6,442 research outputs found
Geometric control of bacterial surface accumulation
Controlling and suppressing bacterial accumulation at solid surfaces is
essential for preventing biofilm formation and biofouling. Whereas various
chemical surface treatments are known to reduce cell accumulation and
attachment, the role of complex surface geometries remains less well
understood. Here, we report experiments and simulations that explore the
effects of locally varying boundary curvature on the scattering and
accumulation dynamics of swimming Escherichia coli bacteria in
quasi-two-dimensional microfluidic channels. Our experimental and numerical
results show that a concave periodic boundary geometry can decrease the average
cell concentration at the boundary by more than 50% relative to a flat surface.Comment: 10 pages, 5 figure
Lattice Boltzmann simulations of soft matter systems
This article concerns numerical simulations of the dynamics of particles
immersed in a continuum solvent. As prototypical systems, we consider colloidal
dispersions of spherical particles and solutions of uncharged polymers. After a
brief explanation of the concept of hydrodynamic interactions, we give a
general overview over the various simulation methods that have been developed
to cope with the resulting computational problems. We then focus on the
approach we have developed, which couples a system of particles to a lattice
Boltzmann model representing the solvent degrees of freedom. The standard D3Q19
lattice Boltzmann model is derived and explained in depth, followed by a
detailed discussion of complementary methods for the coupling of solvent and
solute. Colloidal dispersions are best described in terms of extended particles
with appropriate boundary conditions at the surfaces, while particles with
internal degrees of freedom are easier to simulate as an arrangement of mass
points with frictional coupling to the solvent. In both cases, particular care
has been taken to simulate thermal fluctuations in a consistent way. The
usefulness of this methodology is illustrated by studies from our own research,
where the dynamics of colloidal and polymeric systems has been investigated in
both equilibrium and nonequilibrium situations.Comment: Review article, submitted to Advances in Polymer Science. 16 figures,
76 page
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
Machine learning-based models to predict product state distributions from a
distribution of reactant conditions for atom-diatom collisions are presented
and quantitatively tested. The models are based on function-, kernel- and
grid-based representations of the reactant and product state distributions.
While all three methods predict final state distributions from explicit
quasi-classical trajectory simulations with R > 0.998, the grid-based
approach performs best. Although a function-based approach is found to be more
than two times better in computational performance, the kernel- and grid-based
approaches are preferred in terms of prediction accuracy, practicability and
generality. The function-based approach also suffers from lacking a general set
of model functions. Applications of the grid-based approach to nonequilibrium,
multi-temperature initial state distributions are presented, a situation common
to energy distributions in hypersonic flows. The role of such models in Direct
Simulation Monte Carlo and computational fluid dynamics simulations is also
discussed
Master equation simulation of O2-N2 collisions on an ab-initio potential energy surface
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143056/1/6.2017-3163.pd
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