Integrating direct air capture with algal biofuel production to reduce cost, energy, and GHG emissions
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In this work, we present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.
Nickel-rich (Ni≥90%) layered oxides materials have emerged as a promising candidate for next-generation high-energy-density lithium-ion batteries (LIBs). However, their widespread application is hindered by structural fatigue and lattice oxygen loss. In this work, an epitaxial surface rock-salt nanolayer is successfully developed on the LiNi 0.9 Co 0.1 O 2 sub-surface via heteroatom anchoring utilizing high-valence element molybdenum modification. This in-situ formed conformal buffer phase with a thickness of 1.2nm effectively suppresses the continuous interphase side-reactions, and thus maintains the excellent structure integrity at high voltage. Furthermore, theoretical calculations indicate that the lattice oxygen reversibility in the anion framework of the optimized sample is obviously enhanced due to the higher content of O 2p states near the Fermi level than that of the pristine one. Meanwhile, the stronger Mo–O bond further reduces cell volume alteration, which improves the bulk structure stability of modified materials. Besides, the detailed charge compensation mechanism suggests that the average oxidation state of Ni is reduced, which induces more active Li + participating in the redox reactions, boosting the cell energy density. As a result, the uniquely designed cathode materials exhibit an extraordinary discharge capacity of 245.4 mAh g −1 at 0.1 C, remarkable rate performance of 169.3 mAh g −1 at 10 C at 4.5V, and a high capacity retention of 70.5% after 1000 cycles in full cells at a high cut-off voltage of 4.4V. Further, this strategy provides an valuable insight into constructing distinctive heterostructure on high-performance Ni-rich layered cathodes for LIBs.
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Due to the capacity to offer abundant catalytic sites within porous solids featuring high surface areas, metal-organic frameworks (MOFs) and their derivatives have garnered considerable attention as prospective catalysts in environmental catalysis. Here, to promote the industrial application of MOFs, there is an urgent need for an effective and environmental-friendly preparation approach. Breaking through the limitation of the traditional two-step preparation method that Pd was introduced to the already prepared Ce-BTC (Pd/Ce-BTC, BTC = 1, 3, 5 benzenetricarboxylate), in this work, we present a novel one-pot solvothermal method for synthesizing the Pd material supported by Ce-BTC (Pd@Ce-BTC). After pyrolysis in N 2 flow or air flow, Pd-CeO 2 catalysts derived from Pd@Ce-BTC exhibited much higher CO oxidation activity than those from Pd/Ce-BTC. Moreover, Pd/Ce-BTC and Pd@Ce-BTC pyrolyzed in N 2 flow (Pd/Ce-BTC-N and Pd@Ce-BTC-N) could better catalyze the oxidation of CO than Pd/Ce-BTC and Pd@Ce-BTC pyrolyzed in air flow (Pd/Ce-BTC-A and Pd@Ce-BTC-A). Further characterizations revealed that the abundant surface Ce 3+ species, rich surface adsorbed oxygen species and superior redox properties were the main reasons for the superior CO oxidation activity of Pd@Ce-BTC-N.
Global projections of lake evaporation are typically based on simulations using single mechanistic models. However, because of its complex interactions with various lake physical properties, environmental and anthropogenic drivers, lake evaporation is highly variable and sensitive to the choice of model used. In this study, we present a multi-model analysis to investigate differences across global simulations of lake evaporation during the warm-season using three different lake models driven by outputs from four general circulation models (GCM) (i.e. 12 model combinations in total) for historic and future scenarios. Our results suggest substantial differences among lake-climate model simulations of lake evaporation. These differences varied throughout the 20th and 21st century, with model driver data explaining 74% of the variance in future projections of warm- season lake evaporation. Our projections indicate that, by the end of the 21st century (2070–2099), global annual lake evaporation rates will increase by 10–27% under Representative Concentration Pathways (RCPs) 2.6–8.5. Finally, we highlight the importance of using a multi-model approach for the prediction of future global lake evaporation responses to climate change.
Reservoirs play a vital role in regulating streamflow timing and variability for hydroelectricity, flood control, water supply, irrigation, and recreation. Despite their importance, many reservoirs lack comprehensive operational guidelines, making their management complex due to conflicting operational objectives. Hence traditional policy-based reservoir models often fail to capture real-world conditions accurately and they depend on perfect streamflow predictions, which are not always available. In contrast, data-driven models like Long Short-Term Memory (LSTM) networks offer a robust alternative. This study introduces an approach that integrates reservoir characteristics—such as main use, climate, and maximum capacity—into the LSTM model to enhance reservoir release predictions. Using data from nearly 200 reservoirs in the contiguous United States (CONUS), our conditioned LSTM model (LSTM_cond) was compared with both the vanila LSTM and a traditional policy-based approach. Furthermore, our results show that while both LSTM_cond and LSTM perfoms better than the policy-based approach, LSTM_cond consistently outperforms LSTM for hydroelectric, water supply, irrigation, and recreation reservoirs. The KGE median values for LSTM_cond for out-sample reservoirs are 0.764, 0.565, 0.821, and 0.779, respectively, for the aforementioned reservoir types, which are consistently higher that the corresponding KGE values of 0.737, 0.413, 0.775, and 0.713 of LSTM, demonstrating its advantages in improving generalizability.
Earth system models are advancing toward kilometer-scale resolution to capture local climate impacts and extremes. High-resolution land and river modeling depends on multiple factors, including mesh, surface datasets, and atmospheric forcing, but their relative effects at kilometer scales remain unquantified. We evaluated five Energy Exascale Earth System Model land and river configurations over the Mid-Atlantic region using two mesh (1/8° structured versus variable-resolution unstructured mesh), two surface datasets (default versus newly developed), and three atmospheric forcings (NLDAS2, MSWX, GSWP). Evaluation against satellite, reanalysis, and in situ benchmarks across water, energy, and carbon cycles quantifies how these factors affect model performance. Forcing selection produces the largest bias reductions (12-99% across variables), followed by surface datasets (7-75%) and mesh (up to 21%). Forcing effects vary by variable, with MSWX reducing biases for snow water equivalent, evapotranspiration, albedo, temperature, and gross primary productivity, GSWP for snow cover and runoff, and NLDAS for soil moisture and streamflow. The use of newly developed surface datasets improves gross primary productivity (58% bias reduction) and evapotranspiration but increase soil moisture and albedo biases due to current modeling limitations. Variable-resolution unstructured mesh improves the simulation of small-basin streamflow through better capturing drainage networks, though mesh minimally affects other land variables. These findings provide important guidance for high-resolution modeling development and actionable science.
Understanding the particle capture mechanism in the laser directed energy deposition additive manufacturing process provides a foundation for improving productivity and reducing material loss. Capturing this phenomenon is challenging due to its highly transient time interval. To analyze the impact behavior of spherical Ti-6Al-4V powder, a custom laser directed energy deposition system was designed to control the deposition of individual particles. Synchrotron X-ray imaging at 24 kHz monitored the impact dynamics of individual powder particles. A multi-physics model based on the Smoothed-Particle Hydrodynamics scheme supported the analysis by providing the temperature and velocity fields of the molten pool. Results revealed that in laser directed energy deposition, surface tension forces dominated the powder–molten pool interaction. Hydrophobic Ti-6Al-4V powder particles had an equilibrium contact angle of 121 degrees with their molten state. Furthermore, Ti-6Al-4V powder particles required a high impact velocity (6 to 12 m/s) to transition from oscillation to submergence behavior. Lastly, oscillating powder particles that impacted near the location of the laser beam exhibited faster melting, highlighting the contribution of the laser beam to the melting mechanism. This work lays the foundation for investigations of powder particle impact in laser directed deposition and supports the validation of numerical models of powder–molten pool interactions.
Plasma-facing components (PFCs) are among the most critical gaps for fusion energy to establish technical and economic feasibility. Tungsten as a first wall/blanket material in PFCs requires to be integrating with reduced activation ferritic martensitic (RAFM) steels as a structural component. Currently, major drawbacks are the requirement of brazing, the formation of a brittle interface, and a large difference between the coefficients of thermal expansion of tungsten and steel. Here, a novel transitional multilayer structure was designed and investigated to join tungsten and RAFM steels using three interlayers. The composition of each interlayer was selected based on computational thermodynamics and diffusion kinetics to ensure a body-centered cubic (bcc) single-phase structure and prevent the formation of a brittle intermetallic phase region in the temperature range of 600–1150 °C. Although the transitional layer structure was designed for additive manufacturing, spark plasma sintering (SPS) as proof of concept was used to bond the individual layers. Interfaces were investigated using scanning and transmission electron microscopy methods but no layered intermetallic phase was observed. Nanoindentation maps across the interface suggest major hardness differences at the interface between tungsten and the vanadium interlayer, as well as the interface between RAFM steel and the FeCrAl interlayer.
In this work, to develop advanced reduced-activation ferritic-martensitic (RAFM) steels for fusion reactor structural applications, both carbonitride- and carbide-strengthened castable nanostructured alloys (CNAs) were explored for higher densities of MX (M = Ti/Ta/V/etc. and X = C/N) nanoprecipitates. Additionally, systematic comparisons between the two types of CNAs indicated generally similar microstructures and comparable tensile properties and creep resistance. However, the carbide-CNAs did show some advantages over the carbonitride-CNAs in terms of the uniformly distributed higher density of MC nanoprecipitates, greater Charpy impact upper shelf energies, less deuterium retention and swelling, and potentially less transmutation-induced composition changes and consequently thermodynamically more stable carbides. The carbide-CNAs showed the best-balanced high performance in the examined properties, in contrast to the significantly varied performance of oxide-dispersion-strengthened alloys and the generally lower performance of current RAFM steels.
Understanding the effects of helium on microstructures and mechanical properties of reduced-activation ferritic-martensitic steels is important to use of these steels in fusion reactor structures. The 9Cr-2WVTa steels were doped with 58Ni and 60Ni isotopes at 2 weight percent to control the rate of transmutation helium generation. The samples were irradiated in the High Flux Isotope Reactor to ~24 displacements per atom at nominal temperatures of 300, 400, and 500°C, producing 228 and 7 atomic parts-per-million helium in the 58 Ni- and 60 Ni-doped samples, respectively. Transmission electron microscopy revealed a variety of precipitates and the radiation-induced dislocation loops and cavities (voids or helium bubbles). Tensile tests of the irradiated samples at the irradiation temperatures showed radiation-induced hardening at 300°C and radiation-induced softening at 400°C. Analysis indicates that the hardening primarily originated from the loops and cavities. The 58 Ni-doped samples had greater strengthening contributions from loops and cavities, leading to higher hardening with lower ductility than the 60 Ni-doped samples. The greater helium production of 58 Ni did not show pronounced reductions in ductility of the samples.
A newly developed nano-structured high-Mn 9Cr ferritic-martensitic (FM) steel designed for additive manufacturing (Additive-manufactured Nanostructured Alloy, ANA) was fabricated via the direct energy deposition method. The as-printed ANA2 showed tensile properties (strength and elongation) and Charpy impact toughness (upper-shelf energy and ductile-brittle transition temperature) comparable to oxide-dispersion-strengthened (ODS) Eurofer and PM2000. Compared with wrought P91, the as-built ANA2 showed significantly higher yield strength but inferior Charpy impact toughness and ductility. Microstructural characterization revealed uniformly distributed sub-micron pores, an extremely high dislocation density, and a high density of ultrafine nano-structured precipitates in the matrix. The microstructures indicated the dislocations as the dominant strengthening element and the ultrafine precipitates as the primary contribution to estimated irradiation sink density. This work demonstrates the ability to exploit the unique consolidation properties of additive manufacturing to fabricate steels which marry the benefits of both ODS alloys and conventionally processed FM steels.
Here, a reduced activation ferritic/martensitic steel, Eurofer97, was neutron irradiated in the vicinity of 300 °C in the High Flux Isotope Reactor (HFIR) up to 72 dpa. Advanced analytical scanning transmission electron microscopy and conventional transmission electron microscopy were applied to investigate the radiation-induced segregation and phase instability behavior after neutron irradiation. Amorphization was observed in M 23 C 6 carbides. Cr-rich clusters were seen within the matrix, near the lath boundaries and close to the M 23 C 6 carbides. Cr enrichment and Fe depletion were detected at both prior austenite grain boundaries and lath boundaries, despite different segregation magnitude. In addition, the enrichment of Ni, the depletion of V, and tiny cavities (presumably helium bubbles) are also found at lath boundaries. This work interrogates the evolution of microstructures after neutron irradiation, which provides detailed understanding on the microstructural aspects controlling the mechanical integrity of Eurofer97 under high-dose neutron damage.