degree of polymerization, branching, solubility, and presence of impurities. These features were correlated with xylan glycotypes’ reactivity toward functionalization with succinic anhydride in an optimized DMSO/KOH condition, achieving carboxyl contents of up to 1.46. We used principal component analysis and hierarchical clustering to identify key structural features of xylan that promote its reactivity. Our findings reveal that xylans with higher xylose content and lower degrees of branching exhibit enhanced reactivity, achieving higher carboxyl content and yields. Structural analyses confirmed successful modification, and light scattering analyses showed dramatic changes in the solution properties. Succinylation improves the solubility and film-forming properties of native xylans. This study shows key structure–reactivity relationships in xylan succinylation, establishing that low branching, high xylose content, and reduced lignin impurity enhance chemical functionalization. The results offer a framework for selecting optimal biomass feedstocks and support future efforts in genetic and synthetic biology to design plants with tunable xylan architectures. These findings advance the hemicellulose valorization for applications in coatings and packaging.

Acylation↗
DOE OSTI2025

Corrosion Resistance of an AlCeMg/Stainless-Steel Reactive Bond

A major issue for metal components in many industries is corrosion as it can substantially reduce their lifetime. This issue is especially problematic for materials used in heat exchanger applications. Al–Ce–Mg alloys, which exhibit corrosion resistance and can reactively bond with other metals, may be a viable solution to this problem. This investigation studied the corrosion behavior of Al–2Ce–6Mg (atomic percent)/stainless-steel (SS) reactive bond interfaces after full immersion in nitric, sulfuric, formic, and mixed acid for 267 h. This particular Al–Ce–Mg alloy was chosen due to its good castability. The results of scanning electron microscope characterization showed that reactive bond formations repeatedly occurred throughout the length of the casting in the as-cut samples and that these formations maintained a secure bonding between the alloy and the stainless-steel tubes. Transmission electron microscopy results showed that there was a clear compositional and microstructural transition across the reactive bond. The results of the immersion tests indicated that the nitric, sulfuric, and the mixed acid did not have an observably negative effect on the reactive bond structure. As for the sample exposed to formic acid only, noticable changes were seen in both the microstructural appearance and the elemental profile across the bond, suggesting that oxide formation occurred.

Brechtl, Jamieson [ORNL] (ORCID:0000000217394283)↗
DOE OSTI2025

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. Further, this physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. In conclusion, not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations.

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

Understanding how defects and dopant atoms in copper surface oxides affect reactivity

Copper and its oxides are key catalytic materials, on which reactions often occur at the metal/oxide interface. Here, in this work, we directly connect the induction period observed during methanol-driven reduction of thin-film copper oxides to their atomic-scale structural order. Using temperature-programmed desorption (TPD) methanol titrations combined with scanning tunneling microscopy, we show that highly ordered oxide phases – particularly the “29” structure with its low defect density – exhibit long induction periods and initially low reactivity. The induction period, defined as the number of methanol TPD cycles required to reach half of the maximum formaldehyde yield, scales with oxide order and oxygen coverage. Enhanced reactivity of well-ordered oxides emerges only after repeated methanol adsorption/desorption cycles generate oxygen vacancies and new Cu(111)/Cu x O interfacial sites. In contrast, disordered or sub-stoichiometric oxides, which contain more intrinsic defects and interfaces, are active from the first TPD cycle. We further examine how dilute Pt and Rh dopants influence oxide order and reactivity: 1% Pt increases defect density and catalytic activity, while 1% Rh promotes oxide ordering and longer induction periods. These findings demonstrate that dilute alloying provides a potential method for tuning the structure and reactivity of Cu(111)/Cu x O interfaces.

Cu(111)Methanol oxidation↗
DOE OSTI2024

CO–induced roughening of Cu(111): formation and detection of reactive nanoclusters on metal surfaces

The formation of nanoclusters on metal surfaces in the presence of reactive environments is a phenomenon with important implications for catalysis. These nanoclusters are composed of atoms ejected from undercoordinated sites such as step edges, and their presence alters the catalytic properties of solid materials. We perform density functional theory (DFT) and kinetic Monte Carlo (KMC) simulations to investigate the formation and reactivity of copper clusters on Cu(111). Our results indicate a considerably higher reactivity of small copper nanoclusters, with up to seven atoms in size on roughened copper surfaces than on pristine Cu(111) and Cu(211). Regarding the restructuring events that give rise to nanoclusters under CO atmospheres, we determine that the ejection of Cu atoms from step edges and their migration therefrom to adjacent Cu(111) terraces are, by and large, driven by CO coverage effects. By means of KMC simulations, which account for CO–CO lateral interactions and CO–induced surface restructuring, we show that temperature programmed desorption (TPD) holds promise for the detection of highly reactive nanoclusters. Furthermore, our approach showcases how surface restructuring and surface–adsorbate bond breaking can be combined when modeling surface reactions and contributes to the development of an advanced understanding of the nature of active site under reaction conditions.

catalyst dynamic restructuring↗
DOE OSTI2025

Reactive CO 2 capture and mineralization of magnesium hydroxide to produce hydromagnesite with inherent solvent regeneration

Valorization of multiple low value streams including CO 2 emissions and magnesium-hydroxide bearing mine tailings to produce magnesium carbonate through reactive CO 2 capture and mineralization provides a less explored opportunity to manage several gigatons of CO 2 emissions. To resolve the feasibility of converting magnesium hydroxide to magnesium carbonate through reactive CO 2 capture and mineralization, CO 2 capture solvents such as sodium glycinate are harnessed to capture CO 2 and react directly with Mg(OH) 2 to produce hydromagnesite (Mg 5 [(CO 3 )4(OH) 2 ]·4H 2 O). This approach eliminates the energy-intensive step of producing high purity CO 2 associated with regenerating the solvent, and redissolving CO 2 to produce magnesium carbonate. Interestingly, while temperatures below 50 °C facilitate CO 2 capture, the mineralization kinetics are slow. However, at higher temperatures, accelerated carbon mineralization is favored by the faster kinetics of Mg(OH) 2 dissolution and precipitation of magnesium carbonate. Reacting Mg(OH) 2 at 90 °C with 15 wt% solids in the presence of 2.5 M sodium glycinate after 3 hours under well-stirred conditions results in an extent of carbon mineralization of 75.5%. The theoretical maximum extent of carbon mineralization when hydromagnesite is formed is 80%. Pre-loading CO 2 on the solvent is also an effective approach to ensure that sufficient CO2 is available for reactive CO 2 capture and mineralization, particularly when dilute CO 2 and N 2 mixtures are used. Higher extents of carbon mineralization are associated with an increase in the particle size and a reduction in the cumulative pore volume. These insights unlock the feasibility of harnessing reactive CO 2 capture and mineralization as a pathway to convert magnesium-hydroxide bearing resources into industrially relevant magnesium carbonate products.

Reactive CO2↗
DOE OSTI2026

Assembly Bowing Reactivity Calculation Methodology Applied to Lead Fast Reactor

Ducted assemblies bow during operation due to power and temperature gradients which can be influenced by operating flow rates. For fast spectrum cores using ducted assemblies, the bowing behavior follows that of the duct and because there are gaps between the ducts, the bowing can result in compaction or expansion of the active core. This local displacement can have a positive or negative impact and knowing the net effect during transients is important for system reactivity control. Keeping the net bowing reactivity worth low is possible with attentive placement of load pads above the active core and selecting load pad gap thicknesses that result in a desired behavior at standard operating conditions. This paper considers a Lead Fast Reactor (LFR) concept fueled by HALEU UO2 developed by Westinghouse Electric Company (WEC) and applies a workflow of Argonne-developed codes to estimate the core bowing reactivity worth. Using orifice flow rates grouped by assembly type, the net reactivity impact due to core assembly bowing for the LFR was found to be small and in line with other liquid metal fast reactors: +29/+32/+35 pcm, or about +$0.049/+$0.055/+$0.059, for BOEC/MOEC/EOEC, respectively.

bowing reactivity↗
DOE OSTI2024

Corrosion Behavior of a Reactive Bond Between Stainless Steel and a Cast AlCeMg Alloy

Corrosion is a longstanding issue for metal components, especially those used in heat exchanger applications. Al–Ce–Mg alloys may provide a potential solution to this problem due to their good mechanical properties and potential reaction bonding with other metals. The reaction bonding involves a “reactive” bond that occurs upon casting of Al–Ce–Mg alloy over stainless steel (SS). Here this study examined the corrosion response of Al–2Ce–6Mg (atomic percent)/(SS) reactive bond interfaces after samples were completely submerged in nitric, sulfuric, formic, and mixed acids for 267 h. Scanning electron microscopy revealed that in the as-cut condition, reactive bond formations were seen frequently throughout the length of the casting and maintained a secure bond between the alloy and the SS tubes. Furthermore, the nitric, sulfuric, and the mixed acids did not have a deleterious effect on the reactive bond structure. However, formic acid did produce changes in both the microstructural appearance and the elemental profile across the bond due to the formation of corrosion reaction products on the acid-exposed surface.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mechanochemical synthesis of hydraulically reactive calcium silicate minerals via thermally-assisted mechanical grinding

This study explores a thermally assisted mechanochemical approach alternative to conventional cement synthesis as a potential to produce hydraulically reactive calcium silicate phases. Ball milling of mixed CaO/SiO 2 feedstocks at temperature ranges 100-300 °C increases the formation of the Ca-O-Si bonds and precursor reactivity. Spectroscopic analyses (FTIR, MAS-NMR, UV-Vis DRS) indicate increasing amorphization with milling temperature, attributed to improved mixing and thermally assisted diffusion. Upon hydration, all treated samples exhibit exothermic heat release, with the sample prepared at 300 °C showing the most pronounced reactivity. Thermal analysis reveals weight loss consistent with C-S-H formation, confirming cement-like behavior. In summary, moderate thermal input during milling promotes structural activation and enhances downstream hydraulic reactivity, providing a proof-of-concept for energy-reduced cement precursor processing.

Alite↗
DOE OSTI2025

Combining Reactive Quantum-Mechanical Molecular-Dynamics Simulations with Mutagenesis, Crystallography, and Enzyme Kinetics to Reveal Plausible Steps of Isocyanide Hydratase Catalysis

A complete understanding of enzyme mechanisms requires atomistic details of chemical reactions. Quantum-based molecular dynamics simulations (QMD) are a potential source of this information, but trade-offs between accuracy and computational cost have limited their use. We previously developed extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) methods that leverage a negligible compromise in accuracy to substantially decrease the cost of QMD simulations. Here, we develop a reactive QMD approach using the latest XL-BOMD formulation, which enables efficient simulations of highly reactive systems, and use it to investigate mechanisms of intermediate formation in isocyanide hydratase (ICH) catalysis. In QMD simulations, molecular analogs of ICH active site residues reacted with para-nitrophenyl isocyanide, forming a thioimidate. Analysis of simulated atomic configurational and charge dynamics revealed a pathway where protonation of the isocyanide carbon occurs prior to thioimidate formation and suggested a possible role of Asp17 as a proton donor in the early phase of ICH catalysis. To test whether the pathway seen using the reactive QMD approach might be relevant to ICH catalysis, we performed X-ray crystallography and pre-steady-state enzyme kinetics studies of wild-type and D17N mutant ICH. Both the structure and kinetics are sensitive to the D17N mutation in a manner that is consistent with the order of the reaction steps seen in the simulations. Mobile protons play essential roles in many enzymes, yet they are difficult to observe experimentally, making the ordering of proton-dependent steps ambiguous in many enzyme mechanisms. The ability to directly simulate model reactions for the design of experiments that provide information about enzyme mechanisms involving mobile protons demonstrates the significance of our reactive QMD approach and motivates further biological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗