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This work presents a new RIS scheme for W with ∼15% useful yield using three tunable Ti:Sa lasers as determined by conducting spectroscopic surveys and saturation experiments.
Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.
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We report on one-dimensional simulations of a staged Z-pinch (SZP) fusion scheme using the radiation-hydrodynamics code HYDRA [Marinak et al., Phys. Plasmas 8, 2275 (2001)]. A pulsed-power driver, based on the Z-machine at Sandia National Laboratories, with a peak current around 20 MA, is used to implode a high-Z gas liner surrounding a cylindrical DT fuel target. For the best conditions used in this work, excluding some physical processes, such as mix, we find a “clean” fusion yield of 7.05 MJ (2.5 ×1018 neutrons), thus showing the potential for high yields using this scheme. The design achieving this yield was determined through a study of different liner materials, liner masses, and fuel masses. We also address previous simulations of the SZP setup that used ad hoc limits of the total internal energy of the liner that can artificially increase fusion yields.
A catalytic architecture, comprising a mesoporous silica shell surrounding platinum nanoparticles (NPs) supported on a solid silica sphere (mSiO2/Pt-X/SiO2; X is the mean NP diameter), catalyzes hydrogenolysis of melt-phase polyethylene (PE) into a narrow C23-centered distribution of hydrocarbons in high yield using very low Pt loadings (~10 -5 g Pt/g PE). During catalysis, a polymer chain enters a pore and contacts a Pt NP where the C–C bond cleavage occurs and then the smaller fragment exits the pore. mSiO 2 /Pt/SiO 2 resists sintering or leaching of Pt and provides high yields of liquids; however, many structural and chemical effects on catalysis are not yet resolved. Here, we report the effects of Pt NP size on activity and selectivity in PE hydrogenolysis. Time-dependent conversion and yields and a lumped kinetics model based on the competitive adsorption of long vs short chains reveal that the activity of catalytic material is highest with the smallest NPs, consistent with a structure-sensitive reaction. Remarkably, the three mSiO 2 /Pt-X/SiO 2 catalysts give equivalent selectivity. We propose that mesoscale pores in the catalytic architecture template the C 23 -centered distribution, whereas the active Pt sites influence the carbon–carbon bond cleavage rate. This conclusion provides a framework for catalyst design by separating the C–C bond cleavage activity at catalytic sites from selectivity for chain lengths of the products influenced by the structure of the catalytic architecture. The increased activity, selectivity, efficiency, and lifetime obtained using this architecture highlight the benefits of localized and confined environments for isolated catalytic particles under condensed-phase reaction conditions.
The pincer-ligated ruthenium complex ( iPr PN H P)Ru(CO)H 2 ( iPr PN H P = ( i Pr 2 PC 2 H 4 ) 2 NH) is an active catalyst for the transfer hydrogenation of alkyl formates (HCO 2 R) and organic esters (RCO 2 R′) to the corresponding alcohols under base-free reaction conditions at mild temperatures. Specifically, a range of alkyl formate esters were reduced to MeOH and the corresponding alcohols in high yields using ( iPr PN H P)Ru(CO)H 2 as the catalyst and isopropanol ( i PrOH) as the hydrogen donor at 30 °C. The first step in the process is the metal-catalyzed transesterification of the alkyl formate with i PrOH to generate isopropyl formate, which is then reduced. The use of i PrOH as the hydrogen donor is crucial. ( iPr PN H P)Ru(CO)H 2 can also catalyze the transfer hydrogenation of a broad range of organic esters, including cyclic, acyclic, heteroatom-substituted, and long-chain bio-derived esters, to the corresponding alcohols in good yields using ethanol (EtOH) as the hydrogen donor at 55 °C. Computational studies were used to elucidate the proposed pathway for alkyl formate reduction and the underlying reasons why i PrOH is the most effective hydrogen donor for alkyl formate reduction, while EtOH is optimal for organic ester reduction. Overall, this work describes a highly active catalyst for alkyl formate and organic ester transfer hydrogenation and provides mechanistic insight into the factors responsible for the strong catalytic performance. Finally, these findings will be valuable for designing catalysts for both transfer hydrogenation and related reactions.
Chlorosilanes are cheap and abundant raw materials as crucial building blocks in silicon chemistry, yet the metal-mediated activation and functionalization of Si–Cl bonds typically require precious metal sources due to their thermodynamic inertness. Herein, we report the stoichiometric, facile activation, and hydrogenolysis of chlorosilanes mediated by a series of low-valent NHC–Ni (NHC = N-heterocyclic carbene) complexes. Treatment of a Ni(0) complex (IPr)Ni(η 6 -toluene) (IPr = 1,3-bis(2,6-diisopropylphenyl)imidazole-2-ylidene) with chlorosilanes (R 1 R 2 SiCl 2 , R 1 = Cl, R 2 = Cl, Me, Ph, or R 1 = R 2 = Me, Et, Ph, 4-MePh) rapidly afforded di-Ni(I) complexes with a bridging silyl ligand ([(IPr)Ni] 2 (μ-SiR 1 R 2 Cl)(μ-Cl), 1 R1,R2 ) in high yields. Use of a bulkier chlorosilane, Ph 2 SiCl 2 , allowed the isolation of the mono-Ni(II) silyl complex (IPr)Ni(SiPh 2 Cl)Cl (2 Ph ) as an intermediate generated via Si–Cl oxidative addition, which underwent comproportionation with (IPr)Ni(η 6 -toluene) to form 1 Ph,Ph in nearly quantitative yield. Interestingly, 1 R1,R2 was found to react with H 2 at room temperature to form mono- or di-hydrosilanes in moderate to high yields, and the product selectivity was found to be highly dependent on the identity of substituents on Si. In conclusion, these results demonstrate a novel example of facile Si–Cl activation and hydrogenolysis mediated by low-valent mono- and dinuclear NHC–Ni complexes under mild conditions.
Topological crystalline insulator tin telluride (SnTe) provides a rich playground to examine interactions of correlated electronic states, such as ferroelectricity, topological surface states, and superconductivity. The study of SnTe nanowires may lead to even richer physics owing to the one-dimensional (1D) confinement effect and an increased contribution from the topological surface states. Thus, for transport measurements, SnTe nanowires must be synthesized with reduced diameters and high crystalline quality to ensure 1D confinement and phase coherence of the topological surface electrons. Here, we report a facile growth method to produce narrow SnTe nanowires with a high yield using alloy nanoparticles as growth catalysts. The average diameter of the SnTe nanowires grown using alloy nanoparticles is 85 nm, nearly a factor of three reduction compared to the average diameter of 240 nm when using gold nanoparticles as growth catalysts. Transport measurements reveal the effect of the nanowire diameter on the residual resistance ratio and magnetoresistance. Particularly, the ferroelectric transition temperature for SnTe evolves systematically with the nanowire diameter. In situ cryogenic cooling of narrow SnTe nanowires in a transmission electron microscope directly reveals the cubic to rhombohedral structural transition, which is associated with the ferroelectric transition. Thus, these narrow SnTe nanowires represent a model system to study electronic states arising from 1D confinement, such as 1D topological superconductivity and potential multiband superconductivity.
Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. Further, the yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.
Frustraums have a higher laser-to-capsule x-ray radiation coupling efficiency and can accommodate a large capsule, thus potentially generating a higher yield with less laser energy than cylindrical Hohlraums for a given Hohlraum volume [Amendt et al., Phys. Plasmas 26, 082707 (2019]. Frustraums are expected to have less m = 4 azimuthal asymmetries arising from the intrinsic inner-laser-beam geometry on the National Ignition Facility. An experimental campaign at Lawrence Livermore National Laboratory to demonstrate the high-coupling efficiency and radiation symmetry tuning of the Frustraum has been under way since 2021. Simulations benchmarked against experimental data show that implosions using Frustraums can achieve more yield with higher ignition margins than cylindrical Hohlraums using the same laser energy. Hydrodynamic jets in capsules along the Hohlraum axis, driven by radiation-flux asymmetries in a Hohlraum with a gold liner on a depleted uranium (DU) wall, are present around stagnation, and these “polar” jets can cause severe yield degradation. The early-time Legendre mode P4<0 radiation-flux asymmetry is a leading cause of these jets, which can be reduced by using an unlined DU Hohlraum because the shape of the shell is predicted to be more prolate. Magnetization can increase the implosion robustness and reduce the required hotspot ρR for ignition; therefore, magnetizing the Frustraum can maintain the same yield while reducing the required laser energy or increase the yield using the same laser energy—all under the constraint that the ignition margin is preserved. Reducing polar jets is particularly important for magnetized implosions because of the intrinsic toroidal hotspot ion temperature topology.
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
Abstract Measuring the isotopic composition of trace Zr in presolar stardust grains allows us to study the environment of slow neutron-capture nucleosynthesis in asymptotic giant branch stars. Here, we present a newly characterized Zr resonance ionization scheme that can be saturated with state-of-the-art titanium-sapphire lasers and yields a useful yield of (5.4 ± 0.4)%. Resonance ionization is achieved in two steps: after first being excited with a photon at 319.215 nm, neutral Zr atoms are then ionized with a photon at 388.988 nm, where both wavelengths are reported as in vacuum.
Nuclear data is foundational to several fields, including nuclear forensics. Nuclear forensics investigations of fission events, relies heavily on the cumulative fission yields, however several highly useful fission products suffer from very poor nuclear data including cumulative fission yields or associated uncertainties in that value. Cesium-136 is a highly relevant example of this issue, where the cumulative fission yield may be accurate but its uncertainty high enough to preclude its usefulness. In this work we evaluate and calculate an updated cumulative fission yield for 136 Cs for 235 U, 238 U, and 239 Pu at multiple neutron energies or sources using data obtained from irradiation campaigns. In all cases these newly determined cumulative fission yields provided small changes to the actual fission yields but had dramatic improvements to the uncertainty in those yields. These improvements in uncertainty will enable nuclear forensics end users to use 136 Cs data with more confidence.
Rare earth elements (REEs) are critical materials in electronics and clean technologies. With the diminishing of easily accessible minerals for mining, the REE recovery from waste is an alternative toward a circular economy. Present methods for REE recovery suffer from lengthy purifications, low extractability, and high wastewater streams. Here, we report an ultrafast electrothermal process (~3000°C, ~1 s) based on flash Joule heating (FJH) for activating wastes to improve REE extractability. FJH thermally degrades or reduces the hard-to-dissolve REE species to components with high thermodynamic solubility, leading to ~2× increase in leachability and high recovery yields using diluted acid (e.g., 0.1 M HCl). The activation strategy is feasible for various wastes including coal fly ash, bauxite residue, and electronic waste. The rapid FJH process is energy-efficient with a low electrical energy consumption of 600 kWh ton –1 . The potential for this route to be rapidly scaled is outlined.
Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.
Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.
The computational tool described in this report helps identify promising biological pathways that produce SAF platform molecules (either a drop-in SAF, or a precursor that can be easily converted to a drop-in SAF). The workflow the computational tool follows first identifies possible biological pathways from a user-defined metabolite. These pathways may, or may not lead to a SAF platform molecule, thus the second step involves insilico testing of the end product of each pathway to assess whether it is, or is not, a SAF platform molecule. The identification of biological pathways performed in the first step is facilitated by linking the metabolite to a biological reaction database. Pathways are found by identifying pathways in the reaction database that include the metabolite. The computational tool includes an alternative way to find pathways. The alternative way develops a Flux Balanced Analysis (FBA), and modifying the FBA to include reactions that transform the metabolite. These modifications serve as a basis for understanding, in a semi-quantitative way, if there is an increase in the flux to desirable products. The second step, in silico testing of the end-products, is accomplished by estimating key physical properties relevant to SAF. When good models are available, we have integrated those models into the computational tool. In a few instances, we have developed our own models. In all instances, we have validated the models against available measured data. Finally, we have evaluated the effectiveness of our computational tool by genetically engineering Rhodosporidium toruloides. Validation occurred without the use of a FBA, and further validation is required.