Description:

Our second ChBE seminar of the Spring Quarter will be presented by two of our grad students.

Austin Isner, Ottino/Lueptow Lab
Modeling Size-based Particle Segregation in Free Surface Granular Flows

Free surface granular flows constitute a broad class of solids flow problems important to geophysics as well as industry, e.g. in the form of avalanches and deep sea lahars, or heap flows in silos and chutes. Such flows exhibit the tendency to spatially segregate based on differences in particle properties such as particle size, presenting challenges in the rational design of mixing equipment or storage bins for bulk solids. In this talk, I will present a recent continuum-based modeling approach that can be used to quantitatively predict the final segregation pattern in quasi-two-dimensional bounded heap flows of bidisperse granular mixtures consisting of two particle sizes. The successful application of the continuum theory requires detailed knowledge of the kinematics (including the mean velocity field, flowing layer thickness, diffusion coefficient, and the individual species’ segregation velocity), which can be obtained using Discrete Element Method (DEM) simulations. Scaling relations for the kinematics in terms of a wide range of physical control parameters are determined using a GPU-accelerated version of the DEM algorithm (with a 10x speed up compared to previous simulations) for simulations involving 106 particles. In the final part of my talk, I will also discuss an extension to the theory to model size segregation in multi-disperse and polydisperse systems described by a continuous particle size distribution. These extensions offer one possible approach to study the essential behavior of real segregating granular systems, with the current aim to improve our understanding of the dominant segregation mechanisms.
Funded by The Dow Chemical Company

James Jeffryes, Tyo Lab
Illuminating and Detecting Dark Metabolism

Metabolomics, the study of the population of small molecules in a cell, has drawn intense interest in fields from medicine to synthetic biology because it can provide a fine-grain representation of cellular state and activity. For this reason, metabolomics has great potential to drive the discovery of novel chemistry and pathways that have, to date, eluded discovery by other approaches. Today, the major bottleneck preventing the broader use of metabolomics data in systems biology is the identification of metabolites from their characteristic mass spectra. This process largely relies on incomplete biochemical models of the cell, which biases studies towards rediscovery of known compounds and ensures that new metabolites and pathways are seldom discovered in untargeted studies.

To address this challenge, we have constructed Metabolic In silico Network Expansions (MINEs) that expand existing metabolic models by using expert curated reaction rules to propose novel metabolites and reactions. The reaction rules include an enzymatic set, which has been demonstrated to reproduce a large fraction of known biochemical reactions and a set describing spontaneous (non-enzymatic) chemical transformations of metabolites under physiological conditions. We have applied these generalized reaction rules to compounds from various biochemical databases and resulting MINEs are freely accessible for noncommercial use at https://urldefense.proofpoint.com/v2/url?u=http-3A__minedatabase.mcs.anl&d=DwIFaQ&c=yHlS04HhBraes5BQ9ueu5zKhE7rtNXt_d012z2PA6ws&r=MYyeiQQvLWiHcXw46iyq5q34iML17wBlEqZXKlNMi3AAm7TKg3_d4AuXJrPghAqX&m=VkkagTVj84-ZFzqXvj4FzsnmxvOSvIhEuxpiIJvvZZw&s=RHM5xuUMR8NDqWHeHpH2MX2wHCzctxIySRIyQA1_xZ0&e= . The databases contain over 750,000 putative metabolites; over 90% of which are not found in PubChem, the largest freely available database of chemicals. MINEs have been used to annotate novel metabolites from 4 diverse organisms and inform our exploration of the reactions that damage labile metabolites. MINE databases shine a light on unannotated enzymatic functions and undiscovered metabolic pathways, enabling more complete and predictive models of cellular systems.