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Organic field-effect transistors (OFETs) as nonvolatile memory units are essential for lightweight and flexible electronics, yet the practical application remains a great challenge. The positively charged defects in pentacene film at the interface between pentacene and polymer caused by environmental conditions, as revealed by theoretical and experimental research works, result in unacceptable high programming/erasing (P/E) gate voltages in pentacene OFETs with polymer charge-trapping dielectric. Here, we report a pentacene OFET in which an n-type semiconductor layer was intercalated between a polymer and a blocking insulator. In this structure, the hole barrier caused by the defect layer can be adjusted by the thickness and charge-carrier density of the n-type semiconductor interlayer based on the electrostatic induction theory. This idea was implemented in an OFET structure Cu/pentacene/poly(2-vinyl naphthalene) (PVN)/ZnO/SiO

This study explores the possibility of using an ANN-based model for the rapid numerical simulation and seismic performance prediction of reinforced concrete (RC) columns. The artificial neural network (ANN) method is implemented to model the relationship between the input features of RC columns and the critical parameters of the commonly used lumped plasticity (LP) model: The strength and the yielding, capping and ultimate deformation capacity. Cyclic test data of 1163 column specimens obtained from the PEER and NEEShub database and other sources are collected and divided into the training set, test set and validation set for the ANN model. The effectiveness of the proposed ANN model is validated by comparing it with existing explicit formulas and experimental results. Results indicated that the developed model can effectively predict the strength and deformation capacities of RC columns. Furthermore, the response of two RC frame structures under static force and strong ground motion were simulated by the ANN-based, bi-linear and tri-linear LP model method. The good agreement between the proposed model and test results validated that the ANN-based method can provide sufficiently accurate model parameters for modeling the seismic response of RC columns using the LP model.

Support vector machine models are generally considered as “black box” procedures, which sometimes render them unexplainable and prone to over-fitting or under-fitting in absence of sufficient data. In this study, an empirical-based support vector machine (EM-based SVM) method is proposed to model the complicated relationships among the basic characteristics of reinforced concrete columns and their corresponding critical performance metrics on hysteretic curves. Moreover, a database of historical pseudo-static cyclic tests is established for model training and test after rigorous screening of specimens. The developed model is then validated by comparing with the results of common SVM method and semi-empirical formulas, as well as pseudo-static and shake table tests. Finally, its applicability and effectiveness for seismic damage assessment and simulation are explored, which demonstrates its potential value in engineering applications.

Highway bridge networks (HBN) are one of the critical components of a transportation system that supports the operation of modern society. The traffic-carrying capacity of a transportation system is essential for emergency responses in a catastrophic seismic hazard. In this article, a methodology is proposed to assess the regional seismic performance of HBN considering the post-disaster traffic demand of a transportation system in emergency conditions. First, the framework of the methodology is introduced. Then, topological properties and vulnerability analyses are conducted to understand the connectivity of the HBN and the traffic-carrying capacity of the damaged bridge networks. In addition, traffic flow analysis is employed to consider the traffic situation in an emergency condition. Then, the entropy-right method is adopted to integrate these factors and to assess the importance of each bridge link in an emergency condition. Last, the seismic performance of HBN is also evaluated considering th...

In this paper, Fe X Al 1-X films have been deposited on silicon substrates using electron beam evaporation, which have been not reported to prepare Fe X Al 1-X films before. Subsequently, the films were annealed in vacuum better than 3×10 -4 Pa for 1 hour at 100°C, 280°C, 330°C, 450°C and 500°C, respectively. Electrical resistivities of the samples were been measured by four point probe, and microstructures of the samples were characterized by X-ray diffraction(XRD). The results show that, the resistivity of films reduces gradually with increasing of the annealing temperature, and the structure of films can be improved after annealing. In addition, the resistivity of film reduces gradually with increasing thickness and comes closed to the that of bulk when the film thickness becomes thicker than 100nm.