Polyacrylonitrile (PAN) is among the most promising precursor polymers to produce strong and lightweight carbon fiber. Conformations in solution and the extent of binding to carbon nanotubes (CNTs) are critical during gel spinning and for alignment of graphitic layers upon carbonization. First quantitative insights into these processes are reported using molecular dynamics simulations from the atomic scale including virtual π electrons and comparisons to experimental data. Common solvents for fiber spinning induce significant differences in PAN conformations in dilute solution at 25 C with persistence lengths between 0.5 and 2 nm.Variations in conformation become smaller at 75 C, in the presence of CNTs, and at higher PAN concentration. "Aging" of PAN conformations in dimethylformamide and dimethylsulfoxide at higher temperature is explained and a correlation between extended polymer conformations and increased binding to CNTs identified in dilute solutions. PAN is overall barely attracted to CNTs under common solution conditions and enters significant surface contact only at higher concentration as solvent is physically removed. The impact of temperature is small, whereby binding increases at lower temperature. The results provide first guidance to control interactions of polymers with CNTs to induce distinct conformations and specific binding at the early stages of assembly.

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Abstract. Wind plant layout optimization is a difficult, complex problem with a large number of variables and many local minima. Layout optimization only becomes more difficult with the addition of solar generation. In this paper, we propose a parameterized approach to wind and solar hybrid power plant layout optimization that greatly reduces problem dimensionality while guaranteeing that the generated layouts have a desirable regular structure. Thus far, hybrid power plant optimization research has focused on system sizing. We go beyond sizing and present a practical approach to optimizing the physical layout of a wind–solar hybrid power plant. We argue that the evolution strategy class of derivative-free optimization methods is well-suited to the parameterized hybrid layout problem, and we demonstrate how hard layout constraints (e.g., placement restrictions) can be transformed into soft constraints that are amenable to optimization using evolution strategies. Next, we present experimental results on four test sites, demonstrating the viability, reliability, and effectiveness of the parameterized evolution strategy approach for generating optimized hybrid plant layouts. Completing the tool kit for parameterized layout generation, we include a brief tutorial describing how the parameterized evolutionary approach can be inspected, understood, and debugged when applied to hybrid plant layouts.

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