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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Factors Affecting Bait Site Visitation: Area of Influence of Baits

Baiting is a fundamental strategy for the global management of wild pigs (Sus scrofa); however, little information exists on how anthropogenic bait affects wild pig movements on a landscape. We investigated factors that are important in determining the spatial area of attraction for wild pigs to bait (‘area of influence’ of a bait site) using data from Global Positioning System (GPS) collars and locations of bait sites. We monitored movements of wild pigs in 2 distinct study areas in the United States from February to September 2016 and used locational data using GPS collars to analyze the influence of habitat quality (dependent on site), home range size, number of bait sites in the home range, distance to a bait site, and sex in relation to movement in time and space. We determined the average area of influence by calculating the area of a circle with the radius as the average maximum distance travelled by wild pigs to reach a bait site. The average area of influence for our bait sites was 6.7 km 2 (or a radius of approximately 1.5 km), suggesting a bait spacing of approximately 1.5 km would be adequate to capture visitation by most wild pigs and a spacing of 3 km could allow substantial visitation while minimizing redundant effort depending on the spatial structure of the populations. Eighty percent of wild pigs first visited bait sites within 8.9 days after bait deployment; and they visited earlier when their home range size was larger. As the number of bait sites in an individual's home range increased, individual pigs visited more bait sites, and the probability of a visit increased dramatically up to approximately 5 bait sites and much less thereafter. Wild pigs travelled farther distances to visit bait sites in lower quality habitat. Our results support the hypothesis that habitat quality can mediate the efficacy of baiting programs for wildlife by influencing their movement patterns and motivation to use anthropogenic resources. Our results suggest wild pigs will travel extensively within their home range to visit bait sites, and that in lower quality habitat, most animals will find bait sites more quickly. Determining the area of influence of bait sites can increase the efficacy of planning and monitoring management programs. Overall, our study provides new information to help managers plan baiting designs to attract the greatest number of pigs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Revisiting the unique structure of autonomously replicating sequences in Yarrowia lipolytica and its role in pathway engineering

Production of industrially relevant compounds in microbial cell factories can employ either genomes or plasmids as an expression platform. Selection of plasmids as pathway carriers is advantageous for rapid demonstration but poses a challenge of stability. Yarrowia lipolytica has attracted great attention in the past decade for the biosynthesis of chemicals related to fatty acids at titers attractive to industry, and many genetic tools have been developed to explore its oleaginous potential. Here, our recent studies on the autonomously replicating sequences (ARSs) of nonconventional yeasts revealed that the ARSs from Y. lipolytica showcase a unique structure that includes a previously unannotated sequence (spacer) linking the origin of replication (ORI) and the centromeric (CEN) element and plays a critical role in modulating plasmid behavior. Maintaining a native 645-bp spacer yielded a 2.2-fold increase in gene expression and 1.7-fold higher plasmid stability compared to a more universally employed minimized ARS. Testing the modularity of the ARS sub-elements indicated that plasmid stability exhibits a pronounced cargo dependency. Instability caused both plasmid loss and intramolecular rearrangements. Altogether, our work clarifies the appropriate application of various ARSs for the scientific community and sheds light on a previously unexplored DNA element as a potential target for engineering Y. lipolytica.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Tuning crack-inclusion interaction with an applied T -stress

The interaction between cracks and inclusions plays an important role in the fracture behavior of particulate composites. It is commonly recognized that an inclusion stiffer than the matrix tends to deflect an approaching crack away while a softer inclusion attracts the crack. Here, we demonstrate by analytical modeling and numerical simulations that the crack-inclusion interaction can be tuned by an applied T-stress. Under a sufficiently large compressive applied T-stress, cracks can be attracted to stiffer inclusions while repelled by softer ones, therefore reversing the conventional trend. Potential applications of this work include composite electrodes in lithium-ion batteries and hydraulic fracturing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Phase Relationships in the Carbon–Titanium–Uranium System for Ultra-High Temperature Nuclear Fuels

Carbides of uranium have attracted interest as fuels for nuclear thermal propulsion (NTP) to drive deep-space exploration owing to their attractive thermal and neutronic properties. Optimization of NTP technology, however, requires ultra-high temperature reactor environments to maximize the ratio of thrust to propellant to achieve peak rocket engine efficiency. Incorporation of transition metals into uranium carbides offers a pathway to increase the melting point of carbide fuels to address the operational challenges posed by NTP. Here, a thermodynamic model has been developed to examine the phase relationships in the C–Ti–U system at NTP conditions. Calculated phase relationships at ultra-high temperatures predict a stable (U, Ti)C solid solution. Additionally, experimental work on C–Ti–U synthesis via arc melting has been carried out, providing data on phase constitution and compositions that can be used to further refine the thermodynamic model that has been developed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Applying design principles to improve hydrogen storage capacity in nanoporous materials

Hydrogen is an attractive option for energy storage because it can be produced from renewable sources and produces environmentally benign byproducts. However, the volumetric energy density of molecular hydrogen at ambient conditions is low compared to other storage methods like batteries, so it must be compressed to attain a viable energy density for applications such as transportation. Nanoporous materials have attracted significant interest for gas storage because they can attain high storage density at lower pressure than conventional compression. Here, we examine how to improve the cryogenic hydrogen storage capacity of a series of porous aromatic frameworks (PAFs) by controlling the pore size and increasing the surface area by adding functional groups. We also explore tradeoffs in gravimetric and volumetric measures of the hydrogen storage capacity and the effects of temperature swings using grand canonical Monte Carlo simulations. We also consider the effects of adding functional groups to the metal–organic framework NU-1000 to improve its hydrogen storage capacity. We find that highly flexible alkane chains do not improve the hydrogen storage capacity in NU-1000 because they do not extend into the pores; however, rigid chains containing alkyne groups do increase the surface area and hydrogen storage capacity. Finally, we demonstrate that the deliverable capacity of hydrogen in NU-1000 can be increased from 40.0 to 45.3 g/L (at storage conditions of 100 bar and 77 K and desorption conditions of 5 bar and 160 K) by adding long, rigid alkyne chains into the pores.

08 HYDROGEN↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2022

Biocatalytic gateway to convert glycerol into 3-hydroxypropionic acid in waste-based biorefineries: Fundamentals, limitations, and potential research strategies

Microbial conversion of bioenergy-derived waste glycerol into value-added chemicals has emerged as an important bioprocessing technology due to its eco-friendliness, feasible technoeconomics, and potential to provide sustainability in biodiesel and bioethanol production. Glycerol is an abundant liquid waste from bioenergy plants with a projected volume of 6 million tons by 2025, accounting for about 10% of biodiesel and 2.5% of bioethanol yields. 3-Hydroxypropionic acid (3-HP) is a major product of glycerol bioconversion, which is the third largest biobased platform compound with expected market size and value of 3.6 million tons/year and USD 10 billion/year, respectively. Despite these biorefinery values, 3-HP biosynthesis from glycerol is still at an immature stage of commercial exploitation. The main challenges behind this immaturity are the toxic effects of 3-HPA on cells, the distribution of carbon flux to undesirable pathways, low tolerance of cells to glycerol and 3-HP, co-factor dependence of enzymes, low enzyme activity and stability, and the problems of substrate inhibition and specificity of enzymes. To address these challenges, it is necessary to understand the fundamentals of glycerol bioconversion and 3-HP production in terms of metabolic pathways, related enzymes, cell factories, midstream process configurations, and downstream 3-HP recovery, as discussed in this review critically and comprehensively. It is equally important to know the current challenges and limitations in 3-HP production, which are discussed in detail along with recent research efforts and remaining gaps. Finally, possible research strategies are outlined considering the recent technological advances in microbial biosynthesis, aiming to attract further research efforts to achieve a sustainable and industrially exploitable 3-HP production technology. Here, by discussing the use of advanced tools and strategies to overcome the existing challenges in 3-HP biosynthesis, this review will attract researchers from many other similar biosynthesis technologies and provide a common gateway for their further development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Design and additive manufacturing of optimized electrodes for energy storage applications

Supercapacitors exhibit fast charging/discharging ability and have attracted considerable attention within the automotive, aerospace, and telecommunication industries. Porous carbons, prized for their high electrical conductivity and high surface area, have been attractive candidates for supercapacitor electrodes. Moving to thick electrodes is one strategy to further increase energy density due to a higher volume fraction of active material. However, thick electrodes suffer from sluggish charged species transport, which is why thin electrodes are currently favored. In this work, we investigate the use of computational optimization and additive manufacturing to design and fabricate thick porous electrodes with improved performance. Electrode performance was maximized by designing their morphologies via topology optimization and printing by projection micro stereolithography (PμSL) using commercial resin (PR48). The PR48 resin was then pyrolyzed (PR48-P) to create the final conductive electrode. The optimized PR48-P electrodes exhibited 99% improvement in capacitance compared to control electrodes printed with cubic lattice morphologies. To further improve performance, we formulated a resin combining graphene oxide (GO) and trimethylolpropane triacrylate (TMPTA). Electrodes printed with 3 wt% GO in TMPTA exhibited improved capacitance retention after pyrolysis compared to the PR48-P electrodes. Finally, this work demonstrates the benefits of using topology optimization to design electrodes and material development to improve functional properties of 3D printable electrodes.

25 ENERGY STORAGE↗
DOE OSTI2021

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simu-lations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-di?usion equations using a non-negative finite element formulation for di?erent input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion strength/contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R2-score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction-diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O(107) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate.

Mudunuru, Maruti K.↗
DOE OSTI2019

The effect of wood composition and supercritical CO 2 extraction on charcoal production in ferroalloy industries

This work demonstrates that the integration of supercritical carbon dioxide extraction with slow pyrolysis is an effective method for the production of value-added chemicals and charcoal that is an attractive alternative to coke for industry. Integration of technologies is key for the development of holistic biorefineries that exploit all parts of the biomass feedstock and generate little or ideally no waste. In fact, the use of waste or low valued wood fractions is attractive due to their plentiful abundance and lack of exploitation. Supercritical carbon dioxide has been demonstrated to be effective at the removal of over half of extractives from low quality wood and forestry wastes, which can account for up to 11 wt %, of the dried biomass in waste needles. High extractive yields by supercritical carbon dioxide extraction illustrates the potential of utilizing low quality wood as an alternative feedstock for the sustainable production of value-added chemicals. We report high yields of steroids and derivatives, terpenes and other plant metabolites were obtained in the extracts of needles, branches and bark. Importantly, supercritical carbon dioxide extraction had little impact neither on the physical properties of original wood nor on the yield of solid charcoal. This indicates that extraction by supercritical carbon dioxide can be used as a method for adding further value to the process by removal of bio-based chemicals, whilst still maintaining the yield of the solid fuel product. Moreover, the heat treatment temperature and supercritical carbon dioxide extraction had a significant impact on the tar yields during pyrolysis, leading to an increase in naphthalene, polycyclic aromatic hydrocarbons, aromatic and phenolic fractions with greater temperature. These results are promising as they show that the charcoal obtained from this renewable feedstock could be used as an alternative to fossil-based coke in applications including ferroalloy industries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Probing the shear-induced microstructure of a smectite clay aqueous suspensions using rheo-USANS and rheo-SIPLI measurements

Hypothesis: The static microstructure of aqueous sodium-montmorillonite (Na-Mt) suspensions at low ionic strengths (where Particle Size/Debye Length ≈1) exhibits both the particle–particle ordering as well as aggregation with repulsive ordered domains having characteristic optical birefringence and attractive aggregated entities larger than 20 µm resulting in ever-increasing yield stresses also known as physical aging-rejuvenation behavior. We hypothesize that the attractive particle–particle aggregation is the underlying cause behind the physical aging-rejuvenation behavior observed in Na-Mt suspensions with no contribution from structural dynamics driven by repulsive particle–particle ordering or jamming. Experiments: We investigate the shear-induced microstructure of aqueous Na-Mt suspensions in the sol and gel state using rheo-ultra-small angle neutron scattering (rheo-USANS) experiments at shear rates of 1, 50, 500, and 2000 s −1 . We also perform rheo-shear-induced polarization light imaging (rheo-SIPLI) experiments to relate ordering with shearing and aging. Findings: Shearing the suspensions at low to moderate shear rates induces particle–particle aggregation and shearing at high shear rates induces the breakage of particle–particle aggregation in the sol and gel states, suggesting the microstructural aggregation in the sol and gel state is shear sensitive and a full rejuvenation or breakage of particle–particle aggregation is only achieved at a minimum critical shear rate. The rheo-SIPLI experiments reveal that the sol and gel state exhibited strong Maltese cross patterns at a shear rate of 1000 s −1 , indicating particle–particle ordering. Post shearing, the gel exhibited temporal evolution of storage modulus without any noticeable influence on the appearance of the Maltese cross pattern indicating physical aging and particle ordering are distinct length scale phenomena in Na-Mt suspensions and the physical aging-rejuvenation behaviour is a feature of particle–particle aggregation as opposed to ordering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A method for generating moving, orthogonal, area preserving polygonal meshes

A new method for generating locally orthogonal polygonal meshes from a set of generator points is presented in which polygon areas are a constraint. The area constraint property is particularly useful for particle methods where moving polygons track a discrete portion of material. Because Voronoi polygon meshes have some very attractive mathematical and numerical properties for numerical computation, a generalization of Voronoi polygon meshes was formulated that enforces a polygon area constraint. Area constrained moving polygonal meshes allow one to develop hybrid particle-mesh numerical methods that display some of the most attractive features of each approach. It is shown that this mesh construction method can continuously reconnect a moving, unstructured polygonal mesh in a pseudo-Lagrangian fashion without change in cell area/volume, and the method's ability to simulate various physical scenarios is shown. Overall, the advantages are identified for incompressible fluid flow calculations, with demonstration cases that include material discontinuities of all three phases of matter and large density jumps.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Nuclear waste Educator's workshop: What and how do we teach about nuclear waste?

A workshop was held at the Massachusetts Institute of Technology (MIT) on July 25th and 26th, 2022. The objective was to develop a blueprint for educating next-generation engineers and scientists about nuclear waste management and disposal, which requires knowledge from diverse disciplines, including nuclear, chemical, civil, environmental, and geological science and engineering. The 49 participants included university professors, researchers, industry experts, and government officials from different areas. First, we have developed a list of key fundamental knowledge on waste management and disposal across the nuclear fuel cycle. In addition, we discussed strategies on how to teach students with diverse backgrounds through innovative teaching strategies as well as how to attract students into this area. Through the workshop, we identified the critical needs to (1) develop community resources for nuclear waste education; (2) synthesize historical perspectives, including past contamination and the management of general hazardous waste; (3) emphasize a complete life-cycle perspective, including proper waste management as the key component for energy sustainability; (4) teach students how to communicate about the key facts and risks to technical and non-technical audiences; and (5) accelerate the use of the state-of-art-technologies to attract and retain a young workforce. Furthermore, we aim to build a diverse, inclusive community that supports students in developing their own narratives about nuclear waste, particularly in recognizing that antagonistic views have been important to improving safety and protecting public health and the environment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Atomistic simulations of He bubbles in Beryllium

Formation of He bubbles can have a significant effect on the microstructural evolution and properties of irradiated materials. In this report, we use atomistic simulations based on machine learning potentials to investigate the fundamental behavior of He bubbles in Be, with a specific focus on the shape, stability, and diffusivity of bubbles. Stability of He bubbles is quantified in terms of formation energies, which are determined as a function of the ratio of He/V. We find that He bubbles become unstable with respect to plastic deformation through punch-out dislocations around the bubble when the He/V ratio is larger than ~1.25, and the punch-out process induces the change of the regular bubble shape. In general, the bubble shape of He in Be is found to be ellipsoid-like. It is also found that for a fixed He/V ratio, the bubble attracts vacancies to become larger in size. If the bubble size is constant, the bubble attracts additional He atoms until the punch-out reaction occurs. The dominant diffusion mechanism of He bubbles changes from surface diffusion to volume diffusion as the temperature is increased, with a crossover occurring at about 900 K.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Fine-tuned thermally cross-linkable 6FDA-based polyimide membranes for aggressive natural gas separation

Glassy 6FDA-based polyimides are attractive membrane materials for many industrially important gas separations; however, plasticization in aggressive natural gas often causes significant loss in CO 2 /CH 4 selectivity. In this study, a family of thermally cross-linkable 6FDA-based polyimides containing carboxyl groups were synthesized and cross-linked as dense films via decarboxylation-induced thermal crosslinking. As well as chemical structure, crosslinking temperatures above and below T g are shown to be synergistic tools. Our results demonstrate that structure evolution during the decarboxylation-induced crosslinking process is affected significantly by the 3,5-diaminobenzoic acid (DABA) content in the polymer backbone. Even for a 50/50 CO 2 /CH 4 binary mixture with feed pressure up to 800 psia, decarboxylation-induced crosslinking provides attractive gas separation performance and plasticization resistance. Combined facile structure tunability, crosslinkability and high asymmetric fiber spinnability make the cross-linkable 6FDA-based polyimides a versatile platform for diverse aggressive natural gas feeds encountered in practice. Finally, our study provides insights into material design of thermally cross-linkable polymer membranes for such a broad spectrum of aggressive gas separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Two-stage membrane-based process utilizing highly CO 2 -selective membranes for cost and energy efficient carbon capture from coal flue gas: A process simulation study

Membrane technology for CO 2 capture has become an attractive strategy due to its cost and energy efficiency and low materials costs. In the past decade, membrane-based process designs for post-combustion power plant CO 2 capture have been developed, utilizing existing highly CO 2 -permeable membranes with relatively low CO 2 /N 2 selectivity (<50), and have obtained reasonably economic carbon capture. However, few membrane-based process designs were proposed for moderate to highly CO 2 -selective membranes (CO 2 /N 2 selectivity of 50–300, and >300, respectively), which have vastly emerged in recent years, such as various facilitated transport membranes (FTMs). Herein, we proposed a two-stage membrane-base process design targeting economic carbon capture from coal-fired flue gas. This process design features the utilization of highly CO 2 -selective membranes for one-stage CO 2 enrichment to 95% dry-base purity in the first stage and recycle of the remaining CO 2 by a highly CO 2 -permeable membrane in the second stage, in order to achieve economic CO 2 capture with 90% capture rate and >95% CO 2 product purity. Through an integration-iteration membrane model and the Aspen Plus process simulation, a sensitivity study of operating pressures (feed and permeate pressures) and membrane properties (CO 2 permeance and CO 2 /N 2 selectivity) was conducted. Critical CO 2 /N 2 selectivity of 300–400 was found for the highly CO 2 -selctive membranes to meet the demand for cost and energy efficient results. The lowest possible membrane area of 4.8 × 10 5 m 2 and fractional energy of 19.3% were obtained, which is comparable to or even more attractive than reported membrane-based process designs. Here, this work provides a new membrane process design option for highly CO 2 -selective membranes and gives insights on the influence of membrane performance and operation condition.

42 ENGINEERING↗
DOE OSTI2023

Finely tuning the microporosity in phosphoric acid doped triptycene-containing polybenzimidazole membranes for highly permselective helium and hydrogen recovery

High-performance polymer membranes with well-defined microporosity and size-sieving ability are especially attractive for helium and hydrogen recovery. Here, in this study, we report novel macromolecular engineering of polybenzimidazole (PBI) membranes that integrate hierarchical triptycene units for high permeability and polyprotic acid doping for size sieving via controllable manipulation of microporous architecture. The triptycene moieties disrupt chain packing and introduce additional configurational free volume, leading to significantly boosted He and H 2 permeabilities compared to previously reported PBI membranes. The acid doping resulted in crosslinked PBI membranes via hydrogen bonding and proton transfer with dramatically enhanced gas selectivities. Via adjusting the H 3 PO 4 -doping level, triptycene-based polybenzimidazole (TPBI) composite membranes (TPBI-(H 3 PO 4 ) x ) exhibit the highest gas selectivities for He enrichment (i.e., α(He/CH 4 ) = 7052 ± 156) and H 2 purification (i.e., α(H 2 /CH 4 ) = 5128 ± 110) among existing polymeric gas separation membranes. Additionally, under mixed-gas conditions at 150 °C, the TPBI-(H 3 PO 4 ) 0.98 membrane displays a H 2 permeability of 46.7 Barrer and a H 2 /CO 2 selectivity of 16, far beyond the Robeson's 2008 upper bound for H 2 /CO 2 separation. The facile and diverse tunability and excellent gas separation performance make TPBI-(H 3 PO 4 ) x membranes highly attractive for helium and hydrogen separation.

42 ENGINEERING↗
DOE OSTI2025

Microporous pentiptycene-based polybenzimidazole membranes for high temperature H 2 /CO 2 separation

Separating H 2 from syngas at elevated temperatures (100–250 °C) have attracted significant attention in recent years as a means to reduce energy consumption and capital costs in precombustion carbon capture processes, such as those following steam reforming of natural gas or coal gasification. Polybenzimidazole (PBI), particularly m-PBI, has been reported as a leading membrane material for high-temperature H 2 /CO 2 separation. However, m-PBI exhibits extremely low H 2 permeability, even at high temperatures, which limits its productivity in H 2 /CO 2 separation applications. Here, to address this limitation, this work introduces a new pentiptycene-based polybenzimidazole (PPBI) featuring significantly enhanced H 2 permeability and attractive high-temperature H 2 /CO 2 separation performance, which stems from the unique configurational free volume elements introduced by pentiptycene moieties. Further tuning of the free volume architecture of PPBI films is achieved via acid doping with phosphoric acid (PA) or trans-aconitic acid (TaA), which introduces crosslinking among PPBI chains. Under mixed-gas environment (50/50 mol% H 2 /CO 2 ) at 180 °C, the acid-doped PPBI films exhibit a ∼230 % increase in H 2 /CO 2 selectivity compared to pristine PPBI film while maintaining high H 2 permeability that is nearly 500 % of m-PBI. These properties approach the predicted upper bound for membrane operating at 180 °C, highlighting its great potential for high-temperature H 2 /CO 2 separation.

Liu, Mengdi [University of Notre Dame, IN (United ↗