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Results for “operando modeling”

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 OSTI2025

Mechanistic insights into nitrogen activation on atomic Ru clusters in self-pillared pentasil using operando atomistic models and experimental kinetics

Alternative catalysts to the industrial Haber Bosch process have been of significant interest in the field of heterogeneous catalysis, yet realizing ammonia synthesis under mild conditions (e.g., 300 °C and 10 bar) is challenging due to the low per-pass conversion. One strategy is to promote the associative ammonia synthesis mechanism which eschews direct N-N bond cleavage. Here, in this work, we use self-pillared pentasil, a self-pillared hierarchical zeolite built by thin MFI zeolite nanosheets, as a support for subnanometric Ru clusters to synthesize ammonia. We show that Ru remains well-dispersed during reaction and further demonstrate that ammonia synthesis rates are higher than Cs-Ru/MgO. Reaction kinetics show a positive order in H 2 providing evidence for the associative mechanism, which then becomes negative in H 2 if Ru is allowed to aggregate into nanoparticles. Operando Density Functional Theory models for Ru speciation in SPP, free energy diagrams, and microkinetic modeling were then applied to develop a reaction mechanism that involves sequential hydrogenation of N 2 from metallic Ru clusters. For this hydrogenation to occur, there are site requirements for N 2 to adopt a bridge-bound configuration that facilitates sequential hydrogenation on single sites and metal clusters. These site requirements in turn inform the design of improved zeolite-supported ammonia synthesis catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Heterogeneous catalysis: Optimal performance at a phase boundary?

Most of the industrially used heterogeneous catalysts have been discovered by trial and error, and despite decades of experience, the discovery of new catalysts continues to be extremely challenging. The drive to uncover guiding principles in catalyst design is more present than ever. We share a series of observations indicating that optimal catalysts typically function at characteristic phase boundaries (e.g., abrupt changes in adsorbate coverage, catalyst structure, etc.) accessed in the reaction conditions. The catalyst exploits the associated instability—the desire to exist in multiple states simultaneously—as a driving force for chemical transformations. In other words, phase boundaries are good places to start the catalyst search, and indeed, we should focus on at least two phases at once rather than just one. Here, we substantiate this claim with several studies that combine statistical operando modeling and experiments. Transpiring from these observations is a hitherto unrecognized vector in catalyst discovery.

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

Harnessing metastability for grain size control in multiprincipal element alloys during additive manufacturing

Abstract Controlling microstructure in fusion-based metal additive manufacturing (AM) remains a significant challenge due to the many parameters that directly impact solidification condition. Multiprincipal element alloys (MPEAs), also known as high entropy alloys, offer a vast compositional space to design for microstructural engineering due to their chemical complexity and exceptional properties. Here, we use the FeMnCoCr system as a model platform for exploring alloy design in MPEAs for AM. By exploiting the decreasing stability of the face-centered cubic phase with increasing Mn content, we achieve notable grain refinement and breakdown of epitaxial columnar grain growth. We employ a multifaceted approach encompassing thermodynamic modeling, operando synchrotron X-ray diffraction, multiscale microstructural characterization, and mechanical testing to gain insight into the solidification physics and its ramifications on the resulting microstructure of FeMnCoCr MPEAs. This work aims toward tailoring desirable grain sizes and morphology through targeted manipulation of phase stability, thereby advancing microstructure control in AM applications.

Wakai, Akane↗
DOE OSTI2024

Operando real-space imaging of a structural phase transformation in the high-voltage electrode LixNi0.5Mn1.5O4

Abstract Discontinuous solid-solid phase transformations play a pivotal role in determining the properties of rechargeable battery electrodes. By leveraging operando Bragg Coherent Diffractive Imaging (BCDI), we investigate the discontinuous phase transformation in Li x Ni 0.5 Mn 1.5 O 4 within an operational Li metal coin cell. Throughout Li-intercalation, we directly observe the nucleation and growth of the Li-rich phase within the initially charged Li-poor phase in a 500 nm particle. Supported by the microelasticity model, the operando imaging unveils an evolution from a curved coherent to a planar semi-coherent interface driven by dislocation dynamics. Our data indicates negligible kinetic limitations from interface propagation impacting the transformation kinetics, even at a discharge rate of C/2 (80 mA/g). This study highlights BCDI’s capability to decode complex operando diffraction data, offering exciting opportunities to study nanoscale phase transformations with various stimuli.

Sun, Yifei (ORCID:0000000295625120)↗
DOE OSTI2025

Modeling High Current Pulsed Discharge in AA Battery Cathodes: The Effect of Localized Charging during Rest

During high current operation, substantial heterogeneity develops within battery cathodes, particularly when their thickness is large. Heterogeneity relaxation during subsequent rest is important for understanding battery performance under pulsed conditions. Localized charge balancing phenomena within batteries at zero net current are not well understood and merit investigation. In this work, the heterogeneity within cathodes of commercial alkaline Zn–MnO 2 batteries is measured during discharge and monitored during rest using energy dispersive X-ray diffraction (EDXRD). Significant gradients in protonation form during discharge and partially relax under rest. It is demonstrated that the proton gradient relaxation is through local redox activity at zero net current, where local (de)protonation works to redistribute charge across the cathode thickness. To support this redox-based relaxation, a fundamental kinetic study on prismatic MnO 2 cathodes is conducted to determine an appropriate model to describe both discharge and charge kinetics of MnO 2 . These kinetics are incorporated into a computational model to simulate the proton gradient formation and partial relaxation under identical discharge conditions as the operando EDXRD experiments. Model and experimental data are found to be in excellent agreement, correctly predicting localized charge balancing at rest.

Batteries↗
DOE OSTI2026

Operando neutron radiography validates a parameter-free transport–kinetics model for thick solid-state battery cathodes

Tortuosity-weighted interfacial flux for lithium (TWIF-Li) predicts through-thickness Li gradients in thick composite all-solid-state cathodes without fitted parameters. Image-derived microstructures, GITT-derived concentration-dependent solid diffusion, and tortuosity-weighted interfacial kinetics reproduce operando neutron radiography across practical rates, delivering transferable design rules to suppress transport-limited reaction fronts.

Adam, Andre [ORNL] (ORCID:0000000245023033)↗
DOE OSTI2025

Dynamic Features of Cu-Ceria Interface under CO 2 Hydrogenation to Methanol

It is generally accepted that metal–support interaction is very important for the hydrogenation of CO 2 to methanol, but little has been revealed about the feature of interfacial active sites under real reaction conditions since there are only limited techniques that can be applied under high-pressure conditions. Here, in this work, by combining multiple in situ and operando techniques on a model Cu/ceria catalyst, we have tracked Cu and ceria sites for methanol formation. Under the reaction condition, it is found that upon reaching the reaction temperature, oxidized Cu species in the as-synthesized catalyst immediately change into metallic Cu species. Following this, it is the gradual formation of methanol, the changing rate of which coincides with the formation of a unique Ce 3+ species. The combined experimental results and density functional theory (DFT) calculations have determined that the formed Ce 3+ sites driven by the reaction conditions are bound to hydrides, adsorbed carbonate species, and interfacial active Cu sites. The Cu-ceria interaction in this complex moiety is weak and can be easily disturbed with reaction environment variations, leading to dynamic changes at the interface upon the hydrogenation of active carbonate intermediates, which are precursors for the formation of methanol. The formation of this unique Cu–Ce 3+ interface and its dynamicity lead to an increase of methanol selectivity from less than 20% to 60%. These results suggest that reactant-derived species (H – and carbonate in this work) can be essential components of the active center with the functions of manipulating the metal−oxide interaction and directing reaction pathways.

CO2 hydrogenation to methanol↗
DOE OSTI2026

Non-Invasive Electrode Impedance Estimation for Optimized Charge Profile Parameterization of Lithium-Ion Batteries

This work presents a non-invasive method for parameterizing a physically motivated equivalent circuit model of lithium-ion batteries using operando electrochemical impedance spectroscopy and time-domain data. The proposed model consists exclusively of linear circuit elements, enabling computationally efficient simulation and real-time implementation on battery management system chips. By integrating frequency- and time-domain measurements, the model accurately estimates internal states such as the negative electrode potential, achieving a root mean square error of 12.3 mV during fast charging validation. Parameterization requires only rate tests with sinusoidal perturbations at three different ambient temperatures, making the approach experimentally accessible. The model reveals key insights into battery behavior, including rate-dependent overpotentials primarily governed by charge transfer kinetics at the positive electrode, and temperature-dependent impedance contributions from both charge transfer and solid-state diffusion processes. Validation using reference electrodes confirms the model’s ability to detect lithium plating onset and reproduce impedance behavior across a wide range of operating conditions. The approach enables in situ optimization of fast charging profiles and lays the foundation for future extensions incorporating aging effects and plating dynamics.

25 ENERGY STORAGE↗
DOE OSTI2025

Cross-Scale Catalyst Modeling Applied to H 2 Storage and Release via Formic Acid

Here, we propose the Systems-to-Atoms (S2A) modeling framework that integrates the kinetics of reaction chemistry and structural configurations across various length scales with the aim of establishing a versatile template for multiscale modeling of reactive flow problems and to predict the operando activity of catalyst materials. The approach encompasses a microkinetic model to analyze surface reactions on individual facets of catalyst nanoparticles coupled with the computation of average surface reaction rates for catalyst nanoparticles of specific size distributions. Macro-homogeneous surface reaction kinetics are derived as a function of catalyst loading and used as input parameters for the continuum-scale reactor model. The cross-scale framework enables the optimization of catalyst utilization through reactor design and operating strategy. To demonstrate the framework, we studied the storage and release of hydrogen from formic acid, a promising liquid organic hydrogen carrier (LOHC), over Pd, Pt, and Cu catalysts. The framework predicts observed trends in formic acid dehydrogenation activity for catalysts with comparable weight loadings and metal particle diameters, demonstrating satisfactory quantitative alignment. Finally, the seamless transmission of parameter uncertainties between scales is also discussed.

08 HYDROGEN↗
DOE OSTI2025

Operando FTIR investigation of salt dynamics in Li-ion batteries during fast charging

Li-ion batteries, when charged at fast-charging rates ($>$2C), suffer from reduced lifetimes and can undergo catastrophic failure. During high-rate charging, Li-ions are unable to rapidly transport through high-loading electrodes ($>$4 mAh cm −2 ). This results in unequal charge distributions, potentials, and utilization of the active material, which can lead to Li plating. Li-ion concentration polarization, in which Li-ions deplete in the anode and accumulate in the cathode during charging, precedes Li plating. An operando FTIR-ATR graphite/NMC cell developed in this research captured Li-ion concentration polarization in real-time. During fast charging, decreases in Li-ion concentration ($>$95%) were measured at the back of the anode. This is the first verification of complete Li-ion depletion within the anode at high C-rates. The measurements also showed graphite stage transition. A P2D model was developed for comparison to the operando measurements. The measurements agreed with the model in some cases, but disparities existed at high C-rates and loadings. In the experiment, the Li-ion concentration often failed to recover to 1.2 M until several hours after charging, whereas the model Li-ion concentration rapidly recovered. The contrast between the model and experiment results indicates that further investigation is required to improve understanding of Li-ion concentration dynamics during fast charging.

25 - ENERGY STORAGE↗
DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗
DOE OSTI2026

BMINN: Learning chemical potentials and parameters from voltage data for multi-phase battery modeling

Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.

25 ENERGY STORAGE↗
DOE OSTI2026

Probing Terra Incognita of Ni–P Catalysts: Operando Explorations during Hydrogen Evolution Reaction

We have developed two Ni phosphide preparation methods allowing operando XAS surface-sensitive studies of well-defined bulk systems. For Ni K-edge XAS, a Ni 2 P phase-pure powder was sintered into a high-density pellet and polished for grazing incidence XAS. Ni sites were mildly affected by the acidic electrolyte prior to the HER, while the applied cathodic potential caused the reduction of Ni surface states beyond the states of as-prepared Ni 2 P. The computed fully H-covered Ni 2 P [0001] model describes the difference in the operando Ni K-edge GIXAS spectrum well. Upon turning the applied bias off, the Ni sites became immediately oxidized, forming NiO on the surface. Thus, the active phase during the HER is covalent Ni 0 close to that in the intermetallic phosphides, and Ni 2+ oxides formed after, and not during, the HER. For P K-edge XAS, Ni foam was phosphorized to form a thin Ni 3 P layer while preserving its high surface area. Upon immersion in the acidic electrolyte, the P sites underwent removal of P 5+ phosphates and formed new P coordination, possibly due to the adsorption of protons from the electrolyte. These new P surface states were not affected by turning the cathodic current on and off as soon as the sample was immersed in the acidic electrolyte. However, the removal of the sample from the electrochemical cell and drying in air resulted in substantial depletion and oxidation of surface P. Echoing the observed Ni site chemistry during HER, XAS and XPS suggest that the in situ active P sites are different from the oxidized P states observed under ex situ conditions.

Kong, Seongyoung [Iowa State Univ., Ames, IA (Unit↗
DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗
DOE OSTI2026

Finite-element-based simulations of electrodes for CO 2 cascade reduction reactions

The multielectron reduction of CO 2 to liquid fuels could be a path to scalable energy storage, but reaching this goal requires major advances in catalysis and systems engineering. Cascade catalysis, which couples sequential reactions without isolating intermediates, has emerged as a promising route to enhance selectivity and efficiency in CO 2 reduction (CO 2 R). In this review, we examine how finite-element-based simulations of continuum model [finite element method (FEM)] approaches are being used to analyze and guide CO 2 R cascade systems. We first outline the fundamentals of cascade catalysis and recent advances in catalytic materials (metallic, molecular, and hybrid architectures). We then focus on FEM developments at the electrode and device scales, emphasizing how these models capture transport phenomena, local microenvironments, and geometry-dependent effects. To clarify design principles, we present case studies of cascade electrodes organized in systems without and with integrated semiconductors. We further emphasize the integration of FEM with multiscale frameworks (density functional theory, molecular dynamics, kinetic Monte Carlo) and its role in bridging atomic-level insights with device-level performance. Finally, we identify current limitations and future prospects, including improved boundary conditions, coupling with operando experiments, and machine learning-accelerated model development. Together, these insights provide design principles for next-generation CO 2 R cascade systems for efficient solar fuel production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Electronic and Structural Properties of the Polymer-Electrolyte Interphase in Electrochemically Doped Polymers

The advance of soft, polymer-based (photo)electrochemical energy transformation and storage applications requires a framework for polymer and electrolyte design that includes a deep understanding of the polymer–electrolyte interphase. Here, we report on the investigations of two napthalenediimide (NDI)–bithiophene (T2)-based semiconducting copolymers using computational modeling and in situ, ex situ, and operando techniques to reveal how changes in electrolyte and polymer chemistry modulate the electronic and structural properties of polymer electrodes during electrochemical (de)doping. These systems are shown to host an ensemble of polarons, in contrast with the single polaron-like character often reported, whose properties vary with the nature of the local environment. Importantly, these polarons serve as reporters of the nanoscale environments in which they reside. We demonstrate that controlling the polymer and electrolyte chemistry regulates the nature of the charge carriers generated upon electrochemical doping and/or exciton dissociation in a photoelectrochemical solar cell: For instance, divalent counterions enable polaron and bipolaron formation at lower reducing potentials, while supporting more bipolaron formation than monovalent counterions. A novel application of NEXAFS reveals insights into charge (de)localization, providing a pathway for future investigation of electron transport mechanisms. Finally, simulations of polymer swelling of an amorphous interphase show that charge formation has a large impact on polymer swelling and ion penetration. These studies deliver insights to enable the control of charge-carrier and ion transport, the rates of electron transfer and catalytic efficiency, device stability, and overall device performance.

14 SOLAR ENERGY↗
DOE OSTI2025

Operando probing dynamic migration of copper carbonyl during electrocatalytic CO2 reduction

Single crystals and shape-controlled nanocrystals are well known to exhibit facet-dependent catalytic properties. However, few studies have investigated how those nanocrystals evolve and (de)activate during reactions, calling for the development of nanoscale time-resolved operando methods. In this context, we have designed Cu nanocubes as a model system to elucidate the underlying driving force of dynamic nanocatalyst reconstruction during the CO2 reduction reaction (CO2RR). Operando electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) and synchrotron-based X-ray spectroscopy reveal the size- and potential-dependent complete transformation from (100)-oriented Cu@Cu2O nanocubes to polycrystalline metallic Cu nanograins under CO2RR conditions. In addition, machine learning-assisted operando four-dimensional STEM reveals that large Cu nanograins derived from nanocubes form mainly crystalline domains, while their smaller counterparts are more amorphous due to faster evolution kinetics. In situ Raman spectroscopy and density functional theory calculations suggest that CO drives the ejection of single Cu atoms, resulting in few-nanometre Cu clusters and the surface migration of highly mobile copper carbonyl (Cu–CO) species. Combined, these multimodal operando methods and theoretical approaches pave the way for understanding the complex structural evolution of energy-related nanocatalysts under electrochemical conditions.

Yang, Yao↗
DOE OSTI2025

Low-Temperature Catalyst Redispersion: A Route to Enhanced Stability of Supported Metal Catalysts?

Sintering poses a significant challenge to achieving the long-term stability of supported metal catalysts under reaction conditions. Here, in this study, we report a low-temperature catalyst redispersion mechanism, in which platinum single atoms, which aggregate into nanoparticles under Reverse Water Gas Shift (RWGS) conditions at elevated temperatures, fragment into atomically dispersed species upon cooling. Using multimodal operando characterization combined with first-principles theoretical modeling, we track the structural evolution of Pt single atoms supported on ceria nanodomes, deposited either on ceria or ceria–titania mixed oxides. We find that fragmentation is more pronounced when cooling occurs under RWGS conditions compared to CO alone, owing to a synergistic interplay of the effects of H 2 , CO 2 , and CO. The support architecture has a strong influence on the extent of redispersion: while CO alone induces fragmentation on ceria, interfacial confinement and vacancy pinning at the ceria–titania interface suppress restructuring. In contrast, RWGS conditions overcome these barriers, enabling redispersion across both supports. These findings point toward a pathway for catalyst stabilization via reaction-induced redispersion under mild conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗