In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption.

The increased use of transition fuels, such as natural gas, and the resulting increase in methane emissions have resulted in a need for novel methane storage materials. Metal-organic frameworks (MOFs) have shown promise as efficient storage materials. A virtually limitless number of potential MOFs can be hypothesized, which exhibit a wide variety of different structural and chemical characteristics. Because of the numerous possibilities, identification of the best MOF for methane storage can be a potentially challenging problem. In this work, determination of the best such MOF was cast as an inverse function problem. The function, a random forest (RF) model using 12 structural and chemical descriptors, was trained on 10% of a data set consisting of 130 398 hypothetical MOFs (hMOFs) to predict simulated methane uptake. The RF model was tested on the remaining 90% of the data. After validation, a genetic algorithm (GA) was used to evolve in silico the best MOFs for methane adsorption. The RF model was imbedded into the GA as the fitness function to predict the methane uptake of the evolved MOFs (eMOFs). The best 15 eMOFs matched hMOFs found in the top 1% of the database. Nine of the 15 eMOFs were found in the top 0.1%. More impressively, two of the eMOFs matched the top two hypothetical MOFs with the highest methane uptake values out of the entire database of 130 398 MOFs. Further, by leveraging the ensemble nature of the GA, it was possible to characterize the importance of the different material properties for methane adsorption, providing fundamental insight for future material design strategies.

Enhancing iCVD Modification of Electrospun Membranes for Membrane Distillation Using a 3D Printed Scaffold.

Electrospun membranes have shown promise for use in membrane distillation (MD) as they exhibit exceptionally low vapor transport. Their high porosity coupled with the occasional large pore can make them prone to wetting. In this work, initiated chemical vapor deposition (iCVD) is used to modify for electrospun membranes with increased hydrophobicity of the fiber network. To demonstrate conformal coating, we demonstrate the approach on intrinsically hydrophilic electrospun fibers and render the fibers suitable for MD. We enable conformal coating using a unique coating procedure, which provides convective flow of deposited polymers during iCVD. This is made possible by using a 3D printed scaffold, which changed the orientation of the membrane during the coating process. The new coating orientation allows both sides as well as the interior of the membrane to be coated simultaneously and reduced the coating time by a factor of 10 compared to conventional CVD approaches. MD testing confirmed the hydrophobicity of the material as 100% salt rejections were obtained.

Highly Conductive In-SnO2/RGO Nano-Heterostructures with Improved Lithium-Ion Battery Performance.

The increasing demand of emerging technologies for high energy density electrochemical storage has led many researchers to look for alternative anode materials to graphite. The most promising conversion and alloying materials do not yet possess acceptable cycle life or rate capability. In this work, we use tin oxide, SnO2, as a representative anode material to explore the influence of graphene incorporation and In-doping to increase the electronic conductivity and concomitantly improve capacity retention and cycle life. It was found that the incorporation of In into SnO2 reduces the charge transfer resistance during cycling, prolonging life. It is also hypothesized that the increased conductivity allows the tin oxide conversion and alloying reactions to both be reversible, leading to very high capacity near 1200 mAh/g. Finally, the electrodes show excellent rate capability with a capacity of over 200 mAh/g at 10C.