Interplay of 3d and 4f Magnetism in Chiral Y 6 FeSi 2 S 14 and Tb 6 FeSi 2 S 14 Chalcogenides
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Traditional lost foam casting has been around for decades. The process uses foam forms blown by an injection molding like process or CNC milled into the desired shape, then pouring molten metal over the foam pattern to create a metallic object. Additive Manufacturing Evaporative Casting (AMEC) is a new process that eliminates foam forms by using additive manufacturing to 3D print the desired cast geometry. This not only saves time and money but allows for more advanced and complex designs that can’t be achieved by carving foam. Additionally, AMEC doesn’t require molds and tooling like other casting and foundry options. Because the AMEC process is new, extensive testing is needed to develop a better understanding of the process to minimize defects, quantify material properties, and start computer modeling for the process. This CRADA (collaborative research and development agreement) between ORNL and Skuld seeks to improve the process, develop a computational model, characterize material properties, and explore new applications.
A method and system for manufacturing a cast iron crankshaft for a vehicle are provided. The system comprises a molding unit arranged to form a negative sand cast mold of the cast iron crankshaft. The mold comprising at least one molded cavity having a pattern with dimensions of the cast iron crankshaft. The system further comprises a feeding mechanism comprising a riser having a connector through which molten metallic material flows to the cast mold. The feeding mechanism feeds the molten metallic material at a riser connection angle in the at least one mold cavity. The riser connection angle corresponds to a connector modulus. The connector modulus is 20% greater than a cast modulus. The riser geometry corresponds to a riser modulus. The riser modulus is 20% greater than the connector modulus. The system further comprises a furnace, a cooling area, a separation unit, a controller and a power source.
Motivated by the discovery of the ultra-strong emission-line starburst galaxies (EELGs) known as “green pea galaxies,” in this work we consider their contribution to the intergalactic flux of ionizing UV at high redshifts. Most galaxies that have been observed show a precipitous drop in the flux blueward of their Lyman limit. However, recent observations of EELGs have discovered that many more Lyman continuum photons escape from them into intergalactic space than previously suspected. We calculate their contribution to the extragalactic background light. We also calculate the effect of these photons on the absorption of high-energyγ-rays. For the more distantγ-ray sources, particularly at z >/ 3, an intergalactic opacity above a few GeV is significantly higher than previous estimates which ignored the Lyman continuum photons. We calculate the results of this increased opacity onobservedγ-ray spectra, which produce a high-energy turnover starting at lower energies than previously thought, and a gradual spectral steepening that may also be observable.
A novel, variable displacement, low-speed high-torque hydraulic motor is being developed that is expected to be highly efficient across a broad operating range. To ensure the final hardware achieves the expected performance, the models used in the development of the motor must be experimentally validated and revised, as necessary. The specific focus in this work is on mechanical energy loss models that were used to guide the design of a single-cylinder motor prototype and on experimental tests used for model validation. Ideally each model, whether friction loss in a piston/cylinder interface or energy loss due to leakage in a valve , would be individually validated by an independent test. This granular approach would remove any question of where the error lies and result in highly accurate models. However, many motor components have multiple forms of energy loss, creating difficulty in validating individual losses. Additionally, it is not physically realizable to divide many components into an individual model equivalent test. Testing the motor as a full assembly is possible, but pinpointing the source of discrepancies between the model and the hardware becomes difficult with dozens of models potentially being partially responsible. A compromise was found by separating the motor into functional component groups that are characterized by the type of loss and ability to test each subcomponent independently. By checking for correlation between test observations and model predictions, revisions could be implemented into the models. This allows future solutions to be more accurately predicted in the design phase to drive the design of better machines.
Difference in g factors in multidot structures can form the basis of dot-selective spin manipulation under global microwave irradiation. Here employing electric dipole spin resonance facilitated by strong spin-orbit interaction (SOI), we observe differences in the extracted values of the single-hole effective g factors of the constituent quantum dots of a GaAs/AlGaAs double quantum dot device at the level of ~ 5 %–10%. We examine the continuous change in the hole g factor with electrical detuning over a wide range of interdot tunnel couplings and for different out-of-plane magnetic fields. The observed tendency of the quantum dot effective g factors to steadily increase on decreasing the interdot coupling or on increasing the magnetic field is attributed to the impact on the SOI of changing the dot confinement potential and heavy-hole light-hole mixing.
Many earth science modeling applications employ continuous input data fields derived from satellite data. Environmental factors, sensor limitations and algorithmic constraints lead to data products of inherently variable quality. This necessitates interpolation of one form or another in order to produce high quality input fields free of missing data. The present research tests several interpolation techniques as applied to satellite-derived leaf area index, an important quantity in many global climate and ecological models. The study evaluates and applies a variety of interpolation techniques for the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf-Area Index Product over the time period 2001-2006 for a region containing the conterminous United States. Results indicate that the accuracy of an individual interpolation technique depends upon the underlying land cover. Spatial interpolation provides better results in forested areas, while temporal interpolation performs more effectively over non-forest cover types. Combination of spatial and temporal approaches offers superior interpolative capabilities to any single method, and in fact, generation of continuous data fields requires a hybrid approach such as this.
The National Aeronautics and Space Administration (NASA) Space Technology Mission Directorate (STMD) has been developing a new Strategic Framework to guide investment prioritization and communication of STMD strategic goals to stakeholders. STMD’s analysis of global trends identified four overarching drivers which are anticipated to shape the needs of civilian space research for years to come. These Mega-Drivers form the foundation of the Strategic Framework. The Increasing Access Mega-Driver reflects the increase in the availability of launch options, more capable propulsion systems, access to planetary surfaces, and the introduction of new platforms to enable exploration, science, and commercial activities. Accelerating Pace of Discovery reflects the exploration of more remote and challenging destinations, drives increased demand for improved abilities to communicate and process large datasets. The Democratization of Space reflects the broadening participation in the space industry, from governments to private investors to citizens. Growing Utilization of Space reflects space market diversification and growth. This paper will further describe the observable trends that inform each of these Mega Drivers, as well as the interrelationships between them within STMD’s new Strategic Framework.
Current silicon waveguide Bragg gratings typically introduce perturbation to the optical mode in the form of modulation of the waveguide width or cladding. However, since such a perturbation approach is limited to weak perturbations to avoid intolerable scattering loss and higher-order modal coupling, it is difficult to produce ultra-wide stopbands. In this Letter, we report an ultra-compact Bragg grating device with strong perturbations by etching nanoholes in the waveguide core to enable an ultra-large stopband with apodization achieved by proper location of the nanoholes. With this approach, a 15 µm long device can generate a stopband as wide as 110 nm that covers the entire C + L band with a 40 dB extinction ratio and over a 10 dB sidelobe suppression ratio (SSR). Similar structures can be further optimized to achieve higher SSR of > <#comment/> 17 d B for a stopband of about 80 nm.
The National Aeronautics and Space Administration (NASA) Space Technology Mission Directorate (STMD) has been developing a new Strategic Framework to guide investment prioritization and communication of STMD strategic goals to stakeholders. STMD’s analysis of global trends identified four overarching drivers which are anticipated to shape the needs of civilian space research for years to come. These Mega-Drivers form the foundation of the Strategic Framework. The Increasing Access Mega-Driver reflects the increase in the availability of launch options, more capable propulsion systems, access to planetary surfaces, and the introduction of new platforms to enable exploration, science, and commercial activities. Accelerating Pace of Discovery reflects the exploration of more remote and challenging destinations, drives increased demand for improved abilities to communicate and process large datasets. The Democratization of Space reflects the broadening participation in the space industry, from governments to private investors to citizens. Growing Utilization of Space reflects space market diversification and growth. This paper will further describe the observable trends that inform each of these Mega Drivers, as well as the interrelationships between them within STMD’s new Strategic Framework.
Due to its low cost, stiffness, and recyclability, isotactic polypropylene (iPP) is an excellent candidate for packaging applications. However, iPP is notoriously difficult to thermoform due to its low melt strength. The addition of just 10 thin layers of high-molecular-weight, linear low-density polyethylene (LLDPE) into iPP sheets by coextrusion significantly increased extensional viscosity and reduced sag. Both LLDPE and iPP were metallocene-catalyzed with excellent adhesion as measured in our previous work. Here, we performed a series of hot tensile tests and sheet sag measurements to determine the properties of the iPP sheet and the multilayer sheet between 130 and 180 °C. To evaluate the thermoformability of these multilayer sheets, truncated conical cups were positive vacuum formed at different temperatures and heating times, and the crush strength was measured. Cups that released easily from the mold with good shape retention and a crush strength within 80% of the maximum value were used to define a temperature–time thermoformability window. We estimated the maximum stress that occurred during the thermoforming process to be 5 MPa. Layer thicknesses before and after thermoforming were used to estimate an average strain of 0.78. The thin LLDPE layers decreased the yield stress below 5 MPa. This enabled thermoforming at sheet temperatures as low as 150 °C. The immiscible LLDPE interfaces increased extensional viscosity, which decreased sag in the multilayer sheets compared to iPP. This broadened the thermoforming range to temperatures as high as 180 °C and allowed longer heating times. These highly thermoformable, layered sheets may be recycled as iPP since they contain only 8% of LLDPE.
Neutron star mergers can form a hypermassive neutron star (HMNS) remnant, which may be the engine of a short gamma-ray burst (SGRB) before it collapses to a black hole, possibly several hundred milliseconds after the merger.During the lifetime of an HMNS, numerical relativity simulations indicate that it will undergo strong oscillations and emit gravitational waves with frequencies of a few kilohertz, which are unfortunately too high for detection to be probable with the Advanced Laser Interferometer Gravitational-Wave Observatory. Here we discuss the current and future prospects for detecting these frequencies as modulation of the SGRB. The understanding of the physical mechanism responsible for the HMNS oscillations will provide information on the equation of state of the hot HMNS, and the observation of these frequencies in the SGRB data would give us insight into the emission mechanism of the SGRB.
The third session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort, held on November 18, 2025, focused on developing mitigation strategies for digital assurance risks identified in previous workshops. Hosted by Idaho National Laboratory (INL) and ScottMadden, the session emphasized the application of Cyber-Informed Engineering (CIE) to data center infrastructure, particularly at the utility–data center interface. Participants revisited and ranked key digital assurance risks, including architecture and interface weaknesses, governance gaps, and AI-enabled threats. The workshop introduced the 12 principles of CIE, advocating for consequence-focused design, engineered controls, and secure information architecture to proactively reduce cyber-physical vulnerabilities. These principles were applied to critical data center systems such as power distribution, UPS, cooling, SCADA/BMS, and grid-forming batteries. The session also addressed governance challenges at the interconnection boundary, highlighting the need for clear roles in telemetry sharing, firmware management, and trip settings. Special attention was given to emerging risks from behind-the-meter (BTM) generation, including reverse-power flow and the integration of small modular reactors (SMRs), which shift data centers from large loads to complex generation nodes. Participants explored how interconnection agreements can serve as enforceable instruments for digital assurance, and reviewed gaps in current standards such as NERC CIP, IEC 62443, and IEEE 1547. The workshop concluded with pathways to standardization, including model agreement language, state-level programs, and expanded NERC guidance. INL also presented tools and frameworks for secure procurement and supplier risk management, reinforcing the need for integrated engineering and policy solutions to secure the evolving data center–grid ecosystem. Session 3 of 3.
This paper presents a combined Monte Carlo and analytic approach to the calculation of the pixel-to-pixel distribution of proton-induced damage in a HgCdTe sensor array and compares the results to measured dark current distributions after damage by 63 MeV protons. The moments of the Coulombic, nuclear elastic and nuclear inelastic damage distribution were extracted from Monte Carlo simulations and combined to form a damage distribution using the analytic techniques first described in [I]. The calculations show that the high energy recoils from the nuclear inelastic reactions (calculated using the Monte Car10 code MCNPX [2]) produce a pronounced skewing of the damage energy distribution. The nuclear elastic component (also calculated using the MCNPX) has a negligible effect on the shape of the damage distribution. The Coulombic contribution was calculated using MRED [3,4], a Geant4 [4,5] application. The comparison with the dark current distribution strongly suggests that mechanisms which are not linearly correlated with nonionizing damage produced according to collision kinematics are responsible for the observed dark current increases. This has important implications for the process of predicting the on-orbit dark current response of the HgCdTe sensor array.
Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.
In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods.
Photovoltaics (PV), or solar electricity generation, has become the cheapest form of energy in many locations worldwide and, combined with energy storage, has the potential to satisfy most of our electricity needs. PV has grown at an annual compounded growth rate of approximately 30% in the last three decades. Solar energy systems will continue their impressive growth in distributed energy, microgrids, and utility scale, as efforts are made for dependable electricity in an age with increasing extreme weather. However, within this remarkable success lies a new challenge. The growth curve, combined with rapid product innovation and scale up, means that the majority of PV systems are new, without the three years of performance data that have been required in the past to estimate product lifetime. PV reliability has to address this challenge. In this review we present a brief summary of PV reliability starting with brief historical synopsis, detailing some of the technological challenges and present a framework required for long lifetime.
The National Alliance for Water Innovation (NAWI) is a research consortium formed to accelerate transformative research in desalination and treatment to lower the cost and energy required to produce clean water from nontraditional water sources and realize a circular water economy. Prior to developing this master roadmap, a team of NAWI researchers and water professionals engaged in a detailed process of evaluating water uses, state-of-the-art technologies, emerging technologies and existing uses of desalination and advanced water technologies within five major water user categories in the United States. The resulting Water User Sector Roadmaps were complemented by a Baseline Analysis of a suite of representative treatment systems conducted by NAWI researchers working in collaboration with the hub’s Industrial Partners. This Master Roadmap, which synthesizes the findings from these different efforts as well as feedback from members of NAWI’s Research Advisory Council, serves as the basis for identifying NAWI’s research Areas of Interest (AOIs).