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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Distribution of nickel(II) ions adsorbed at the muscovite mica (001)-water interface determined by in-situ resonant anomalous X-ray reflectivity

Mineral-water interfaces mediate adsorption, ion exchange, and secondary mineral formation that control element mobility in natural and engineered systems. Reliable prediction and control of these processes require a fundamental understanding of the interfacial structure that links adsorbed ion speciation to macroscopic sorption capacity and strength. Here, we determine atomic-scale changes in hydration and distribution of Ni(II) at the muscovite mica (001)-water interface using in situ high-resolution X-ray reflectivity (XR) and resonant anomalous X-ray reflectivity (RAXR) at 1 mM NiCl2 and pH 5.7. XR reveals reorganization of the primary hydration structure relative to that in deionized water: the water layer adsorbed in the cavity sites at a height of ~1.3 Å disappears, while distinct solution layers emerge at ~2.3, ~4.1, and ~5.6 Å above the basal oxygen plane. RAXR resolves three interfacial Ni(II) species: a dominant outer-sphere complex at 3.65 Å (~80% of the total coverage), a minor inner-sphere complex at 0.75 Å, and a low-coverage, more distant outer-sphere species at 5.63 Å. These three adsorbed Ni(II) species account for a total Ni(II) coverage of 0.54 ± 0.02 ion per unit cell area that compensates for the surface charge. These results highlight the role of interfacial hydration in controlling the speciation and stability of adsorbate cations on the negatively charged mica surface, providing quantitative insight into predicting the geochemical behavior of divalent metal cations in the aqueous environments.

Lee, Sang Soo↗
DOE OSTI2025

De Nive Quinquangula: Pentagonal Snowflake Metal Nanocrystals as an Example of Emergence Phenomenon in Nanotechnology Complex Systems

The formation of snowflakes (SFL) is one of the most captivating phenomena in nature. The fabrication of multimetallic gold (Au) nanoparticles containing copper (Cu) and iron (Fe) is reported, which adopt snowflake-like morphology after nanoparticle aggregation. The synthesis of these snowflake-like Au microcrystals occurs mainly by kinetic control. In this process, a surfactant-assisted salt reduction method is employed on a heated substrate. Here, the metal salts react within seconds to form nanoparticles. This process results in a hierarchical structure with pseudo-pentagonal symmetry and chiroptical activity. We discuss how large superstructures emerge from nanoparticle aggregation, resembling natural snowflakes. However, unlike ice snowflakes, which exhibit hexagonal symmetry, the Au snowflakes display pentagonal symmetry. It is proposed that the formation of these snowflake-like microcrystals is the result of the complexity involved in the crystal growth process and represents an example of the phenomenon of emergence, which has become very significant in several fields of modern science, including physics, biology, chemistry, economics, philosophy, and poetry. The term emergent is used to evoke the collective behavior of a large number of microscopic constituents that is qualitatively different than the behaviors of the individual constituents.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Lewis Acid Site Engineering in Chromite Spinels Orchestrated Surface Reconstruction and Surpasses RuO 2 in Oxygen Evolution

Atomic-scale engineering of chromite spinels featuring redox-active tetrahedral A-sites and strong Cr–O covalency offers a promising route to superior platinum-group-metal-free oxygen evolution reaction (OER) catalysts. However, comprehensive studies addressing how cation substitution influences surface chemistry and governs OER activity and durability in chromite spinels remain limited. Here, in this work, a systematic investigation of the multicationic chromite series Ni x Fe y Cr 3−x−y O 4 is presented, identifying composition-dependent Lewis acidity as a descriptor of superior OER performance. It is further demonstrated that tuning surface acidity directly controls dynamic reconstruction processes and lattice-oxygen participation during spinel-based electrocatalysis. Following activation, the optimized Ni 0.8 Fe 0.3 Cr 1.9 O 4 catalyst delivers a current density of 10 mA cm −2 at an overpotential of 235 mV, surpassing RuO 2 , with excellent long-term stability. Integrating microscopic and spectroscopic analysis with operando impedance spectroscopy, it shows that activation generates an oxyhydroxide overlayer and reveals a previously unrecognized link between surface Lewis acidity and the growth kinetics and activity of these shells. Density functional theory calculations indicate that Fe incorporation at octahedral sites raises the O 2p-band center and lowers oxygen-vacancy formation energy, promoting lattice-oxygen activation and triggering reconstruction, yielding enhanced OER. This work integrates cation-driven surface-acidity modulation, acidity-governed reconstruction, and OER activity enhancement into a unified predictive framework for designing earth-abundant spinel-based catalysts.

operando impedance spectroscopy↗
DOE OSTI2024

In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination

Abstract Synchrotron X‐ray‐based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on‐site, on‐the‐fly analysis needed for real‐time process monitoring. Here, a weighted lagged cross‐correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X‐ray diffraction metrology to monitor the calcination process of the archetypal nickel‐based cathode, LiNiO 2 . The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO 2 , providing information for agile on‐site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early‐stage phase nucleation and growth. Expanding this data‐powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Controlling mechanical properties of laser powder bed fused AlSi10Mg through manipulation of laser scan rotation

The microstructure and mechanical properties of laser powder bed fused (LPBF) AlSi10Mg alloys can be controlled by many processing parameters. This study focuses on the scan rotation angle, α, between adjacent layers, and establishes the relationship between α and tensile behavior of the as-built LPBF-processed AlSi10Mg alloy. Near-full density cubic coupons were manufactured using the same processing parameters but with systematic variation of α from 0° to 90°. Microscopic observations and X-ray diffraction analysis showed that differences among various coupons mainly include orientations of the melt pools with respect to the build direction and development of the crystallographic texture. The α=0° coupon and α=30° coupon showed the highest and lowest texture index, although the overall crystallographic texture was mild. Tensile specimens were manufactured horizontally and vertically using either α=0° or α=30°, but with various first layer laser direction with respect to the build plate. Notably, the α=0° specimen that was tested along the laser scan direction showed the largest yield strength (283 MPa) and highest tensile ductility (10.1 %). Quantitative image analysis and fractography were performed on all specimens. Results showed that the melt pool orientation with respect to the tensile direction affected the tensile behavior across the different specimens. This was closely related to the localized strain distribution within the melt pool and along the melt pool boundary for the different melt pool orientations observed. Further, these results demonstrate that the laser scan rotation angle between layers can be used to fine tune the mechanical properties of LPBF AlSi10Mg alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In situ probing of structure and deagglomeration of SnO 2 colloids via small-angle X-ray scattering

Transforming dry nanopowders into stable colloidal dispersions remains challenging due to the cohesive forces between the nanoparticles (NPs) which promote agglomeration. Effective dispersion and deagglomeration of these agglomerates is a critical process in the formulation and preparation of nanoparticle-based functional materials via the colloidal route. Understanding the deagglomeration dynamics provides information to improve microstructural quality in various applications by enabling engineering of agglomerate size, structure and morphology. However, the deagglomeration process dynamics with respect to the evolution of the fractal agglomerate structures, particularly for very small NPs, is still poorly understood and requires further investigation. This study employs in situ small-angle X-ray scattering (SAXS) to investigate the sonication-induced deagglomeration of SnO 2 NPs. Electrostatically stabilized SnO 2 colloids with varying primary particle size (6–21 nm) are investigated in a specifically designed in situ cell using synchrotron-based SAXS to study the influence of sonication time and intensity on the nanoscaled agglomerates. Complete structural analysis via SAXS reveals a direct correlation between changes in agglomerate size and structure, size-dependent deagglomeration behavior and a dependence on the overall energy introduced during sonication into the dispersion regardless of the actual power as well as ultrasonic process parameters in case of SnO 2 NPs. The results suggest that control of the dispersion process during ultrasonic deagglomeration results in tailoring agglomerates with respect to size and structure.

Deagglomeration↗
DOE OSTI2025

Pointing stabilization of a 1 Hz high-power laser via machine learning

Abstract High-power lasers are vital for particle acceleration, imaging, fusion and materials processing, requiring precise control and high-energy delivery. Laser plasma accelerators (LPAs) demand laser positional stability at focus to ensure consistent electron beams in applications such as X-ray free-electron lasers and high-energy colliders. Achieving this stability is especially challenging for the low-repetition-rate lasers in current LPAs. We present a machine learning method that predicts and corrects laser pointing instabilities in real-time using a high-frequency pilot beam. By preemptively adjusting a correction mirror, this approach overcomes traditional feedback limits. Demonstrated on the BELLA petawatt laser operating at the terawatt level (30 mJ amplification), our method achieved root mean square pointing stabilization of 0.34 and 0.59 $\unicode{x3bc} \mathrm{rad}$ in the x and y directions, reducing jitter by 65% and 47%, respectively. This is the first successful application of predictive control for shot-to-shot stabilization in low-repetition-rate laser systems, paving the way for full-energy petawatt lasers and transformative advances across science, industry and security.

Amodio, Alessio↗
DOE OSTI2025

Single-Atom Doping at the Molecule–Metal Interface: How Rh Affects Surface Explosion Kinetics

Controlling chemical reactions at interfaces is central to many fields, including atmospheric, environmental, and biological chemistry as well as catalysis and corrosion. We performed a fundamental study of how catalytically active Rh atoms placed at and above a molecule-metal interface influence the surface reaction kinetics of tartaric acid (TA) decomposition. Specifically, TA decomposition on Cu(100) exhibits autocatalytic decomposition kinetics involving a slow initiation step followed by fast decomposition of the molecular layer. These so-called “surface explosions” are extremely sensitive to initial conditions, making it fundamentally interesting to study how the placement of reactive atoms affects the reaction rate. Temperature-programmed reaction (TPR) experiments reveal that Rh atoms embedded in the Cu(100) surface beneath TA (TA/Rh/Cu) or atop the TA layer (Rh/TA/Cu) reduce the decomposition temperature and modify the reaction rate constants. Isothermal TPR analysis reveals that Rh enhances both the initiation rate constant, k i , and the explosion rate constant, k e , facilitating explosive decomposition at lower temperatures. This result provides evidence that both the initiation and explosion steps occur at the metal-molecule interface and are accelerated by the presence of Rh at this interface. This study illustrates how small amounts of reactive elements may be used to control nonlinear kinetic processes at interfaces.

desorption↗
DOE OSTI2026

Ternary and Quaternary Nickel-Iron-Oxide-Based Thin Films as Oxygen Evolution Reaction Catalysts in Alkaline Media

Alkaline water electrolyzers are a promising technology for energy storage and conversion through the electrochemical production of hydrogen, however the slow kinetics of the oxygen evolution reaction (OER) pose a major challenge. The alkaline environment enables the use of non-Pt-group metals as OER catalysts, in particular NiFe-based materials. In this study, Mn and/or Cr were elementally mixed with NiFe to further improve the OER catalyst properties through a controlled, systematic synthesis process involving thin films prepared by electron beam-induced physical vapor deposition (PVD). By exploring both simultaneous co-deposition and serial deposition of the different metals, the OER performance was investigated as a function of both the location of Mn or Cr in the multi-metal NiFe-based catalysts and the difference between surface versus bulk mixing. The distinct differences in the redox dynamics, surface oxidation, and stability of the seven studied catalysts reveal metal Cr- and/or Mn-overlayers as a promising synthesis approach for fine-tuning of catalyst properties and improving the OER performance.

Alkaline Water Electrolysis↗
DOE OSTI2024

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

58 GEOSCIENCES↗
DOE OSTI2025

Dynamic realization of emergent high-dimensional optical vortices

The dimensionality of vortical structures has recently been extended beyond two dimensions, providing additional topological complexity and robustness for high-capacity information processing and turbulence control. The generation of high-dimensional vortical structures has mostly been demonstrated in classical systems through the complex interference of fluidic, acoustic, or electromagnetic waves. However, natural materials rarely support three- or higher-dimensional vortical structures and their physical interactions. Here, we experimentally demonstrate a high-dimensional gradient thickness optical cavity (GTOC) in which the optical coupling of planar metal-dielectric multilayers implements topological interactions across multiple dimensions. At non-trivial topological phases, high-dimensional GTOC induces high-dimensional vortical structures in generalized parameter space in three, four dimensions, and beyond. These emergent high-dimensional vortical structures are observed under electro-optic tomography as optical vortex dynamics in two-dimensional real-space, employing the optical thicknesses of dielectric layers as synthetic dimensions. Our findings hold significant promise for emulating high-dimensional physics and developing active topological photonic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simulating topological quantum gates in two-dimensional magnet-superconductor hybrid structures

The creation of topological quantum gates using Majorana zero modes—an outstanding problem in the field of topological quantum computing—relies on our ability to control the braiding process in time and space. Here, we propose two-dimensional magnet-superconductor hybrid structures as a new platformfor the successful implementation of topologically protected √σ z -, σ z - and σ x -quantum gates using Majorana zero modes. Employing a novel theoretical formalism to compute the full timedependent many-body wave-function and utilizing a braiding protocol motivated by recent advances in electron-spin-resonance techniques we simulate quantum gates in 2D systems up to 600 sites, on timescales from a few femto- to nanoseconds. We demonstrate that the braiding process can be visualized in time and space by computing the non-equilibrium local density of states, which is proportional to the time-dependent differential conductance measured in scanning tunneling spectroscopy experiments, allowing us to directly image Majorana world lines.

Superconducting properties and materials↗
DOE OSTI2025

Complex carbonate phases drive geologic CO2 mineralization

Abstract Geologic carbon sequestration in mafic and ultramafic reservoirs is a scalable strategy for carbon dioxide removal, offering permanent storage via mineralization as stable carbonates. However, there is limited information on the structure and composition of key mineralization endpoints during sequestration. Here, we unravel the atomic structure, composition, and nanoscale morphology of carbonates recovered from a field-scale demonstration of CO 2 mineralization in basalt. Using transmission electron microscopy, we mapped mineralogical variations from the initial to later stages of subsurface carbonate growth and identified a previously unknown cation-ordered ankerite phase that exerts a primary control over carbonation processes. This study has provided a new understanding of subsurface carbonation pathways which will impact the parameterization of predictive geochemical models for future sequestration efforts in basalt formations.

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

Geologic hydrogen: a review of resource potential, subsurface dynamics, exploration, production, transportation, and research opportunities

Hydrogen is a versatile resource with critical roles in decarbonization, industrial manufacturing, and energy integration. However, most hydrogen today is produced from fossil fuels, resulting in high emissions and energy consumption. Although low-carbon hydrogen production methods, such as steam methane reforming with carbon capture and renewable-powered electrolysis, are advancing, their high costs hinder large-scale deployment. Identifying alternative pathways for producing low-cost, low-emission hydrogen is therefore essential. Geologic hydrogen, referring to natural and stimulated hydrogen generated in the Earth's subsurface, has attracted growing attention as a potential source of sustainable, economically viable, and environmentally favorable hydrogen. This paper provides a comprehensive review of geologic hydrogen, covering its resource potential, origins, migration and trapping mechanisms, exploration techniques, production strategies, and pipeline transportation. It also identifies key knowledge gaps and proposes a roadmap for future research. The review indicates that geologic hydrogen has vast resource potential and can leverage existing subsurface technologies and geophysical exploration methods. However, major challenges persist, including uncertain hydrogen generation rates, limited understanding and control of serpentinization processes, costly transportation infrastructure, the lack of validated techno-economic analysis, and potential social and environmental issues. As the field is still in its early stages, progress will require interdisciplinary collaboration spanning geoscience, engineering, economics, environmental science, and policy and regulation.

42 ENGINEERING↗
DOE OSTI2025

Unraveling spin entanglement using quantum gates with scanning tunneling microscopy-driven electron spin resonance

Quantum entanglement is a fundamental resource for quantum information processing, and its controlled generation and detection remain key challenges in scalable quantum architectures. Here, we numerically demonstrate the deterministic generation of entangled spin states in a solid-state platform by implementing quantum gates via electron spin resonance combined with scanning tunneling microscopy (ESR-STM). Using two titanium atoms on a MgO/Ag(100) substrate as a model, we construct a two-qubit system whose dynamics are coherently manipulated through tailored microwave pulse sequences. We generate Bell states by implementing a Hadamard gate followed by a controlled-NOT gate, and evaluate its fidelity and concurrence using the quantum-master equation-based code TimeESR. Our results demonstrate that ESR-STM can create entangled states with significant fidelity. This study paves the way for the realization of atom-based quantum circuits and highlights ESR-STM as a powerful tool for probing and engineering entangled states on surfaces.

Switzer, Eric D. [Donostia International Physics C↗
DOE OSTI2026

Morphology-driven oxygen evolution performance of NiO x nanostructures and implications for hole transport in perovskite solar cells

Morphology-controlled nanostructures provide an effective strategy to modulate both oxygen evolution reaction (OER) activity and photovoltaic performance in perovskite solar cells (PSCs). However, achieving low OER overpotentials and high power conversion efficiency (PCE) simultaneously through morphology engineering remains challenging. In this work, nickel oxide (NiO x ) nanostructures with spindle-like (NiO x -NS) and plate-like (NiO x -NP) morphologies were synthesized and evaluated as bi-functional OER catalysts and hole transport layers (HTLs) in inverted PSCs. Structural and thermal analyses reveal that NiO x -NS crystallizes into a cubic phase at a lower temperature (300 °C), whereas NiO x -NP requires higher calcination temperatures, reflecting differences in precursor microstructure. Electrochemical measurements indicate that NiO x -NS calcined at 300 °C delivers the lowest OER overpotential (395 mV at 10 mA cm −2 ), outperforming NiO x -NP calcined at 400 °C (565 mV) and 500 °C (474 mV). This enhanced activity is ascribed to favorable surface strain, increased defect density, and advantageous facet exposure. When used as HTLs, NiO x -NS also delivers the highest PCE (13.25%) among all tested devices, exceeding those based on NiO x -NP and commercial NiO x , owing to improved hole extraction and interfacial contact. Overall, this study highlights the importance of morphology control and thermal processing in tailoring NiO x for multifunctional nanomaterials in electrocatalytic and photovoltaic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cesium exhibits different mesoscale segregation and ion pairing than lithium, sodium, or potassium in concentrated alkaline aqueous nitrite solutions

Understanding ion pairing in concentrated alkaline electrolytes facilitates the prediction and control of chemical processes in nuclear waste. While sodium is the dominant alkali metal in such systems, cesium often exhibits distinct bulk behavior relative to lighter alkali cations. Aqueous mixtures of cesium hydroxide and sodium nitrite were compared to alkali hydroxide analogs using small-angle x-ray scattering, revealing that cesium disrupts the sodium nitrite electrolyte structure, forming cesium-rich domains. This mesoscale segregation contrasts with the near-ideal mixing observed in other mixed hydroxide–nitrite systems. Raman spectroscopy indicates cesium–nitrite ion pairing, evidenced by vibrational shifts distinct from those associated with sodium. Multinuclear magnetic resonance spectroscopy further supports cesium–nitrite and cesium–hydroxide association, revealing a distinct local environment for nitrite in cesium-containing solutions. Together, these findings show that cesium promotes a unique solution structure dominated by specific ion pairing within segregated domains. This structural organization may influence radical generation pathways in high-ionic-strength alkaline media relevant to nuclear waste processing and management.

Nienhuis, Emily T. [Pacific Northwest National Lab↗