Rational Design and Characterization of Solution-Processable Organic Photovoltaic Devices: A Study of Both Organic and Inorganic Architectures

In this dissertation we report the synthesis and photovoltaic characterization of a number of semiconducting polymers and colloidal inorganic nanomaterials and their implementation into organic solar cells with different architectures (Schottky single layer, bilayer heterojunction, and bulk heterojunction), with research emphasis on the mechanisms underlying material and device optimization, which sheds light on future material design for high efficiency solar cells and other organic electronic devices, such as organic light emitting diodes (OLEDs) and organic field effect transistors (OFETs). In the first part, the synthesis, characterization, and photovoltaic applications of a new conjugated copolymer (C12DPP-Pi-BT) are reported. The energy levels of C12DPP-Pi-BT were designed to be intermediate to those of popular electron donor and acceptor photovoltaic materials, P3HT and PCBM. The unique ambipolar nature of C12DPP-Pi-BT was then explored in two different photovoltaic systems where C12DPP-Pi-BT serves as either an electron donor or an acceptor when paired with PCBM or P3HT to form junctions with large built-in potentials. Optical, electrical, and structural characterization have been carried out to understand the photoinduced charge separation, charge carrier transport and recombination mechanism in different device configurations. The influence of polymers\u27 molecular weight and processing condition on device performance has also been explored. In addition, preliminary studies of OLED and OFET application of the C12DPP-Pi-BT have been carried out. In the second part, the synthesis, surface ligand treatment and photovoltaic application of inorganic PbSe and CdSe nanocrystals have been investigated. In Chapter 3, photoluminescence quenching, current-voltage characterization and electrochemical measurements have been used to study the mechanism of photoinduced charge transfer between PbSe and P3HT, which confirmed material incompatibility and suggested new directions for the design of inorganic material as electron acceptor. In Chapter 4, the photovoltaic application of thiocyanate capped CdSe nanocrystals in combination with P3HT in bilayer hybrid devices has been explored. Important factors such as nanocrystal size and bilayer interfacial mixing on the device performance have been investigated and discussed. Bilayer solar cells with ligand exchanged CdSe nanocrystals and P3HT achieved 1.3% power conversion efficiency with good tunability in performance parameters and promising optimization potential

Local Red Culture under the Background of the Whole Environment Research and Practice on the Enlightenment of Red Morality of Preschool Children in Rural Areas—A Case Study of Kindergartens in Zhaojue County, Liangshan Prefecture, Sichuan Province

This study focuses on the study and practice of local red culture on the enlightenment of rural preschool children's red morality under the background of the whole environment. Taking the kindergarten of Zhaojue County, Liangshan Prefecture, Sichuan Province as an example, this paper discusses how to incorporate the local red culture into the creation of the kindergarten environment, and generate the kindergart-based curriculum with local characteristics, so as to promote the moral development of rural preschool children

PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels

Electric faults may trigger blackouts or wildfires without timely monitoring and control strategy. Traditional solutions for locating faults in distribution systems are not real-time when network observability is low, while novel black-box machine learning methods are vulnerable to stochastic environments. We propose a novel Physics-Preserved Graph Network (PPGN) architecture to accurately locate faults at the node level with limited observability and labeled training data. PPGN has a unique two-stage graph neural network architecture. The first stage learns the graph embedding to represent the entire network using a few measured nodes. The second stage finds relations between the labeled and unlabeled data samples to further improve the location accuracy. We explain the benefits of the two-stage graph configuration through a random walk equivalence. We numerically validate the proposed method in the IEEE 123-node and 37-node test feeders, demonstrating the superior performance over three baseline classifiers when labeled training data is limited, and loads and topology are allowed to vary