Computational study of boron nitride nanotube synthesis: how catalyst morphology stabilizes the boron nitride bond

In an attempt to understand why catalytic methods for the growth of boron nitride nanotubes work much worse than for their carbon counterparts, we use first-principles calculations to study the energetics of elemental reactions forming N2, B2 and BN molecules on an iron catalyst. We observe that in the case of these small molecules, the catalytic activity is hindered by the formation of B2 on the iron surface. We also observe that the local morphology of a step edge present in our nanoparticle model stabilizes the boron nitride molecule with respect to B2 due to the ability of the step edge to offer sites with different coordination simultaneously for nitrogen and boron. Our results emphasize the importance of atomic steps for a high yield chemical vapor deposition growth of BN nanotubes and may outline new directions for improving the efficiency of the method.Comment: submitted to physical review

Nonequilibrium dissipation-free transport in F1-ATPase and the thermodynamic role of asymmetric allosterism

F1-ATPase (or F1), the highly-efficient and reversible biochemical engine, has motivated physicists as well as biologists to imagine the design principles governing machines in the fluctuating world. Recent experiments have clarified yet another interesting property of F1; the dissipative heat inside the motor is very small, irrespective of the velocity of rotation and energy transport. Conceptual interest is devoted to the fact that the amount of internal dissipation is not simply determined by the sequence of equilibrium pictures, but also relies on the rotational-angular dependence of nucleotide affinity, which is a truly nonequilibrium aspect. We propose that the totally asymmetric allosteric model (TASAM), where adenosine triphosphate (ATP) binding to F1 is assumed to have low dependence on the angle of the rotating shaft, produces results that are most consistent with the experiment. Theoretical analysis proves the crucial role of two time scales in the model, which explains the universal mechanism to produce the internal dissipation-free feature. The model reproduces the characteristic torque dependence of the rotational velocity of F1, and predicts that the internal dissipation upon the ATP synthesis direction rotation becomes large at the low nucleotide condition.Comment: 10 pages, 5 figures + Supplementary Material (9 pages, 9 figures

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 253 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1975

Power-to-Syngas: A Parareal Optimal Control Approach

A chemical plant layout for the production of syngas from renewable power, H2O and biogas, is presented to ensure a steady productivity of syngas with a constant H2-to-CO ratio under time-dependent electricity provision. An electrolyzer supplies H2 to the reverse water-gas shift reactor. The system compensates for a drop in electricity supply by gradually operating a tri-reforming reactor, fed with pure O2 directly from the electrolyzer or from an intermediate generic buffering device. After the introduction of modeling assumptions and governing equations, suitable reactor parameters are identified. Finally, two optimal control problems are investigated, where computationally expensive model evaluations are lifted viaparareal and necessary objective derivatives are calculated via the continuous adjoint method. For the first time, modeling, simulation, and optimal control are applied to a combination of the reverse water-gas shift and tri-reforming reactor, exploring a promising pathway in the conversion of renewable power into chemicals

Theoretical study of correlation between structure and function for nanoparticle catalysts

textThe science and technology of catalysis is more important today than at any other time in our history due to the grand energy and environment challenges we are facing. With the explosively growth of computation power nowadays, computer simulation can play an increasingly important role in the design of new catalysts, avoiding the costly trail-and-error attempts and facilitating the development cycle. The goal to inverse design of new materials with desired catalytic property was once far off, but now achievable. The major focus of this dissertation is to find the general rules that govern the catalytic performance of a nanoparticle as the function of its structure. Three types of multi-metallic nanoparticles have been investigated in this dissertation, core-shell, random alloy and alloy-core@shell. Significant structural rearrangement was found on Au@Pt and Pd@Pt nanoparticle, which is responsible for a dramatic improvement in catalytic performance. Nonlin- ear binding trends were found and modeled for random alloy nanoparticles, providing a prescription for tuning catalytic activity through alloying. Studies of ORR on Pd/Au random alloy NP and hydrogenation reaction on Rh/Ag random alloy NP revealed that binding on individual ensemble should be in- vestigated when large disparity of adsorbate affinity is presented between two alloying elements. In the alloy-core@shell system, I demostrated a general linear correlations between the adsorbate binding energy to the shell of an alloy-core@shell nanoparticle and the composition of the core. This relation- ship allows for interpolation of the properties of single-core@shell particles and an approach for tuning the catalytic activity of the particle. A series of promising catalysts were then predicted for ORR, HER and CO oxidation. As a first attempt to bridge the material gap, bimetallic nano clus- ter supported on CeO₂(111) was investigated for CO oxidation. A strong support-metal interaction induces a preferential segregation of the more reac- tive element to the NC-CeO₂ perimeter, generating an interface with the Au component. (Au-Cu)/CeO₂ was found to be optimal for catalyzing CO oxida- tion via a bifunctional mechanism. O₂ preferentially binds to the Cu-rich sites whereas CO binds to the Au-rich sites. A method called distributed replica dynamics (DRD) is proposed at last to utilize enormous distributed computing resources for molecular dynamics simulations of rare-event in chemical reac- tions. High efficiency can be achieved with an appropriate choice of N [subscript rep] and t [subscript rep] for long-time MD simulation.Chemistr

From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media

The modeling of flow and transport in porous media is of the utmost importance in many chemical engineering applications, including catalytic reactors, batteries, and CO2 storage. The aim of this study is to test the use of fully connected (FCNN) and convolutional neural networks (CNN) for the prediction of crucial properties in porous media systems: The permeability and the filtration rate. The data-driven models are trained on a dataset of computational fluid dynamics (CFD) simulations. To this end, the porous media geometries are created in silico by a discrete element method, and a rigorous setup of the CFD simulations is presented. The models trained have as input both geometrical and operating conditions features so that they could find application in multiscale modeling, optimization problems, and in-line control. The average error on the prediction of the permeability is lower than 2.5%, and that on the prediction of the filtration rate is lower than 5% in all the neural networks models. These results are achieved with at least a dataset of ~ 100 CFD simulations

Constrained Allocation Flux Balance Analysis

New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an "ensemble averaging" procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws.Comment: 21 pages, 6 figures (main) + 33 pages, various figures and tables (supporting); for the supplementary MatLab code, see http://tinyurl.com/h763es

Beyond the two-state model of switching in biology and computation

The thesis presents various perspectives on physical and biological computation. Our fundamental object of study in both these contexts is the notion of switching/erasing a bit. In a physical context, a bit is represented by a particle in a double well, whose dynamics is governed by the Langevin equation. We define the notions of reliability and erasing time-scales in addition to the work required to erase a bit for a given family of control protocols. We call bits “optimal” if they meet the required reliability and erasing time requirements with minimal work cost. We find that optimal bits always saturate the erasing time requirement, but may not saturate the reliability time requirement. This allows us to eliminate several regions of parameter space as sub-optimal. In a biological context, our bits are represented by substrates that are acted upon by catalytic enzymes. We define retroactivity as the back-signal propagated by the downstream system when connected to the upstream system. We analyse certain upstream systems that can help mitigate retroactivity. However, these systems require a substantial pool of resources and are therefore not optimal. As a consequence, we turn our attention to insulating networks called push-pull motifs. We find that high rates of energy consumption are not essential to alleviate retroactivity in push-pull motifs; all we need is to couple weakly to the upstream system. However, this approach is not resilient to cross-talk caused by leak reactions in the circuit. Next, we consider a single enzyme-substrate reaction and analyse its mechanism. Our system has two intermediate states (enzyme-substrate complexes). Our main question is “How should we choose binding energies of the intermediates to minimize sequestra- tion of substrates (retroactivity), whilst maintaining a minimum flux at steady-state?”. Choosing very low binding energies increases retroactivity since the system spends a considerable proportion of time in the intermediate states. Choosing binding energies that are very high reduces retroactivity, but hinders the progress of the reaction. As a result, we find that the the optimal binding energies are both moderate, and indeed tuned with each other. In particular, their difference is related to the free energy difference between the products and reactants.Open Acces