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Multimodal synchrotron analysis uncovers how Co, Mn, and Al dopants mitigate degradation and reinforce structural integrity in LiNiO 2 cathodes.
In fiscal year (FY) 2024, the National Renewable Energy Laboratory (NREL) took a major leap forward with the completed full buildout of Kestrel, the Office of Energy Efficiency and Renewable Energy's newest high-performance computing (HPC) system. Kestrel is already supporting science across the portfolio, bringing roughly 44 petaflops of computing power, which is more than five times the capacity of our previous supercomputer, Eagle. By delivering greater GPU capacity, Kestrel enables faster progress in artificial intelligence (AI) and opens new avenues in energy research - from defining long-term planning scenarios to accommodate a growing power system to material discovery to improving energy efficiency in photovoltaics (PV). Across the portfolio, research is being accelerated by Kestrel's impressive power. During FY24, 427 projects and more than 700 researchers used NREL's HPC, supporting the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy across 13 funding areas. Through these collaborations, researchers produced more than 450 technical outputs, including 195 articles in peer-reviewed publications, pushing the boundaries of science and engineering. This year's report features new sections spotlighting the expanding roles of Artificial Intelligence and Accelerated Computing. We also introduce an early career section to celebrate the accomplishments of our up-and-coming researchers, whose pioneering work is shaping the future of energy. We hope you enjoy the new insights and discoveries highlighted in these pages.
Due to its unrivaled ability to predict the dynamical evolution of interacting atoms, molecular dynamics (MD) is a widely used computational method in theoretical chemistry, physics, biology, and engineering. Despite its success, MD is only capable of modeling time scales within several orders of magnitude of thermal vibrations, leaving out many important phenomena that occur at slower rates. The Temperature Accelerated Dynamics (TAD) method overcomes this limitation by thermally accelerating the state-to-state evolution captured by MD. Due to the algorithmically complex nature of the serial TAD procedure, implementations have yet to improve performance by parallelizing the concurrent exploration of multiple states. Here we utilize a discrete event-based application simulator to introduce and explore a new Speculatively Parallel TAD (SpecTAD) method. We investigate the SpecTAD algorithm, without a full-scale implementation, by constructing an application simulator proxy (SpecTADSim). Finally, following this method, we discover that a nontrivial relationship exists between the optimal SpecTAD parameter set and the number of CPU cores available at run-time. Furthermore, we find that a majority of the available SpecTAD boost can be achieved within an existing TAD application using relatively simple algorithm modifications.
Time-resolved X-ray photoelectron spectroscopy (XPS) is used to track the photodissociation dynamics of 2-iodothiophene following 262 nm excitation. The transient XPS features include both direct ionization of the initially populated excited states and pronounced satellite peaks arising from shake-down processes. While the direct ionization signals exhibit only minimal energy shifts during C−I bond cleavage, the shake-down transitions undergo a substantial, 5 eV, shift over the reaction coordinate. By correlating these shifts with simulated C−I bond lengths, a direct structural mapping is established that reveals the exceptional sensitivity of shake-down channels to molecular geometry. These results demonstrate that shake-down transitions provide a new and powerful probe of ultrafast structural dynamics.
The photophysics of nucleobases has been the subject of both theoretical and experimental studies over the past decades due to the challenges posed by resolving the steps of their radiationless relaxation dynamics, which cannot be described in the framework of the Born–Oppenheimer approximation (BOA). In this context, the ultrafast dynamics of 2-thiouracil has been investigated with a time-resolved NEXAFS study at the Free Electron Laser FLASH. Near Edge X-ray Absorption Fine Structure spectroscopy (NEXAFS) can be used to observe electronic transitions in ultrafast molecular relaxation. We performed time-resolved UV-pump/X-ray probe absorption measurements at the sulfur 2s (L1) and 2p (L2/3) edges. We are able to identify absorption features corresponding to the S2 (ππ*) and S1 (nπ*) electronic states. We observe a delay of 102 ± 11 fs in the population of the nπ* state with respect to the initial optical excitation and interpret the delay as the time scale for the S2 → S1 internal conversion. We furthermore identify oscillations in the absorption signal that match a similar observation in our previous X-ray photoelectron spectroscopy study on the same molecule.
Purpose: To predict potential failures of hardware within the Varian TrueBeam linear accelerator in order to proactively replace parts and decrease machine downtime. Methods: Machine downtime is a problem for all radiation oncology departments and vendors. Most often it is the result of unexpected equipment failure, and increased due to lack of in-house clinical engineering support. Preventative maintenance attempts to assuage downtime, but often is ineffective at preemptively preventing many failure modes such as MLC motor failures, the need to tighten a gantry chain, or the replacement of a jaw motor, among other things. To attempt to alleviate downtime, software was developed in house that determines the maximum value of each axis enumerated in the Truebeam trajectory log files. After patient treatments, this data is stored in a SQL database. Microsoft Power BI is used to plot the average maximum error of each day of each machine as a function of time. The results are then correlated with actual faults that occurred at the machine with the help of Varian service engineers. Results: Over the course of six months, 76,312 trajectory logs have been written into the database and plotted in Power BI. Throughout the course of analysis MLC motors have been replaced on three machines due to the early warning of the trajectory log analysis. The service engineers have also been alerted to possible gantry issues on one occasion due to the aforementioned analysis. Conclusion: Analyzing the trajectory log data is a viable and effective early warning system for potential failures of the TrueBeam linear accelerator. With further analysis and tightening of the tolerance values used to determine a possible imminent failure, it should be possible to pinpoint future issues more thoroughly and for more axes of motion.
Plastic waste accumulation poses significant environmental challenges due to a lack of economical solutions for the molecular deconstruction of diverse synthetic polymers. Biological‐based degradation offers promise but is hindered by the crystallinity, hydrophobicity, and additive complexity of plastics, which restrict biocatalyst access and activity. To address these problems, we propose a multi‐scale framework that combines detailed materials characterization, optimization of plastic‐biomolecular interfacial interactions, and enhancement of biocatalytic kinetics to develop effective plastic‐deconstructing enzymes. This approach leverages principles from reaction kinetics, transport and interfacial phenomena, and enzyme engineering to systematically address barriers across diverse plastic types. Our framework aims to accelerate the discovery and optimization of biocatalysts capable of scalable, selective, and efficient deconstruction of plastic waste. These advances hold potential to enable sustainable biological recycling and upcycling pathways, contributing to global efforts in mitigating plastic pollution and promoting circular material economies.
To address challenges of high costs and scalability for fuel cells, it is essential to develop af-fordable and earth-abundant materials that are Pt-group-metal (PGM)-free. Recent advance-ments in PGM-free electrocatalysis for the oxygen reduction reaction (ORR) in alkaline media show that high catalyst loadings are needed to achieve high reaction rates; however, there are associated mass transport limitations. To address this issue, we develop low-loading ionomer-less Ag-bimetallic thin films by alloying with 3d block elements or Sn. We bridge knowledge from fundamental rotating disk electrode (RDE) studies to gas-diffusion cathodes in high-temperature anion-exchange membrane fuel cells (HT-AEMFCs), resulting in high-activity ORR catalysis and peak power densities up to 1.2 W cm-² geo for the Ag-Sn and Ag-Co alloys. Here, experimental post-characterization and theoretical calculations reveal small Co or Sn oxide nano-islands on Ag as likely active sites enhancing ORR activity, ultimately offering these low-loading PGM-free ORR materials as a viable path towards sustainable energy conversion.
At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.
Thlapsi arvense L. (pennycress) is being developed as a profitable oilseed cover crop for the winter fallow period throughout the temperate regions of the world, controlling soil erosion and nutrients run-off on otherwise barren farmland. We demonstrate that pennycress can serve as a user-friendly model system akin to Arabidopsis that is well-suited for both laboratory and field experimentation. We sequenced the diploid genome of the spring-type Spring 32-10 inbred line (1C DNA content of 539 Mb; 2n = 14), identifying variation that may explain phenotypic differences with winter-type pennycress, as well as predominantly a one-to-one correspondence with Arabidopsis genes, which makes translational research straightforward. We developed an Agrobacterium-mediated floral dip transformation method (0.5% transformation efficiency) and introduced CRISPR-Cas9 constructs to produce indel mutations in the putative FATTY ACID ELONGATION1 (FAE1) gene, thereby abolishing erucic acid production and creating an edible seed oil comparable to that of canola. We also stably transformed pennycress with the Euonymus alatus diacylglycerol acetyltransferase (EaDAcT) gene, producing low-viscosity acetyl-triacylglycerol-containing seed oil suitable as a diesel-engine drop-in fuel. Adoption of pennycress as a model system will accelerate oilseed-crop translational research and facilitate pennycress’ rapid domestication to meet the growing sustainable food and fuel demands.
PeleLMeX is a solver for high fidelity reactive flow simulations, namely direct numerical simulation (DNS) and large eddy simulation (LES). The solver combines a low Mach number approach, adaptive mesh refinement (AMR), embedded boundary (EB) geometry treatment and high performance computing (HPC) to provide a flexible tool to address research questions on platforms ranging from small workstations to the world's largest GPU-accelerated supercomputers. PeleLMeX has been used to study complex flame/turbulence interactions in RCCI engines and hydrogen combustion or the effect of sustainable aviation fuel on gas turbine combustion. PeleLMeX is part of the Pele combustion Suite (https://amrex-combustion.github.io/)
In general, excitonic and plasmonic nanoscale materials in close proximity show high potential for significant breakthroughs in energy related materials research. The interactions between these two kinds of materials result in coupled optical transitions (plexcitons), distinct from those of both the individual exciton and plasmon as well as from those of the sum of their constituents (synergistic effects). By linking together materials-research and physical-research approaches, this project contributes to a concerted approach on nanomaterials energy research. The project’s overall goal is to accelerate the development of well-defined plexcitonic model systems consisting of carefully engineered plasmonic and excitonic nanomaterial— essential for both gaining a fundamental understanding of plexcitonic nanomaterials and the development of novel design principles for biomimetic solar energy harvesting. During the 3-year project period and the terminal renewal with limited support for a 12-month period, we successfully synthesized and characterized (1) a robust excitonic nanomaterial and (2) a library of plasmonic nanoparticles as well as developed (3) a microfluidic platform for homogenous nanosynthesis as summarized below: (1) Robust Excitonic Nanomaterial. Supramolecular assemblies are Nature’s most successful material system for solar energy harvesting. However, photovoltaic devices based on artificial supramolecular assemblies continue to be stymied by disappointing efficiencies and poor stability. The conceptual failure may lie in current solar cell architectures, which rely on solidifying supramolecular assemblies as an ensemble into a solid matrix, neglecting the intrinsic fragility of the assemblies’ internal structure, thus disrupting or even destroying their delicate optoelectronic properties, that is, delicate Frenkel excitonic properties. Supramolecular assemblies may finally serve as usable light harvesting material systems for solar energy conversion technologies, only if they meet the following criteria: (a) Stability, that is, the fragile structure including its delicate Frenkel excitonic character needs to be stable, (b) Robustness, that is, resistant against elevated and fluctuating temperatures, and (c) Viability for device integration, that is, capable of being immobilized onto solid substrates. Here, by developing a nanocomposite via a tunable, cage-like scaffold design, we successfully provided stable supramolecular nanocomposites, that inhabit robust Frenkel excitons despite harming environmental conditions such as extreme heat stress. (2) Library of Plasmonic Nanoparticles. Naturally, current models describing plasmonic hybrid quantum states—plasmonic hybridizations—parallel those developed for molecular orbitals, equating individual plasmonic nanostructures with “atoms” and the plasmonic nanoassemblies with “molecules.” In analogy to organic synthesis, a suitably robust fabrication method would allow for “atom-like” manipulation of “molecule-like” plasmonic nanoassemblies; of high value for next-generation energy nanotechnologies. Despite this frequent comparison, current plasmonic nanoassembly fabrication methods favor top-down templating over wet-chemical synthesis, however, achieving precise control over nanostructure’s geometry and surface characteristics remain an art and a scientific challenge. The conceptual failure may lie in the current wet-chemical synthesis paradigm, as it relies on the accessibility of a multi-dimensional synthesis parameter space through limited, rather one-dimensional synthesis procedures by employing step-by-step approaches. Solution-based nanoarchitectonics for rational design of precisely built plasmonic nanoassemblies via solution-based fabrication may finally be possible only if multi-dimensional syntheses approaches are available that allow for comprehensive control over the plasmonic nanomaterials’ (a) Structural Properties and (b) Surface Properties. Here, by developing an innovative multidimensional 1,3-propanediol based polyol synthesis, we successfully provided control over the plasmonic building-block’s geometry (size and shape) together with its surface characteristics. Our results present a critical step toward the vision of a “periodic table-like” system for plasmonic materials based on straightforward wet-chemical syntheses for energy nanotechnologies. Developing deliberate modifications on this synthesis, we generated a library of plasmonic nanostructures covering the vast parameter space—opening the door for fundamental investigation of plexcitonic model systems. (3) Microfluidic Platform for Homogenous Nanosynthesis. Control over structural properties of plexcitonic nanocomposites remains a challenge due to current limitations in nanosynthesis techniques. Slight variations in nanostructure’s geometry impact their optoelectronic properties, demanding precise synthesis beyond the capabilities of solution-based (batch) synthesis processes. In contrast, the small, confined liquid volumes used in microfluidics—a reaction technique where the manipulation of fluids takes place in channels with dimensions of tens of micrometers—allows for homogenous synthesis conditions, providing excellent control of the reaction and, as a result, of the materials’ geopmetry and composition. However, thus far, the majority of plexcitonic systems has been developed via batch synthesis. Here, by successfully developing a two-channel microreactor, our microfluidic-supported synthesis approach combines the advantages of both microfluidics and batch platforms, allowing for precise spatio-temporal control over all synthesis parameters opening the possibility for homogenous nanosythnesis of well-defined plexcitonic model systems.
Nuclear energy is identified as a valuable candidate for decarbonizing industrial facilities as a non-emitting baseload energy source capable of dispatching energy as needed for an integrated facility. As industrial facilities are unique, analyzing what nuclear technology is the best candidate should be evaluated on a case-by-case basis. For two identified facilities of the Dow Chemical Company, an ideal mix of nuclear energy resources is calculated, and integration strategies are put forward. This research was funded through a Gateway for Accelerated Innovation in Nuclear (GAIN) Nuclear Energy Voucher, specifically seeking near-term engineering solutions for the technical issue of introducing nuclear generated energy into the Dow chemical facilities. The research is split into four main phases: background on reactors and chemical facilities, preliminary engineering analysis determining how reactor technologies may be integrated with specific facilities, site characterization and discrete hazards analysis, and finally an optimization of reactor-facility selection through the use of Framework for Optimization of ResourCes and Economics (FORCE) tools.
CO2 capture from coal- or natural gas-derived flue gas has been widely considered as the next opportunity for the large-scale deployment of gas separation membranes. Despite the tremendous progress made in the synthesis of polymeric membranes with high CO2/N2 separation performance, only a few membrane technologies were advanced to the bench-scale study or above from a highly idealized laboratory setting. Therefore, the recent progress in polymeric membranes is reviewed in the perspectives of capture system energetics, process synthesis, membrane scale-up, modular fabrication, and field tests. These engineering considerations can provide a holistic approach to better guide membrane research and accelerate the commercialization of gas separation membranes for post-combustion carbon capture.
This study compared fuel economy and emissions between heavy-duty hybrid electric vehicles (HEVs) and equivalent conventional diesel vehicles. In-use field data were collected from daily fleet operations carried out at a FedEx facility in California on six HEV and six conventional 2010 Freightliner M2-106 straight box trucks. Field data collection primarily focused on route assessment and vehicle fuel consumption over a six-month period. Chassis dynamometer testing was also carried out on one conventional vehicle and one HEV to determine differences in fuel consumption and emissions. Route data from the field study was analyzed to determine the selection of dynamometer test cycles. From this analysis, the New York Composite (NYComp), Hybrid Truck Users Forum Class 6 (HTUF 6), and California Air Resource Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) drive cycles were chosen. The HEV showed 31% better fuel economy on the NYComp cycle, 25% better on the HTUF 6 cycle and 4% worse on the CARB HHDDT cycle when compared to the conventional vehicle. The in-use field data indicates that the HEVs had around 16% better fuel economy than the conventional vehicles. Dynamometer testing also showed that the HEV generally emitted higher levels of nitric oxides than the conventional vehicle over the drive cycles, up to 77% higher on the NYComp cycle (though this may at least in part be attributed to the different engine certification levels in the vehicles tested). The conventional vehicle was found to accelerate up to freeway speeds over ten seconds faster than the HEV.
The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is investigating a significant energy upgrade utilizing Fixed-Field Alternating-gradient (FFA) recirculating arcs. This upgrade requires the design of complex horizontal beam splitters to manage up to six concurrent beam passes. This paper presents the conceptual design of these splitters, which are subject to severe physical constraints imposed by the existing accelerator tunnel and multifaceted beam dynamics requirements for matching into the permanent-magnet FFA arcs. The design methodology, centered on multi-pass simulations in the Bmad toolkit, is detailed from the initial geometric layout through the advanced optics matching. Key results include a robust geometric arrangement that fits within the spatial boundaries and the development of multiple, flexible optics matching solutions. Furthermore, the design integrates a viable scheme for extracting high-energy beams for the experimental halls, a critical operational requirement. This work establishes a comprehensive and viable conceptual design, forming a baseline for future engineering and performance optimization studies.
Oak Ridge National Laboratory (ORNL) has recently completed the resonance parameter evaluation of four tungsten isotopes, i.e., {sup 182,183,184,186}W, in the neutron energy range of thermal up to several keV. This nuclear data work was performed with support from the US Nuclear Criticality Safety Program (NCSP) in an effort to provide improved tungsten cross section and covariance data for criticality safety analyses. The evaluation methodology uses the Reich-Moore approximation of the R-matrix formalism of the code SAMMY to fit high-resolution measurements performed in 2010 and 2012 at the Geel linear accelerator facility (GELINA), as well as other experimental data sets on natural tungsten available in the EXFOR library. In the analyzed energy range, this work nearly doubles the resolved resonance region (RRR) present in the latest US nuclear data library ENDF/B-VII.1. In view of the interest in tungsten for distinct types of nuclear applications and the relatively homogeneous distribution of the isotopic tungsten - namely, {sup 182}W(26.5%), {sup 183}W(14.31%), {sup 184}W(30.64%), and {sup 186}W(28.43%) - the completion of these four evaluations represents a significant contribution to the improvement of the ENDF library. This paper presents an overview of the evaluated resonance parameters and related covariances for total and capture cross sections on the four tungsten isotopes. (authors)
Rayleigh-Taylor (RT) instability is one of the major concerns in inertial confinement fusion (ICF) because it amplifies target modulations in both acceleration and deceleration phases of implosion, which leads to shell disruption and performance degradation of imploding targets. This article reviews experimental results of the RT growth experiments performed on OMEGA laser system, where targets were driven directly with laser light. RT instability was studied in the linear and nonlinear regimes. The experiments were performed in acceleration phase, using planar and spherical targets, and in deceleration phase of spherical implosions, using spherical shells. Initial target modulations consisted of 2-D pre-imposed modulations, and 2-D and 3-D modulations imprinted on targets by the non-uniformities in laser drive. In planar geometry, the nonlinear regime was studied using 3-D modulations with broadband spectra near nonlinear saturation levels. In acceleration-phase, the measured modulation Fourier spectra and nonlinear growth velocities are in good agreement with those predicted by Haan's model [Haan S W 1989 Phys. Rev. A 39 5812]. In a real-space analysis, the bubble merger was quantified by a self-similar evolution of bubble size distributions [Oron D et al 2001 Phys. Plasmas 8, 2883]. The 3-D, inner-surface modulations were measured to grow throughout the deceleration phase of spherical implosions. RT growth rates are very sensitive to the drive conditions, therefore they can be used to test and validate drive physics in hydrodynamic codes used to design ICF implosions. Measured growth rates of pre-imposed 2-D target modulations below nonlinear saturation levels were used to validate non-local thermal electron transport model in laser-driven experiments.