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Assessing the Performance of a Physically-Based Soil Moisture Module Integrated within the Soil and Water Assessment Tool
A Richards equation-based soil moisture module was developed and integrated within the Soil and Water Assessment Tool (SWAT). Four years of daily soil moisture measurements from 10 monitoring stations at three depths (i.e., 5, 10, and 50 cm) in the Choptank River watershed, Maryland, were used to test the module performance. Results show that, as compared with the original SWAT soil moisture module, the Richards-equation-based soil moisture module improved R2 from 0.12 to 0.45 and reduced soil moisture simulation bias from -0.10 to -0.02, averaged across the 10 stations at soil surface layer (i.e., 5 cm depth). Noticeable improvements were also observed for deeper soil layers, and for both dry and wet periods. Notably, the soil moisture coupling strength between different soil layers was substantially improved with the new module. The enhanced SWAT model is expected to better inform soil water and irrigation management.
Assessing feasible H2–CO2 sources in the US as Feedstocks for Sustainable Aviation Fuel Precursors: Acetic Acid and Ethanol Production via Hydrogenotrophic Pathways
The environmental impact of carbon dioxide emissions is significant, and research is focused on mitigating these emissions and developing eco-friendly technologies in line with green chemistry principles. Waste-to-energy technologies play a crucial role in converting waste into renewable energy and valuable biofuels and bioproducts. This study specifically explores the utilization of waste gas emissions, particularly carbon dioxide, from various sources in the United States for the production of sustainable aviation fuel (SAF) precursors, such as ethanol and acetic acid. The study categorizes and quantifies the volumes of carbon dioxide emissions into three types: non-biogenic, biogenic, and biogenic emissions from ethanol production facilities. Stoichiometric calculations are applied to compare the amounts of carbon dioxide from each category with the available hydrogen production capacity, determining if sufficient hydrogen is present for converting carbon dioxide into SAF precursors. The study reveals two key findings. Firstly, there is a significant reserve of carbon dioxide, approximately 1648 million metric tons per year (MMTy), combining all three categories, which would require a substantial increase of approximately 35–40 times in the existing hydrogen production capacity of 4.988 MMTy. This increased hydrogen production has the potential to yield approximately 1067.82 MMTy of acetic acid and 189.19 MMTy of ethanol annually. Secondly, upon analyzing the quality and application of the three sources of carbon dioxide with the currently available hydrogen production capacity, it is found that biogenic carbon dioxide from ethanol plants is the most suitable choice for immediate production of SAF precursors. This would theoretically result in an annual production of 1.36 MMTy of ethanol and 1.772 MMTy of acetic acid. The other two sources of carbon dioxide can be considered potential reserves for future utilization when additional hydrogen production facilities are established. The study provides a foundation for assessing the aggregation potential required for acetic acid and ethanol production. By optimizing the use of waste gases as raw materials, the study not only enables the production of SAF precursors but also contributes to the passive reduction of greenhouse gas emissions.
Assessment of structures and stabilities of defect clusters and surface energies predicted by nine interatomic potentials for UO2
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Laser ultrasonic assessment of the effects of porosity and microcracking on the elastic moduli of nuclear graphites
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Biojet fuels and emissions mitigation in aviation: An integrated assessment modeling analysis
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Assessing High-Throughput Descriptors for Prediction of Transparent Conductors
The growth of materials databases has offered significant quantities of data to mine for new energy materials using high-throughput screening methodologies. One application of interest to energy and optoelectronics is the prediction of new high performing p-type transparent conductors (TCs). Yet, screening methods for such materials have never been validated over the breadth of computed materials properties. In this study, we compile an experimental data set of 74 bulk crystal structures corresponding to known state-of-the-art n-type and p-type TCs and compute a series of corresponding computational descriptor properties. Our goals are to (1) compare computational descriptors to experimentally demonstrated properties of real materials in the data set, (2) determine the ability of ground state, density functional theory (DFT)-based computational screening methodologies to identify these experimentally realized TCs, and (3) use this understanding to estimate reasonable screening cutoffs for four commonly used descriptors. First, stability calculations demonstrate that most materials in the data set have an energy above the convex hull ( E hull ) of <100 meV/atom, and we also propose a Pourbaix analysis technique to estimate moisture stability. Second, we find a lenient cutoff for the DFT PBE band gap of 0.5 eV is sufficient to include a majority of the wide gap candidates and exclude narrow gap compounds. Next, the effective mass, m *, is found to correlate weakly to conductivity in the p-type materials as compared with n-type materials, which may motivate an increase in the m * cutoff as well. Lastly, we perform an uncertainty analysis and literature comparison for the branch point energy (BPE), a qualitative descriptor for dopability. We discover the BPEs of most n-type materials to lie near the conduction band and those of most p-type materials to lie at midgap; this is a significant distinction, implying BPE to be a more definitive descriptor for n-type TC materials. By assessing the validity of this simple screening methodology via comparing experimental data to computational descriptors, we aim to motivate and strengthen future materials discovery efforts in the field of transparent conductors and beyond.
Electrochemical Methods for Assessing Kinetic Factors in the Reduction of CO 2 to Formate: Implications for Improving Electrocatalyst Design
hermochemical insights are often employed in the rationalization of reactivity and in the design of electrocatalysts for CO 2 reduction reactions targeting C–H bond-containing products. This work identifies experimental methods for assessing kinetic aspects of reactivity. These methods are illustrated using [Fe 4 N(CO) 12 ] - , which produces formate from CO 2 at -1.2 V versus SCE in either a MeCN/H 2 O solvent (95:5) or pH 6.5 buffered water. Elementary rates for each reaction step are identified along with the rate-determining step (RDS) as C–H bond formation. Transition state kinetics were determined from an Eyring analysis for the rate-determining C–H bond formation step using temperature-dependent electrochemical measurements. A lower measured Δ G ‡ (298 K, 12.3 ± 0.1 kcal mol –1 ) in a pH 6.5 aqueous solution, compared with a Δ G ‡ (298 K) of 15.0 ± 0.1 kcal mol –1 in a MeCN/H 2 O solvent (95:5), correlates with faster observed reaction rates and provides a kinetic rationalization for the solvent-dependent chemistry. Taken together, the experimentally determined kinetic insights highlight that enhancement of the local concentration of CO 2 at catalyst–hydride sites should be a primary focus of ongoing catalyst design. This will both enhance reaction rates and increase selectivity for C–H bond formation over competing H–H bond formation, because that step is fast in H 2 evolution reactions.
Uptake of Uranium from Seawater by Amidoxime-Based Polymeric Adsorbent: Field Experiments, Modeling, and Updated Economic Assessment
Uranium recovery from seawater has been investigated for several decades for the purpose of securing nuclear fuel for energy production. In this study, field column experiments have been performed at the Marine Sciences Laboratory of the Pacific Northwest National Laboratory (PNNL) using a laboratory-proven, amidoxime-based polymeric adsorbent developed at the Oak Ridge National Laboratory (ORNL). The adsorbent was packed either in in-line filters or in flow-through columns. The maximum amount of uranium uptake from seawater was 3.3 mg of U/g of adsorbent after 8 weeks of contact between the adsorbent and seawater. This uranium adsorption amount was about 3 times higher than the maximum amount achieved in this study by a leading adsorbent developed at the Japan Atomic Energy Agency (JAEA).
Time-Dependent Density Functional Theory Assessment of UV Absorption of Benzoic Acid Derivatives
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Towards a systematic assessment of errors in diffusion Monte Carlo calculations of semiconductors: Case study of zinc selenide and zinc oxide
The fixed node diffusion Monte Carlo (DMC) method has attracted interest in recent years as a way to calculate properties of solid materials with high accuracy. However, the framework for the calculation of properties such as total energies, atomization energies, and excited state energies is not yet fully established. Several outstanding questions remain as to the effect of pseudopotentials, the magnitude of the fixed node error, and the size of supercell finite size effects. Here, we consider in detail the semiconductors ZnSe and ZnO and carry out systematic studies to assess the magnitude of the energy differences arising from controlled and uncontrolled approximations in DMC. The former include time step errors and supercell finite size effects for ground and optically excited states, and the latter include pseudopotentials, the pseudopotential localization approximation, and the fixed node approximation. We find that for these compounds, the errors can be controlled to good precision using modern computational resources and that quantum Monte Carlo calculations using Dirac-Fock pseudopotentials can offer good estimates of both cohesive energy and the gap of these systems. We do however observe differences in calculated optical gaps that arise when different pseudopotentials are used.
Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data
New technologies, such as connected and automated vehicles, have attracted more and more researchers for improving the energy efficiency and environmental impact of current transportation systems. The green routing strategy instructs a vehicle to select the most fuel-efficient route before the vehicle departs. It benefits the current transportation system with fuel saving opportunity through identifying the greenest route. This paper introduces an evaluation framework for estimating benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption of actual routes and comparing to routes procured by navigation systems. A route-based fuel consumption estimation model, considering road traffic conditions, functional class, and road grade is proposed and used in the framework. An experiment using a large-scale data set from the California Household Travel Survey global positioning system trajectory data base indicates that 31% of actual routes have fuel savings potential with a cumulative estimated fuel savings of 12%.
Assessing Gaussian Assumption of PMU Measurement Error Using Field Data
Gaussian PMU measurement noise has been assumed for many power system applications, such as state estimation, oscillatory modes monitoring, voltage stability analysis, to cite a few. This letter proposes a simple yet effective approach to validate this assumption by using the stability property of a probability distribution and the concept of redundant measurement. Extensive results using field PMU data from WECC system reveal that the Gaussian assumption is questionable.
A Data-Driven Framework for Assessing Cold Load Pick-Up Demand in Service Restoration
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