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Seminar for the KEPCO International Nuclear Graduate School (KINGS) in South Korea. The presentation will cover a brief introduction to INL, opportunities for advanced nuclear, and integrated energy systems.
Modification of fossil-fueled industrial gas turbines to accept no/low carbon fuels (Hydrogen, H2/natural gas blends) is a significant undertaking. Successful deployment of this technology sits at the intersection of three design criteria (1) new functional fuel injectors that can burn these fuels, (2) manufacturability to meet cost and time-to-market targets, and (3) durability in the harsh environment of an operating turbine. Additive manufacturing (AM) provides accelerated product development. However, uncertainty remains around the durability of parts with rough AM surfaces. A fully experimental approach towards quantifying fatigue performance of rough AM microstructures is costly and laborious. Instead, Solar Turbines Incorporated (Solar) proposes the use of a crystal plasticity finite element (CPFE) model to quantify the factors that drive AM surface fatigue behavior. Solar will use the CPFE results, along with targeted experimental data, to train a computationally efficient surrogate model that can be incorporated into existing turbine part lifing methods.
Anion exchange membrane water electrolyzers (AEMWEs) represent a promising technology for hydrogen production. The big advantage of the technology is that it allows for the use of platinum group metal-free (PGM-free) electrocatalysts at both electrodes, including catalysts for the hydrogen evolution reaction (HER) at the cathode. In addition to fulfilling the cost requirement, PGM-free HER catalysts need to meet the activity and durability targets of the AEMWEs. Here, in this work, we developed several carbon-supported, xerogel-derived nickel (Ni) HER electrocatalysts and evaluated the effect of various synthesis conditions, such as the type of carbon support, Ni-to-carbon ratio, and heat-treatment temperature and time, on their performance. Scanning transmission electron microscopy combined with energy-dispersive X-ray spectroscopy (STEM-EDS), X-ray diffraction spectroscopy (XRD), and X-ray photoelectron spectroscopy (XPS) revealed the formation of Ni nanoparticles with an oxygen-rich layer on the outside. Durability of the best-performing catalyst was assessed via a constant-current hold at 10 mA cm –2 over 100 h. This catalyst was found to be more active and durable than the reference PGM-free material, a commercial Ni catalyst supported on a Vulcan XC-72. The catalyst was also tested in the cathode of a fully PGM-free AEMWE, allowing to reach 1.90 V (1.84 V HFR-free) at 1 A cm –2 at 80 °C.
The long-term strategy of the United States targets reaching economy-wide net-zero emissions by 2050 and a carbon-neutral electricity grid by 2035 (U.S. Department of State and U.S. Executive Office of the President, 2021). Meeting these targets would require considerable changes to the energy system. Some key characteristics of illustrative net-zero energy systems include increased penetration of renewable energy and carbon sources, use of CO2 capture and storage (CCS) in hard-to-abate sectors, and a greater role for energy carriers such as electricity and hydrogen (Davis et al, 2018). Another common feature of such energy systems is the need for carbon dioxide removal (CDR) approaches (Horowitz et al, 2022). Across all these characteristics of net-zero energy systems, bioenergy and biomass feedstock is anticipated to play an important role. Biomass feedstock serves as a renewable carbon source. This can enable conversion of such feedstock into fuels and energy carriers for hard-to-abate sectors such as aviation. Indeed, the U.S. Government has a target to meet all jet fuel demand by 2050 from sustainable aviation fuel (SAF), where biofuel pathways are likely to have an important role (EERE, 2020). Bioenergy is also highly versatile with the possibility to convert feedstock into electricity, hydrogen, liquid fuels, heat or high-value products, based on biomass type, demand and technology availability (Clarke et al, 2022). Combination of bioenergy with CCS can also nominally deliver CDR (Fuhrman et al, 2023). As such, the share of bioenergy is expected to grow by at least five time across scenarios studied for the long-term strategy of the U.S. between 2020 and 2050 (Horowitz et al, 2022). Notwithstanding the role of bioenergy in the energy systems, its deployment, costs and scalability are influenced by a number of factors. Some of these factors pertain to policy interventions such as imposition of a binding decarbonization target either at an economy-wide level or the sectoral level. Resource availability and type of biomass feedstock also varies considerably across regions. From a technological perspective, the readiness of bioenergy conversion pathways is subject to high variability. This influences the costs of deployment. Moreover, the sourcing of feedstock, grid carbon intensity, and co-product handling approaches all affect the life cycle efficacy of bioenergy. The latter, in turn, is particularly important in determining the extent to which bioenergy with CCS or BECCS can effectively deliver CDR (Fajardy and Mac Dowell, 2017).
Duke Energy has partnered with the National Renewable Energy Laboratory (NREL) to evaluate pathways to achieving their carbon-free targets and to assess the operational impacts of the resulting system. This report details findings from Phase II of the Duke Low Carbon Resource Integration study, which consisted of three separate but interrelated analyses: (1) a resource assessment exploring the technical and economic potential and characteristics of wind and solar resources in the Carolinas; (2) capacity expansion modeling identifying the least-cost investment pathways for achieving 70% CO 2 emissions reductions in North Carolina by 2030 and a net-zero electricity system by 2050; and (3) detailed production cost modeling of power system operations at the higher shares of low- and zero-carbon emitting generation sources, informed by the capacity expansion modeling portion of the analysis. The analysis finds that Duke Energy can approach the 2030 and 2050 emissions target in North Carolina through investment in a combination of solar, wind, and storage along with maintaining its existing nuclear fleet. The average cost of CO 2 abatement in the Carolinas through 2021-2050 is on the order of $\$27-33$ per metric ton (range of $\$9-34$ per metric ton across key sensitivities).Duke Energy can expected increased interchange with neighbors to help balance higher levels of solar, although the ability to do this will depend on whether neighboring regions also move to integrate more carbon-free resources. As Duke Energy moves toward both the 2030 and 2050 targets, addressing energy needs during the winter peak period becomes particularly important, and the system relies on the availability of resources such as renewable or hydrogen combustion turbines, seasonal storage, or other similar technologies that are dispatchable but able to operate at low capacity factors.
RTI Team, proposes an approach for demonstrating carbon-neutral methanol (MeOH) produced from carbon dioxide (CO 2 ) removed from air by direct air capture (DAC) and green hydrogen (H 2 ) from water (H 2 O) electrolysis using renewable electricity. The proposed project will include completing a feasibility study that informs a conceptual design of an integrated process that meets technical and economic targets set forth.
The hydrogenation of CO 2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO 2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.
Here in this work, the UEDGE edge transport code is used to examine conditions in the SPARC divertor and edge plasma for various levels of carbon impurity and power from the core (P SOL ). A double-null magnetic configuration is simulated assuming up-down symmetry in geometry and physics. The anomalous heat and particle transport coefficients are tuned to match empirical predictions for SPARC's midplane density profiles, target plate heat flux profiles, and inner/outer divertor power sharing. Convective transport is included on the low-field side, while on the high-field side the transport is modeled as purely diffusive. Hydrogen neutrals are modeled as a fluid with inertial effects, and a carbon impurity is included using the fixed-fraction model. We find that detachment induced by impurity seeding could significantly reduce the heat flux to the divertor surfaces in the SPARC tokamak. At P SOL = 28 MW (the value predicted for SPARC's full-power H-mode scenario) cases with both divertor legs detached were obtained with a carbon impurity fraction between 0.3%–1.4%, far below Z eff limits for SPARC. When the plasma in the outer leg is detached, the peak heat flux density perpendicular to the target plate is below 1 MW m -2 , electron and ion temperatures are less than 1.5 eV, and momentum detachment is observed. However, the detachment state is found to be sensitive to the side-wall boundary conditions, the level of neutral pumping, and the target plate tilt. Finally, a broadly similar SOLPS simulation of SPARC is used to assess the appropriateness of the simpler impurity and neutral models used in UEDGE.
Wide-spread commercialization of fuel cell electric vehicles using proton exchange membrane fuel cell (PEMFC) power sources requires that several existing limitations be addressed. These include: (1) a reduction in platinum (Pt) loading in the catalytic electrodes, (2) improvements in reactant and electronic mobility throughout the catalytic electrodes, (3) reduction in the reliance on materials derived from polluting “forever chemicals”, and (4) a significant improvement in the operational longevity of catalytic electrode components. In this project, a team of two universities, Drexel University and Texas A&M University, one national lab, National Renewable Energy Laboratory, and one company, General Motors, collaborated to develop a new cathode ionomer chemistry that would address these limitations and result in an improvement in performance over existing ionomer materials. The key technology developed through this collaborative project was a composite cathode ionomer encompassing an ionic liquid interlayer between Pt catalysts and a sulfonated polymerized ionic liquid block co-polymer (S-PILBCP) that possess the orthogonal properties of protonic conductivity and ionic liquid enhanced kinetics and durability (see schematic in Figure 1). The composite S-PILBCP ionomer eliminates many of the existing issues with perfluorosulfonic acid-based ionomers including active site blocking by sulfonate specific adsorption, restricted O 2 transport through ionomer films, limited humidity tolerance and active area loss for carbon pore confined catalyst particles, and use of polluting “forever chemicals”. Following successful integration of the developed composite ionic liquid into a PEMFC cathode catalyst layer, we demonstrate enhanced performance over Nafion containing cathodes with Pt/C and PtCo/C at both low and high current density. The performance with our composite S-PILBCP ionomer meets the Department of Energy (DOE) targets for light duty vehicle applications
Abstract Boron‐enriched scaffolds have demonstrated unique features and promising performance in the field of catalysis towards the activation of small gas molecules. However, there is still a lack of facile approaches capable of achieving high B doping and abundant porous channels in the targeted catalysts. Herein, construction of boron‐ and nitrogen‐enriched nanoporous π‐conjugated networks (BN‐NCNs) was achieved via a facile ionothermal polymerization procedure with hexaazatriphenylenehexacarbonitrile [HAT(CN) 6 ] sodium borohydride as the starting materials. The as‐produced BN‐NCN scaffolds were featured by high heteroatoms doping (B up to 23 wt. % and N: up to 17 wt. %) and permanent porosity (surface area up to 759 m 2 g −1 mainly contributed by micropores). With the unsaturated bonded B species acting as the active Lewis acid sites and defected N species acting as the active Lewis base sites, those BN‐NCNs delivered attractive catalytic performance towards H 2 activation/dissociation in both gaseous and liquid phase, acting as efficient metal‐free heterogeneous frustrated Lewis pairs (FLPs) catalysts in hydrogenation procedures.
Boron-enriched scaffolds have demonstrated unique features and promising performance in the field of catalysis towards the activation of small gas molecules. However, there is still a lack of facile approaches capable of achieving high B doping and abundant porous channels in the targeted catalysts. Here, in this paper, construction of boron- and nitrogen-enriched nanoporous π-conjugated networks (BN-NCNs) was achieved via a facile ionothermal polymerization procedure with hexaazatriphenylenehexacarbonitrile [HAT(CN) 6 ] sodium borohydride as the starting materials. The as-produced BN-NCN scaffolds were featured by high heteroatoms doping (B up to 23 wt. % and N: up to 17 wt. %) and permanent porosity (surface area up to 759 m 2 g -1 mainly contributed by micropores). With the unsaturated bonded B species acting as the active Lewis acid sites and defected N species acting as the active Lewis base sites, those BN-NCNs delivered attractive catalytic performance towards H 2 activation/dissociation in both gaseous and liquid phase, acting as efficient metal-free heterogeneous frustrated Lewis pairs (FLPs) catalysts in hydrogenation procedures.
The rate of the final step in the astrophysical αp process, the 34 Ar(α,p) 37 K reaction, suffers from large uncertainties due to a lack of experimental data, despite having a considerable impact on the observable light curves of x-ray bursts and the composition of the ashes of hydrogen and helium burning on accreting neutron stars. Here, we present the first direct measurement constraining the 34Ar(α,p)37K reaction cross section, using the Jet Experiments in Nuclear Structure and Astrophysics gas jet target. The combined cross section for the 34 Ar,Cl(α,p) 37 K,Ar reaction is found to agree well with Hauser-Feshbach predictions. The 34 Ar(α,2p) 36 Ar cross section, which can be exclusively attributed to the 34 Ar beam component, also agrees to within the typical uncertainties quoted for statistical models. This indicates the applicability of the statistical model for predicting astrophysical (α,p) reaction rates in this part of the αp process, in contrast to earlier findings from indirect reaction studies indicating orders-of-magnitude discrepancies. This removes a significant uncertainty in models of hydrogen and helium burning on accreting neutron stars.
Hydrodynamic plasma waveguides initiated by optical field ionization have recently become a key component of multi-GeV laser wakefield accelerators. Here, we present the most complete and accurate experimental and simulation-based characterization to date, applicable to current multi-GeV experiments and future 100 GeV-scale laser plasma accelerators. Crucial to the simulations is the correct modeling of intense Bessel beam interaction with meter-scale gas targets, the results of which are used as initial conditions for hydrodynamic simulations. The simulations are in good agreement with our experiments measuring evolving plasma and neutral hydrogen density profiles using two-color short pulse interferometry, enabling realistic determination of the guided mode structure for application to laser-driven plasma accelerator design. Published by the American Physical Society 2024
Abstract We study the production of very light elements ( Z < 20) in the dynamical and spiral-wave wind ejecta of binary neutron star mergers by combining detailed nucleosynthesis calculations with the outcome of numerical relativity merger simulations. All our models are targeted to GW170817 and include neutrino radiation. We explore different finite-temperature, composition-dependent nuclear equations of state, and binary mass ratios, and find that hydrogen and helium are the most abundant light elements. For both elements, the decay of free neutrons is the driving nuclear reaction. In particular, ∼0.5–2 × 10 −6 M ⊙ of hydrogen are produced in the fast expanding tail of the dynamical ejecta, while ∼1.5–11 × 10 −6 M ⊙ of helium are synthesized in the bulk of the dynamical ejecta, usually in association with heavy r -process elements. By computing synthetic spectra, we find that the possibility of detecting hydrogen and helium features in kilonova spectra is very unlikely for fiducial masses and luminosities, even when including nonlocal thermodynamic equilibrium effects. The latter could be crucial to observe helium lines a few days after merger for faint kilonovae or for luminous kilonovae ejecting large masses of helium. Finally, we compute the amount of strontium synthesized in the dynamical and spiral-wave wind ejecta, and find that it is consistent with (or even larger than, in the case of a long-lived remnant) the one required to explain early spectral features in the kilonova of GW170817.
This project targeted advanced low NOx combustion for advanced gas turbines capable of 65%, or greater, efficiency in combined cycle application. This technology advancement has further potential to benefit gas turbines used in coal based IGCC applications with pre-combustion carbon capture and hydrogen as the resulting fuel. The program developed and synthesized the most advanced combustion system capable of achieving low NOX emissions up to turbine inlet temperatures of 3100F while also supporting the load-following needs of a modern grid. The combustion system contributes to the overall gas turbine efficiency goal by setting the maximum cycle temperature achievable for a given NOX level and by minimizing the through-combustor air flow pressure drop. Focus areas for this project targeted maximizing the turbine inlet temperature entitlement, as constrained by emissions considerations. The design also minimized parasitic air flow pressure drop by using advanced cooling techniques and performance materials selections and by minimizing hot surface area. These two technology objectives (maximum, emissions-compliant cycle temperature and minimum air flow pressure drop) were integrated into a prototype design. The primarily analytical project sought to identify the most promising technologies to meet these objectives. Additional critical “jugular” data were obtained from multi-tube mixer tests to realize the potential of leveraging “micro flames” for minimizing overall hot surface area. This data was used, in conjunction with an understanding of advanced material and cooling design technologies, to analytically develop multiple design concepts. Phase I focused on in-depth engineering analysis and design, with minimal supporting laboratory testing to enable a selection of the top three combustion architectures for achieving these overall objectives. Phase II of the program developed the selected design through a combination of sub-scale testing and analytical efforts. Early tests included a cold-flow cascade to establish aerodynamic performance characteristics and a sub-scale fired test at GE Global Research in Niskayuna, NY, to establish cooling and heat transfer characteristics in conjunction with combustion performance. The data from these tests validated the analytical models to ultimately design a full-scale, test article to evaluate at prototypical pressure and temperature conditions at GE Gas Power’s Gas Turbine Technology Laboratory in Greenville, SC. GE Gas Power also developed, tested, and recommended a suitable seal design to be applied to the unique features of the combustor. To assess the technology challenges from prospective future production of the combustor from a ceramic matrix composite material, screening tests of Environmental Barrier Coatings were completed.
ABSTRACT Accurate biomolecular structure prediction enables the prediction of mutational effects, the speculation of function based on predicted structural homology, the analysis of ligand binding modes, experimental model building, and many other applications. Such algorithms to predict essential functional and structural features remain out of reach for biomolecular complexes containing nucleic acids. Here, we report a quantitative and qualitative evaluation of nucleic acid structures for the CASP16 blind prediction challenge by 12 of the experimental groups who provided nucleic acid targets. Blind predictions accurately model secondary structure and some aspects of tertiary structure, including reasonable global folds for some complex RNAs; however, predictions often lack accuracy in the regions of highest functional importance. All models have inaccuracies in non‐canonical regions where, for example, the nucleic‐acid backbone bends, deviating from an A‐form helix geometry, or a base forms a non‐standard hydrogen bond (not a Watson‐Crick base pair). These bends and non‐canonical interactions are integral to forming functionally important regions such as RNA enzymatic active sites. Additionally, the modeling of conserved and functional interfaces between nucleic acids and ligands, proteins, or other nucleic acids remains poor. For some targets, the experimental structures may not represent the only structure the biomolecular complex occupies in solution or in its functional life cycle, posing a future challenge for the community.
Near-surface nitrogen vacancy centres are critical to many diamond-based quantum technologies such as information processors and nanosensors. Surface defects play an important role in the design and performance of these devices. The targeted creation of defects is central to proposed bottom-up approaches to nanofabrication of quantum diamond processors, and uncontrolled surface defects may generate noise and charge trapping which degrade shallow NV device performance. Surface preparation protocols may be able to control the production of desired defects and eliminate unwanted defects, but only if their atomic structure can first be conclusively identified. This work uses a combination of scanning tunnelling microscopy (STM) imaging and first-principles simulations to identify several surface defects on H:C(100)—2 × 1 surfaces prepared using chemical vapour deposition (CVD). The atomic structure of these defects is elucidated, from which the microscopic origins of magnetic noise and charge trapping are determined based on the modeling of their paramagnetic properties and acceptor states. Rudimentary control of these deleterious properties is demonstrated through STM tip-induced manipulation of the defect structure. Furthermore, the results validate accepted models for CVD diamond growth by identifying key adsorbates responsible for the nucleation of new layers.
Due to their high volumetric hydrogen storage capacity under moderate storage conditions, TiFe alloys have been widely investigated as candidates for practical solid-state hydrogen storage. Partially substituting Ti or Fe sites can improve the key characteristics of TiFe alloys, such as the first hydrogen absorption step (activation) and the equilibrium hydrogen pressure (thermodynamic properties). However, the selection of substitution elements has heavily relied on intuition and trial-and-error. Also, conventional substitution strategies have mainly focused on single-element substitution within the TiFe alloy, limiting the design space and tunability for target applications. Here, to address this limitation, we report a multi-element substitution strategy motivated by an efficient, data-driven machine learning (ML) approach combined with corroborating density functional theory (DFT) calculations. Our models successfully predict experimentally measured hydride stability in five selected alloys using only compositional descriptors. Most importantly, the multi-element substitution leads to enhanced activation properties compared to pure TiFe, achieving near room-temperature activation behaviour. This work provides a method for on-demand tuning of hydrogen storage and activation properties, which may have broad implications for data-driven discovery of energy storage materials.