Patterning of Discotic Liquid Crystals with Tunable Molecular Orientation for Electronic Applications

The large‐area formation of functional micropatterns with liquid crystals is of great significance for diversified applications in interdisciplinary fields. Meanwhile, the control of molecular alignment in the patterns is fundamental and prerequisite for the adequate exploitation of their photoelectric properties. However, it would be extremely complicated and challenging for discotic liquid crystals (DLCs) to achieve the goal, because they are insensitive to external fields and surface chemistry. Herein, a simple method of patterning and aligning DLCs on flat substrates is disclosed through precise control of the formation and dewetting of the capillary liquid bridges, within which the DLC molecules are confined. Large‐area uniform alignment occurs spontaneously due to directional shearing force when the solvent is slowly evaporated and programmable patterns could be directly generated on desired substrates. Moreover, the in‐plane column direction of DLCs is tunable by slightly tailoring their chemical structures which changes their self‐assembly behaviors in liquid bridges. The patterned DLCs show molecular orientation–dependent charge transport properties and are promising for templating self‐assembly of other materials. The study provides a facile method for manipulation of the macroscopic patterns and microscopic molecular orientation which opens up new opportunities for electronic applications of DLCs.     

Atomic disorders in layer structured topological insulator SnBi<Subscript>2</Subscript>Te<Subscript>4</Subscript> nanoplates

Identification of atomic disorders and their subsequent control has proven to be a key issue in predicting, understanding, and enhancing the properties of newly emerging topological insulator materials. Here, we demonstrate direct evidence of the cation antisites in single-crystal SnBi₂Te₄ nanoplates grown by chemical vapor deposition, through a combination of sub-ångström-resolution imaging, quantitative image simulations, and density functional theory calculations. The results of these combined techniques revealed a recognizable amount of cation antisites between Bi and Sn, and energetic calculations revealed that such cation antisites have a low formation energy. The impact of the cation antisites was also investigated by electronic structure calculations together with transport measurement. The topological surface properties of the nanoplates were further probed by angle-dependent magnetotransport, and from the results, we observed a two-dimensional weak antilocalization effect associated with surface carriers. Our approach provides a pathway to identify the antisite defects in ternary chalcogenides and the application potential of SnBi2Te4 nanostructures in next-generation electronic and spintronic devices.

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Reconstruction of intersecting curved solids from 2D orthographic views

This paper presents a new approach to reconstruct curved solids composed of elementary volumes intersecting with one another from three-view engineering drawings. Intersection curves arising from two intersecting curved surfaces are mostly higher order spatial curves, which cannot be described exactly by 2D orthographic projections and normally represented as smooth curves passing through several key points or even simplified as arcs or lines. Approximated sketches of higher order intersection curves in 2D views result in the invalidation of existing methods that need the exact projection information as input. Based on some heuristic hints, our method is able to recover the complete and correct half-profiles of the intersecting elementary volumes using the least traces left by them, which ensure the correctness of solution solids constructed finally. Several examples are provided to show the validation of the described method.     

A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors

Ultra-wideband (UWB) has been widely recommended for significant commercial and military applications. However, the well-derived coherent structures for UWB signal detection are either computationally complex or hardware impractical in the presence of the intensive multipath propagations. In this article, based on the nonparametric Parzen window estimator and the probabilistic neural networks, we suggest a low-complexity and noncoherent UWB detector in the context of distributed wireless sensor networks (WSNs). A novel characteristic spectrum is firstly developed through a sequence of blind signal transforms. Then, from a pattern recognition perspective, four features are extracted from it to fully exploit the inherent property of UWB multipath signals. The established feature space is further mapped into a two-dimensional plane by feature combination in order to simplify algorithm complexity. Consequently, UWB signal detection is formulated to recognize the received patterns in this formed 2-D feature plane. With the excellent capability of fast convergence and parallel implementation, the Parzen Probabilistic Neural Network (PPNN) is introduced to estimate a posteriori probability of the developed patterns. Based on the underlying Bayesian rule of PPNN, the asymptotical optimal decision bound is finally determined in the feature plane. Numerical simulations also validate the advantages of our proposed algorithm.