Synthesis and Characterization of Acceptor Polymers for All-Polymer Solar Cells and Photodetectors

The development of polymer semiconductors has become an important topic due to its advantages oflow cost, easy fabrication, light weight, and capability to fabricate flexible large-area devices. Forexample, as the need for new clean energy sources is increasing, polymer solar cells (PSCs) are beingdeveloped rapidly and becoming a promising alternative to silicon solar cells. This thesis focuses onthe applications of polymer semiconductors in two active fields of polymeric optoelectronics: PSCs andpolymer photodetectors (PPDs). Heretofore, PSCs and PPDs were fabricated commonly using a blendof a conjugated polymer and a fullerene derivative as the active layer. Despite the wide use of fullerenederivatives, their limitations such as low absorption, morphological instability, and high costs, createda strong need to develop new acceptor materials. Therefore, all-polymer solar cells (all-PSCs) and allpolymerphotodetectors (all-PPDs) based on a blend of conjugated polymers acting as both electrondonor and acceptor are being actively pursued.We have made concerted efforts to prepare high-performance all-PSCs and all-PPDs, by specificallymodifying the acceptor molecular structure, and rationally choosing suitable donor and acceptorcombinations. This aspect of our work had two main facets:\ua0* Material synthesis: the design, synthesis and characterization of novel acceptor polymers.\ua0* Device engineering: the fabrication, optimization and characterization of all-PSCs and all-PPDs.Our efforts in the design of novel acceptor polymers focused on crystallinity and energy levelengineering via structural modifications like backbone and sidechain modulation. Also, acomprehensive comparison of the characteristic functional properties of acceptor polymers wasundertaken. Binary devices using donor and acceptor polymers with complementary absorption orsuitable energy level offset, and ternary devices were studied to further improve the performance of all-PSCs. High efficiencies of 8.0% and 9.0% are achieved for binary all-PSCs and ternary all-PSCs,respectively. Additionally, high-performance all-PPDs exhibiting low dark current density (Jd) and highresponsivity (R) under -5 V bias were demonstrated. Based on the results presented herein, we are nowmoving closer to understanding the correlation between the polymer structure, blend morphology, anddevice performance. This thesis also provides a guideline for developing all-PSCs and all-PPDs withimproved performance

Automaticity in processing spatial-numerical associations: Evidence from a perceptual orientation judgment task of Arabic digits in frames.

Human adults are faster to respond to small/large numerals with their left/right hand when they judge the parity of numerals, which is known as the SNARC (spatial-numerical association of response codes) effect. It has been proposed that the size of the SNARC effect depends on response latencies. The current study introduced a perceptual orientation task, where participants were asked to judge the orientation of a digit or a frame surrounding the digit. The present study first confirmed the SNARC effect with native Chinese speakers (Experiment 1) using a parity task, and then examined whether the emergence and size of the SNARC effect depended on the response latencies (Experiments 2, 3, and 4) using a perceptual orientation judgment task. Our results suggested that (a) the automatic processing of response-related numerical-spatial information occurred with Chinese-speaking participants in the parity task; (b) the SNARC effect was also found when the task did not require semantic access; and (c) the size of the effect depended on the processing speed of the task-relevant dimension. Finally, we proposed an underlying mechanism to explain the SNARC effect in the perceptual orientation judgment task

Reliability Assessment of CNC Machining Center Based on Weibull Neural Network

CNC machining centers, as the key device in modern manufacturing industry, are complicated electrohydraulic products. The reliability is the most important index of CNC machining centers. However, simple life distributions hardly reflect the true law of complex system reliability with many kinds of failure mechanisms. Due to Weibull model’s versatility and relative simplicity and artificial neural networks’ (ANNs) high capability of approximating, they are widely used in reliability engineering and elsewhere. Considering the advantages of these two models, this paper defined a novel model: Weibull neural network (WNN). WNN inherits the hierarchical structure from ANNs which include three layers, namely, input layer, hidden layer, and output layer. Based on more than 3000 h field test data of CNC machining centers, WNN has been successfully applied in comprehensive operation data analysis. The results show that WNN has good approximation ability and generalization performance in reliability assessment of CNC machining centers