Nanoproducts represent a potential growing sector and nanofibrous materials are widely requested in industrial, medical, and environmental applications. Unfortunately, the production processes at the nanoscale are difficult to control and nanoproducts often exhibit localized defects that impair their functional properties. Therefore, defect detection is a particularly important feature in smart-manufacturing systems to raise alerts as soon as defects exceed a given tolerance level and to design production processes that both optimize the physical properties and control the defectiveness of the produced materials. Here, we present a novel solution to detect defects in nanofibrous materials by analyzing scanning electron microscope images. We employ an algorithm that learns, during a training phase, a model yielding sparse representations of the structures that characterize correctly produced nanofiborus materials. Defects are then detected by analyzing each patch of an input image and extracting features that quantitatively assess whether the patch conforms or not to the learned model. The proposed solution has been successfully validated over 45 images acquired from samples produced by a prototype electrospinning machine. The low computational times indicate that the proposed solution can be effectively adopted in a monitoring system for industrial productio
Electrospinning is affected by high variability, and an accurate setting of process parameters is fundamental for producing high quality nanofibers. This work aims at determining the optimal values of the main process parameters (polymer concentration, polymer feed rate, and voltage) as a function of the environmental factors (temperature and humidity), in order to obtain nanofibrous materials within a specific range of fiber diameter and porosity, and at the same time to minimize production defects. The response surfaces of diameter, porosity, and defects are first determined with a central composite design. These surfaces are then employed as an input for the optimization problem: diameter and porosity surfaces are used to constrain an admissible region, where the minimum of the defect surface is searched. The approach is tested on a prototype electrospinning machine. The estimated response surfaces capture the variability of the process with respect to both production parameters and environmental factors, and are capable of getting the optimal values of the process parameters. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44740.
In this article we address the study of ion charge transport in the biological channels separating the intra and extracellular regions of a cell. The focus of the investigation is devoted to including thermal driving forces in the wellknown velocity-extended Poisson-Nernst-Planck (vPNP) electrodiffusion model. Two extensions of the vPNP system are proposed: the velocity-extended Thermo-Hydrodynamic model (vTHD) and the velocity-extended Electro-Thermal model (vET). Both formulations are based on the principles of conservation of mass, momentum and energy, and collapse into the vPNP model under thermodynamical equilibrium conditions. Upon introducing a suitable one-dimensional geometrical representation of the channel, we discuss appropriate boundary conditions that depend only on effectively accessible measurable quantities. Then, we describe the novel models, the solution map used to iteratively solve them, and the mixedhybrid flux-conservative stabilized finite element scheme used to discretize the linearized equations. Finally, we successfully apply our computational algorithms to the simulation of two different realistic biological channels: 1) the Gramicidin-A channel considered in [10]; and 2) the bipolar nanofluidic diode considered in [40].
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