Some effects of porous structure in an oxygen consuming cathode
Porous structure effects in oxygen consuming cathode, discussing electrode thickness, effective pore diameter, pressure difference and electrode performance
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Porous structure effects in oxygen consuming cathode, discussing electrode thickness, effective pore diameter, pressure difference and electrode performance
The recycle, reuse, or reclamation of single cell protein from liquid and solid agricultural waste fibers by a fermentation process is reported. It is shown that cellulose comprises the bulk of the fibers at 50% to 55% of the dry weight of the refuse and that its biodegradability is of prime importance in the choice of a substrate. The application of sodium hydroxide followed by heat and pressure serves to de-polymerize and disrupt lignin structure while swelling the cellulose to increase water uptake and pore volume. Some of the lignin, hemi-celluloses, ash, and cellulose of the material is hydrolized and solubilized. Introduction of microorganisms to the substrate fibers mixed with nutrients produces continuous fermentation of cellulose for further protein extraction and purification.
Highly porous, fibrous bulk sound absorbing materials are studied with a view toward understanding their acoustical properties and performance in a wide variety of applications including liners of flow ducts. The basis and criteria for decoupling of acoustic waves in the pores of the frame and compressional waves in the frame structure are established. The equations of motion are recast in a form that elucidates the coupling mechanisms. The normal incidence surface impedance and absorption coefficient of two types of Kevlar 29 and an open celled foam material are studied. Experimental values and theoretical results are brought into agreement when the structure factor is selected to provide a fit to the experimental data. A parametric procedure for achieving that fit is established. Both a bulk material quality factor and a high frequency impedance level are required to characterize the real and imaginary part of the surface impedance and absorption coefficient. A derivation of the concepts of equivalent density and dynamic resistance is presented.
The development of the monogroove heat pipe, a fundamentally new high-performance device suitable for multi-kilowatt space radiator heat-rejection systems, is reported. The design separates heat transport and transfer functions, so that each can be separately optimized to yield heat transport capacities on the order of 25 kW/m. Test versions of the device have proven the concept of heat transport capacity control by pore dimensions and the permeability of the circumferential wall wick structure, which together render it insensitive to tilt. All cases tested were for localized, top-side heat input and cooling and produced results close to theoretical predictions.
Improved cadmium electrode substrates with precisely controlled microstructures for possible use in aerospace nickel-cadmium cells were prepared. The preparative technique was a powder metallurgical process in which a fugitive pore-former and a nickel powder were blended, then isostatically compacted, and subsequently sintered. Cadmium electrodes prepared from such substrates were cycle tested using an accelerated tortuous test regime. It was discovered that plaques of 60% or 80% porosity prepared with a 25 micron pore-former were better than state-of-the-art electrodes in terms of efficienty and/or mechanical strength. The 60% structures were particularly outstanding in this respect in that they had efficiencies only 5-10 percentage points lower than state-of-the-art electrodes and vastly superior mechanical properties. This added strength was observed to eliminate cracking and physical degradation of the electrodes during processing and cycling. The cadmium electrodes prepared from the 80% porous substrates proved to be the best electrodes made during the course of the work from the point of view of highest efficiency. Three-point bend tests were used to measure mechanical properties of the plaques produced and also as a general characterization tool. In addition, the BET surface areas of selected specimens was determined. The SEM was used for judging microscopic uniformity and quantitatively determining the induced pore size and various other fine structures in the substrates. The technique of X-ray radiography was used to follow the bulk uniformity of the substrates at various stages of their processing.
Certain anions OH(-), F(-) and Gl(-) are shown to enhance grain growth in MgO. The magnitude of their effect decreases in the order in which the anions are listed and depends on their location (solid-solution, second phase) in the MgO lattice. As most anions exhibit relatively high vapor pressures at sintering temperatures, they retard densification and invariably promote residual porosity. The role of anions on grain growth rates was studied in relation to their effect on pore mobility and pore removal; the atomic process controlling the actual rates was determined from observed kinetics in conjunction with the microstructural features. With respect to controlling mechanisms, the effects of all anions are not the same. OH(-) and F(-) control behavior through creation of a defect structure and a grain boundary liquid phase while Cl(-) promotes matter transport within pores by evaporation-condensation. Studies on an additional anion, S to the minus 2nd power gave results which were no different from undoped MgO, possibly because of evaporative losses during hot pressing. Hence, the effect of sulphur is negligible or undetermined.
The assembled state of nanoparticles (NPs) within porous matrices plays a governing role in directing their biological, electronic, and catalytic properties. However, the effects of the spatial confinement and environmental factors, such as salinity, on the NP assemblies within the pores are poorly understood. In this study, we use adsorption isotherms, spectrophotometry, and small-angle neutron scattering to develop a better understanding of the effect of spatial confinement on the assembled state and catalytic performance of gold (Au) NPs in propylamine-functionalized SBA-15 and MCM-41 mesoporous silica materials (mSiO 2 ). We carry out a detailed investigation of the effect of pore diameter and ionic strength on the packing and spatial distribution of AuNPs within mSiO 2 to get a comprehensive insight into the structure, functioning, and activity of these NPs. We demonstrate the ability of the adsorbed AuNPs to withstand aggregation under high salinity conditions. We attribute the observed preservation of the adsorbed state of AuNPs to the strong electrostatic attraction between oppositely charged pore walls and AuNPs. The preservation of the structure allows the AuNPs to retain their catalytic activity for a model reaction in high salinity aqueous solution, here, the reduction of p-nitrophenol to p-aminophenol, which otherwise is significantly diminished due to bulk aggregation of the AuNPs. This fundamental study demonstrates the critical role of confinement and dispersion salinity on the adsorption and catalytic performance of NPs.
Microstructural changes induced by neutron irradiation of superfine grain graphite G347A (Tokai Carbon, Japan) were examined by nitrogen adsorption at 77K and by three microscopy techniques (SEM, TEM and FIB-SEM tomography). The specimens were irradiated at doses of up to 30 dpa, covering stages before and after the turnaround fluence at three temperatures (300, 450, 750 °C) of their irradiation envelope. The initial graphite densification at low fluences did not produce any detectable effect in the pore size range (<350 nm) measured by gas adsorption. However, graphite irradiated at high fluences, after turnaround, showed severe structural changes. At all three temperatures and high irradiation fluences, gas adsorption revealed significant increase of the volume of narrow mesopores (<5–20nm) and up to five times increase of BET surface area, both in linear relationship with the relative volume expansion. Analysis of microscopy images showed multiplication of fine macropores (>50 nm) at high irradiation fluences and more structural changes on multiple scales, from nanometers to microns. This work demonstrates the unique ability of gas adsorption techniques to analyze open pores with sizes between sub-nanometer and sub-micron in bulk nuclear graphite, with supporting microscopy results.
The development of large pore single-crystalline covalently linked organic frameworks is critical in revealing the detailed structure-property relationship with substrates. One emergent approach is to photo-crosslink hydrogen-bonded molecular crystals. Introducing complementary hydrogen-bonded carboxylic acid building blocks is promising to construct large pore networks, but these molecules often form interpenetrated networks or non-porous solids. In this report we introduced heteromeric carboxylic acid dimers to construct a non-interpenetrated molecular crystal. Crosslinking this crystal precursor with dithiols afforded a large pore single-crystalline hydrogen-bonded crosslinked organic framework H C OF-101. X-ray diffraction analysis revealed H C OF-101 as an interlayer connected hexagonal network, which possesses flexible linkages and large porous channels to host a hydrazone photoswitch. Multicycle Z/E-isomerization of the hydrazone took place reversibly within H C OF-101, showcasing the potential use of H C OF-101 for optical information storage.
While surface segregation has been intensively studied in thin films, relatively little has been investigated in filament geometries. In contrast to thin films, the filament surface forms a continuum, with interactions both at the free air and internal filament interface, which makes it difficult to achieve an equilibrium configuration. This asymmetry has become particularly apparent with the increasing use of additive manufacturing where entire structures are constructed via layered deposition of filamentous layers from a moving thermal nozzle. In addition, this process is highly non-equilibrated with poor thermal retention and is responsible for insufficient filament–filament interfacial fusion and internal pore formation along the printing direction. These features all contribute to poor mechanical and structural properties, which are exacerbated in semi-crystalline polymers, such as Polylactic Acid (PLA), where the melting point, T m , is much higher than the glass transition, T g . Even though system is liquid the molecules become ordered within the crystal structure and this ordering interferes with interdiffusion, which in turn prevents proper interfacial fusion. Capitalizing on the differential in surface tension, we show that addition of less than 1%, or trace amounts, of longer chain polymers, an oriented core shell structure, will be formed with a self-limiting thickness of three time the radius of gyration, R g , of the tracer chains. The lower T g of the tracers and their orientation makes them poised for interdiffusion when subsequent layers are deposited producing structures with significant improvement of the mechanical properties and internal structural integrity.
The purification of C 2 H 4 from C 2 H 6 /C 2 H 4 /C 2 H 2 mixtures is of great significance in the chemical industry for C 2 H 4 production but remains a daunting task. Guided by powerful reticular chemistry principles, herein a systematic study is carried out to engineer pore dimensions and pore functionality of fcu-type Y-based metal–organic frameworks (Y-MOFs) through the construction of a series of eight new structures using linear dicarboxylate linkers with different length and functional groups. This study illustrates how delicate changes in pore size and pore surface chemistry can effectively influence the adsorption preference of C 2 H 6 , C 2 H 4 , and C 2 H 2 by the MOFs. Importantly, clear relations between pore size/pore surface polarity and C 2 adsorption selectivities of this series of MOFs are established. In particular, HIAM-326 built on a linker decorated with trifluoromethoxy group shows notably preferential adsorption of C 2 H 6 and C 2 H 2 over C 2 H 4 , with balanced C 2 H 2 /C 2 H 4 and C 2 H 6 /C 2 H 4 selectivities. Furthermore, this endows the compound with the capability of one-step purification of C 2 H 4 from C 2 H 6 /C 2 H 4 /C 2 H 2 ternary mixtures, which is validated by breakthrough measurements where high purity C 2 H 4 (99.9%+) can be obtained directly from the separation column. Its adsorption thermodynamics and underlying selective adsorption mechanisms are further revealed by ab initio calculations.
Type A γ-aminobutyric acid receptors (GABA A Rs) are the principal inhibitory receptors in the brain and the target of a wide range of clinical agents, including anaesthetics, sedatives, hypnotics and antidepressants. However, our understanding of GABA A R pharmacology has been hindered by the vast number of pentameric assemblies that can be derived from 19 different subunits and the lack of structural knowledge of clinically relevant receptors. Here, we isolate native murine GABA A R assemblies containing the widely expressed α1 subunit and elucidate their structures in complex with drugs used to treat insomnia (zolpidem (ZOL) and flurazepam) and postpartum depression (the neurosteroid allopregnanolone (APG)). Using cryo-electron microscopy (cryo-EM) analysis and single-molecule photobleaching experiments, we uncover three major structural populations in the brain: the canonical α1β2γ2 receptor containing two α1 subunits, and two assemblies containing one α1 and either an α2 or α3 subunit, in which the single α1-containing receptors feature a more compact arrangement between the transmembrane and extracellular domains. Interestingly, APG is bound at the transmembrane α/β subunit interface, even when not added to the sample, revealing an important role for endogenous neurosteroids in modulating native GABA A Rs. Together with structurally engaged lipids, neurosteroids produce global conformational changes throughout the receptor that modify the ion channel pore and the binding sites for GABA and insomnia medications. Our data reveal the major α1-containing GABA A R assemblies, bound with endogenous neurosteroid, thus defining a structural landscape from which subtype-specific drugs can be developed.
Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study.
Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.
Global warming due to climate change is a threat to humankind. Nuclear energy is one of the promising solutions to reduce fossil fuel usage. Nuclear energy can handle the base load, compensating for the volatility of renewable energy. If nuclear energy could achieve load following capability, the combination with renewable energy would be more suitable. Thermal energy storage (TES) is one of the options for enabling load following of nuclear reactors. The TES makes it possible to store surplus nuclear thermal energy and release it later as needed. In Idaho National Laboratory (INL), a new concept of latent heat TES integrated with high-temperature heat pipe has been proposed and is under development, which is called Heat pipe-Integrated Thermal Battery (HITB). HITB exchanges thermal energy between the reactor system and TES via heat pipe. The heat transferred to TES medium, made of phase change material (PCM), stores energy as sensible heat and/or latent heat. As PCM typically has poor thermal conductivity, however, various heat transfer enhancement techniques are required to achieve a rapid charging cycle. There are many techniques to enhance the heat transfer ability of TES medium such as disk, fin, and metal foam. Among them, metal foam is an appropriate option to enhance the heat transfer because it maximizes the heat transfer area through metal wicks. Metal foam is a lightweight metal structure that has a high porosity of over 0.9. The typical materials for metal foam are Aluminum, Copper, Nickel, and Silicon Carbide (SiC). Metal foam not only enhances heat transfer via conduction but also increases contact surface area. In the HITB design , the metal foam is being considered as one of the options to enhance the heat transfer of TES medium (PCM) [1]. To predict the enhanced thermal performance of TES, one should properly estimate the effective thermal conductivity of metal foam combined with PCM material or calculate heat transfer in distributed model. There are many experimental works that provides effective thermal conductivity of metal foam with various PCM [2,3]. Also, many theoretical models were developed based on the unit cell model of metal foam [4,5]. With a distributed model, on the other hand, detail heat transfer characteristics between metal foam and PCM material can be analyzed considering the geometry or buoyancy effect. However, due to the complex geometry of metal foam pores, the computational cost for three-dimensional modeling highly increases. Therefore, if metal foam structure can be modeled in simple and repetitive design, the computational cost would decrease Among the various metal foam models [2], lattice model is one of the simple and extendable design. The porosity and pores per inch (PPI) can be characterized by the size and spatial distance of lattice structure. If the three-dimensional metal foam model consists of lattice structure could properly estimate the heat transfer, which is characterized by effective thermal conductivity, it would be a good option to assess the thermal performance of metal foam with PCM. In this study, a three-dimensional numerical model was developed to simulate conductive heat transfer between metal foam and PCM. The three-dimensional lattice structure of square pillars was selected as a basic structure of the metal foam. The calculation result was characterized by the effective thermal conductivity of the whole domain. A sensitivity study was conducted for mesh size, domain size, and PPI to check whether the calculation result gives a converged result or not. Lastly, the effective thermal conductivity from the lattice model was compared with existing experimental data to validate the model result
Accurate prediction of water adsorption in micro- and mesoporous materials with hydrophobic pores is essential for the design and characterization of advanced adsorbent materials for separation and energy applications. Here, we assess the reproducibility and consistency of water adsorption isotherms in two microporous all-silica MFI zeolite structures (MFI-K and MFI-O) using two different zeolite force fields and three simulation approaches: grand canonical Monte Carlo (GCMC), Gibbs ensemble Monte Carlo (GEMC), and transition matrix Monte Carlo (TMMC). We demonstrate that consistent treatment of the bulk fluid phase in GCMC and TMMC simulations is critical for reconciling isotherms across methods, and we construct simulation-based equations of state for the TIP4P water model to enable rigorous fugacity-to-pressure conversions. Large shifts in the isotherms are observed for two zeolite force fields developed using different parametrization strategies, with the GCS force field representing implicitly a defect-containing all-silica zeolite, whereas the TraPPE-zeo force field accurately represents an essentially defect-free all-silica zeolite. While water in the van Koningsveld structure of MFI exhibits a first-order phase transition and condensation-like step for adsorption near room temperature, water in the Olson structure of MFI displays continuous adsorption, attributed to differences in the adsorption free energy landscapes. Structural analysis reveals that small geometric variations, particularly Si–O–Si bond angles near the strongest adsorption sites, lead to these substantial differences in adsorption behavior. Furthermore, our results highlight the sensitivity of simulated water adsorption isotherms in hydrophobic frameworks to seemingly small differences in the framework structures, force field parametrization, and simulation approaches.
The phase behavior of hydrocarbon fluids confined in porous media has been discussed to deviate significantly from that in the bulk environment due to the existence of sub-10nm pores. Though experiments and simulations have measured the bubble/dew points and sorption isotherms of hydrocarbons confined in both natural and synthetic nanopores, the confinement effects in terms of the strength of fluid-pore interactions tuned by surface wettability and chemistry have received comparably less discussion. More significantly, the underlying physics of confinement-induced phenomena remain obfuscated. In this work, we studied the phase behavior and capillary condensation of n-hexane to understand the effects of confinement at the molecular level. To systematically investigate the pore effects, we constructed two types of wall confinements; one is a structureless virtual wall described by the Steele potential and the other one is an all-atom amorphous silica structure with surface modified by hydroxyl groups. Our numerical results demonstrated the importance of fluid-pore interaction, pore size, and pore morphology effects in mediating the pressure-volume-temperature (PVT) properties of hydrocarbons. The most remarkable finding of this work was that the saturation pressure predicted from the van der Waals-type adsorption isothermal loop could be elevated or suppressed relative to the bulk phase, as illustrated in the graphical abstract. As the surface energy (i.e., fluid-pore interaction) decreased, the isothermal vapor pressure increased, indicating a greater preference for the fluid to exist in the vapor state. Sufficient reduction of the fluid-pore interactions could even elevate the vapor pressure above that of the bulk fluid.
The nature of ion–ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion–ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene’s electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes.