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An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The board game industry revenue has seen rapid growth from 6.2 billion USD to a projected 8.6 billion USD in revenue from 2018 to 2024 indicating an increased demand for this style of product. Inflation, shipping and the cost of raw materials have increased the cost for producing board games. This increased the barrier of entry and reduces profit margins. Many people lose the chance to experience much of the catalogue of modern hobby board games because of this. There is a need for a digital and low-cost solution for buyers to fulfill demand and create a steady supply while maintaining the characteristics that make board games attractive to consumers. Lowering the barrier of entry will allow for more consumers to enter the market and provide a catalyst for growth in the board game community.
The Signal Amplification By Reversible Exchange (SABRE) technique provides enhancement of Nuclear Magnetic Resonance (NMR) signals up to several orders of magnitude using chemical exchange of a substrate and parahydrogen on an iridium complex. Therefore, the availability of such a catalytic complex to a broader community is an absolutely vital step for dissemination of the groundbreaking SABRE methodology. The most common SABRE catalyst, which is activated in situ, is based on Ir-IMes system (IMes = 1,3-Bis(2,4,6-trimethylphenyl)imidazol-2-ylidene). Earlier approaches for the synthesis of this catalyst often relied on specialized equipment and were limited to a comparatively small scale. This, in turn, increased the barrier of entry for new scientists to the area of SABRE hyperpolarization. Here, we present a robust, inexpensive, and easy to reproduce synthetic procedure for the preparation of this SABRE catalyst, which does not require specialized inert atmosphere equipment like a glove box or Schlenk line. The synthesis was validated on the scale of several grams vs. tens of milligrams scale in the reported approaches. The resulting SABRE catalyst, [Ir(IMes)(COD)Cl], was activated in situ and further evaluated in hyperpolarization experiments resulting in signal enhancements comparable to (or higher than) those for the catalyst prepared using Schlenk line equipment.
Infrasound, low‐frequency sound below 20 Hz, has been a key technology to monitor explosion events in the atmosphere. The International Monitoring System (IMS) of the Comprehensive Nuclear‐Test‐Ban Treaty Organization provides the means for continuous monitoring of infrasonic events worldwide. Infrasonic techniques for event location and size estimation can also complement other observational techniques for the detection and characterization of the entry of asteroids or large meteoroids. In this study, we describe the detection capability of IMS infrasound stations for an explosive event in the middle of the atmosphere. Full‐waveform simulations are performed with the specification of atmospheric conditions and incorporated into the event location and explosion yield estimation. We applied it to the 2023 April 20 SpaceX Starship explosion at 29 km altitude. Starship is a super heavy‐lift space vehicle constructed by SpaceX and known as the largest and most powerful rocket ever built. The Starship explosion created huge pressure disturbances in the atmosphere, and its infrasound was detected by the IMS arrays in North America between 2000 and 4000 km. Independent observational data and available ground‐truth information provide a rare opportunity to evaluate the monitoring capability of the IMS network for elevated sources in the atmosphere. We also demonstrate the capability of full‐waveform simulation for infrasound wavefield characterization and prediction to improve event location and yield estimation.
This report was prepared as an account of work sponsored by the Office of Fossil Energy and Carbon Management of U.S. Department of Energy under Funding Opportunity Announcement Number DE-FOA-0002332 “Energy Storage for Fossil Power Generation”. The work aimed to explore and advance an innovative hydrogen energy storage system – the synergistically integrated hydrogen energy storage system (SIHES) – that has the following characteristics: • Compatible with existing or new coal and gas fuel electricity generation units, • best suited for intermediate to long duration energy storage, from 12 hours to weeks even months, and • capable of storing energy at the utility scale – hundreds of MWh to GWh energy storage with power output level in tens to hundreds of MW. Preliminary front-end engineering design (Pre-FEED) studies was carried out to develop and refine a site-specific SIHES as peaking power generation units (so named as HyPeaker) as the first market entry point, to demonstrate both the technical feasibility and the economic viability to integrate the HyPeaker “within the fence” of a fossil power plant. This specific site was TVA’s Johnsonville Combustion Turbine Plant. The HyPeaker was designed and engineered to integrate with a 60MW aeroderivative gas turbine unit already available at TVA’s Johnsonville site. This site-specific HyPeaker consists of an alkaline electrolyzer to produce hydrogen from CO 2 free electricity sources, an innovative low-cost high-pressure hydrogen storage system (Big-Ton) and the aero gas turbine to generate electricity using blend of hydrogen and natural gas. A holistic system level technoeconomic analysis tool specific to HyPeaker was developed to optimize the engineering design of the Johnsonville site-specific HyPeaker for cost and performance. The optimal design and specification of the Johnsonville site-specific HyPeaker are the following: • Alkaline electrolyzer: 3MW • Big-Ton storage vessel: 11,000kg H 2 at 3000psi. • 4-stage diaphragm hydrogen compressor: 55kg-H 2 /hr from 150psi to 3000psi. The HyPeaker is designed to provide sufficient hydrogen for 90% continuous operation of the HyPeaker. All major components have design life of 30 years. The capex of HyPeaker is estimated at $\$$7.1M. This included $\$$1.5M for the electrolyzer, $\$$5.6M for the storage vessel and compressor. The cost of aero gas turbine was included as it is already available at the site. Key findings are: • HyPeaker can be designed, manufactured, installed and integrated with the fossil power plants, with sub-systems and components commercially available on the market today, even when it is scaled up to an order of magnitude larger than the one at the Johnsonville site. HyPeaker is a technologically viable solution to cover a wide range of energy storage duration needs, from daily peaking operation to seasonal shifting for fossil fueled assets. • The cost advantage of SCCV based Big-Ton H 2 storage vessel made it possible to “oversize” the H 2 storage subsystem to achieve overall system level cost optimization. The benefits are two-fold. First, it allows to significantly reduce the capacity and cost of electrolyzer by spreading H 2 production over a much longer period of time when the fuel cost for electricity production is low. Second, it allows to balance the hydrogen production and usage shift over weeks to months to meet the peak demands. As such, the capital cost of HyPeaker system using the Big-Ton was less than half of the cost of a system with today’s steel tube based H 2 storage system. The HyPeaker has even better cost advantage Li-ion battery based energy storage system. The estimated capital cost of Li-Ion battery system would be at $\$$38M, under the same projected 20-year electricity generation profile of the Johnsonville site. This is over 5 times more expensive than the HyPeaker system. • Since industry scale energy storage systems do not have 100% energy conversion and storage efficiency, energy storage systems using fossil fuel generated electricity would increase the CO 2 emission. This is particularly the case for HyPeaker due to its low round trip efficiency. Therefore, a more sensible solution would be to the excessive or curtailed electricity from CO 2 emission free sources such as solar farms, wind farms or nuclear power plants, to produce hydrogen, and integrate them with the HyPeaker. Electricity from TVA’s nuclear power plants was used for the Johnsonville HyPeaker. • The economic viability of HyPeaker is expected to be further improved when global supply chains are taken into consideration. For the same Johnsonville site specific HyPeaker, the capex would be reduced to ~$\$$3.6M from ~$\$$7.1M, and the added LCOE is reduced to ~$\$$85/MWh. With the bipartisan Infrastructure Investment and Jobs Act, the cost of domestically produced HyPeaker sub-systems would be at the level of today’s global suppliers. Since the peaking units generally operate at peak usage period, thereby demanding higher price, the projected $\$$85/MWh LCOE would be within the realm of financial viability for utility operators.
Abstract The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP. However, among these leading DL models, there is a wide variance in both the training settings and architecture used. Further, the lack of thorough ablation studies makes it hard to discern which components are most critical to success. In this work, we show that it is possible to attain high forecast skill even with relatively off-the-shelf architectures, simple training procedures, and moderate compute budgets. Specifically, we train a minimally modified Swin Transformer V2 (SwinV2) on ERA5 data and find that it attains superior skill in terms of mean-square errors of deterministic forecasts when compared against the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS). Almost all DL–NWP systems share a core set of hyperparameters and design decisions. To aid and expedite future DL–NWP research, we present an in-depth, systematic exploration of different loss functions, model sizes and depths, patch sizes, and multistep training objectives. We also examine the model performance with metrics beyond the typical accuracy (ACC) and RMSE and investigate how the performance scales with model size. Through our open-source code, scoring pipelines, and models, we share our findings on key aspects of the training pipeline. These ablations reduce the necessity for expensive hyperparameter tuning and lower the barrier to entry for future DL–NWP research. Significance Statement This study investigates the potential of using large-scale transformer-based models for weather prediction, showing that it is possible to achieve high forecast accuracy with simpler, off-the-shelf architectures. By training a minimally modified SwinV2 transformer on ERA5 data, we show that the model achieves competitive forecast skill in terms of mean-square error for key variables, outperforming the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS) at all lead times. Our findings suggest that effective training strategies, such as multistep fine-tuning and channel-weighted losses, significantly enhance the model’s performance. However, we also highlight that these improvements come with trade-offs in other areas, such as ensemble spread and high-frequency spatial detail. This work highlights the promise of deep learning in improving weather forecasts, which could lead to better preparedness and response to weather events, ultimately benefiting society by providing more reliable weather predictions.
Informed decision-making using computational fluid dynamics (CFD) results requires quantifying the errors and uncertainties of a simulation. Verification, validation, and uncertainty quantification (VVUQ) methods were developed to address this need and have matured. However, these VVUQ analyses are often non-trivial and require CFD analysts and practitioners to have specific skill sets. This has led to the uneven adoption of VVUQ analyses, in part, based on the availability of software tools to aid CFD analysts and practitioners. Solution verification, a procedure to evaluate the accuracy of a simulation by estimating potential errors arising from the computational model and computing the uncertainties without comparing to results from a physical system, is one of the lagging VVUQ analyses as the absence of software has forced CFD analysts and practitioners to develop their own codes or piece together incomplete software from across the internet. This work presents an opensource Python package, CFDverify, to lower the barrier of entry and fill in the technological gap in solution verification. CFDverify also provides a streamlined framework to remove some potential errors in post-processing CFD results. The hope is that CFDverify can improve the quality and quantity of CFD solution verification in scientific and research studies and attract interest in developing a communal tool. This paper describes the design, features, and an example use of CFDverify.
Solar thermal technologies are a promising technology to supply low-cost thermal energy to industrial processes, but there are often significant barriers to entry to industrial owners considering these technologies for their energy demands. To overcome this barrier and convey economic value to customers, NREL and Sandia National Laboratories developed Solar Thermal Energy Planner (STEP 1), a new web-based decision support tool for solar industrial process heat systems. At SolarPACES 2024, the STEP 1 tool was still under development; progress, methodologies, and a preliminary case study was presented. With the STEP 1 tool launch in May 2025, in this work, the initial version of the full public tool will be presented with demonstrations of its capabilities using a few case studies. First, the user's process heat needs such as location, process media (e.g., steam, air), process temperature, land availability, electricity and fuel costs, among other parameters. STEP 1 features a mapping interface that allows users to draw land and roof boundaries. The process media and temperature inform technology selection criteria modules that determine the appropriate solar thermal collection technologies, as well as congruent heat transfer media (e.g., hot water, oil, salt). Once the solar thermal technology selected, its nominal thermal production for the given site is characterized using NREL's System Advisor Model (SAM). Then, a modified version of NREL's REopt optimal sizing and dispatch optimization tool determines cost-optimal sizing. Within minutes, the user receives the results of the technoeconomics analysis, including the size and performance of the cost-optimal solar-plus-storage system. The cost of the system is compared to business-as-usual (e.g., an existing, standalone natural gas boiler). Users can download key results to store for sensitivity analyses. Examples of flat plate collector, parabolic trough, and molten salt tower applications with and without PV hybridization for different industrial facility types are presented in this work. The STEP 1 tool aims to reduce barriers to the adoption of solar heating solutions stemming from a lack of familiarity and technical background with solar system design options and costs among industry stakeholders.
This report outlines methods vendors can use to incorporate security-by-design (SeBD) into their microreactor facility design to support and address security for both U.S. and international deployment. The team developed a hypothetical below-grade microreactor with a physical protection system (PPS) to protect the microreactor against acts of theft and sabotage and evaluated it against two adversary attack scenarios defined by a group of adversary subject matter experts (SMEs). The hypothetical microreactor facility consists of two distinct buildings. The first is the above-grade protected area (PA) entry control point (ECP) building, which houses security personnel responsible for conducting screenings and managing access to the PA. The second building is the reactor building, which features both an above-grade floor and a below-grade floor.
We examine the state of publicly available information about known exploitable vulnerabilities applicable to operational technology (OT) environments. Specifically, we analyze the Known Exploitable Vulnerabilities Catalog (KEVC) maintained by the US Department of Homeland Security Cybersecurity and Infrastructure Security Agency (CISA) to assess whether currently available data is sufficient for effective and reliable remediation in OT settings. Our team analyzed all KEVC entries through July 2025 to determine the extent to which OT environments can rely on existing remediation recommendations. We found that although most entries in the KEVC could affect OT environments, only 13% include vendor workarounds or mitigations as alternatives to patching. This paper also examines the feasibility of developing such alternatives based on vulnerability and exploit characteristics, and we present early evidence of success with this approach.
This technical report provides for a case study for the safe installation of a second-life, or repurposed, battery, that has been reconfigured for use as a stationary energy storage systems (ESS). Driven by legislative requirements such as California Senate Bill 615 and projections that retired EV batteries could meet a substantial portion of U.S. grid ESS needs beginning in 2035, the repurposing of EV batteries is anticipated to grow significantly. However, safety concerns arise from the effects of aging, unknown prior usage history, and changes in thermal runaway behavior, which may increase failure risks compared to new batteries. NFPA 855, the predominant U.S. standard for ESS installation, mandates that second-life batteries meet all requirements for new batteries, with repurposers complying with UL 1974 in addition to obtaining UL 9540 and UL 1973 listings. These are certifications that few repurposers have achieved and represent a regulatory barrier to entry for the market as a whole.