Ultraclean two-dimensional hole systems with mobilities exceeding 10 7 cm 2 / Vs
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In the recent past, MnTe has proven to be a crucial component of the intrinsic magnetic topological insulator (IMTI) family [MnTe] m [Bi2Te 3 ] n , which hosts a wide range of magneto-topological properties depending on the choice of m and n. However, bulk crystal growth allows only a few combinations of m and n for these IMTIs due to the strict limitations of the thermodynamic growth conditions. One way to overcome this challenge is to utilize atomic layer-by-layer molecular beam epitaxy (MBE) technique, which allows arbitrary sequences of [MnTe]m and [Bi 2 Te 3 ] n to be formed beyond the thermodynamic limit. For such MBE growth, finding optimal growth templates and conditions for the parent building block, MnTe, is a key requirement. Here, we report that two different hexagonal phases of MnTe - nickeline (NC) and zinc-blende/wurtzite (ZB-WZ) structures, with distinct in-plane lattice constants of 4.20 ± 0.04 Å and 4.39 ± 0.04 Å, respectively - can be selectively grown on c-plane Al 2 O 3 substrates using different buffer layers and growth temperatures. Moreover, we provide the first comparative studies of different MnTe phases using atomic-resolution scanning transmission electron microscopy and show that ZB and WZ-like stacking sequences can easily alternate between the two. Surprisingly, In 2 Se 3 buffer layer, despite its lattice constant (4.02 Å) being closer to that of the NC phase, fosters the ZB-WZ instead, whereas Bi 2 Te 3 , sharing the same lattice constant (4.39 Å) with the ZB-WZ phase, fosters the NC phase. Furthermore, these discoveries suggest that lattice matching is not always the most critical factor determining the preferred phase during epitaxial growth. Overall, this will deepen our understanding of epitaxial growth modes for chalcogenide materials and accelerate progress toward new IMTI phases as well as other magneto-topological applications.
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The emergence of wide-bandgap (WBG) power devices opens opportunities for current source inverters (CSIs) to provide a promising alternative drive configuration for motor drive applications. The sinusoidal output voltage of CSI offers significant advantages for numerous motor drive applications, including reducing the risks associated with motor terminal overvoltages and bearing discharge currents. This paper compares the performance of CSIs and voltage source inverters (VSIs) with the same performance capabilities. More specifically, sine filters are added to the VSI output terminals to match the CSI performance, and a front-end boost converter is added to the VSI to match the CSI’s voltage-boost capability. The results show that the CSI active components require 48% less volume than the VSI, combined with other performance advantages that make it an appealing candidate for some future motor drive applications.
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The Thermo Scientific P385 Neutron Generator is a compact neutron source, producing 14 MeV neutrons through the deuterium-tritium (DT) fusion reaction. It is important to measure and understand the dependence of the neutron production rate on the accelerator current and voltage. In this study we evaluated neutron production with an absolutely calibrated liquid scintillator neutron spectrometer (BTI N-Probe), an absolutely calibrated He-3 detector surrounded by HDPE shells (Detec Nested Neutron Spectrometer, NNS), and two uncalibrated ZnS fast neutron scintillators (EJ-410), for both A3082 and A3083 sealed tubes. Here we also modeled the neutron yield using the TRIM code, which calculates the trajectory and the energy loss of deuterons and tritons within the target. Experimental results showed an essentially linear dependence on beam current, as expected. A 3.59 ±0.08 power law dependence on the operating voltage was measured, in effective agreement with the modeled value of 3.5. A series of absolute NNS and N-Probe measurements, matched against MCNP calculations, showed that the A3083 and A3082 tubes provide a maximum neutron yield of 8.2 × 10 8 n/s and 4.7 × 10 8 n/s respectively, with estimated uncertainty of ±10%.We showed, through modeling, that tritium decay is not a significant consideration for tubes, such as these, with lifetimes of less than 10 years.
The first automatic twisting heliostat, with 8 m2 reflector, was completed and tested on-sun in January 2024. It was set up on a target-oriented dual-axis mount, with the target-axis aimed at a target 113 m to the west. The shape-twisting is purely automatic, made by a mechanical cam coupling to the cross-axis, which turns as the angle of incidence of the sun’s rays on the reflector. Three unsaturated images taken at different times of day were recorded of the sun on the target, reflected at angles of incidence of 5°, 49° and 68°. The measured FWHM of three images is very similar, about 1.09 m or 9.5 mrad, only slightly larger than an ideal solar disc, indicating that the different twisted reflector shapes are close to the ideal biconics needed to image the sun. The encircled energy measured for all three images was similar, 87% within approximately 1.30 m diameter. We report here also on a novel, tracking camera that employs a semitransparent beamsplitter fixed perpendicular to the mirror surface and to the plane of incidence. Over 2.5 hours of measurement of closed loop tracking, errors were ≤ 0.36 mrad rms, in both tracking axes.
In this work, I discuss various situations in which perturbative expansions are used in Yang-Mills theories with asymptotic freedom and establish the limits of its applicability.
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Deep learning neuroarchitecture and hyperparameter search are important in finding the best configuration that maximizes learned model accuracy. However, the number of types of layers, their associated hyperparameters, and the myriad of ways to connect layers poses a significant computational challenge in discovering ideal model configurations. Here, we assess two different approaches for neuroarchitecture search for a LeNet style neural network, one that uses a fixed-length approach where there is a preset number of possible layers that can be toggled on or off via mutation, and a variable-length approach where layers can be freely added or removed via special mutation operators. We found that the variable-length implementation trained better models while discovering unusual layer configurations worth further exploration.
High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.
Anion exchange membrane water electrolyzers (AEMELs) have recently received significant attention due to their potential advantages over proton exchange membrane electrolyzers (PEMELs). However, some AEMELs feed an aqueous salt solution to the cell where PEMELs typically feed deionized (DI) water. DI water is preferred to keep the system and maintenance costs low. Because of this, many AEMEL researchers report performance both in the salt solution (typically KOH) and DI water. However, the methodology for switching between KOH and DI water is often poorly defined, and it is unclear what impact the residual salt has on cell performance after switching from salt to DI water. Having a fully deionized environment is important because the presence of salts in the water feed increase the effective electrochemical surface area of the catalyst in the three-dimensional electrode and residual salt remaining after switching to DI water feed can have a misleading transient effect on cell performance. This paper focuses on understanding the transition from KOH to DI water testing in AEMELs. It is shown that when switching from salt to DI water feed, a large volume of DI water must be fed over several hours to achieve true DI-water performance. It is also shown that starting AEMELs from the beginning with DI water feed (without any KOH ever being fed to the cell) results in better cell durability. Lastly, a cell is demonstrated having operated exclusively on DI water at 1.0 A cm −2 for 500 h at an operating voltage of ca. 2 V and a low degradation rate.
The formation and evolution of the dynamic solid electrolyte interphase (SEI) at the Si anode/electrolyte interface are yet to be completely understood to solve irreversible capacity loss and increase battery cycle life. Herein, the evolution of SEI and its dynamic properties at the Si anode/electrolyte interface are investigated in two electrolyte systems, a 1.2 M LiPF6 in EC: EMC 3:7 (wt%) electrolyte (referred to as Gen2) and a 1.2 M LiTFSI in EC: EMC 3:7 (wt%) electrolyte (referred to as LiTFSI). Two lithiation stages are studied: the pre-lithiation (pre-Li) SEI stage and the post-lithiation (post-Li) stage. Findings reveal at the pre-Li, SEI formation starts at an early potential and contributes to the greater mass gain in the Si/Gen2, and it is dominated by the formation of a non-uniform F- and P-rich layer in Si/Gen2, in contrast to a homogeneous F- and C-containing layer at the Si/LiTFSI interphase. The initially formed SEI in LiTFSI further benefits the charge transfer kinetics. At the post-Li stage, a more substantial SEI evolution is observed at Si/LiTFSI. This paper offers a foundational understanding of the SEI dynamic evolution with electrolyte dependence. Findings from this report offer important insights into solving the complex SEI stability issues on Si.
Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).
Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14–36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year–1 from the atmosphere, deliver up to 16 Tg CE year–1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.
The U.S. Transuranium and Uranium Registries performs autopsies on each of its deceased Registrants as a part of its mission to follow up occupationally-exposed individuals. This provides a unique opportunity to explore death certificate misclassification errors, and the factors that influence them, among this small population of former nuclear workers. Underlying causes of death from death certificates and autopsy reports were coded using the 10 th revision of the International Classification of Diseases (ICD-10). These codes were then used to quantify misclassification rates among 268 individuals for whom both full autopsy reports and death certificates with legible underlying causes of death were available. When underlying causes of death were compared between death certificates and autopsy reports, death certificates correctly identified the underlying cause of death’s ICD-10 disease chapter in 74.6% of cases. The remaining 25.4% of misclassified cases resulted in over-classification rates that ranged from 1.2% for external causes of mortality to 12.2% for circulatory disease, and under-classification rates that ranged from 7.7% for external causes of mortality to 47.4% for respiratory disease. Neoplasms had generally lower misclassification rates with 4.3% over-classification and 13.3% under-classification. A logistic regression revealed that the odds of a match were 2.8 times higher when clinical history was mentioned on the autopsy report than when it was not. Similarly, the odds of a match were 3.4 times higher when death certificates were completed using autopsy findings than when autopsy findings were not used. This analysis excluded cases where it could not be determined if autopsy findings were used to complete death certificates. The findings of this study are useful to investigate the impact of death certificate misclassification errors on radiation risk estimates and, therefore, improve the reliability of epidemiological studies.
This dataset contains strain change rate versus depth data acquired using a Rayleigh frequency shift (RFS) distributed strain sensing (DSS) system during hydraulic stimulation of well 16A(78)-32 at the Utah FORGE site in April 2024. The data were collected from an optical fiber installed in the annulus of production well 16B(78)-32, approximately 300 feet from the injection well. The dataset includes tabulated strain data and an explanation of the methodology used to generate the frac log, which integrates strain change rate signals over selected time windows to identify fracture events.