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Photocurrent enhancements of organic solar cells by altering dewetting of plasmonic Ag nanoparticles

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Incorporation of metal nanoparticles into active layers of organic solar cells is one of the promising light trapping approaches. The size of metal nanoparticles is one of key factors to

Incorporation of metal nanoparticles into active layers of organic solar cells is one of the promising light trapping approaches. The size of metal nanoparticles is one of key factors to strong light trapping, and the size of thermally evaporated metal nanoparticles can be tuned by either post heat treatment or surface modification of substrates. We deposited Ag nanoparticles on ITO by varying nominal thicknesses, and post annealing was carried out to increase their size in radius. PEDOT:PSS was employed onto the ITO substrates as a buffer layer to alter the dewetting behavior of Ag nanoparticles. The size of Ag nanoparticles on PEDOT:PSS were dramatically increased by more than three times compared to those on the ITO substrates. Organic solar cells were fabricated on the ITO and PEDOT:PSS coated ITO substrates with incorporation of those Ag nanoparticles, and their performances were compared. The photocurrents of the cells with the active layers on PEDOT:PSS with an optimal choice of the Ag nanoparticles were greatly enhanced whereas the Ag nanoparticles on the ITO substrates did not lead to the photocurrent enhancements. The origin of the photocurrent enhancements with introducing the Ag nanoparticles on PEDOT:PSS are discussed.

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Date Created
  • 2015-09-21

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Feature and statistical model development in structural health monitoring

Description

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects.

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.

The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.

Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.

The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.

Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.

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Date Created
  • 2016