Physicochemical influences on electrohydrodynamic transport in compressible packed beds of colloidal boehmite particles

Production and processing of colloidal particles require a deeper understanding of surface charge of particles and interaction of mass and charge transport in packed beds. The assessment of fundamental parameters is rather complex due to the additional influence of the particle charge on the structure of a packed bed. The combination of different measurement techniques (streaming potential and electroosmosis) allows for separating the effects, based on the postulation of a new method to quantify the ratio of surface conductance to liquid conductance. The purpose of this paper is to investigate the influence of the pH value and compression on the electrohydrodynamic transport parameters.Comment: 13 pages including 7 figures, accepted by Journal of Colloid and Interface Scienc

Agglomeration and filtration of colloidal suspensions with DVLO interactions in simulation and experiment

Cake filtration is a widely used solid-liquid separation process. However, the high flow resistance of the nanoporous filter cake lowers the efficiency of the process significantly. The structure and thus the permeability of the filter cakes depend on the compressive load acting on the particles, the particles size, and the agglomeration of the particles. The latter is determined by the particle charge and the ionic strength of the suspension, as described by the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. In this paper, we propose a combined stochastic rotation dynamics (SRD) and molecular dynamics (MD) methodology to simulate the cake formation. The simulations give further insight into the dependency of the filter cakes' structure on the agglomeration of the particles, which cannot be accessed experimentally. The permeability, as investigated with lattice Boltzmann (LB) simulations of flow through the discretized cake, depends on the particle size and porosity, and thus on the agglomeration of the particles. Our results agree qualitatively with experimental data obtained from colloidal boehmite suspensions.Comment: revised version, 30 pages, 11 figures, 62 reference

Metrics for measuring distances in configuration spaces

In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. We show that these metrics correlate well with the RMSD between two configurations if this RMSD is obtained from a global minimization over all translations, rotations and permutations of atomic indices. We introduce a Monte Carlo approach to obtain this global minimum of the RMSD between configurations

Finding Reaction Pathways with Optimal Atomic Index Mappings

Finding complex reaction and transformation pathways involving many intermediate states is, in general, not possible on the density-functional theory level with existing simulation methods, due to the very large number of required energy and force evaluations. For complex reactions, it is not possible to determine which atom in the reactant is mapped onto which atom in the product. Trying out all possible atomic index mappings is not feasible because of the factorial increase in the number of possible mappings. We use a penalty function that is invariant under index permutations to bias the potential energy surface in such a way that it obtains the characteristics of a structure seeker, whose global minimum is the reaction product. By performing a minima-hopping-based global optimization on this biased potential energy surface, we rapidly find intermediate states that lead into the global minimum and allow us to then extract entire reaction pathways. We first demonstrate for a benchmark system, namely, the Lennard-Jones cluster LJ 38 , that our method finds intermediate states relevant to the lowest energy reaction pathway, and hence we need to consider much fewer intermediate states than previous methods to find the lowest energy reaction pathway. Finally, we apply the method to two real systems, C 60 and C 20 H 20 , and show that the reaction pathways found contain valuable information on how these molecules can be synthesized

Computationally efficient characterization of potential energy surfaces based on fingerprint distances

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This method allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling. Published by AIP Publishing

Changes in the genetic structure of an invasive earthworm species (Lumbricus terrestris, Lumbricidae) along an urban – rural gradient in North America

European earthworms were introduced to North America by European settlers about 400 years ago. Human-mediated introductions significantly contributed to the spread of European species, which commonly are used as fishing bait and are often disposed deliberately in the wild. We investigated the genetic structure of Lumbricus terrestris in a 100km range south of Calgary, Canada, an area that likely was devoid of this species two decades ago. Genetic relationships among populations, gene flow, and migration events among populations were investigated using seven microsatellite markers and the mitochondrial 16S rDNA gene. Earthworms were collected at different distances from the city and included fishing baits from three different bait distributors. The results suggest that field populations in Alberta established rather recently and that bait and field individuals in the study area have a common origin. Genetic variance within populations decreased outside of the urban area, and the most distant populations likely originated from a single introduction event. The results emphasise the utility of molecular tools to understand the spatial extent and connectivity of populations of exotic species, in particular soil-dwelling species, that invade native ecosystems and to obtain information on the origin of populations. Such information is crucial for developing management and prevention strategies to limit and control establishment of non-native earthworms in North America.Peer reviewe

A fingerprint based metric for measuring similarities of crystalline structures

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell we introduce crystal fingerprints that can be calculated easily and allow to define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method is an useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms and high-throughput screenings

Staged treatment of a comminuted femoral fracture with Masquelet technique and 3D printed reposition guides.

Background Comminuted femoral fractures pose a challenge to the trauma surgeon due to the absence of bony references during surgery. Therefore, malalignment of length and axis can occur and necessitate revision surgery. During the last decade, 3D-planning has evolved as a surgical aid in difficult cases. Case report An 18-year-old male patient suffered a polytrauma following a motorcycle accident. This report is about the treatment of a 3rd degree open and comminuted fracture of the left distal femur. The fracture was treated with Masquelet's two-staged technique. With the intent of avoiding malalignment, the second stage surgery was performed with the aid of 3D-planned reduction guides. Despite complex fracture pattern, complete fracture union was achieved with acceptable final alignment (side-to-side comparison of length, axis and femoral torsion). Conclusion In this case, performing Masquelet's two-staged surgery with the aid of 3D-printed reposition guides yielded favorable results in regards to rotational malalignment. The malrotation of the femur was reduced after the second operation to a clinically acceptable side-to-side difference (10°). This technique remains technically challenging due to soft tissue tension and limited possibility of soft tissue release

Application of a genome-based predictive CHO model for increased mAb production and Glycosylation control

Monoclonal antibody therapeutics continue to grow in both number and market share with recent forecasts of global sales reaching ~$125MM by 2020. Most mAb products currently on the market are produced using cultured mammalian cells, typically Chinese Hamster Ovary (CHO) cells, which provide the necessary post-translational modifications to make the antibody efficacious. Many post-translational modifications such as the oligosaccharide profile are considered critical quality attributes (CQAs) that must be tightly controlled throughout the manufacturing process to ensure product safety and effectiveness. Therefore, the ability to predict how cell culture media components, including potential contaminants like trace metals, will affect product formation and glycosylation is important from both a process development and process control viewpoint. A detailed genome-based, predictive CHO model from the Insilico Cells™ library was adapted by the reconstruction software Insilico Discovery™ for a representative fed-batch process through a collaborative effort leveraging the computational and experimental expertise of two companies. The final, compartmentalized network model contained 1900 reactions (including transport reactions), 1300 compounds and contains stoichiometric descriptions of anabolic pathways for amino acids, lipids and carbohydrate species. The genome-scale model was constrained using several assumptions on the cell physiology and then used to compute time-resolved flux distributions by the software module Insilico Inspector™. The Insilico Designer™ module was then used to subsequently reduce the large model to a computationally manageable reduced model able to describe all flux distributions using 5 flux modes, of which 4 combined several metabolic functions and one is independently responsible for product synthesis. Using Insilico Designer™, the kinetic parameters of the reduced model were estimated by fitting the model-predicted metabolite concentrations to the experimentally determined values. The calibrated model was able to properly describe the time-dependent trajectories of biomass, product and most metabolites. Simulations using the reduced model were run and a media composition predicted to improve mAb production was identified and experimentally verified. Furthermore, experiments probing the effects of trace metals on product glycosylation were used to extend the model’s glycosylation predictability. The ability to identify both metabolic signatures, as well as media components, that correlate to specific glycan profiles will allow for fine-tuning of desired CQAs and enable more robust control strategies in upstream processes