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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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749 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Strain and Defect-Tailored Magnetotransport in NiCo 2 O 4 Thin Films and Freestanding Membranes

Magnetic spinel NiCo 2 O 4 is promising for developing spintronic applications due to its high magnetic Curie temperature, high spin polarization, fast spin dynamics, and strain-tunable magnetic anisotropy, while its electronic and magnetic properties depend sensitively on epitaxial strain and disorder. Here, in this study, we use epitaxial NiCo 2 O 4 thin films and freestanding NiCo 2 O 4 membranes as model systems to reveal the complex interplay of strain and defects in determining the metallicity and magnetotransport properties of the ferrimagnetic spinel. NiCo 2 O 4 on perovskite substrates and NiCo 2 O 4 membranes exhibit insulating behaviors and spin canting, in sharp contrast to the metallic NiCo 2 O 4 films on spinel substrates that possess strong perpendicular magnetic anisotropy. Anisotropic magnetoresistance studies provide critical information about disorder-induced spin scattering and strain-induced tetragonal magnetocrystalline anisotropy, which is corroborated by comprehensive electron microscopy characterizations. Our study presents a promising venue for designing flexible magnetic memory, sensor, and spintronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Active Asteroids Citizen Science Program: Overview and First Results

Abstract We present the Citizen Science program Active Asteroids and describe discoveries stemming from our ongoing project. Our NASA Partner program is hosted on the Zooniverse online platform and launched on 2021 August 31, with the goal of engaging the community in the search for active asteroids—asteroids with comet-like tails or comae. We also set out to identify other unusual active solar system objects, such as active Centaurs, active quasi-Hilda asteroids (QHAs), and Jupiter-family comets (JFCs). Active objects are rare in large part because they are difficult to identify, so we ask volunteers to assist us in searching for active bodies in our collection of millions of images of known minor planets. We produced these cutout images with our project pipeline that makes use of publicly available Dark Energy Camera data. Since the project launch, roughly 8300 volunteers have scrutinized some 430,000 images to great effect, which we describe in this work. In total, we have identified previously unknown activity on 15 asteroids, plus one Centaur, that were thought to be asteroidal (i.e., inactive). Of the asteroids, we classify four as active QHAs, seven as JFCs, and four as active asteroids, consisting of one main-belt comet (MBC) and three MBC candidates. We also include our findings concerning known active objects that our program facilitated, an unanticipated avenue of scientific discovery. These include discovering activity occurring during an orbital epoch for which objects were not known to be active, and the reclassification of objects based on our dynamical analyses.

Astronomy & Astrophysics↗
DOE OSTI2026

Direct observation of diamond formation in a shock-compressed high explosive

Understanding the formation timescale and structure of carbonaceous reaction products is critical for modeling the high-pressure equation-of-state of organic materials. We use the National Ignition Facility to shock-compress polycrystalline TATB (C 6 H 6 N 6 O 6 ) samples to ~70–130 GPa and ~4000–5500 K, employing in situ nanosecond X-ray diffraction to probe reaction products and velocimetry to measure transmitted compression wave profiles. Our diffraction data is consistent with the formation of diamond over timescales less than ~60 ns. This represents carbon condensation from a molecular explosive on timescales three times faster than previously reported and the earliest observation of diamond produced from reacting TATB. Reactive flow simulations with explicit chemistry reproduce the observed temporal structure within wave profiles to inform the distribution of P-T states. These findings provide direct evidence of ultrafast diamond formation in a reactive system at extreme conditions and provide new constraints for models of shock and detonation chemistry.

Clarke, Samantha M. [Lawrence Livermore National L↗
DOE OSTI2025

Exact block encoding of imaginary time evolution with universal quantum neural networks

We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the nonunitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave-function is closed under the action of the imaginary time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the yields the ground state of the system. The number of qubits grows linearly with the number of interactions and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via midcircuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and number of interactions, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the number of interactions, and circuit depth grows linearly with imaginary time only. Every midcircuit measurement has a postselection success probability, and the overall success probability is equal to the product of the probabilities of the midcircuit measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Explainable machine learning reveals that local structural motifs encode the thermodynamic state across the CuZr metallic glass-forming range

Metallic glasses derive their properties from the statistics of local atomic motifs rather than from long-range order, yet a quantitative, chemistry-specific link between motif populations and the underlying glassy state has remained elusive. In this work we combine large-scale molecular dynamics, Voronoi tessellation, deep neural networks, and SHapley Additive exPlanations (SHAP) to identify which local structural motifs define the glassy state of Cu—Zr metallic glasses. A dataset of 17,180 atomistic configurations spanning ten compositions (Cu 20 Zr 80 –Cu 80 Zr 20 ) and four quench rates (10 9 –10 12 K/s) is used to train a feed-forward neural network that regresses temperature across the 50–2000 K liquid–supercooled–glass range, achieving a mean absolute error of 19.89 K and R 2 = 0.9974, confirming that the local structural state is faithfully encoded in motif-level structure. SHAP analysis then reveals that a tightly coupled near-icosahedral family of motifs (coordination numbers (CN) 11–13, including the full icosahedron 001200 and its single-atom-perturbation sibling 10930) collectively encodes the thermodynamic state of the system across the full glass-forming range. The CN = 11–13 ordered members carry negative SHAP values at high populations, tracking the most deeply-quenched configurations, while 10930 shows the reversed signature consistent with its role as a soft-spot host whose population shrinks as the icosahedral network deepens. The analysis demonstrates that explainable machine learning can isolate the minimal motif vocabulary defining the glassy state and recovers the near-icosahedral building blocks previously identified by data-driven analyses of Cu—Zr. The approach provides a general, chemistry-specific route for characterizing the structural state of disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Development of large-scale, 3D Printed high temperature ceramic material

Through this collaborative effort, a new 3D printing platform for ceramics called laser-induced slip casting (LIS) was explored and developed for improving the processing and manufacture of silicon carbide (SiC), a high temperature ceramic material. This method prints layers of ceramic slips/slurries with subsequent selective-laser heating to dry each layer to build a 3D structure. The completed and dried part is then sintered. This manufacturing process is inherently lower cost than alternative ceramic printing methods such as binder jet technology or stereolithography when it comes to the feedstock material but is more expensive than robocasting or direct ink writing. However, it has the potential to make more controlled parts with less defects compared to robocasting. The largest cost is the heating source for the printer. With this technique, there is potential to make large ceramic parts in a near-net shape. Further, there is no known commercial manufacturing of 3D printed ceramics that creates a large range of different high-density ceramics at large scale. The goals and outcomes for the development of the new printing technology were: 1) assessing the technology with alumina by characterizing coupons, 2) printing and sintering of silicon carbide (SiC), and 3) characterization and properties testing of materials printed and a scale-up of SiC part(s). Through this collaborative project, it was sought to provide the best solution to achieving highly dense and near-net shaped ceramic parts at large scale and reduced cost. The goal was to produce high density sintered materials for applications in defense, energy generating systems, armor, and wear parts using 3D printing methods. Variables including powder particle sizes, dispersant molecular weights (MWs), binders, printing parameters, and post processing were explored as well as the characterization of the physical properties (add specifics here).

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
DOE OSTI2026

Atomic Evolution of Hydrogen Intercalation Wave Dynamics in Palladium Nanocrystals Revealed by Liquid-Phase Transmission Electron Microscopy

Solute-intercalation-induced phase separation creates spatial heterogeneities in host materials, a phenomenon ubiquitous in batteries, hydrogen storage, and other energy devices. Despite many efforts, probing intercalation processes at the atomic scale has been a significant challenge. By utilizing liquid-phase transmission electron microscopy (TEM), we study hydrogen (de)intercalation in palladium nanocrystals as a model system and have achieved unprecedented atomic-resolution imaging of hydrogen intercalation wave dynamics. Our observations reveal that intercalation wave mechanisms, instead of shrinking-core mechanisms, prevail at ambient temperature for palladium nanocubes ranging from ∼60 nm down to ∼10 nm. Systematic image analysis uncovers the atomic evolution of the hydrogen intercalation wave, transitioning from nonplanar and inclined boundaries to those closely aligned with {100} planes. Our kinetic Monte Carlo simulations demonstrate that the observed intercalation wave dynamics correspond to sorption pathways minimizing the lattice mismatch strain at the phase boundary. In conclusion, unveiling the atomic intercalation pathways holds profound implications for engineering intercalation-mediated devices and advancements in energy sciences.

Lee, Daewon [Lawrence Berkeley National Laboratory↗
DOE OSTI2026

Solid neon as a noise-resilient host for electron qubits above 100 mK

Solid neon can be used as a solid host for single-electron qubits. At temperatures of around 10 mK, electron-on-solid-neon charge qubits exhibit long coherence times and high operation fidelities. However, a systematic characterization of the noise features of such systems is needed for the development of scalable quantum information architectures. Here, in this work, we show that solid neon can be used as a noise-resilient host for electron qubits above 100 mK. We examine the resilience of solid neon against charge and thermal noise when electron-on-solid-neon charge qubits are operated away from the charge-insensitive sweet spot and at elevated temperatures. We show that the extracted high-frequency charge noise density of electron-on-solid-neon qubits, projected as voltage fluctuations on nearby electrodes, is between 10 −4 μV 2 Hz −1 and 10 −6 μV 2 Hz −1 at 0.01 MHz to 1 MHz, which is comparable to common semiconductor hosts. We also show that the electron-on-solid-neon charge qubits operating at frequencies of around 5 GHz can maintain echo coherence times of over 1 μs at temperatures up to 400 mK.

42 ENGINEERING↗
DOE OSTI2026

Directing Assembly of Mesoscale Multi‐Shell Morphologies of DNA Origami Crystals

Nature builds hierarchically ordered materials, such as seashells, wood, and bones, through spatially and temporally regulated growth. Mimicking such a level of control in synthetic systems remains challenging, particularly in achieving multiscale organizations with prescribed nanoscale arrangements and desired material morphologies. In this study, we introduce a DNA-based self-assembly strategy for constructing diverse multi-shell mesoscale morphologies from nanoscale lattices, enabling prescribed structural, and compositional 3D material patterns. Using DNA origami frames as modular monomers, we direct anisotropic epitaxial growth through addressable DNA frame binding motifs and encapsulate nanoparticles (NPs) in desired 3D patterns. Sequential monomer addition under thermodynamically favorable conditions enables shell growth through heterogeneous nucleation while minimizing unwanted homogeneous nucleation. Here, we demonstrate that DNA-encoded addressability enables epitaxial shell growth along specific lattice directions, yielding crystals with multilayered mesoscale organization, including tube-like (sushi roll) and plate-like (macaron) morphologies. Shell-specific NP configurations and compositions are achieved through addressable and differentiated placement of NPs within each shell, as validated by small-angle x-ray scattering and cross-sectional scanning transmission electron microscopy. We further demonstrate addressable NP release and reveal that shells modulate release kinetics. Together, these findings establish a platform for fabricating DNA origami crystals with programmable mesoscale morphologies, nanoscale structure, composition, and transport properties.

3D patterning↗
DOE OSTI2021

Design and Realization of Ohmic and Schottky Interfaces for Oxide Electronics

Understanding band alignment and charge transfer at complex oxide interfaces is critical to tailoring and utilizing their diverse functionality. Toward this goal, both Ohmic- and Schottky-like charge transfers at oxide/oxide semiconductor/metal interfaces are designed and experimentally validated. A method for predicting band alignment and charge transfer in ABO 3 perovskites is utilized, where previously established rules for simple semiconductors fail. The prototypical systems chosen are the rare class of oxide metals, SrBO 3 with B = V–Ta, when interfaced with the multifaceted semiconducting oxide, SrTiO 3 . For B = Nb and Ta, it is confirmed that a large accumulation of charge occurs in SrTiO 3 due to the higher energy Nb and Ta states relative to Ti. Furthermore, this gives rise to a high mobility metallic interface, which is an ideal epitaxial oxide/oxide Ohmic contact. On the contrary, for B = V, there is no charge transfer into the SrTiO 3 interface, which serves as a highly conductive epitaxial gate metal. Going beyond these specific cases, this work opens the door to integrating the vast phenomena of ABO 3 perovskites into a wide range of practical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation

Fibrillated cellulose derived from forestry feedstocks represents a renewable and high-strength materials platform for circular bioeconomies. However, its practical implementation is hindered by the irreversible aggregation of nanocellulose architectures, including cellulose nanofibers (CNFs). Solvent-based dispersion offers a simple and practical route to prevent CNF aggregation. Here, in this work, we integrate classical and enhanced sampling molecular dynamics (MD) simulations with experimental suspension rheology and atomic force microscopy (AFM) to elucidate how solvent environments tune CNF–CNF interactions and dispersion stability. CNF–CNF contact free energies computed from MD simulations reveal reduced aggregation in acetone/water, γ-valerolactone (GVL)/water, and tetrahydrofuran (THF)/water and pure acetone compared with pure water, reflecting stronger CNF-solvent relative to inter-CNF interactions. Correspondingly, CNF-solvent suspensions in these solvent systems exhibit stronger inter-fibril network structures and enhanced recovery compared to water, indicating improved CNF-solvent affinity. Liquid cell AFM imaging in acetone–water mixtures and in pure acetone further confirm the presence of well-dispersed CNFs. By combining multiscale computation with targeted experiments, this study establishes a rational framework for solvent design to achieve stable nanocellulose dispersions for high-strength biobased materials and efficient bioenergy conversion.

cellulose↗
DOE OSTI2025

From Pollutant Removal to Renewable Energy: MoS2-Enhanced P25-Graphene Photocatalysts for Malathion Degradation and H2 Evolution

The widespread presence of pesticides—especially malathion—in aquatic environments presents a major obstacle to conventional remediation strategies, while the ongoing global energy crisis underscores the urgency of developing renewable energy sources such as hydrogen. In this context, photocatalytic water splitting emerges as a promising approach, though its practical application remains limited by poor charge carrier dynamics and insufficient visible-light utilization. Herein, we report the design and evaluation of a series of TiO2-based ternary nanocomposites comprising commercial P25 TiO2, reduced graphene oxide (rGO), and molybdenum disulfide (MoS2), with MoS2 loadings ranging from 1% to 10% by weight. The photocatalysts were fabricated via a two-step method: hydrothermal integration of rGO into P25 followed by solution-phase self-assembly of exfoliated MoS2 nanosheets. The composites were systematically characterized using X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), UV-Vis diffuse reflectance spectroscopy (DRS), and photoluminescence (PL) spectroscopy. Photocatalytic activity was assessed through two key applications: the degradation of malathion (20 mg/L) under simulated solar irradiation and hydrogen evolution from water in the presence of sacrificial agents. Quantification was performed using UV-Vis spectroscopy, gas chromatography–mass spectrometry (GC-MS), and thermal conductivity detection (GC-TCD). Results showed that the integration of rGO significantly enhanced surface area and charge mobility, while MoS2 served as an effective co-catalyst, promoting interfacial charge separation and acting as an active site for hydrogen evolution. Nearly complete malathion degradation (~100%) was achieved within two hours, and hydrogen production reached up to 6000 µmol g−1 h−1 under optimal MoS2 loading. Notably, photocatalytic performance declined with higher MoS2 content due to recombination effects. Overall, this work demonstrates the synergistic enhancement provided by rGO and MoS2 in a stable P25-based system and underscores the viability of such ternary nanocomposites for addressing both environmental remediation and sustainable energy conversion challenges.

Chemistry↗
DOE OSTI2026

Quantum control of Hubbard excitons

Quantum control of the many-body wavefunction is a central challenge in quantum materials research, as it could yield a precise control knob to manipulate emergent phenomena. Floquet engineering, the coherent dressing of quantum states with periodic non-resonant optical fields, has become an important strategy for quantum control. Most applications to solid-state systems have targeted weakly interacting or single-ion states, leaving the manipulation of many-body wavefunctions largely unexplored. Here, in this work, we use Floquet engineering to achieve quantum control of a strongly correlated Hubbard exciton in the one-dimensional Mott insulator Sr 2 CuO 3 . A nonresonant midinfrared optical field coherently dresses the exciton wavefunction, driving its rotation between bright and dark states. We use resonant third-harmonic generation to quantify ultrafast π/2 rotations on the Bloch sphere spanned by these exciton states. Our work advances the quest towards programmable control of correlated states and exciton-based quantum sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Distributed Mafic Rock Resources for Carbon Mineralization in Arizona

Ex-situ carbon mineralization is a process by which CO2 is reacted with alkaline silicate minerals and rocks to produce stable carbonate materials, which can be used for other industrial processes. Arizona, U.S.A., hosts abundant surficial mafic rocks in three young volcanic fields, Geronimo-San Bernardino, San Francisco, and Springerville, and other distributed locations throughout the state. We created a Mafic Rock Resource Inventory (MRRI) that categorizes geochemical, physical, and textural characteristics of a diverse suite of surficial mafic rock samples and provide a benchmark reaction dataset parameterizing the temperature, pressure, and pH conditions best suited ex-situ mineralization in different rock types. MRRI data is publicly available online via a map-viewer. We establish two reaction condition sets, varied in temperature, pressure, and pH, where crystal-rich and glassy rocks reach maximum reaction extent and different carbonate phases are formed. Systematic ex-situ mineralization experiments on 21 diverse rock types show trends in geochemical, mineralogical, and reactivity behavior and establish maximum effective capture capacity. From this, scoria cones in three Arizona volcanic fields have a ~62 Gt effective CO₂ storage capacity with one of the fields having a ~42 Gt storage capacity in lava flows. Reactivity results have applications to alkaline mafic rock resources exposed globally, including producing additional effective storage capacity estimates and scaled commercialization of mafic rock ex-situ mineralization, should reaction extents be improved through advances in mineralization techniques. MRRI data were used to create a Direct Air Capture to Mineralization (DACM) systems model, technoeconomic analysis, and life-cycle assessment. These documents are presented as three appendices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Integration of ultra-low coverage whole-genome sequences for reconstructing the evolutionary history of Galapagos giant tortoises

Genomic data from contemporary and historical samples often need to be coupled for evolutionary reconstructions of multitaxon complexes. However, the genetic data recovered from historical samples may result only in ultra-low coverage whole-genome sequences (ulcWGS; <0.15× depth), leading to inaccurate evolutionary inferences given a preponderance of missing data. Using the Galapagos giant tortoise radiation as a study system (Chelonoidis spp., composed of 13 extant and four extinct lineages), we assembled a novel methodological pipeline that removes potential noise introduced by the missing data and enhances the evolutionary signal from ulcWGS samples. We leveraged existing tools for phylogenomic placement (EPA-ng), population genomic structure (smartsnp) and admixture (Admixfrog, NGSadmix) to demonstrate that the evolutionary history of samples can be uncovered with sequencing depths as low as 0.008–0.139×. Importantly, these approaches do not use genotype imputation of the ulcWGS samples, which would require extensive reference datasets. Our application to two cases of extinct lineages of Galapagos giant tortoises, with and without references from the same lineage, demonstrates the general value of the approach. We confirm where the extinct lineages from San Cristóbal and Santa Fe islands fit into the Galapagos giant tortoise radiation, and that these lineages were evolutionarily distinct entities.

ancient DNA↗
DOE OSTI2026

Scattering theory of frequency-entangled biphoton states facilitated by cavity polaritons

The use of quantum light to probe exciton properties in semiconductor and molecular nanostructures typically occurs in the low-intensity regime. A substantial enhancement of exciton-photon coupling can be achieved with photonic cavities, where excitons hybridize with cavity modes to form polariton states. Here, to provide a theoretical framework for interpreting emerging experimental efforts in this direction, we develop a scattering theory describing the interaction of frequency-entangled photon pairs with cavity polariton and bipolariton states under various coupling regimes. Employing the Tavis-Cummings model in combination with our scattering approach, we present a quantitative analysis of how the interaction of the entangled photon pair with the polariton or bipolariton modifies its joint spectral amplitude (JSA). Specifically, we examine the effects of the cavity-mode steady-state population, exciton-cavity coupling strength, and different forms of the input photon JSA. Our results show that the entanglement entropy of the scattered photons is highly sensitive to the interplay between the input JSA and the spectral line shapes of the polariton resonances, emphasizing the cavity filtering effects. We suggest that biphoton-scattering quantum light spectroscopy best serves as a sensitive probe of polariton and bipolariton states in the photon-vacuum cavity state. Our approach is not only robust to various regimes of cavity-exciton coupling, but also amenable to extensions beyond the Tavis-Cummings model, enabling the representation of a broad class of molecular systems and solid state quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Enhancing NO Reduction by CO Over NiO/CeO 2 Catalyst by Optimizing the Metal Oxide–Support Interaction

Noble metal catalysts are widely used in three-way catalysts (TWCs) due to their high efficiency, but their scarcity and high cost drive the need for effective, low-cost alternatives. Here, in this work, a series of NiO/CeO 2 catalysts were engineered by modulating the OH content and crystallite size of the CeO 2 support. This approach yielded Ni species ranging from isolated single atoms to small clusters and larger aggregates, with varied NiO/CeO 2 interaction strengths. Evaluation of their performance in NO reduction by CO, CO oxidation, and the water–gas shift reaction revealed that the NiO/CeO 2 -300 catalyst, featuring small CeO 2 crystallites and highly dispersed NiO clusters, delivered superior activity for NO reduction and CO oxidation. It was demonstrated that small NiO clusters, due to their efficient CO adsorption and activation, were more active than Ni single atoms. Furthermore, the presence of water was found to influence the catalyst stability, with NiO clusters less stable than single atoms likely due to the hydroxylation effect leading to the formation of Ni(OH) 2 . Oxygen storage capacity measurements revealed that performance was governed by a combination of CeO 2 crystallite size and the abundance of NiO/CeO 2 interfaces. These findings demonstrated that the catalytic performance of Ni/CeO 2 systems could be maximized by optimizing the Ni nanostructure and its interaction with CeO 2 support, positioning them as a promising, multifunctional nonprecious alternative for three-way catalysis application.

36 MATERIALS SCIENCE↗