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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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Biotemplating of a Highly Porous Cellulose–Silica Composite from Apium graveolens by a Low-Toxicity Sol–Gel Technique
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Engineering the atomic interface of porous ceria nanorod with single palladium atoms for hydrodehalo
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Comparative Study of Flash and Acid Hydrolysis of Microalgae (Scenedesmus sp.) for the Recovery of Biochemicals and Production of Porous Biocarbon Nanosheets
An integrated biorefinery concept is a novel and economical process intensification methodology for efficient utilization of biomass components. In this research, microalgae (Scenedesmus sp.) slurry with biomass concentration of 8.5 wt.% was parallelly fractionated using two techniques: 'flash hydrolysis (FH)' and 'acid hydrolysis (AH)'. FH was performed at 240 degrees C with a residence time of 10 +/- 2 s in a continuous flow reactor, whereas AH was performed at 155 degrees C and reaction time of 15 min in a batch reactor. About 63% of microalgal biomass was solubilized in liquid hydrolysate through both FH and AH. However, AH had an advantage over FH in recovering microalgae proteins and carbohydrates. FAME recovery through solvent extraction from FH and AH derived wet solids (insoluble microalgae) was 40 and 63%, respectively. Finally, the FH- and AH-derived post extraction solid residue was thermally activated using K2CO3 to produce highly microporous biocarbon nanosheets with BET surface areas of 712 and 1289 m2 g-1, respectively. Overall, an integrated process was developed using two potential hydrolysis techniques to maximize utilization of microalgae components.
3D-ink-extruded titanium scaffolds with porous struts and bioactive supramolecular polymers for orthopedic implants
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Hierarchically-porous metallic scaffolds via 3D extrusion and reduction of oxide particle inks with
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Hierarchical 3D Printed Porous Silicones with Porosity Derived from Printed Architecture and Silicone-Shell Microballoons
A method is reported to prepare siloxane microballoons that can be loaded into a direct ink write siloxane ink for 3D printing. The resulting structure is subjected to a solvent removal and supercritical drying process to provide a final printed siloxane having both macroscopic and microscopic porosity. Core-shell particles are generated by an emulsion template and phase separation process. Intra-strand closed-cell porosities can be achieved between 10 and 40 vol% thus facilitating additional compressive strain before lockup. Here short term compression set testing highlights the excellent shape retention properties of these elastomers, which have less than 8% relative compression set under load.
Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media
Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution. Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O ( n log n ) instead of the typical O ( n 3 ) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Finally, although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.
3D porous graphene/NiCo2O4 hybrid film as an advanced electrode for supercapacitors
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Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media
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