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At least 829 records · Page 46

A PCB-Embedded 1.2 kV SiC MOSFET Half-Bridge Package for a 22 kW AC–DC Converter

This article presents the design and analysis of a double-side-cooled printed circuit board (PCB) embedded silicon carbide (SiC) MOSFET half-bridge package with low loop inductances and an integrated gate driver. The 1.2 kV SiC MOSFET dies used in the half-bridge package are embedded in the PCB using AT&S's patented technique. The dies are cooled and electrically connected to traces in the PCB through copper-filled microvias. The design methodology accounts for both electrical and thermal performance, limiting the power-loop inductance to 2.3 nH and the maximum package temperature to less than the 175 °C limit. The integration of the gate drive circuitry allows for a high power density and 2.2 nH gate-loop inductances. At 0.12 K/W, the measured junction-to-case thermal resistance with double-sided cooling is 57% lower than that of a TO-247 package. Under similar operating conditions, the PCB-embedded half-bridge package also achieves a 5.6 times lower voltage overshoot and a 0.5% higher peak efficiency than a TO-247-based half-bridge. This article reports the first demonstration of PCB-embedded 1.2 kV SiC MOSFET packages in buck, boost, and ac–dc converters. Furthermore, the prototype three-phase ac–dc converter for an electric vehicle on-board charger is composed of six PCB-embedded half-bridge packages and achieves an efficiency of 98.2% and a power density of 182 W/in 3 .

42 ENGINEERING↗

A framework for estimating the energy-saving potential of occupant behaviour improvement

Energy-related occupant behaviour in buildings has demonstrated considerable energy-saving potential. However, the current modelling method of occupant behaviour does not give sufficient considerations on the implementation difficulty of behaviour and provide a holistic map from survey data to various behaviour models. This article proposes a holistic survey-and-simulation-based framework for estimating the energy-saving potential of occupant behaviour improvement. In the framework, seven typical categories of occupant behaviour models are identified based on the survey results. According to the implementation difficulty, the models are integrated into four behaviour styles (baseline, wasteful, moderate and austere) to represent different levels of energy-saving consciousness of occupants. Based on a case study with a nationwide survey in Singapore, there are remarkable energy savings potential if occupant behaviour is improved; the building energy consumption can be reduced by up to 9.5% with the moderate behaviour improvement, and up to 21.0% with the aggressive behaviour improvement. The simulation results accord well with the measured results within a reasonable range of deviation. The framework can be applied to estimate the energy-saving potential of occupant behaviour improvement in a building with affordable cost, and the findings can inform a behaviour improvement programme with effective and efficient measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics

As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current-voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.

14 SOLAR ENERGY↗

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

36 MATERIALS SCIENCE↗

Numerical Mixing Suppresses Submesoscale Baroclinic Instabilities Over Sloping Bathymetry

Abstract The impacts of spurious numerical salinity mixing on the larger‐scale flow and tracer fields are characterized using idealized simulations. The idealized model is motivated by realistic simulations of the Texas‐Louisiana shelf and features oscillatory near‐inertial wind forcing. can exceed the physical mixing from the turbulence closure in frontal zones and within the mixed layer. This suggests that simulated mixing processes in frontal zones are driven largely by . Near‐inertial alongshore wind stress amplitude is varied to identify a base case that maximizes the ratio of to in simulations with no prescribed horizontal mixing. We then test the sensitivity of the base case with three tracer advection schemes (MPDATA, U3HC4, and HSIMT) and conduct ensemble runs with perturbed bathymetry. Instability growth is evaluated using the volume‐integrated eddy kinetic energy and available potential energy . While all schemes have similar total mixing, the HSIMT simulations have over double the volume‐integrated and 20% less relative to other schemes, which suppresses the release of and reduces the by roughly 25%. This results in reduced isohaline variability and steeper isopycnals, evidence that enhanced suppresses instability growth. Differences in and between the MPDATA and U3HC4 simulations are marginal. However, the U3HC4 simulations have 25% more . Experiments with variable horizontal viscosity and diffusivity coefficients show that small amounts of prescribed horizontal mixing improve the representation of the ocean state for all advection schemes by reducing the and increasing the .

54 ENVIRONMENTAL SCIENCES↗

Investigation of two-dimensional radio-frequency sheath properties using a microscale fluid model

Abstract In previous work (Kohno and Myra 2023 Comput. Phys. Commun. 291 108841), we developed a numerical scheme based on a two-dimensional microscale radio-frequency (RF) sheath model with periodically curved wall boundaries. Here, we expand the capability of this scheme through modification of the boundary conditions (BCs) on the conducting walls, which allows the ion flow to turn back to the plasma at locations on the walls where the electromagnetic force on the ions is reversed from its usual direction. Numerical simulations are carried out to investigate the dependences of the surface-integrated admittances on the wall bump height, ion magnetization, ion mobility, and the magnetic field angle, and to visualize the sheath structures in several cases. One of the main results is the ion cyclotron admittance resonance observed under the condition of low ion mobility (high normalized frequency). It is shown that the amplitude of the resonance peak depends on the wall bump height and the ion velocity is reversed on the sides of the bump in an RF cycle for the resonance cases. Furthermore, the differences in the admittances between the one- and two-dimensional microscale models are assessed for the purpose of understanding non-locality of the sheath near the wall surface for the parameters considered in this study. This information will be essential for improving the sheath BC for macroscale calculations in the future.

Kohno, H. (ORCID:0000000332690010)↗

From quantitative trait loci towards mechanisms: Linkage Integration Hypothesis Testing (LIgHT) sheds light on the mechanisms of genetically modulated stress tolerance

The goal of this work is to assess the mechanistic bases of natural genetic variations in plant responses of photosynthesis to stress. To achieve this goal, we devised the Linkage Integration Hypothesis Testing (LIgHT) approach, comparing chromosomal locations of quantitative trait loci (QTLs) for multiple phenotypes to distinguish between hypothetical mechanisms. As a use case, we explored genetic variations in photosynthesis-related processes under chilling stress in recombinant inbred lines of cowpea ( Vigna unguiculata L. Walp.). We focused on photosynthesis-related parameters measurable in high throughput and indicative of proposed chilling responses, including the states of PSI and PSII, photoprotective non-photochemical quenching, PSII photodamage, and nyctinastic leaf movements (NLMs). The patterns of QTL linkages indicated that chilling stress tolerance is genetically controlled by avoiding PSII photodamage rather than PSI damage or NLMs. This model was validated in a separate experiment measuring the rates of PSII photodamage and repair. Additional linkages suggest that chilling-induced damage to PSII is controlled by the thylakoid proton motive force and redox state of PSII. This regulation appears to be modulated by thylakoid fatty acid composition, previously associated with the same genetic loci and now supported by broader mechanistic evidence. We propose that the LIgHT approach can be broadly applied to test mechanisms underlying genetic variations.

MultispeQ↗

Separability of the Dirac equation in the STU black hole spacetime: Pairwise-equal charge case study

We study the separability of the Dirac equation in the background of four dimensional charged rotating asymptotically flat black hole solution of supergravity and the low energy string theory known as STU black hole. In particular, we analyze in detail the separability conditions in the pairwise equal charge STU black hole space-time. While in the latter case the minimally coupled Dirac equation is not separable, the introduction of a specific torsion term ensures the separability. The source of the torsion is the Kalb-Ramond field, which is an integral part of string theory, but further aspects of its properties and coupling to fermionic fields remain to be studied. To derive the torsion, two different approaches are used in conformally related frames, showing that the torsion is not unique. The correspondingly modified Dirac equations in the Einstein and string frames are shown to be separable. Furthermore, the massless radial and angular wave equations are examined; they show close similarity with corresponding equations for the standard Kerr background. A generalization of the Teukolsky equation for the pairwise equal case is conjectured. We also briefly analyze a technically sophisticated radial equation in the massive case.

Dirac equation↗

Techno-Economic Analysis of a Cryogenic Flux Capacitor for Grid Storage

Abstract The Cryogenic Flux Capacitor (CFC) is a cold, dense fluid storage core with integrated design features that afford the designer flexibility and provide new possibilities for the storage and discharge of energy. The stored energy, in this case, is represented by hydrogen physically bonded within the nanoscale pores within the aerogel composite blanket material, and the process of bonding or debonding is governed by principles of physical adsorption (physisorption) and thermodynamics. The large surface area afforded by the nanoporous aerogel (∼1,000 m2/g) allows for storage densities close to, or in some cases exceeding, that of normal boiling point liquids. Its performance easily exceeds what can be achieved via ambient temperature, high-pressure gas storage for an equivalent volume. CFC storage is predicted to be easily scalable, is constructed from readily available commercial materials, lends itself to a range of pressure applications, and is geometry insensitive. The high energy density of CFC-stored hydrogen allows long durations of storage, such as daily to monthly cycling, which corresponds to approximately 10 to 100 hours of duration, respectively. The techno-economic analysis examines different types of hydrogen storage for commercial viability. The assessment includes the use of hydrogen blended with natural gas in a combined cycle gas turbine. The study provides results for a range of blends from 100% natural gas to 100% hydrogen. The required fuel usages are estimated for the intended application in a grid scenario that features large amounts of renewable penetration, 10 hours, and as a baseload, 100 hours. The study presents preliminary estimates of the capital cost of storage and operating cost of storage. In addition, the cost of firing natural gas and capturing the carbon dioxide (CO2) in a carbon capture and sequestration (CCS) system is assessed. Based on the study, it is shown that CFC can competitively meet performance and cost needs for grid-based energy storage.

08 HYDROGEN↗

Development of an Advanced Hydrogen Energy Storage System using Aerogel in a Cryogenic Flux Capacitor (CFC)

The Cryogenic Flux Capacitor (CFC) is a cold, dense fluid storage core with integrated design features that afford the designer flexibility and provide new possibilities for the storage and discharge of energy. The stored energy, in this case, is represented by hydrogen physically bonded within the nanoscale pores within the aerogel composite blanket material, and the process of bonding or debonding is governed by principles of physical adsorption (physisorption) and thermodynamics. The large surface area afforded by the nanoporous aerogel (~1,000 m2/g) allows for storage densities close to, or in some cases exceeding, that of normal boiling point liquids. Its performance easily exceeds what can be achieved via ambient temperature, high-pressure gas storage for an equivalent volume. CFC storage is predicted to be easily scalable, is constructed from readily available commercial materials, lends itself to a range of pressure applications, and is geometry insensitive. They can also be used in modular designs, affording even more flexibility for potential deployment. In addition to the aerogel adsorbent within the blanket material, a CFC includes thermally-conductive membrane layered with the aerogel, which acts as a large-area, quick-response thermal management system. The system conducts heat throughout the volume to discharge the unit quickly. This same thermal management system can also be connected to a refrigeration system, or cold fluid such as liquid nitrogen (LN2), to facilitate the charging up of the CFC. The charging and discharging can be performed as fast or as slow as desired by controlling the cooling or heating supplied to the CFC.

08 HYDROGEN↗

Technoeconomic Evaluation of Solid Oxide Fuel Cell Hydrogen-Electricity Co-Generation Concepts

This report evaluates the cost and performance of several types of Integrated Energy Systems (IES) based on solid oxide fuel cells (SOFCs) to generate power and solid oxide electrolysis cells (SOECs) to produce hydrogen. All cases feature carbon capture at rates exceeding 97 percent. The report also describes the development of optimized steady state process models for each system. These are used to calculate overall electricity and hydrogen production costs using a consistent methodology that facilitates comparisons of these cases to one another and to prior NETL cost and performance estimates. The SOFC and SOEC costs and performance are based on nth-of-a-kind systems and include research and development and learning associated with mass-scale commercial deployment of the technology over the next decade required for commercial utility-scale systems.

08 HYDROGEN↗

User Access to Scientific Facilities via 5G: A Cyber Security Thought Experiment

5G is more than an over-the-air radio technology upgrade. It is a strategy to extend Mobile Network Operator service offerings beyond traditional voice, instant messaging and Internet access. 5G Mobile Network Operators will offer new telecommunication services that include enhanced guarantees of confidentiality, integrity and availability. How could such services change the way Science collaborations connect scientists to supercomputers and other scientific facilities? Current scientific collaborations implicitly trust cloud service providers to securely store and process data. The perceived risks of outsourcing Science data security are counterbalanced by assurances that cloud providers operate at a scale that allows them to implement security measures impractical for Science collaborations (e.g. continuous system administrator behavioral monitoring and strict individual separation of duties). If that is true for a cloud service provider like Amazon Web Services (2018 revenue: $25.7 billion), could it also be true for Mobile Network Operators like Verizon Wireless (2018 revenue: $91.7 billion) or AT&T Mobility (2018 revenue: $71.3 billion)? DOE Leadership Class supercomputer facility users currently access them from the public Internet via Secure Shell. The sponsors and operators of the supercomputer facilities have determined that the public Internet path between the Scientist’s Device and the Login Node does not natively provide enough confidentiality or integrity to protect those communications. Therefore, the facilities achieve additional confidentiality and integrity by requiring Secure Shell encryption across those untrusted network paths. Using 5G Network Slice technology, a Mobile Network Operator may offer communication services between supercomputer users and facilities that natively provide confidentiality and integrity guarantees. Sponsors and operators of supercomputer facilities may determine that these guarantees provide enough confidentiality and integrity to protect those communications. If so, a 5G Network Slice could replace an SSH session running over the public Internet. Finally, this use case could be extended to other Office of Science user facility access requirements. Consider microscopy instruments at (e.g.) the Center for Nanoscale Materials or the Environmental Molecular Sciences Laboratory. The embedded systems controlling such instruments may not always support encrypted network access technologies like SSH. 5G Network Slices may offer an alternative to current VPN or SSH tunneling techniques, with additional benefits like guaranteed minimum bandwidth.

5G↗

Workshop Summary Report on Using AI Tools to Improve the Efficiency and Outcomes of the NEPA Process: AI for Permitting Workshop at the 2025 National Association of Environmental Professionals (NAEP) Annual Conference

On April 29, 2025, the U.S. Department of Energy and Pacific Northwest National Laboratory hosted a workshop at the National Association of Environmental Professionals 2025 Conference and Training Symposium in Charleston, South Carolina, titled, “Effective and Responsible Use of Customized AI Tools to Improve the Efficiency and Outcomes of the NEPA Process.” The objectives of this workshop were to make environmental practitioners aware of the potential for using artificial intelligence in the National Environmental Policy Act process, demonstrate examples of how artificial intelligence can be integrated effectively to improve efficiency and outcomes and solicit questions and feedback from practitioners. This report summarizes the key points from all talks and case studies, as well as audience questions and feedback on the presentation topics and the broader topic of "AI in permitting". The report concludes by highlighting the key barriers and opportunities for the implementation of AI in permitting, as discussed during the workshop.

54 ENVIRONMENTAL SCIENCES↗

Control Oriented Models for Co-Design: Technical Overview of MT HVDC, MVDC, and Solid State Transformer Building Blocks

The electric power system is shifting toward a power electronics–enabled grid, where converter based “building blocks” (e.g., high voltage direct current (HVDC) links, multi terminal HVDC (MT HVDC) networks, medium voltage DC (MVDC) links, and solid state transformers (SSTs)) provide fast, precise control of power flows, voltage, and frequency. This report develops and applies publicly shareable electromagnetic transient (EMT) and phasor models to examine how such building blocks can be composed and coordinated to support offshore wind integration, inter area transfers, feeder support, and resilience. Section 2 documents a modular multilevel converter (MMC)–based MT HVDC modeling framework and two use cases: a compact WSCC/IEEE 9 bus test system and a 240 bus “mini WECC” case with five offshore wind plants (OWFs). Phasor to EMT transfer, initialization, and sanity checks are summarized, and neutral demonstrations of normal and contingency operation are reported. Section 3 frames the problem of wind plant inertial frequency response (IFR): shaping energy release and recovery to improve nadir while avoiding aerodynamic stall; representative simulations illustrate the issues without disclosing proprietary control. Section 4 develops MVDC concepts through an IEEE 16 bus loop and an Olympic Peninsula case study that compares AC vs. MVDC corridors and shows how feeder headroom can be pooled via DC couplers. Section 5 surveys SST architectures and identifies a gap: scalable, communication free coordination of multiple SSTs for islanded feeder networks. Across the report, novel methods and configurations under separate publication and IP review are not disclosed; only topic oriented, replicable setups and non proprietary results are shown. These models and use cases are intended as foundations for future publications and co design studies on architecture, control, and coordination of PE enabled grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Scalable Solar Fuels Production in A Reactor Train System by Thermochemical Redox Cycling of Novel Nonstoichiometric Perovskites

Hydrogen production via two-step thermochemical water splitting redox cycles using nonstoichiometric redox-active metal oxides has the potential to dramatically increase fuel production rates. At moderate-to-low water splitting temperatures, surface reaction kinetics co-limit the process. In such cases, stable and high surface area microstructures that allow exploitation of the full thermodynamic potential of the materials are essential as is tight thermal integration of the reactor module. This project’s goals were the development of novel nonstoichiometric perovskite oxides with high stability and favorable thermodynamic and kinetic properties, to optimize their microstructure for maximizing the fuel productivity, and to build a prototype reactor train system (RTS) comprising at least one reactor to meet specific performance targets: (1) capable of an in-house solar thermochemical hydrogen (STCH) productivity ≥ 12 mL g -1 for stable continuous operation ≥ 20 cycles; and (2) demonstration of scalable solar fuels production at practical solar reactor level in an industrial-scale concentrated solar tower (CST) using developed perovskites to achieve a hydrogen production rate ≥ 1 g h -1 .

08 HYDROGEN↗

A total of 19 months of daily weather logging on the US east coast: the WFIP3 event log

The Third Wind Forecast Improvement Project (WFIP3) is a multi-institutional field campaign designed to advance the understanding and prediction of the offshore atmospheric boundary layer along the US east coast. Extending from February 2024 through August 2025, WFIP3 combines long-term coastal and offshore measurements with targeted modeling and forecasting efforts. This data paper presents the WFIP3 event log, a curated record of 578 d of meteorological phenomena and field observations that complements the campaign's extensive high-frequency datasets. The event log provides both manually documented daily weather discussions and automatically derived indicators of atmospheric processes – including low-level jets, wind ramps, extreme wind veer, and weak wind conditions – based on observations from scanning lidars deployed at three coastal and offshore sites. The dataset offers structured metadata, standardized time and site identifiers, and consistent terminology to facilitate its integration with WFIP3's observational and modeling data products. The log supports diverse applications, from model evaluation and forecast verification to the selection of case studies on offshore boundary-layer dynamics. The WFIP3 event log is publicly available through the US Department of Energy's Wind Data Hub, providing the research community with a transparent and enduring contextual reference for the interpretation and use of WFIP3 measurements.

17 WIND ENERGY↗

Heat Exchangers Circuitry Optimization using Low-GWP Refrigerants in Reversible Heat Pump Applications

Tube-fin heat exchangers (TFHX) are widely used in heat pump applications. Circuitry optimization can improve system performance. Previous optimizations focus on improving component-level performance under a specific operating condition, i.e., the TFHX either works as a condenser or an evaporator. The optimal circuitry obtained under air conditioning (AC) mode cannot guarantee optimal performance when used in heat pump (HP) mode. This study implements a bi-objective formulation to achieve optimal system performance in both AC and HP modes. An integer permutation-based Genetic Algorithm is integrated with Heat Pump Design Model (HPDM) to perform heat pump optimization. Six refrigerants, i.e., R410A, R452B, R454B, R32, D2Y60 and L41a are investigated. Case studies show that optimal heat exchangers yield 2.1%-6.1% EER improvement under AC mode and 1.9%-8.5% COP improvement under HP mode. Use of this design approach can help assure a preferable performance of reversible heat pumps during both cooling and heating mode usage.

Li, Zhenning↗

Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Amini, Mohammad Reza↗