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At least 667 records · Page 37

False metals, real insulators, and degenerate gapped metals

This paper deals with a significant family of compounds predicted by simplistic electronic structure theory to be metals but are, in fact, insulators. This false metallic state has been traditionally attributed in the literature to reflect the absence of proper treatment of electron-electron correlation (“Mott insulators”) whereas, in fact, even mean-field like density functional theory describes the insulating phase correctly if the restrictions posed on the simplistic theory are avoided. Such unwarranted restrictions included different forms of disallowing symmetry breaking described in this article. As the science and technology of conductors have transitioned from studying simple elemental metals such as Al or Cu to compound conductors such as binary or ternary oxides and pnictides, a special class of degenerate but gapped metals has been noticed. Their presumed electronic configurations show the Fermi level inside the conduction band or valence band, yet there is an “internal band gap” between the principal band edges. The significance of this electronic configuration is that it might be unstable toward the formation of states inside the internal band gap when the formation of such states costs less energy than the energy gained by transferring carriers from the conduction band to these lower energy acceptor states, changing the original (false) metal to an insulator. The analogous process also exists for degenerate but gapped metals with the Fermi level inside the valence band, where the energy gain is defined by transfer of electrons from the donor level to the unoccupied part of the valence band. We focus here on the fact that numerous electronic structure methodologies have overlooked some physical factors that could stabilize the insulating alternative, predicting instead false metals that do not really exist (note that this is in general not a physical phase transition, but a correction of a previous error in theory that led to a false prediction of a metal). Such errors include: (i) ignoring spin symmetry breaking, such as disallowing magnetic spin ordering in CuBi2O4 or disallowing the formation of polymorphous spin networks in paramagnetic LaTiO 3 and YTiO 3 ; (ii) ignoring structural symmetry breaking, e.g., not enabling energy-lowering bond disproportionation (Li-doped TiO 2 , SrBiO 3 , and rare-earth nickelates), or not exploring pseudo-Jahn–Teller-like distortions in LaMnO 3 , or disallowing spontaneous formation of ordered vacancy compounds in Ba 4 As 3 and Ag 3 Al 22 O 34 ; and (iii) ignoring spin–orbit coupling forcing false metallic states in CaIrO 3 and Sr 2 IrO 4 . The distinction between false metals vs real insulators is important because (a) predicting theoretically that a given compound is metal even though it is found to be an insulator often creates the temptation to invoke high order novel physical effects (such as correlation in d-electron Mott insulators) to explain what was in effect caused by a more mundane artifact in a lower-level mean-field band theory, (b) recent prediction of exotic physical effects such as topological semimetals were unfortunately based on the above compounds that were misconstrued by theory to be metal, but are now recognized to be stable insulators not hosting exotic effects, and (c) practical technological applications based on stable degenerate but gapped metals such as transparent conductors or electrides for catalysis must rely on the systematically correct and reliable theoretical classification of metals vs insulators.

36 MATERIALS SCIENCE↗

Microfabricated Devices and Ion Trapping Capabilities

Next generation ion traps will likely need to support tens if not hundreds of ions in order to achieve several logical qubits. As we scale to those sizes, the same problems we face now – rf dissipation, control I/O, and optical access – will only grow and become more complicated. While many of these challenges can potentially be solved with technology integration, independently researching the feasibility of that integration and other solutions may help reduce the time and risk in scaling up to larger traps, by testing on smaller less complex devices. We should also consider other fabrication techniques that may help scale to larger devices, such as: through-substrate-vias (TSVs), different metal coatings, exotic rf routing, on chip laser sources, or even a secondary macroscopic trap to reload ions from. To have these technologies ready for full scale integration when we need them, ion traps with some of these capabilities need to be produced now. Developing the rigorous fabrication methods for producing reliable traps takes time and experimentation. We propose developing larger ion traps and reliable integrated technology in conjunction to make both available faster.

36 MATERIALS SCIENCE↗

Modular High Temperature Gas-cooled Reactor: Accident Analysis

Types of Potential Accidents and Reactor Response Codes and Tools Experimental Validation Safety Analysis Approach Licensing Modernization Project Use of PRA in LMP, ASME/ANS, Non-LWR PRA Standard Methods for Incorporating Passive System Reliability into a PRA

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Advances in PFAS Monitoring and Remediation Using a Functionalized Material Approach - 20080

The growing global concerns about the effects to public health from human exposure to per- and polyfluoroalkyl substances (PFAS) motivates the development of strategies for reliable monitoring of PFAS in environmental streams, as well as for their rapid, effective removal if detected. For the continuous PFAS monitoring, an inexpensive, field-deployable, in situ sensor is urgently needed; yet the prevalent in situ techniques often struggle to strike a balance between the practical sensitivity and selectivity demands of the real world. Similarly, for effective PFAS removal, strategies for their fast, selective, and quantitative capture are desired, yet the present commercially available sorbents are unable to meet the requirements of rapid, quantitative capture of all PFAS components, and are notably inefficient in removing the more toxic smaller chains. To address these twin challenges, Pacific Northwest National Laboratory is developing strategies for improved detection and remediation of PFAS. For the rapid, selective, quantitative removal of PFAS from environmental streams, the strategy relies on designing capture probes with exclusively tailored electronic and spatial affinities for the PFAS that are able to selectively capture them from environmental streams. For the in situ detection and quantification of PFAS in complex, multicomponent matrices such as groundwater, the approach relies on the targeted capture of specific PFAS by these PFAS-specific capture probes immobilized on a platform. The platform acts as an electrode to directly measure PFAS concentration through a proportional change in electrical response upon their capture. A combination of optimization of platform design and incorporation of additional, sensitive detection modalities have allowed us to achieve detection limits as low as 0.5 ng/L for detection of PFAS compounds (compared to the 70 ng/L Health Advisory Limit of the U.S. Environmental Protection Agency). (authors)

47 OTHER INSTRUMENTATION↗

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗

Meshfree simulation and prediction of recrystallized grain size in friction stir processed 316L stainless steel

Friction stir processing (FSP) is a promising solid-phase microstructural modification technique that can repair and enhance damaged stainless steel surfaces exposed to harsh environments. The quality of the repaired material is closely correlated to the recrystallized grain size in the stir zone (SZ), which is influenced by the thermomechanical conditions dictated by FSP process parameters. Thus, establishing a reliable relationship between these parameters and recrystallized grain size in the SZ is crucial for optimizing repair quality. However, existing experimental approaches often rely on indirect temperatures measured far from the SZ, along with rough strain rate estimations, which are imprecise and time-consuming. Meanwhile, existing mesh-based modeling methods usually face numerical challenges when dealing with the large material deformations inherent in FSP. Here, to address these issues, this study introduces a meshfree process model for FSP based on the smoothed particle hydrodynamics (SPH) method, aimed at predicting process conditions under different parameters. The model is validated using experimental data from 11 combinations of tool traverse and rotation speeds on 316 L stainless steel. Correlations between process parameters, material flow, temperature, strain, strain rate, and recrystallized grain size are revealed through SPH simulations and electron backscatter diffraction (EBSD) imaging. The results show that in situ SZ temperatures range from 1071 to 1322°C, which exceed the tool temperature by over 300°C. Furthermore, SZ temperature, strain rate, and grain size increase monotonically with higher tool temperature and faster traverse speed. A relationship is then established between the model-predicted Zener-Hollomon parameter and the recrystallized grain size based on EBSD data, expressed as ln(d) = -0.364 ln(Z) + 14.673. Finally, this relationship exhibits satisfactory accuracy with errors of less than 26.9% in predicting grain sizes at various SZ locations, which offers valuable insights for optimizing FSP repair processes for 316 L stainless steel.

316L stainless steel↗

Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel

The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel proposed to be used for multiple modern nuclear technologies. Therefore, characterizing its safety is vital for the reliable operation of nuclear technologies. However, the TRISO fuel failure probabilities are small and the computational model is time consuming to evaluate them using traditional Monte Carlo-type approaches. In the paper, we present a multifidelity active learning approach to efficiently estimate small failure probabilities given an expensive computational model. Active learning suggests the next best training set for optimal subsequent predictive performance and multifidelity modeling uses cheaper low-fidelity models to approximate the high-fidelity model output. After presenting the multifidelity active learning approach, we apply it to efficiently predict TRISO failure probability and make comparisons to the reference results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media

Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, in this study, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.

36 MATERIALS SCIENCE↗

NEML2: An efficient and modular multiphysics constitutive modeling library for hybrid computing environments

This paper presents NEML2, an open-source, high-performance library developed for constitutive material modeling, designed to support the flexible and modular development of models for complex material behavior. Building on the foundational structure of its predecessor, NEML, the NEML2 library introduces significant improvements, including enhanced vectorization, automatic differentiation, and seamless integration with PyTorch, facilitating the application of machine learning techniques in material simulations. NEML2 provides a C++ backend with Python bindings, enabling users to create custom material models that can be executed efficiently on both CPU and GPU platforms. The library also supports coupling with Multiphysics simulation frameworks like MOOSE, making it suitable for realistic simulations involving coupled physical processes. Rigorous quality assurance through unit and regression testing ensures the reliability of results, while the extensible, user-friendly design encourages collaboration and reproducibility across the scientific community. This paper provides an overview of NEML2’s architecture, core features, and applications, highlighting its impact on accelerating material qualification and advancing computational methods in materials science.

GPU↗

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗

Improved digital construction binder solution utilizing calcium aluminate additive for Infrastructure Scale Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Kerneos Inc., a Division of Imerys USA, Inc. to develop an improved digital construction binder using calcium aluminate additives produced by Kerneos along with locally available concrete materials. When the two-stage (2K) binder formulation was added to commercially available Portland Limestone Cement and sand to form a digital construction mortar, its strength exceeded a number of commercially available digital construction binders both at early and later ages. At the same time, the developed formulation showed precise control of setting time after addition and showed very low shrinkage making it a strong, reliable, and easily customizable alternative to fixed proprietary digital construction binders.

36 MATERIALS SCIENCE↗

Management of Risks Associated with Application of Novel Materials in Novel Operating Environments in Novel Reactor Designs

There is currently no widely agreed, detailed general method for licensing a novel plant incorporating novel materials (or materials being deployed in novel environments); in many such situations, there are no directly applicable engineering code cases for decision-makers (including regulators) to rely on. This paper discusses a framework for solving this problem that is based on the Reliability and Integrity Management (RIM) approach delineated in ASME BPVC Section XI Division 2. NRC Regulatory Guide 1.246, Rev. 0, endorses, with conditions, the subject portion of the ASME Code. The proposed framework is meant to support development of a licensing case by addressing certain technical challenges. The framework discussed here is compatible with the Licensing Modernization Project, but applying it in a specific case will call for advances in the state of practice, if not the state of the art. The RIM approach calls for applicants to (a) allocate reliability targets to plant structures, systems, and components (SSCs), (b) show that they are able to relate the currently observed physical condition of each SSC in the program to its failure probability well enough to determine whether the target reliability allocations are being satisfied, allowing for uncertainty related to the novelty of the materials/designs/operating environments, and (c) be able to demonstrate that the proposed program of surveillances will reliably detect unacceptable degradation of an SSC before SSC failure occurs. These challenges are discussed in the paper, and a potentially applicable modeling approach based on cumulative damage rather than failure rates is briefly illustrated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A Semiconducting Two‐Dimensional Polymer as an Organic Electrochemical Transistor Active Layer

Abstract Organic electrochemical transistors (OECTs) are devices with broad potential in bioelectronic sensing, circuits, and neuromorphic hardware. Their unique properties arise from the use of organic mixed ionic/electronic conductors (OMIECs) as the active channel. Typical OMIECs are linear polymers, where defined and controlled microstructure/morphology, and reliable characterization of transport and charging can be elusive. Semiconducting two‐dimensional polymers (2DPs) present a new avenue in OMIEC materials development, enabling electronic transport along with precise control of well‐defined channels ideal for ion transport/intercalation. To this end, a recently reported 2DP, TIIP, is synthesized and patterned at 10 µm resolution as the channel of a transistor. The TIIP films demonstrate textured microstructure and show semiconducting properties with accessible oxidation states. Operating in an aqueous electrolyte, the 2DP‐OECT exhibits a device‐scale hole mobility of 0.05 cm 2 V –1 s –1 and a µ C * figure of merit of 1.75 F cm –1 V –1 s –1 . 2DP OMIECs thus offer new synthetic degrees of freedom to control OECT performance and may enable additional opportunities such as ion selectivity or improved stability through reduced morphological modulation during device operation.

2D-polymer↗

Surrogate-assisted optimization under uncertainty for design for remanufacturing considering material price volatility

Remanufacturing is a well-established end-of-life (EOL) strategy that promises significant savings in energy and carbon emissions. However, the current design practices are not remanufacturing-inclusive, i.e., the majority of products are designed for a single life cycle. As a result, potential products that can sustain multiple life cycles are deprived of additional benefits of being designed for remanufacturing, such as reduced material usage, lower cost, and improved environmental impact. Moreover, the uncertainty in design, material selection, and economics are not considered to produce remanufacturable designs. Accordingly, this research proposes a design for remanufacturing (DfRem) framework that accounts for design uncertainty and material price volatility. The framework systematically explores the design space, performs design optimization under uncertainty, followed by topology optimization to provide additional mass savings, and finally, a price volatility analysis for plausible design material choices. The candidate designs are evaluated based on their design mass, material price volatility, failure mode characteristics, carbon footprint, and embodied energy impacts. The proposed framework's utility is demonstrated via the use of an engine cylinder head case study subjected to thermo-mechanical loads along with fatigue and wear failure. Considering grey cast iron and aluminum alloy as the design material choices, it was found that the cast iron design reduced the initial design mass by 6% as opposed to a 5% decrease for aluminum. On the other hand, about 8% area of the cast iron design failed due to fatigue, compared to 3% for aluminum. Here, we further observed that although the aluminum design provided better mechanical performance than the cast iron design, this material was more expensive and volatile in price.

36 MATERIALS SCIENCE↗

Thin-Film Fracture Behavior for Diketopyrrolopyrrole Semiconducting Polymeric Films

Fracture energy, which quantifies a material’s resistance to the propagation of a pre-existing crack, is a key parameter for ensuring the mechanical reliability of stretchable organic electronic devices. However, most existing methods, such as a four-point bending fracture energy, utilized for measuring the fracture energy of semiconducting polymeric thin films are complicated by substrate effects, making it challenging to isolate the intrinsic behavior of the film from interfacial influences. In this study, we employed a pseudo free-standing pure shear method to systematically investigate the cohesive fracture energy of poly(diketopyrrolopyrrole-terthiophene) P(DPP-T)-based thin films to examine the effects of nanoconfinement, side chain length, degree of crystallinity, and strain rates. This method effectively eliminates substrate interference, enabling a direct assessment of the cohesive fracture energy of P(DPP-T) thin films. We found that thinner films and those with lower molecular weights exhibited significantly reduced fracture energies due to diminished chain entanglements. Additionally, films with shorter side chains displayed notably higher fracture energies, which were attributed to an increase in the degree of crystallinity. Finally, slower strain rates led to higher fracture energies, consistent with an enhanced stress relaxation. These insights offer practical guidelines for designing mechanically robust semiconducting polymers, contributing to the advancement of reliable, durable, flexible, and wearable electronic devices.

Zhang, Song [Univ. of Southern Mississippi, Hattie↗

Eutectic Processing of Semiconductor Colloidal Nanocrystals for Energy Applications

Colloidal semiconductor nanocrystals (NCs) offer a costeffective platform for light-energy conversion in X-ray scintillators, photovoltaics, lasers, and display technologies. Yet, device-relevant NCs often require complex heterostructured compositions, where lattice imperfections compromise the efficiency and stability of photoconversion processes. Here, we show that a simple synthetic detour through a eutectic state of II−VI semiconductor NCs (e.g., CdSe, ZnSe) with halide salts (e.g., CdCl 2 , ZnCl 2 ) overcomes this limitation by melting and reconstructing NC lattices into defect-free alloyed and core/shell architectures. Applied to ternary CdSeTe NCs, this process produces downconverters with record brightness and minimal line widths, delivering a 3-fold increase in film-side external quantum efficiency of commercial CdTe photovoltaic modules (First Solar Inc.). Meanwhile, eutectic processing of CdSe-based core/shell emitters yields an 8-fold enhancement in their photoluminescence stability under backlight operation, addressing the reliability bottleneck for display technologies. Together, these findings establish eutectic NC processing as a scalable route to efficient, durable photoconversion materials for energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Durability Research Is Pivotal for Perovskite Photovoltaics

Metal halide perovskite solar cells have shown promising power conversion efficiencies, but commercialization requires that decent durability is also demonstrated. Under normal operation, solar cells are subject to a complex combination of stressors, such as visible light, ultraviolet light, heat, humidity, mechanical stress and electric potential, which complicates the understanding of failure mechanisms. Existing stress tests do not act as a time machine. In new materials systems such as perovskite photovoltaics, the tests have no known relationship to field service. In this Perspective we recommend following a durability learning cycle that interleaves photovoltaic module engineering with field testing; accelerated testing; and preconditioning and performance engineering. We advocate for field testing to demonstrate real-world performance and identify field-relevant failure modes, and urge the community to develop accelerated and qualification tests that account for device metastability, variations in material composition and different/various processing methods. In conclusion, these practices are more difficult, but more important, than the simple pursuit of higher initial efficiencies.

14 SOLAR ENERGY↗

Foundations of variational discrete action theory

Variational wave functions and Green's functions are two important paradigms for solving quantum Hamiltonians, each having their own advantages. Here we detail the variational discrete action theory (VDAT), which exploits the advantages of both paradigms in order to approximately solve the ground state of quantum Hamiltonians. VDAT consists of two central components: the sequential product density matrix (SPD) ansatz and a discrete action associated with the SPD. The SPD is a variational ansatz inspired by the Trotter decomposition and characterized by an integer $\mathscr{N}$, recovering many well-known variational wave functions, in addition to the exact solution for $\mathscr{N}$ = ∞. The discrete action describes all dynamical information of an effective integer time evolution with respect to the SPD. We generalize the path integral to our integer time formalism, which converts a dynamic correlation function in integer time to a static correlation function in a compound space. We also generalize the usual many-body Green's function formalism to integer time, which results in analogous but distinct mathematical structures, yielding integer time versions of the generating functional, Dyson equation, and Bethe-Salpeter equation. We prove that the SPD can be exactly evaluated in the multiband Anderson impurity model (AIM) by summing a finite number of diagrams. For the multiband Hubbard model, we prove that the self-consistent canonical discrete action approximation (SCDA), which is the integer time analog of the dynamical mean-field theory, exactly evaluates the SPD for d = ∞. VDAT within the SCDA provides an efficient yet reliable method for capturing the local physics of quantum lattice models, which will have broad applications for strongly correlated electron materials. More generally, VDAT should find applications in various many-body problems in physics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗