Large-Area Nanoimprint Lithography and Applications

Large-area nanoimprint lithography (NIL) has been regarded as one of the most promising micro/nano-manufacturing technologies for mass production of large-area micro/nanoscale patterns and complex 3D structures and high aspect ratio features with low cost, high throughput, and high resolution. That opens the door and paves the way for many commercial applications not previously conceptualized or economically feasible. Great progresses in large-area nanoimprint lithography have been achieved in recent years. This chapter mainly presents a comprehensive review of recent advances in large-area NIL processes. Some promising solutions of large-area NIL and emerging methods, which can implement mass production of micro-and nanostructures over large areas on various substrates or surfaces, are described in detail. Moreover, numerous industrial-level applications and innovative products based on large-area NIL are also demonstrated. Finally, prospects, challenges, and future directions for industrial scale large-area NIL are addressed. An infrastructure of large-area nanoimprint lithography is proposed. In addition, some recent progresses and research activities in large-area NIL suitable for high volume manufacturing environments from our Labs are also introduced. This chapter may provide a reference and direction for the further explorations and studies of large-area micro/nanopatterning technologies

High-Resolution Electric-Field-Driven Jet 3D Printing and Applications

Multi-scale and multi-material 3D printing technique has been regarded as a revolutionary technology and a next-generation manufacturing tool, which can really fulfill the “creating material” and “creating life,” especially subvert the traditional product design and the manufacturing method. However, very few of the established additive manufacturing processes possess the capability to fully implement the fabrication of multi-scale and multi-material. A novel high-resolution 3D printing, named as high-resolution electric-field-driven jet 3D printing, which is based on the induced electric field and EHD cone-jetting behavior, has been developed by our research team. It provides a feasible approach to implement the additive manufacturing of multi-scale and multi-material with high efficiency and low cost. This chapter will introduce this new high resolution 3D printing technique. In particular, many typical applications including transparent conducting electrodes, tissue engineering scaffold, 3D electronics, etc., are presented in detail

Pembelajaran Bahasa Arab Berbasis Media IPAD (I-Learning)

The information technology plays an urgent role in our everyday life and in the practice of education as well. When it is well-planned and prepared, it has effective functions as the media of learning. Therefore, for the sake of making an active and dynamic process of learning and accomplishing the learning objectives, the Arabic lecturers/teachers must create an interesting, inovative, effective and creative learning practices. The technology, media of learning, as well as learning strategy the lectures/teachers take and implement will seriousely influence the output of students\u27 learning. Electronic Learning and i-learning (iPad learning) are two medias of Arabic learning that use internet in learning process. If it is well-prepared it will automatically raise the learning output

PSC 352.01: American Political Thought

In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multiscale strategy to learn the pixel-level correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches and multi-view line drawings rendered from multiple 3D shape datasets