Kinetic characterization and modeling of sequentially entrapped enzymes in 3D-printed PMMA microfluidic reactors for the synthesis of amorphadiene via the isopentenol utilization pathway.

The development of cascade cell-free systems reduces the requirement for extensive metabolic engineering and optimization to increase in vivo pathway flux. For continuous operation and increased stability, direct enzyme entrapment during reactor fabrication by three-dimensional (3D)-printing allows for simple immobilization procedures without enzyme-specific optimization. In this study, the isopentenol utilization pathway (IUP) was selected for the synthesis of amorphadiene, an antimalaria drug precursor, using a 3D-printed, sequentially immobilized, microfluidic reactor. As an initial proof-of-concept, alkaline phosphatase (ALP) was entrapped in a poly(methyl methacrylate) (PMMA)-based matrix during stereolithographic 3D-printing and was kinetically characterized. No significant shift of the kinetically modeled substrate binding affinity was observed during immobilization and continuous operation of an entrapped ALP microfluidic reactor displayed high stability. The IUP enzymes retained moderate activity during entrapment (6.6%-9.6%) relative to the free enzyme solutions, however the sequentially immobilized IUP microfluidic reactor was severely limited by low pathway flux due to the use of stereolithographic 3D-printing which significantly diluted enzyme concentrations for printing. Although this study demonstrated the use of additive manufacturing for the synthesis of amorphadiene using a complex five-enzyme cascade microfluidic reactor, stereolithographic enzyme entrapment remains limited in scope and dependent on advancements to additive manufacturing technologies.

Insights into the Effect of Structural Heterogeneity in Carbonized Electrospun Fibrous Mats for Flow Battery Electrodes by X-Ray Tomography.

Electrospun custom made flow battery electrodes are imaged in 3D using X-ray computed tomography. A variety of computational methods and simulations are applied to the images to determine properties including the porosity, fiber size, and pore size distributions as well as the material permeability and flow distributions. The simulations are performed on materials before and after carbonization to determine the effect it has in the internal microstructure and material properties. It is found that the deposited fiber size is constantly changing throughout the electrospinning process. The results also show that the surfaces of the fibrous material are the most severely altered during carbonization and that the rest of the material remained intact. Pressure driven flow is modeled using the lattice Boltzmann method and excellent agreement with experimental results is found. The simulations coupled with the material analysis also demonstrate the highly heterogeneous nature of the flow. Most of the flow is concentrated to regions with high porosity while regions with low porosity shield other pores and starve them of flow. The importance of imaging these materials in 3D is highlighted throughout.

Versatile and efficient pore network extraction method using marker-based watershed segmentation.

Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of 500^{3} voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided.

Electrocatalytic Oxygen Reduction Performance of Silver Nanoparticle Decorated Electrochemically Exfoliated Graphene.

We have developed a potentiostatic double-pulse technique for silver nanoparticle (Ag NP) deposition on graphene (GRn) with superior electronic and ionic conductivity. This approach yielded a two-dimensional electrocatalyst with a homogeneous Ag NP spatial distribution having remarkable performance in the oxygen reduction reaction (ORR). GRn sheets were reproducibly prepared by the electrochemical exfoliation of graphite (GRp) at high yield and purity with a low degree of oxidation. Polystyrenesulfonate added during exfoliation enhanced the stability of the GRn solution by preventing the restacking of the graphene sheets and increased its ionic conductivity. The potentiostatic double-pulse technique is generally used to electrodeposit Pt nanoparticles and remains challenging for silver metal that exhibits nucleation and growth potentials relatively close to each other. We judiciously exploited this narrow margin of potential, and for the first time we report Ag NP electrodeposited onto graphene with the subsequent ability to control both the density and the size of metallic nanoparticles. Considering the high activity along with the lower cost of Ag compared to Pt, these findings are highly relevant to the successful commercialization of fuel cells and other electrochemical energy devices.

Effect of water sorption on the electronic conductivity of porous polymer electrolyte membrane fuel cell catalyst layers.

A method is described for measuring the effective electronic conductivity of porous fuel cell catalyst layers (CLs) as a function of relative humidity (RH). Four formulations of CLs with different carbon black (CB) contents and ionomer equivalent weights (EWs) were tested. The van der Pauw method was used to measure the sheet resistance (RS), which increased with RH for all samples. The increase was attributed to ionomer swelling upon water uptake, which affects the connectivity of CB aggregates. Greater increases in RS were observed for samples with lower EW, which uptake more water on a mass basis per mass ionomer. Transient RS measurements were taken during absorption and desorption, and the resistance kinetics were fit using a double exponential decay model. No hysteresis was observed, and the absorption and desorption kinetics were virtually symmetric. Thickness measurements were attempted at different RHs, but no discernible changes were observed. This finding led to the conclusion that the conducting Pt/C volume fraction does not change with RH, which suggests that effective medium theory models that depend on volume fraction alone cannot explain the reduction in conductivity with RH. The merits of percolation-based models were discussed. Optical micrographs revealed an extensive network of "mud cracks" in some samples. The influence of water sorption on CL conductivity is primarily explained by ionomer swelling, and its effects on the quantity and quality of interaggregate contacts were discussed.