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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This project sought to select a process configuration, select a commercial technology offering, and complete an initial engineering design of a carbon capture system that recovers and ultimately would store ~190,000 tonnes per year of CO 2 with 90%+ carbon capture efficiency from an existing steam methane reforming (SMR) plant at Phillips 66’s Rodeo Refinery.
A simple co-design example in a reduced parameter space is presented for an oscillating flap device. Initially, the WEC geometry and mass properties are considered along with drivetrain gear ratio, inertia, motor constant and stiffness under both PI and optimal control. This parameter space is reduced to those to which performance is most sensitive for a fixed geometry. The gear ratio, drivetrain stiffness, and flap mass are found to be the most impactful design criteria as they can create orders of magnitude variations in power performance. The performance of the optimized system is compared with several sub-optimal variants in terms of electrical and mechanical power capture, transmission coefficients, and transducer power gain. Notably, though substantial power capture improvements are demonstrated when an optimal controller is employed, this power capture remains sensitive to appropriate selections of drivetrain and flap design parameters, implying that control co-design procedures remain necessary for high-performing WECs. Furthermore, a number of practical caveats and extensions to the presented co-design methodology are suggested, including the characterization of system static friction, especially in the presence of high gear ratios.
Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species’ cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward.
Abstract Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.
Abstract Rigorous studies have characterized the aa-tRNA selection mechanism in bacteria, which is essential for maintaining translational fidelity. Recent investigations have identified critical distinctions in humans, such as the requirement of subunit rolling and a tenfold slower proofreading step. Although these studies captured key intermediates involved in tRNA selection, they did not elucidate the transitions of aa-tRNA between intermediates. Through diverse structure-based simulations, we simulated 1856 aa-tRNA accommodation events into the human ribosomal A site. Here we show the requirement for a distinct ~30° pivoting of aa-tRNA about the anticodon stem within the accommodation corridor. This pivoting is crucial for navigating the crowded accommodation corridor, which becomes more constrained due to subunit rolling. Subunit rolling-dependent crowding increases the steric contributions of the accommodation corridor during aa-tRNA accommodation, consistent with the tenfold reduction in the rate of proofreading. Furthermore, we show that eEF1A interacts with the accommodating aa-tRNA through conserved basic residues, limiting premature aa-tRNA dissociation from the A site. These findings provide a structural description of the human aa-tRNA selection process and demonstrate that the aa-tRNA alignment relative to the ribosomal catalytic sites is a critical determinant of translational fidelity.
Per- and polyfluoroalkyl substances (PFAS) are among the most abundant environmental contaminant species. They are widespread due to uncontrolled industrial and commercial use and have been linked to health risks such as cancer. With rising global concerns about the public health effects of PFAS, there is an incentive to develop strategies for reliable monitoring and effective PFAS removal, particularly in drinking water. Traditional PFAS sensing techniques are inefficient due to long measurement times, high labor input, high costs, and limitations to ex situ analysis. Current commercially available sorbents for PFAS removal generally lack the ability to capture PFAS components rapidly and quantitatively. Furthermore, existing sorbents are notably inefficient in removing the more toxic, smaller PFAS chemical chains. Large-scale applications of these methods tend to be costly and resource-intensive. Pacific Northwest National Laboratory has developed unique strategies for PFAS sensing and removal with capture probe technology that has an affinity for fluorocarbons, including PFAS. For both sensing and removal, the customizable capture probes can target specific PFAS compounds since they used a metal-organic framework (MOF)-based technology that can be fine-tuned and molecularly tailored. This tailoring of the materials enables high PFAS sensitivity, selectivity, and faster uptake. PNNL’s capture probe materials have been developed into a sensing technique based on the interactions of the capture probes with PFAS at the molecular level and further transduced that to a quantifiable electrochemical response. The materials have been integrated into a sensor platform developed by New Jersey Institute of Technology (NJIT). Tunable capture probes with a range of detection sensitivities allow for faster and more sensitive PFAS detection limits as low as what appears to be 0.5 ng/L (compared to the U.S. Environmental Protection Agency health advisory level of 70 ng/L). Customization of the capture probe results in improved sorption capacities (fast kinetics and high capacities) compared to commercial granular activated carbons. This research applied various experimental and modeling tools to improve understanding the molecular level interactions between the sorbents towards PFAS adsorption properties.
Poor selectivity of conventional desalination membranes for boron often necessitates multi-stage treatment trains to achieve desired boron removal for end uses such as irrigation. One approach to membrane design for improved single-pass boric acid removal is via incorporation of chelating ligands that selectively sorb boron. In this study, membranes based on epoxy-amine cross-linked poly(glycidyl glycidyl ether) (PGGE) were synthesized and functionalized with N-methyl-d-glucamine (NMDG), a polyol known to interact selectively with boron. PGGE and PGGE-NMDG membranes exhibited boron sorption isotherms that were well-described by dual mode isotherms. PGGE-NMDG sorbed 2.5 mmol B/g dry polymer from a neutral aqueous solution containing 100 mmol B/L, which was almost three times the adsorption density of a commercial boron selective resin, Amberlite IRA743. Further, the membranes were regenerated in acid without a significant loss of boron sorption capacity over four cycles. Interactions between boron and NMDG and PGGE sites (e.g., epoxides) impacted boron diffusion in both membranes. The use of ligand functionalized membranes to capture target solutes such as boron requires high loading of interacting sites to maximize uptake capacity. Establishing fundamental structure/property rules for boron selectivity could lead to new material designs with improved boron separation properties for water purification.
Direct conversion of point-source CO 2 into fine chemicals over cooperative and bifunctional materials (BFMs) – composed of adsorbents and catalysts – has emerged as a promising approach to improve the energy efficiency of the carbon capture and conversion processes. In this study, a bifunctional material consisting of Cr 2 O 3 /ZSM-5 catalyst and CaO adsorbent was developed and tested in the CO 2 -oxidative dehydrogenation of propane (CO 2 –ODHP) for reactive capture of CO 2 in a fixed bed reactor. First, CaO was prepared using two distinct methods: solid-state and citrate sol–gel. The citrate sol–gel method resulted in small and finely-distributed CaO particles, allowing more accessible sites for CO 2 adsorption. Consequently, a high CO 2 adsorption capacity of ~14 mmol/g was achieved with fast adsorption kinetics compared to CaO prepared by the solid-state method. The CaO adsorbent was then combined with the Cr 2 O 3 /ZSM-5 catalyst for BFM synthesis and tested in the CO 2 –ODHP process, targeting propylene production. The BFM was extensively characterized to provide insights into the BFM’s surface chemistry, morphology, and reaction mechanism in the reactive capture process of CO 2 –ODHP. The results revealed that under isothermal adsorption–reaction conditions at 600 °C, a propane conversion of 22.5%, a propylene selectivity of 55.3%, and an olefin selectivity of 67.3% were achieved. The excellent propylene selectivity was attributed to the catalyst acidity and redox property of the Cr 2 O 3 /ZSM-5 catalyst, which facilitated the reaction pathway of propane dehydrogenation in the process of CO 2 –ODHP. Overall, this study renders Cr 2 O 3 /ZSM-5@CaO as promising BFMs with high CO 2 capture capacity and catalytic activity for integrated CO 2 capture and conversion in the ODHP reaction.
The primary objective of the project was to demonstrate the technical and economic performance of a technology that simultaneously captures CO 2 and water from the air – a Hybrid Direct Air Capture system (HDAC). In HDAC, air is passed over water capture section as well as a CO 2 selective sorbent to remove CO 2 from the air stream. Combining potable water generation and CO 2 capture in a single device with the unique energy conserving features of the proposed design enables long-term projected CAPEX under $\$$750/t-CO 2 and levelized cost of capture (LCOC) of $\$$140/t-CO 2 . Project DE-FE-0031970 "A Combined Water and CO 2 Direct Air Capture System" ran for four years and three months from 10/01/2020 to 12/31/2024 for three budget periods and an extension period. The total project budget at completion was $\$$3,534,408 consisting of a $\$$2,680,064 federal share and $\$$854,344 cost share. The pilot plant was successfully designed, engineered, built and commissioned during the initial three budget periods. The plant demonstrated successful water capture during this period, whereas initial CO 2 capture was well below target values. The project team requested an extension period during which alternate materials and beds were evaluated. By the end of the project, both moisture swing adsorption of CO 2 and water capture had successfully been demonstrated at target capture rates making further scale-up of the technology viable.
Accurate assessment of tritium as a fuel source is critical in fusion reactions, necessitating effective sensor evaluation methods. This study investigates a multi-criteria decision-making framework for selecting tritium sensors, integrating fuzzy logic to enhance decision quality. Initial attempts at applying fuzzy logic were found to be too elementary and failed to capture the complexity of multi-criteria selection; this prompted a refined approach that incorporated expert insights and advanced ranking techniques for sensor evaluation. The research used a two-stage methodology. In the first stage, important criteria and sub-criteria for sensor performance were identified and defined. These criteria were then weighted and scored using a fuzzy best-worst method, drawing upon expert opinions to ensure relevance and validity. The second stage involved interpreting information about varying sensors to rank them based on their overall criteria scores, encouraging the selection of the most suitable options. The result of the study is a proposed method for effective sensor selection in fusion reactors, which in turn will significantly improve the reliability of tritium monitoring in fusion applications.
High-sulfur mixed fly ash residues from semi-dry flue gas desulfurization units in coal-fired power plants are unsuitable for use as supplementary cementitious material (SCM) for concrete production or carbon dioxide utilization. In this work, we explore the potential for upcycling a representative spray dry absorber ash (10.44 wt% SO 3 ) into concrete-SCM by selective sulfur removal via weak acid dissolution while simultaneously exploring the possibility for CO 2 capture. Towards this effort, parametric studies varying liquid-to-solid ratio, acidity, and CO 2 pressure were conducted in a batch reactor to establish the sulfur removal characteristics in de-ionized water, nitric acid, and carbonic acid, respectively. The dissolution studies show that the leaching of sulfur from calcium sulfite hemihydrate, which is the predominant S phase, is rapid and achieves a concentration plateau within 5 min, and subsequently, appears to be controlled by the primary mineral solubility. Here, preferential S removal was sufficient to meet SCM standards (e.g., 5.0 wt% as per ASTM C618) using all three washing solutions with 0.62–0.72 selectivity (S^), defined as the molar ratio of S to Ca in the leachate, for a raw fly ash with bulk S^ = 0.3. Acid dissolution with 1.43 meq/g of ash or under 5 atm CO 2 retained > 18 wt% CaO and other Si-, Al-rich phases in the fly ash. Based on the experimental findings, two sulfur removal schemes were suggested for either integration with CO 2 capture and utilization processes using flue gas or to produce fly ash for use as a SCM.
Direct electrochemical upgrading of CO 2 in capture media is an attractive approach to carbon capture that can bypass the energy requirement for the thermal release of pure CO 2 . Here we investigate the electrocatalytic activity of iron(tetraphenylporphyrin) in the presence of organic solvents that convert into ionic liquids upon exposure to CO 2 . Four different solvent systems were tested, all of which capture CO 2 in the form of an alkyl carbonate (or carbamate) anion and an acidic ammonium cation. The electrocatalytic selectivity exhibited a strong dependence on the acidity of the capture medium, with the most basic solvent affording a high selectivity for production of CO instead of H 2 . Experimental and computational studies support a canonical mechanism in which the catalyst reacts with free CO 2 in solution, as opposed to a reaction with the alkyl carbonate that is present in high concentration. Kinetic analysis indicates that the rate-limiting step is changed from C–O protonolysis in traditional solvents to the binding of CO 2 in the capture media. Quantitative 13 C– 13 C EXSY revealed that the dissociation of the alkyl carbonate into free, solvated CO 2 is very rapid (~15 s –1 ) compared to the interconversion of HCO 3 – /CO 2 in aqueous solution. These results underscore the need to understand the mechanism and kinetics for both the release of captured CO 2 and its electrocatalytic conversion.
Rare earth elements (REEs) are critical materials to modern technologies. They are obtained by selective separation from mining feedstocks consisting of mixtures of their trivalent cation. We are developing an all-aqueous, bioinspired, interfacial separation using peptides as amphiphilic molecular extractants. Lanthanide binding tags (LBTs) are amphiphilic peptide sequences based on the EF-hand metal binding loops of calcium-binding proteins which complex selectively REEs. We study LBTs optimized for coordination to Tb 3+ using luminescence spectroscopy, surface tensiometry, X-ray reflectivity, and X-ray fluorescence near total reflection, and find that these LBTs capture Tb 3+ in bulk and adsorb the complex to the interface. Molecular dynamics show that the binding pocket remains intact upon adsorption. We find that, if the net negative charge on the peptide results in a negatively charged complex, excess cations are recruited to the interface by nonselective Coulombic interactions that compromise selective REE capture. If, however, the net negative charge on the peptide is −3, resulting in a neutral complex, a 1:1 surface ratio of cation to peptide is achieved. Surface adsorption of the neutral peptide complexes from an equimolar mixture of Tb 3+ and La 3+ demonstrates a switchable platform dictated by bulk and interfacial effects. The adsorption layer becomes enriched in the favored Tb 3+ when the bulk peptide is saturated, but selective to La 3+ for undersaturation due to a higher surface activity of the La 3+ complex.
Methods for preventing radioiodine from entering the environment are needed in processes related to nuclear energy and medical isotope production. The development and performance of many different types of sorbents to capture iodine have been reported on for decades; however, there is yet to be a concise overview on the important parameters that should be considered when selecting a material for chemically capturing radioiodine. This paper summarizes several criteria that should be considered when selecting candidate sorbents for implementation into real-world systems. The list of selection criteria discussed are 1) optimal capture performance, 2) kinetics of adsorption, 3) performance under relevant process conditions, 4) properties of the substrate that supports the getter, and 5) environmental stability and disposition pathways for iodine-loaded materials.
Abstract Efficient direct air capture (DAC) of CO 2 will require strategies to deal with the relatively low concentration in the atmosphere. One such strategy is to employ the combination of a CO 2 ‐selective membrane coupled with a CO 2 capture solvent acting as a draw solution. Here, the interactions between a leading water‐lean carbon‐capture solvent, a polyether ether ketone (PEEK)‐ionene membrane, CO 2 , and combinations were probed using advanced NMR techniques coupled with advanced simulations. We identify the speciation and dynamics of the solvent, membrane, and CO 2 , presenting spectroscopic evidence of CO 2 diffusion through benzylic regions within the PEEK‐ionene membrane, not spaces in the ionic lattice as expected. Our results demonstrate that water‐lean capture solvents provide a thermodynamic and kinetic funnel to draw CO 2 from the air through the membrane and into the bulk solvent, thus enhancing the performance of the membrane. The reaction between the carbon‐capture solvent and CO 2 produces carbamic acid, disrupting interactions between the imidazolium (Im + ) cations and the bistriflimide anions within the PEEK‐ionene membrane, thereby creating structural changes through which CO 2 can diffuse more readily. Consequently, this restructuring results in CO 2 diffusion at the interface that is faster than CO 2 diffusion in the bulk carbon‐capture solvent.
Carbon dioxide (CO 2 ) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, in this study, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO 2 in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO 2 solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO 2 solubility (ln(x CO 2 )) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO 2 solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO 2 capture and utilization.
Abstract Liquid metals are being investigated as coolants in many advanced reactor designs because of their high thermal conductivity and effectiveness at high temperatures. However, they often pose challenges to reactor operation and safety because of the complex thermal mixing and stratification in the plenum of pool-type reactor designs. The advanced system analysis code System Analysis Module (SAM) currently under development at Argonne National Laboratory aims to develop and implement thermal mixing models to accurately capture these complex thermal fluid behaviors. In this study, the SAM thermal mixing model was compared against experimental data from the Gallium Thermal-Hydraulic Experiment facility, a scaled liquid metal test facility that uses gallium as a surrogate fluid to investigate the stratification and thermal mixing of low-Prandtl-number fluids in the upper plenum of a liquid metal-cooled reactor. Two cold shock transient cases were used: one with stable stratified flow (Ri = 32) and one with stronger thermal mixing (Ri = 0.5). The resultant temperatures were then compared with the experimental temperatures over the entire plenum to assess the ability of the mixing models to capture the thermal behavior and to better correspond mixing parameters to various flow scenarios. Generally, the zero-dimensional mixing model was more capable of capturing the bulk temperature of the component modeled assuming that an accurate mass flow rate was provided, but it was inherently unable to capture thermal gradients in space. The one-dimensional mixing model was capable of capturing that the thermal gradients provided accurate selection of the mixing coefficients. Further, the temperature at the outlet junction was compared over time for each of the mixing models with the recorded experimental temperature. The implemented mixing models demonstrated the ability to effectively capture the overall thermal behavior for stronger mixing scenarios but struggled with more stably stratified flows. It was found that a system analysis code's covering of the entire range of different operating conditions still remains a challenging task, and it is suggested that further model and closure improvements are necessary to accurately capture complex thermal mixing and stratification phenomena.
Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. Here, this work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO 2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.