Materials Combining Asymmetric Pore Structures with Well-Defined Mesoporosity for Energy Storage and Conversion
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Synthetic mesoporous silica nanoparticle (MSN) is an architected geomaterial that provides confinement-mediated flow and transport properties of fluids needed for environmental research such as subsurface energy storage or carbon capture.
Single-atom metal embedded in nitrogen-doped carbon show high HER performance, with Co-NAC reaching 200 mA cm −2 at 310 mV overpotential.
The porosity of the slip cast Si3N4 is similar to that of pressed Si3N4 formed at 2500 kg/sq cm. The porosity of cast Si oxynitride is equivalent to that of samples stressed at 10,000 kg/sq cm. Crucibles formed from these materials by slip casting have high thermal shock and corrosion resistance.
Solid oxide fuel cells (SOFCs) have tremendous commercial potential because of their high efficiency, high energy density, and flexible fuel capability (ability to use fossil fuels). The drive for high-power-utilizing, ultrathin electrolytes (less than 10 microns), has placed an increased demand on the anode to provide structural support, yet allow sufficient fuel entry for sustained power generation. Concentration polarization, a condition where the fuel demand exceeds the supply, is evident in all commercial-based anode-supported cells, and it presents a significant roadblock to SOFC commercialization.
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Basalt formations are promising candidates for the geologic storage of anthropogenic CO 2 due to their storage capacity, porosity, permeability, and reactive geochemical trapping ability. The Wallula Basalt Carbon Storage Pilot Project demonstrated that supercritical CO 2 injected into >800 m deep Columbia River Basalt Group stacked reservoir flow tops mineralizes to ankerite-siderite-aragonite on month-year timescales, with 60% of the 977 metric tonnes of CO 2 converted within two years. The potential impacts of mineral precipitation and consequent changes on rock porosity, pore structure, pore size, and pore size distributions have likely been underestimated hitherto. Herein, we address these knowledge gaps using X-ray Micro Computed Tomography (XMT) to evaluate the pore network architecture of sidewall cores recovered two years after CO 2 injection. In the present study, we performed a detailed quantitative analysis of the CO 2 -reacted basalt cores by XMT imaging. Reconstructed 3D images were analyzed to study the distribution and volumetric details of porosity and carbonate nodules in the cores along with the various other phases, providing insight into paragenesis and carbonate growth mechanisms, including mineralogic/chemical zonation. Finally, these findings are being used to parametrize multiphase reactive transport models to predict the fate and transport of subsurface CO 2 , enabling scale-up to commercial-scale geologic carbon storage in basalts and other reactive mafic-ultramafic formations.
Platinum group metal (PGM) catalysts are the major electrocatalysts for oxygen reduction reaction (ORR) in the polymer electrolyte membrane fuel cells (PEMFCs). The cost becomes unaffordable if the PEMFC is in massive application. The PGM-Free catalyst shows very promising activity in rotation disk electrode (RDE) testing. The half-wave potential could reach 0.91 V versus standard hydrogen electrode (SHE). However, in a membrane electrode assembly (MEA), the performance of PGM-Free catalysts is not good enough to replace the PGM catalysts. Since the PGM-free catalysts are so different from the PGM catalysts in terms of catalytic activity, stability, surface conditions, particle size, etc., the fabrication of PGM-Free catalyst MEA cannot simply copy the method of making PGM MEA. Here we proposed a novel method of fabricating PGM-Free catalyst MEA, so that the intrinsic catalyst activity from RDE can be translated into MEA performance. The method is based on the catalyst coated membrane (CCM) method using optimized ionomer to carbon (I/C) ratio and solvent mixture of catalyst ink. Using this method, the PGM-free catalyst MEA achieved the current density 44.9 mA cm -2 at 0.9 V iR-free in H 2 /O 2 and 150 mA cm -2 at 0.8 V in H 2 /air, which surpassed the performance targets of US Department of Energy (DOE)for PGM-Free catalyst MEA. The property (solvent composition, dispersion of catalyst and ionomer in an ink), structure (pore structure) and the MEA performance have been characterized using, mercury intrusion porosimetry (MIP), MEA testing. A property-structure-performance relationship has been established.
Nuclear pore complexes (NPCs) are the main conduits for molecular exchange across the nuclear envelope. The NPC is a modular assembly of ~500 individual proteins, called nucleoporins or nups. Most scaffolding nups are organized in two multimeric subcomplexes, the Nup84 or Y complex and the Nic96 or inner ring complex. Working in S. cerevisiae, and to study the assembly of these two essential subcomplexes, we here develop a set of twelve nanobodies that recognize seven constituent nucleoporins of the Y and Nic96 complexes. These nanobodies all bind specifically and with high affinity. We present structures of several nup-nanobody complexes, revealing their binding sites. Additionally, constitutive expression of the nanobody suite in S. cerevisiae detect accessible and obstructed surfaces of the Y complex and Nic96 within the NPC. Overall, this suite of nanobodies provides a unique and versatile toolkit for the study of the NPC.
An anode includes a mixed ionic-electronic conductor (MIEC) with an open pore structure. The open pore structure includes open pores to facilitate motion of an alkali metal into and/or out of the MIEC. The open pore structure thus provides open space to relieve the stresses generated by the alkali metal when charging/discharging a battery. The MIEC is formed from a material that is thermodynamically and electrochemically stable against the alkali metal to prevent the formation of solid-electrolyte interphase (SEI) debris and the formation of dead alkali metal. The MIEC may also be passive (the MIEC does not store or release alkali metal). In one example, the open pore structure may be an array of substantially aligned tubules with a width less than about 300 nm, a wall thickness between about 1 nm to about 30 nm, and a height of at least 10 um arranged as a honeycomb.
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In this paper, the volume control spark plasma sintering tool has been designed and applied to sinter porous U-10Zr metallic fuels, by which the sintered sample volume can be precisely controlled. Ethanol and NH 4 HCO 3 are used to control the powder compact or as pore formers to control the pore size and pore structure. Without pore formers, the fuel pellet displays an inhomogeneous microstructure consisting of highly porous and highly densified areas. Uneven powder stacking in the green body results in a non-uniform microstructure, and in the closed-packed area, Joule heating accelerates the neck formation and densification. The addition of ethanol reduces the friction between the powders, resulting in isolated pores formed by the stacking of powders during the sintering. By adding NH 4 HCO 3 , the pore size, and structure can be well controlled, and an interconnected pore structure can be obtained upon the decomposition of the NH 4 HCO 3 . Further, a uniform microstructure and pore distributions can be achieved through the U-10Zr fuel pellets by controlling current flow during the volume control SPS sintering. The microstructure and phase characterization of the sintered porous U-10Zr pellets show major phases of α-U and α-Zr for the sample with short dwelling. For the sample with long dwelling (30 min), the ω UZr 2 in the Zr-enriched area has been observed. The strategy of volume control SPS sintering with the assistance of pore formers could be used to fabricate porous U-10Zr metallic fuels to mimic the microstructure evolution of irradiated metallic fuels (including porosity) and could enable a possible solution for the design of new sodium-free metallic fuels for high burnup.
This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.
Most porous media, such as geomaterials and biomaterials are highly heterogeneous in nature, and they contain large variations of microscopic pore structures, such as pore sizes, pore distribution, and pore shapes. The oscillation of microscopic structures is a substantial challenge in theoretical characterization and is usually ignored in continuous modeling. However, mechanical behavior of porous media such as deformation and failure, are essentially impacted by the microscopic heterogeneity which needs to be considered in modeling a porous media. Here, this research proposes a numerical modeling framework with a capability to investigate the effect of microscopic heterogeneity on the macroscopic fracture behavior in porous media by using a numerical homogenization technique, combined with the phase-field fracture modeling method. This numerical modeling strategy computes a homogenized elasticity tensor based on microscopic heterogeneous pore structures heterogenous porous domain by solving boundary value problems at microscopic domain. The strain energy and subsequent propagation of macroscopic fractures will be updated using homogenized stiffness information. Using this numerical scheme, the microscopic pore structure’s impact on the fracture behavior through the homogenized elastic tensor will be taken into account. This multiscale technique is benchmarked against classical problems. Finally, the results highlight the importance of the underlying pore structure and reveal that both fracture strength and propagation path can be influenced by the microscopic heterogeneity.
The pore structure of biogenic materials imbues the ability to deliver water and nutrients through a plant from root to leaf. This anisotropic pore granularity can also play a significant role in processes such as biomass pyrolysis that are used to convert these materials into useful products like heat, fuel, and chemicals. Evolutions in modeling of biomass pyrolysis as well as imaging of pore structures allow for further insights into the concerted physics of phase change-induced off-gassing, heat transfer, and chemical reactions. In this work, we report a biomass single particle model which incorporates these physics to explore the impact of implementing anisotropic permeability and diffusivity on the conversion time and yields predicted for pyrolysis of oak and pine particles. Simulation results showed that anisotropic permeability impacts predicted conversion time more than 2 times when the Biot number is above 0.1 and pyrolysis numbers (Py 1 , Py 2 ) are less than 20. Pore structure significantly impacts predicted pyrolytic conversion time (>8 times) when the Biot number is above 1 and the pyrolysis number is below 1, i.e., the “conduction controlled” regime. Therefore, these nondimensional numbers reflect that when internal heat conduction limits pyrolysis performance, internal pyrolysis off-gassing further retards effective heat transfer rates as a closely coupled phenomenon. Overall, this study highlights physically meaningful opportunities to improve particle-scale pyrolysis modeling and experimental validation relevant to a variety of feedstock identities and preparations, guiding the future design of pyrolyzers for efficient biomass conversion.
Abstract Porous polydimethylsiloxane (PDMS) framework with adjustable pore structures has been fabricated by polymerization of the continuous phase in the emulsion templates. Different types of surfactants, including nonionic (Triton X‐100), cationic (Benzalkonium chloride), anionic (sodium dodecyl sulfate), and silicone‐based block copolymer were used to stabilize the water‐in‐oil emulsion system. Deionized water with a wide range of internal phases varying from 10% to 60% by weight was employed to make the low internal phase emulsion and medium internal phase emulsion. The effect of surfactant type, surfactant concentration, and the internal phase volume on the stability of the emulsion, pore morphology, and pore size distribution was explored. The stability of the emulsion was investigated by comparing the pore morphology of the cured sample at different set times, such as 0, 6, and 72 h. Scanning electron microscopy was employed for the characterization of the porous structures. The image analysis was conducted, and the pore size distribution, porosity, and open‐cell ratio of each sample were calculated. Interconnected pores have been seen in the porous PDMS made from emulsions with an internal phase larger than 30%. The results demonstrated that the frequency of open‐cell pores and the pore size is dependent on the surfactant types.