Highlights Chitosan-coated nanocapsules, but not nanoemulsions, influence the -potential of E.coli A fixed number of nanocapsules binds and promotes aggregation of bacteria In silico simulations agree closely with experimental results Chitosan-coated nanocapsules attenuate the bacterial quorum sensing response § These authors contributed equally to this publication * Author for correspondence Colloids and Surfaces B: Biointerfaces 149 (2017) 358-368 AbstractWe examined the interaction between chitosan-based nanocapsules (NC), with average hydrodynamic diameter ~114-155 nm, polydispersity ~0.127, and -potential ~+50 mV, and an E. coli bacterial quorum sensing reporter strain. Dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) allowed full characterization and assessment of the absolute concentration of NC per unit volume in suspension. By centrifugation, DLS, and NTA, we determined experimentally a "stoichiometric" ratio of ~80 NC/bacterium. By SEM it was possible to image the aggregation between NC and bacteria. Moreover, we developed a custom in silico platform to simulate the behavior of particles with diameters of 150 nm and -potential of +50 mV on the bacterial surface. We computed the detailed force interactions between NC-NC and NC-bacteria and found that a maximum number of 145 particles might interact at the bacterial surface. Additionally, we found that the "stoichiometric" ratio of NC and bacteria has a strong influence on the bacterial behavior and influences the quorum sensing response, particularly due to the aggregation driven by NC.
Reactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.
The rational design of carbon fibers with desired properties requires quantitative relationships between the processing conditions, microstructure, and resulting properties. We developed a molecular model that combines kinetic Monte Carlo and molecular dynamics techniques to predict the microstructure evolution during the processes of carbonization and graphitization of polyacrylonitrile (PAN)-based carbon fibers. The model accurately predicts the cross-sectional microstructure of the fibers with the molecular structure of the stabilized PAN fibers and physics-based chemical reaction rates as the only inputs. The resulting structures exhibit key features observed in electron microcopy studies such as curved graphitic sheets and hairpin structures. In addition, computed X-ray diffraction patterns are in good agreement with experiments. We predict the transverse moduli of the resulting fibers between 1 GPa and 5 GPa, in good agreement with experimental results for high modulus fibers and slightly lower than those of high-strength fibers. The transverse modulus is governed by sliding between graphitic sheets, and the relatively low value for the predicted microstructures can be attributed to their perfect longitudinal texture. Finally, the simulations provide insight into the relationships between chemical kinetics and the final microstructure; we observe that high reaction rates result in porous structures with lower moduli.
Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models. Less explored is its use to discover interpretable physical laws from data. We propose parsimonious neural networks (PNNs) that combine neural networks with evolutionary optimization to find models that balance accuracy with parsimony. The power and versatility of the approach is demonstrated by developing models for classical mechanics and to predict the melting temperature of materials from fundamental properties. In the first example, the resulting PNNs are easily interpretable as Newton’s second law, expressed as a non-trivial time integrator that exhibits time-reversibility and conserves energy, where the parsimony is critical to extract underlying symmetries from the data. In the second case, the PNNs not only find the celebrated Lindemann melting law, but also new relationships that outperform it in the pareto sense of parsimony vs. accuracy.
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