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At least 307 records · Page 17

A Continuous-Discontinuous Galerkin Method for Electromagnetic Simulations Based on an All-Frequency Stable Formulation

In this paper, a potential-based partial-differential formulation, called the all-frequency stable formulation, is presented for the accurate and robust simulation of electromagnetic problems at all frequencies. Due to its stability from (near) dc to microwave frequencies, this formulation can be applied to simulate wide-band and multiscale problems without encountering the infamous low-frequency breakdown issue or applying basis function decompositions such as the tree-cotree splitting technique. To provide both efficient and flexible numerical solutions to the electromagnetic formulation, a mixed continuous-discontinuous Galerkin (CDG) method is proposed and implemented. In regions with homogeneous media, the continuous Galerkin method is employed to avoid the introduction of duplicated degrees of freedom (DoFs) on the elemental interfaces, while on the interfaces of two different media, the discontinuous Galerkin method is applied to permit the jump of the normal components of the electromagnetic fields. Numerical examples are provided to validate and demonstrate the proposed numerical solver for problems in a wide electromagnetic spectrum.

Yan, Su↗

Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework

A Lagrangian framework is used to evaluate aerosol–cloud interactions in the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) for measurements taken at Graciosa Island in the Azores where a U.S. Department of Energy Atmosphere Radiation Measurement (ARM) site is located. This framework uses direct measurements of cloud condensation nuclei (CCN) concentration (instead of relying on satellite retrievals of aerosol optical depth) and incorporates a suite of ground-based ARM measurements, satellite retrievals, and meteorological reanalysis products that when applied to over a 1500 trajectories provides key insights into the evolution of low-level clouds and aerosol radiative forcing that is not feasible from a traditional Eulerian analysis framework. Significantly lower concentrations (40 %) of surface CCN concentration are measured when precipitation rates in 48 h back trajectories average above 1.2 mm d -1 in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product. The depletion of CCN concentration when precipitation rates are elevated is nearly twice as large in the ARM observations compared to E3SMv1 simulations. The model CCN concentration bias remains significant despite modifying the autoconversion and accretion rates in warm clouds. As the clouds in trajectories associated with larger surface-based CCN concentration advect away from Graciosa Island, they maintain higher values of droplet number concentrations (N d ) over multiple days in observations and E3SM simulations compared to trajectories that start with lower CCN concentrations. The response remains robust even after controlling for meteorological factors such as lower troposphere stability, the degree of cloud coupling with the surface, and island wake effects. E3SMv1 simulates a multi-day aerosol effect on clouds and a Twomey radiative effect that is within 30 % of the ARM and satellite observations. However, the mean cloud droplet concentration is more than 2–3 times larger than in the observations. While Twomey radiative effects are similar amongst autoconversion and accretion sensitivity experiments, the liquid water path and cloud fraction adjustments are positive when using a regression model as opposed to negative when using the present-day minus pre-industrial aerosol emissions approach. This result suggests that tuning the autoconversion and accretion alone is unlikely to produce the desired aerosol susceptibilities in E3SMv1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Orphaned oil and gas well methane emission rates quantified using Gaussian plume inversions of ambient observations

Abstract. Annually, ∼ 3.6 million abandoned oil and gas wells in the US emit a combined ∼ 2.6 Tg methane (CH4), adversely affecting climate and regional air quality. However, these estimates depend on emission factors derived from measuring subpopulations of wells that vary by orders of magnitude due to very limited field sampling and poorly characterized distributions. Currently, US protocols to remediate orphaned wells lacks standardized quantification methods needed to both prioritize plugging and account for emission reductions. Therefore, sensitive, reliable, affordable, and scalable CH4 flux quantification methods are needed. We report the use of a simple Gaussian plume method where the dispersion parameters are constrained by in situ ground measurements of CH4 concentration at four locations 7.5–49 m downwind of the orphan well as well as local winds to estimate the leak rate from an orphan well. We derive a flux of 10.53 ± 1.16 kg CH4 h−1 during a venting procedure in April 2023 that agrees with the directly measured volumetric flow rate of 9.00 ± 0.25 kg CH4 h−1. This is 71 % greater than the 5.3 kg CH4 h−1 flux measured 7 months prior. Additionally, we discovered a secondary leak through the surface casing inferred as 0.43–0.67 kg CH4 h−1 both by our ground Gaussian analysis and by transecting the plume with an uncrewed aerial system (UAS). We show that in situ determination of the dispersion parameters used in our Gaussian inversions allows us to measure methane emissions to 15 % accuracy, significantly reducing errors when compared to the standard practice of assuming stability class. Our results help develop simpler methods and protocols for robust orphan well emission quantification that can be used for reporting.

Follansbee, Emily↗

Incommensurate Antiferromagnetic Order in the Fe-substituted Bi-2201 Cuprate in the Heavily Overdoped Regime

Elastic neutron scattering experiments showed incommensurate antiferromagnetic peaks in 5% Fe-substituted Bi-2201 cuprate in the non-superconducting heavily overdoped regime. Here, the incommensurability δ ∼ 0.21 is comparable to that observed in Fe-substituted Bi-2201 in the overdoped regime. [Hiraka et al., Phys. Rev. B 81, 144501 (2010).] The magnetic correlation length is comparable between the overdoped and non-superconducting heavily overdoped regimes. It is plausible that incommensurate antiferromagnetic order is induced and stabilized by Fe in the heavily overdoped regime, which suggests a robust antiferromagnetic correlation beyond the superconducting dome in the phase diagram.

Bi-2201↗

Harnessing the Hybridization of a Metal-Organic Framework and Superbase-Derived Ionic Liquid for High-Performance Direct Air Capture of CO 2

Direct air capture (DAC) of CO 2 has emerged as the most promising “negative carbon emission” technologies. Despite being state-of-the-art, sorbents deploying alkali hydroxides/amine solutions or amine-modified materials still suffer from unsolved high energy consumption and stability issues. Here, in this work, composite sorbents are crafted by hybridizing a robust metal-organic framework (Ni-MOF) with superbase-derived ionic liquid (SIL), possessing well maintained crystallinity and chemical structures. The low-pressure (0.4 mbar) volumetric CO 2 capture assessment and a fixed-bed breakthrough examination with 400 ppm CO 2 gas flow reveal high-performance DAC of CO 2 (CO 2 uptake capacity of up to 0.58 mmol g -1 at 298 K) and exceptional cycling stability. Operando spectroscopy analysis reveals the rapid (400 ppm) CO 2 capture kinetics and energy-efficient/fast CO 2 releasing behaviors. The theoretical calculation and small-angle X-ray scattering demonstrate that the confinement effect of the MOF cavity enhances the interaction strength of reactive sites in SIL with CO 2 , indicating great efficacy of the hybridization. The achievements in this study showcase the exceptional capabilities of SIL-derived sorbents in carbon capture from ambient air in terms of rapid carbon capture kinetics, facile CO 2 releasing, and good cycling performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enhanced cycling stability of Ni-rich Li-metal cells enabled by dual vinylene carbonate and tris(trimethylsilyl)borate electrolyte additives

NMC811 (LiNi 0.8 Co 0.1 Mn 0.1 O 2 ) and other high-Ni chemistries are promising cathode candidates for high-performance electric vehicles, owing to their high energy density and reduced cobalt content. However, their long-term cycling stability is hindered by surface degradation, particularly when paired with conventional electrolytes and a lithium metal anode. Electrolyte additives represent a practical approach to enhance interfacial stability and improve overall battery performance by promoting the formation of a robust electrolyte–electrode interphase (EEI). In this study, we revisit the effects of vinylene carbonate (VC) and tris(trimethylsilyl)borate (TMSB) additives on single-crystal SC-NMC811||Li cells. While TMSB only increases the open-circuit voltage and initial overpotential, it delivers superior capacity retention at C/3 compared to cells containing only VC or a dual additive system (VC and TMSB). Notably, under fast-charging conditions (1C, 2C, and 5C), the dual-additive system significantly outperforms other formulations, achieving markedly enhanced long-term capacity retention. Comprehensive electrochemical and spectroscopic analyses reveal that the VC/TMSB dual-additive system suppresses surface transition in NMC811, mitigates structural degradation by forming a thin, LiF-deficient cathode-electrolyte interface (CEI) layer. Moreover, they promote smooth and dense Li deposition and generate a LiF-deficient solid-electrolyte interphase (SEI). Consequently, the synergistic stabilization of both the CEI and SEI effectively limits the overall cell impedance growth during extended cycling. These findings provide key insights into co-additive strategies for engineering stable interfaces in high-energy Ni-rich Li-metal batteries.

36 MATERIALS SCIENCE↗

A Robust Yule-Walker Method for Online Monitoring of Power System Electromechanical Modes of Oscillation

Reliable power system operation requires that system operators maintain a satisfactory small-signal stability margin at all times. Mode meters allow system operators to continuously monitor the stability margin by analyzing synchrophasor measurements. Experience with mode meters at the Bonneville Power Administration (BPA) has revealed that mode meters sometimes suffer severe bias following transient disturbances. In this paper, a robust version of the Yule-Walker mode meter algorithm is proposed to mitigate this bias. The robust capability is introduced by modifying the estimator of the autocovariance sequence (ACS) on which the algorithm depends. Tests with field-measured data demonstrate that the robust Yule-Walker (RYW) algorithm significantly outperforms its conventional counterpart and is better able to provide system operators with accurate information about the system's stability margin.

Follum, James D.↗

Stabilizing Non-Abelian Topological Order Against Heralded Noise via Local Lindbladian Dynamics

An important open question for the current generation of highly controllable quantum devices is understanding which phases can be realized as stable steady states under local quantum dynamics. In this work, we show how robust steady-state phases with both Abelian and non-Abelian mixed-state topological order can be stabilized, in two spatial dimensions, against generic “heralded” noise using active dynamics that incorporate measurement and feedback, modeled as a fully local Lindblad master equation. These topologically ordered steady states are two-way connected to pure topologically ordered ground states using local quantum channels, and preserve quantum information for a time that is exponentially large in the system size. Specifically, we present explicit constructions of families of local Lindbladians for both Abelian (ℤ 2 ) and non-Abelian (𝐷 4 ) topological order whose steady states host mixed-state topological order when the noise is below a threshold strength. As the noise strength is increased, these models exhibit first-order transitions to intermediate mixed-state phases where they encode robust classical memories, followed by (first-order) transitions to a trivial steady state at high noise rates. When the noise is imperfectly heralded, steady-state order disappears but our active dynamics significantly enhances the lifetime of the encoded logical information. To carry out the numerical simulations for the non-Abelian 𝐷 4 case, we introduce a generalized stabilizer tableau formalism that permits efficient simulation of the non-Abelian Lindbladian dynamics.

Monte Carlo methods↗

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

Accelerating Biomimetic Solar - Energy Harvesting: Mapping the Interaction Landscape of Plasmonic-Excitonic Hybrid Nanosystems (Final Report)

In general, excitonic and plasmonic nanoscale materials in close proximity show high potential for significant breakthroughs in energy related materials research. The interactions between these two kinds of materials result in coupled optical transitions (plexcitons), distinct from those of both the individual exciton and plasmon as well as from those of the sum of their constituents (synergistic effects). By linking together materials-research and physical-research approaches, this project contributes to a concerted approach on nanomaterials energy research. The project’s overall goal is to accelerate the development of well-defined plexcitonic model systems consisting of carefully engineered plasmonic and excitonic nanomaterial— essential for both gaining a fundamental understanding of plexcitonic nanomaterials and the development of novel design principles for biomimetic solar energy harvesting. During the 3-year project period and the terminal renewal with limited support for a 12-month period, we successfully synthesized and characterized (1) a robust excitonic nanomaterial and (2) a library of plasmonic nanoparticles as well as developed (3) a microfluidic platform for homogenous nanosynthesis as summarized below: (1) Robust Excitonic Nanomaterial. Supramolecular assemblies are Nature’s most successful material system for solar energy harvesting. However, photovoltaic devices based on artificial supramolecular assemblies continue to be stymied by disappointing efficiencies and poor stability. The conceptual failure may lie in current solar cell architectures, which rely on solidifying supramolecular assemblies as an ensemble into a solid matrix, neglecting the intrinsic fragility of the assemblies’ internal structure, thus disrupting or even destroying their delicate optoelectronic properties, that is, delicate Frenkel excitonic properties. Supramolecular assemblies may finally serve as usable light harvesting material systems for solar energy conversion technologies, only if they meet the following criteria: (a) Stability, that is, the fragile structure including its delicate Frenkel excitonic character needs to be stable, (b) Robustness, that is, resistant against elevated and fluctuating temperatures, and (c) Viability for device integration, that is, capable of being immobilized onto solid substrates. Here, by developing a nanocomposite via a tunable, cage-like scaffold design, we successfully provided stable supramolecular nanocomposites, that inhabit robust Frenkel excitons despite harming environmental conditions such as extreme heat stress. (2) Library of Plasmonic Nanoparticles. Naturally, current models describing plasmonic hybrid quantum states—plasmonic hybridizations—parallel those developed for molecular orbitals, equating individual plasmonic nanostructures with “atoms” and the plasmonic nanoassemblies with “molecules.” In analogy to organic synthesis, a suitably robust fabrication method would allow for “atom-like” manipulation of “molecule-like” plasmonic nanoassemblies; of high value for next-generation energy nanotechnologies. Despite this frequent comparison, current plasmonic nanoassembly fabrication methods favor top-down templating over wet-chemical synthesis, however, achieving precise control over nanostructure’s geometry and surface characteristics remain an art and a scientific challenge. The conceptual failure may lie in the current wet-chemical synthesis paradigm, as it relies on the accessibility of a multi-dimensional synthesis parameter space through limited, rather one-dimensional synthesis procedures by employing step-by-step approaches. Solution-based nanoarchitectonics for rational design of precisely built plasmonic nanoassemblies via solution-based fabrication may finally be possible only if multi-dimensional syntheses approaches are available that allow for comprehensive control over the plasmonic nanomaterials’ (a) Structural Properties and (b) Surface Properties. Here, by developing an innovative multidimensional 1,3-propanediol based polyol synthesis, we successfully provided control over the plasmonic building-block’s geometry (size and shape) together with its surface characteristics. Our results present a critical step toward the vision of a “periodic table-like” system for plasmonic materials based on straightforward wet-chemical syntheses for energy nanotechnologies. Developing deliberate modifications on this synthesis, we generated a library of plasmonic nanostructures covering the vast parameter space—opening the door for fundamental investigation of plexcitonic model systems. (3) Microfluidic Platform for Homogenous Nanosynthesis. Control over structural properties of plexcitonic nanocomposites remains a challenge due to current limitations in nanosynthesis techniques. Slight variations in nanostructure’s geometry impact their optoelectronic properties, demanding precise synthesis beyond the capabilities of solution-based (batch) synthesis processes. In contrast, the small, confined liquid volumes used in microfluidics—a reaction technique where the manipulation of fluids takes place in channels with dimensions of tens of micrometers—allows for homogenous synthesis conditions, providing excellent control of the reaction and, as a result, of the materials’ geopmetry and composition. However, thus far, the majority of plexcitonic systems has been developed via batch synthesis. Here, by successfully developing a two-channel microreactor, our microfluidic-supported synthesis approach combines the advantages of both microfluidics and batch platforms, allowing for precise spatio-temporal control over all synthesis parameters opening the possibility for homogenous nanosythnesis of well-defined plexcitonic model systems.

14 SOLAR ENERGY↗

Enhancing surface activity and durability in triple conducting electrode for protonic ceramic electrochemical cells

With the material system operating at lower temperatures, protonic ceramic electrochemical cells (PCECs) can offer high energy efficiency and reliable performance for both power generation and hydrogen production, making them a promising technology for reversible energy cycling. However, PCEC faces technical challenges, particularly regarding electrode activity and durability under high current density operations. To address these challenges, we introduce a nano-architecture oxygen electrode characterized by high porosity and triple conductivity, designed to enhance catalytic activity and interfacial stability through a self-assembly approach, while maintaining scalability. Electrochemical cells incorporating this advanced electrode demonstrate robust performance, achieving a peak power density of 1.50 W cm −2 at 600 °C in fuel cell mode and a current density of 5.04 A cm −2 at 1.60 V in electrolysis mode, with enhanced stability on transient operations and thermal cycles. The underlying mechanisms are closely related to the improved surface activity and mass transfer due to the dual features of the electrode structure. Additionally, the enhanced interfacial bonding between the oxygen electrode and electrolyte contributes to increased durability and thermomechanical integrity. This study underscores the critical importance of optimizing electrode microstructure to achieve a balance between surface activity and durability.

Protonic Ceramic Electrochemical Cells↗

Identification of multiple eigenmode growth rates towards real time detection in DIII-D and KSTAR tokamak plasmas

Abstract The successful application of three-dimensional (3D) magnetohydrodynamic (MHD) spectroscopy enables us to identify the multi-mode eigenvalues in DIII-D and KSTAR tokamak experiments with stable plasmas. The temporal evolution of the multi-modes’ stabilities have been detected. The new method is numerically efficient allowing the real time detection of MHD modes’ stabilities during the discharge. The method performs active detection of the plasma stability by utilizing the upper and lower rows of internal non-axisymmetric coils to apply a wide variety of 3D fields. Multi-mode eigenvalues are extracted using subspace system identification of the plasma response measured by 3D-field magnetic sensors distributed at different poloidal locations. The equivalence of this new method with the one introduced by Wang (2019 Nucl. Fusion 59 024001) has been numerically corroborated. The more robust and efficient calculation developed here will enable real time monitoring of the plasma stability based on the extracted eigenvalues of stable modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Thermodynamic Water Activity Explains the Unusual Electrochemical Stability of Aqueous Deep Eutectic Solvents

The presence of water in nonaqueous deep eutectic solvent (DES) electrolytes has been debated in recent years, with efforts ranging from its complete removal to willful addition. It was shown that controlled amounts of water can be beneficial, as it not only enhances the physicochemical properties of these electrolytes but also has no significant detrimental effects on their electrochemical stability. Despite these advantages, there is still limited understanding of how water interacts with DES systems at the molecular level. This study examines the water activity in ethylene glycol and glycerol, as well as their binary mixtures with choline chloride to form the DESs ethaline and glyceline, respectively. In this work, we show that the high electrochemical stability of glyceline is related to its lower water activity compared to ethaline and can be attributed to the robust H-bonding network formed by the three hydroxyl groups of glycerol. Its 3D H-bond network effectively integrates water molecules within its solvent structure, reducing degradation and maintaining stability at higher water contents. The deviations from the ideal Raoult's law behavior are reflected in the water activity and activity coefficients, which highlight the intricate H-bond interactions within DES-water mixtures. Water acts like a lubricant within the more viscous DES mixtures without being detrimental to their electrochemical performance. The presented results emphasize the necessity of customizing DES-water compositions to enhance their performance as electrolytes, especially in flow battery applications where electrochemical stability, ionic conductivity, and fluidity are of utmost importance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Insights into catalyst degradation during alkaline water electrolysis under variable operation

Energy conversion technologies that are key to decarbonization efforts face significant durability challenges due to variable operation. Understanding the impact of variable operation on catalytic stability and identifying the key variables that dictate degradation is crucial for developing robust technologies. Here, we present a comprehensive investigation of the effects of variable operation on liquid alkaline water electrolysis. Our findings reveal that variable operation induces severe degradation of Ni, Fe, and Co catalytic films that is not observed during steady-state operation. By systematically interrogating the electrode discharge during simulated shutdown tests using Raman spectroscopy and mass spectrometry techniques, we uncover significant alterations caused by reverse currents in real time. These include changes in crystal structure, composition, film thickness, electronic conductivity, and dissolution rates. Lab-scale electrolyzer experiments further highlight the impact of variable operation on catalyst materials under relevant conditions. Finally, we provide guidelines for leveraging these insights to advance electrocatalysis research. This work underscores the importance of integrating realistic stressors into stability testing and offers practical guidelines for catalyst design, performance evaluation, and industrial implementation. Collectively, insights from this study will drive the development of more resilient energy conversion technologies.

Marquez, Raul A. [Univ. of Texas, Austin, TX (Unit↗

Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids

Polymer–protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer–protein hybrid materials.

36 MATERIALS SCIENCE↗

Comparative neutron-irradiation effects on thermal conductivity degradation and dimensional stability of TiC, TiB 2 , and ZrB 2 at 200–1000 °C

Ultra-high-temperature ceramics (UHTCs), including TiC, Ti 11 B₂, and Zr 11 B₂, show great potential for plasma-facing components due to their excellent high-temperature properties prior to irradiation. However, their response to neutron irradiation remains insufficiently understood, limiting robust assessment of their viability for fusion energy applications. Here, this study examines the thermal conductivity, dimensional stability and microstructure of TiC, TiB₂, and ZrB₂ following neutron irradiation at temperatures of 200–1000 °C and fast neutron fluences of 2.0 × 10 25 to 1.1 × 10 26 n/m 2 (E > 0.1 MeV). Lattice swelling measured by synchrotron X-ray diffraction in all three UHTCs was maximized at 200 °C and decreased with increasing irradiation temperature, with no evidence of amorphization observed at 200 °C. Above 600 °C, significant macroscopic volume swelling was observed in irradiated Ti 11 B₂ and Zr 11 B₂, but not in TiC, likely due to cavity formation in the diborides. The post-irradiation thermal conductivity, measured at the irradiation temperature, ranged from 28 to 45 W/m·K, representing a 34–45% reduction relative to the unirradiated material. Notably, neutron-irradiated UHTCs exhibit recoverable thermal conductivity at elevated temperatures, comparable to ferritic–martensitic steels and potentially superior to W when transmutation effects are considered, highlighting promise for shielding or armor plasma-facing components. At 600 °C, both thermal conductivity degradation and lattice swelling saturated at doses exceeding 2–4 dpa.

fusion materials↗

Universal dwell time optimization for deterministic optics fabrication

Computer-Controlled Optical Surfacing (CCOS) has been greatly developed and widely used for precision optical fabrication in the past three decades. It relies on robust dwell time solutions to determine how long the polishing tools must dwell at certain points over the surfaces to achieve the expected forms. However, as dwell time calculations are modeled as ill-posed deconvolution, it is always non-trivial to reach a reliable solution that 1) is non-negative, since CCOS systems are not capable of adding materials, 2) minimizes the residual in the clear aperture 3) minimizes the total dwell time to guarantee the stability and efficiency of CCOS processes, 4) can be flexibly adapted to different tool paths, 5) the parameter tuning of the algorithm is simple, and 6) the computational cost is reasonable. In this study, we propose a novel Universal Dwell time Optimization (UDO) model that universally satisfies these criteria. First, the matrix-based discretization of the convolutional polishing model is employed so that dwell time can be flexibly calculated for arbitrary dwell points. Second, UDO simplifies the inverse deconvolution as a forward scalar optimization for the first time, which drastically increases the solution stability and the computational efficiency. Finally, the dwell time solution is improved by a robust iterative refinement and a total dwell time reduction scheme. The superiority and general applicability of the proposed algorithm are verified on the simulations of different CCOS processes. A real application of UDO in improving a synchrotron X-ray mirror using Ion Beam Figuring (IBF) is then demonstrated. The simulation indicates that the estimated residual in the 92.3 mm × 15.7 mm CA can be reduced from 6.32 nm Root Mean Square (RMS) to 0.20 nm RMS in 3.37 min. After one IBF process, the measured residual in the CA converges to 0.19 nm RMS, which coincides with the simulation.

36 MATERIALS SCIENCE↗

Engineering collinear magnetization in hexagonal LuFeO 3 and magnetoelectric control of skyrmions in hexagonal 2D epilayers

Cubic, perovskite-structure ABO 3- and A 1–x A’ x BO 3 -type oxides have been investigated extensively while their hexagonal structure versions have not, even though they are multiferroic and can form heterostructures with hexagonal 2D materials. In particular, multiferroic 2D epilayers may lead to strong magnetoelectric coupling. Hexagonal RFeO 3 ferrites, where R is a rare-earth element (Lu, Yb, etc.), are excellent candidates, but their ferromagnetism isweak. In this paper, we employ density-functional-theory (DFT) calculations and first show that heavy electron doping of hexagonal LuFeO 3 (h-LFO), namely Lu 1–x Hf x FeO 3 (h-LHFO), leads to spin-disproportionation of the Fe sublattices and, especially for x = 1/2 and 2/3, to robust, room-temperature, out-of-plane, collinear ferrimagnetism that is stabilized by a Jahn-Teller metal-to-insulator transition. We then show that h-LHFO/h-2D heterostructures, where h-2D is the FE/ FM monolayer MnSTe, stabilize skyrmions without an external magnetic field and control their chirality by an external electric field through the h-LHFO polarization, opening up a new realm for magnetoelectric applications.

36 MATERIALS SCIENCE↗