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At least 847 records · Page 47

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗

Evaluation of an Accident Tolerant Fuel Leak in the Advanced Test Reactor

Accident Tolerant Fuels (ATF), which are nuclear fuel sources designed to withstand operational irregularities and incidents, have been a topic of interest in the nuclear industry for several decades. Interest in ATF technology surged following the 2011 accident at Fukushima Daiichi in Japan. At the Advanced Test Reactor (ATR), one of Idaho National Laboratory’s (INL) four operating nuclear reactors, the ATF program is a collaborative effort between the national laboratory and various stakeholders within the nuclear industry. This program focuses on the research and development of novel fuel compositions, cladding, and component materials with enhanced accident-resistant properties. During one of ATR’s 60-day operating cycles in 2024, the reactor experienced five unplanned shutdowns. Following the fifth shutdown, radiation monitors detected an increase in radiation levels coming from the loop piping. Subsequent water samples confirmed the cause was a leak of fission products from the ATF experiment, designated as ATF-2C. The source of the leak was identified as the instrumented section of the test train. The primary discussions in this presentation are 1) the design of the ATF test train, 2) the operating parameters leading up to and following the detection of the leak, and 3) the quantification and characterization of the released fission products.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Nanoscale Quantum Imaging of Field-Free Deterministic Switching of a Chiral Antiferromagnet

Recently, unconventional spin-orbit torques (SOTs) with tunable spin generation have opened new pathways for designing novel magnetization control for cutting-edge spintronics innovations. A leading research thrust is to develop field-free deterministic magnetization switching for implementing scalable and energy favorable magnetic recording and storage, which have been demonstrated in conventional ferromagnetic and antiferromagnetic material systems. Here, in this work, we extend this advanced magnetization control strategy to chiral antiferromagnet Mn 3 ⁢Sn using spin currents with out-of-plane canted polarization generated from low-symmetry van der Waals (vdW) material WTe 2 . Numerical calculations suggest that dampinglike SOT of spins injected perpendicular to the kagome plane of Mn 3⁢ Sn serves as a driving force to rotate the chiral magnetic order, while the fieldlike SOT of spin currents with polarization parallel to the kagome plane provides the bipolar deterministicity to the magnetic switching in the absence of an external magnetic field. We further introduce scanning quantum microscopy to visualize nanoscale evolutions of Mn 3 ⁢Sn magnetic domains during the field-free switching process, corroborating the exceptionally large magnetic switching ratio up to 90%. Our results highlight the opportunities provided by hybrid SOT material platforms consisting of noncollinear antiferromagnets and low-symmetry vdW spin source materials for developing next-generation spintronic logic devices.

2-dimesional systems↗

Pathways for decarbonization of the buildings sector in Ukraine

The paper focuses on Ukraine’s intention to achieve a two-thirds reduction in buildings’ energy consumption for heating and cooling by 2050, concurrently aiming for net zero greenhouse gas emissions and heightened energy security. Here, the study examines the outcomes of retrofitting existing residential, commercial, and public buildings with highly efficient materials, improving construction standards, and transitioning to advanced heating systems. However, Russia’s invasion in 2022 inflicted substantial damage, prompting a shift from retrofit and decarbonization to reconstruction. The Ukrainian government’s Reconstruction Plan emphasizes clean, sustainable, and resilient energy systems. The study employs energy system and integrated assessment models (TIMES-Ukraine and GCAM-Ukraine) to explore scenarios taking into consideration the war, reconstruction, and a net zero CO 2 pathway. Using two models allowed the inter-model comparison. The analysis addresses vital questions on energy resiliency measures and the compounding effects of decarbonization. Findings indicate that Ukraine’s energy goals can be met through strategic retrofitting and economy-wide decarbonization, emphasizing the importance of low-carbon alternatives like district heating with renewable sources. Electrification with renewables and fuel-switching emerges as crucial for achieving building decarbonization. The study offers valuable insights into navigating energy challenges amidst the war and outlines a pathway for Ukraine’s sustainable energy future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Performance of a dynamic single bubbler in single and two-phase immiscible liquids

Ensuring nonproliferation and safeguards of special nuclear materials (SNM) is a critical aspect of advancing the nuclear fuel cycle. Traditional bubbler systems used to estimate liquid levels and densities in nuclear recycling processes have limitations, particularly in harsh environments where dip-tube corrosion and buildup necessitate frequent maintenance and recalibration. This study explores the Dynamic Single Bubbler (DSB) method, which utilizes a single dip-tube attached to a linear actuator to estimate liquid properties dynamically. This approach is extended to estimate liquid-liquid interfaces in immiscible liquids and employs a linear regression method to reduce uncertainties and improve accuracy. The DSB method achieved density estimate uncertainties of less than 0.5% and surface level estimate uncertainties typically under 0.5%, across various fluids including water, acetone, methanol, mineral oil, glycerol, and aqueous salt solutions. Results indicate that the DSB method provides accurate and robust estimates of liquid density and surface levels with minimal maintenance and without the need for calibration. Additionally, the method's applicability to immiscible liquids and various dip-tube geometries was demonstrated, showing promise for widespread use in nuclear and other industrial applications.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗

Kinetics of photogenerated carbon dangling bonds in organic photovoltaic thin Films: An EPR study

Here, we report an investigation of the early kinetics of photogenerated carbon dangling bond (CDB) formation and annealing in organic photovoltaic bulk heterojunction (BHJ) thin film blends under oxygen- and moisture-free conditions, using X-band electron paramagnetic resonance (EPR) spectroscopy. The study focuses on donor:acceptor BHJ blends of PCE12:PCBM and PCE12:ITIC films, where PCE12 is PBDB-T. The time evolution of CDBs in such drop-cast BHJ films irradiated at 300 nm is monitored. The early kinetics of CDB formation, critical for understanding OPV degradation mechanisms, is studied. Theoretical analysis of the defect growth mechanism suggests a monomolecular defect creation model where the defect count follows a power-law t β with irradiation time t, where β ∼ 0.55–0.58, in excellent agreement with the theoretically expected value of β = 1/2. This model is compatible with CDB formation by the holes in donor sites adjacent to acceptors, likely assisted by energy released from quenching of nearby excitons by the holes, elucidating the physical mechanism underlying CDB formation. This is significant for designing improved materials, which mitigate defect creation, and consequently advancing the development of stable OPV systems.

42 ENGINEERING↗

Photochemistry of Hypervalent Iodoazide Derivatives

The photochemical properties and reaction mechanisms of a series of hypervalent iodoazide compounds (R–IN3) were investigated, with substituents (−CH 3 , −H, −CF 3 ) tuning the electronic density of the phenyl ring. With the help of UV irradiation, ultrafast time-resolved spectroscopy, and density functional theory (DFT) calculations, we elucidated the mechanisms of azide radical (N 3 • ) release and its subsequent reactivity. UV–vis spectroscopy reveals that the photoconversion rates follow the trend CH3 > H > CF3, aligning with DFT-calculated ΔG values for ring-opening transitions. Homolytic cleavage of the I–N bond is identified as the dominant pathway for N3• generation upon UV irradiation, occurring within 400 fs. The released azide radicals react with the solvent molecules, while the iodo radical R–I C • fragments undergo a thermodynamically uphill lactone ring-opening (RO) reaction and subsequent hydrogen atom abstraction from the solvent to form carboxylic acids R–I–COOH, as validated by NMR and IR spectroscopy. The study also highlights the role of substituents in influencing reaction kinetics and intermediate stability, with electron-donating groups accelerating the release of the N 3 • species. This work bridges experimental observations with computational predictions, offering a foundation for future advancements in azide-based reactions and materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Scalable Bottom-Up Synthesis of Nanoporous Hexagonal Boron Nitride ( h -BN) for Large-Area Atomically Thin Ceramic Membranes

Nanopores embedded within monolayer hexagonal boron nitride (h-BN) offer possibilities of creating atomically thin ceramic membranes with unique combinations of high permeance (atomic thinness), high selectivity (via molecular sieving), increased thermal stability, and superior chemical resistance. However, fabricating size-selective nanopores in monolayer h-BN via scalable top-down processes remains nontrivial due to its chemical inertness, and characterizing nanopore size distribution over a large area remains extremely challenging. Here, we demonstrate a facile and scalable approach of exploiting the chemical vapor deposition (CVD) process temperature to enable direct incorporation of subnanometer/nanoscale pores into the monolayer h-BN lattice, in combination with manufacturing compatible polymer casting to fabricate centimeter-scale nanoporous atomically thin ceramic membranes. We leverage diffusive transport of analytes including size-selective Ficoll sieving to characterize subnanometer-scale and nanoscale defects that manifest as pores in centimeter-scale h-BN membranes, overcoming previous limitations in large-area characterization of nanoscale defects in h-BN. Our approach opens a new frontier to advance atomically thin membranes to 2D ceramic materials, such as h-BN via facile and direct formation of nanopores, for size-selective separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dual C–H functionalization of polyolefins for covalent adaptable network formation via cooperative electrolysis

The increasing demand for carbon fibre-reinforced polymer composites with polyolefin thermoset matrices will lead to the generation of large volumes of low molecular weight waste oligomers during the fibre recovery process. To address this challenge, we present a cooperative electrolytic dual C–H bond functionalization strategy for the one-step installation of two key functional groups essential for dynamic linkages of these deconstructed oligomers. Through direct competition studies, we reveal the predominance of tertiary allylic C–H activation along the complex, branched oligomer backbone. The resulting difunctionalized oligomers form covalently adaptable networks with exceptional circularity. By establishing a sustainable pathway for upcycling deconstructed oligomers from carbon fibre-reinforced polymer fibre recovery, this strategy introduces thermoset materials derived directly from waste. Here, it advances sustainable manufacturing practices, contributing to a net-zero waste synthetic ecosystem, and highlights the prospects of mediated electrolysis in macromolecular transformations.

Electrocatalysis↗

Simulation of ultrafast transient absorption spectra of a perylene-based light harvesting antenna

Atomistic simulations of photo-induced responses in artificial light-harvesting molecular systems help to reveal the mechanisms of ultrafast intramolecular energy transfer between individual chromophores. These light-induced processes mimic the primary events occurring in natural photosynthesis. Modeling studies contribute to the design of more efficient molecular architectures enabling performance optimization for applications in light harvesting, energy conversion, and optoelectronics. Within this context, the direct comparison between simulated and experimental transient absorption pump–probe (TA-PP) spectra are especially valuable for validating theoretical approaches and deepening mechanistic understanding. Herein, we investigate the photoinduced dynamics of an antenna system composed of two naphthalene monoimides donor units covalently linked to a perylene derived acceptor. Following photoexcitation, the exciton rapidly self-traps on one of the donor units. Thereafter, efficient ultrafast energy transfer to the acceptor unit takes place via two possible pathways: either through transient exciton localization on the second donor unit or by direct transfer to the acceptor. The simulated TA-PP spectra clearly capture these distinct energy transfer pathways and enable a detailed comparison of their relative efficiencies. This highlights the system's potential for tunable exciton dynamics towards advancing light-harvesting and optoelectronic molecular materials.

36 MATERIALS SCIENCE↗

Obtaining bulk-like correlated oxide surfaces with protective caps

Functional oxides exhibit a diverse range of correlated electron phenomena, some of which are highly attractive for novel electronic, magnetic, and optical devices. Despite decades of advancement of our fundamental understanding of these materials, they consistently fall short of realizing their promise in functional devices. We identify a significant bottleneck toward device realization to be surface overoxidation. Protective caps can effectively prevent overoxidation, but their interfaces with functional oxides are not well understood. These interfaces are critical for effectively using functional oxides in field-effect devices, where “the interface is the device.” This work addresses the chemistry and physics of the interface between protective caps and the correlated metal SrVO3, a model functional oxide. Our comparison of five different cap materials reveals effective protection and similar SrVO3 surface chemistry in all cases. Systematic comparisons of surface and bulk-sensitive photoelectron spectra reveal that negligible interface redox takes place, elucidating the cap-SrVO3 interface chemistry. This work demonstrates a robust and simple solution to the surface overoxidation problem in vanadates, paving the way toward effectively using these materials in field-effect devices. Our conclusions are general and can be applied to numerous other systems, thus moving oxide electronics closer to the realization of functional devices.

Cohen, Amit (ORCID:0009000276477510)↗

Monolithic AlScN/SiC phononic waveguides for scalable acoustoelectric and quantum devices

Unlike conventional surface acoustic wave devices, phononic waveguide systems enable higher circuit density and stronger strain and piezoelectric fields, making them promising for advanced acoustoelectric and quantum applications. One such material system for generating and guiding phonons at gigahertz frequencies is AlScN on SiC, which can be synthesized by sputter depositing AlScN directly onto SiC wafers. The AlScN on the SiC platform allows for tightly vertically-confined acoustic modes with high electromechanical coupling, high speed of sound, and simple fabrication of strip and rib waveguides. Until now, this system has only been studied as a slab waveguide platform, i.e., without any lateral waveguiding. Here, we demonstrate a two-dimensionally confined phononic architecture in AlScN on SiC that supports guided modes at 2.95 and 4.05 GHz. These modes exhibit strong electromechanical coupling coefficients (k 2 = 4.27%) and propagation losses on the order of 10 dB/mm. Furthermore, this architecture is well-suited for phononic routing and power-efficient active or nonlinear devices such as amplifiers, mixers, and oscillators, and is compatible with the integration of quantum systems, including vacancy centers, charge carriers, photons, and spins, either embedded in SiC or heterogeneously integrated on the surface.

Electrical components↗

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗

Multi-Objective Design Optimization of 100 kW Non-Rare-Earth or Reduced-Rare-Earth Machines

The goal of this project is to reduce the size, weight, cost, and losses associated with rotating electric machinery and its associated power electronics for electric and hybrid vehicle applications. In particular, this effort strives to facilitate electric machinery that will meet the requirements set forth in the USDRIVE Electrical and Electronics Technical Team Roadmap of October 2017. This roadmap calls for an electric machine with a peak power of 100 kW, a continuous rated power of 55 kW, a peak speed of less than 20,000 rpm, a volume of no more than 2 liters, a mass of less than 20 kg, and a useful life of 15 years or 300,000 miles of vehicle service. This will be achieved through a combination of (i) new materials, (ii) new electric machine topologies, (iii) advances in power electronics, and (iv) superior design through the use of formal and rigorous multi-objective optimization-based design founded on advanced analysis techniques.

33 ADVANCED PROPULSION SYSTEMS↗

Crystal Calorimetry for Charged Lepton Flavor Violation Searches

The Mu2e experiment at Fermilab aims to search for Charged Lepton Flavor Violation (CLFV) through the coherent conversion of a muon into an electron in the field of an aluminum nucleus. This process, highly suppressed in the Standard Model, would clearly indicate new physics if observed. A crucial component of Mu2e is its electromagnetic calorimeter, which enhances the identification and measurement of conversion electrons. The calorimeter consists of two disks of undoped CsI crystals read out by custom-designed SiPMs and fast front-end electronics. This paper presents an overview of the calorimeter’s design, its custom SiPM technology, the readout and data acquisition system, and the results from commissioning tests.Building on the experience from Mu2e, the proposed Mu2e-II upgrade aims to enhance further the experiment’s sensitivity by an order of magnitude. This requires significant advancements in calorimeter technology, particularly in crystal materials and photodetector performance. Studies on BaF$_2$ and LYSO crystals, as well as the development of radiation-hard Silicon Photomultipliers (SiPMs), are currently underway. Test beam results demonstrate promising improvements in energy resolution and timing capabilities, ensuring the feasibility of next-generation calorimetry solutions for Mu2e-II.

Atanov, N. [Dubna, JINR]↗