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Analytical Test Methods for Polymer Characterization
Published in Nicholas P. Cheremisinoff, Elastomer Technology Handbook, 2020
Nicholas P. Cheremisinoff, Boyko Randi, Leidy Laura
ICP is used for the determination of parts per million levels of metals in liquid samples. It is not suitable for the noble gases, halogens, or light elements such as H, C, N, and O. Sulfur requires a vacuum monochromator. A direct reader ICP excels at the rapid analysis of multielement samples.
Tools and Techniques for Characterizing Nanomaterial Internalization/Uptake in Plants and its Importance in Agricultural Applications
Published in Ramesh Raliya, Nanoscale Engineering in Agricultural Management, 2019
Inductively Coupled Plasma (ICP)-based spectroscopic techniques mainly include mass spectrometry (ICP-MS), Single Particle-Inductively Coupled Plasma-Mass Spectrometry (SP-ICP-MS) and Optical Emission Spectrometry (ICP-OES).
Top-Down Fabrication of ZnO NWFETs
Published in Razali Ismail, Mohammad Taghi Ahmadi, Sohail Anwar, Advanced Nanoelectronics, 2018
Sultan Suhana Mohamed, Ashburn Peter, Chong Harold M. H.
Basically, the mechanism of all these techniques is the same, which is utilizing the plasma to etch the material. RIE etch is typically performed at 10–100 mTorr pressure in an asymmetric parallel plate reactor and is highly anisotropic. The ion density is in the range of 1010−1011 cm−3 [57]. In comparison, electron cyclotron resonance (ECR) etching also produces high anisotropic etch with lower gas pressures of 0.2–10 mTorr and the etch rate is faster than in the RIE. This is due to higher ion density (up to 1012 cm−3). However, ECR technology is expensive; etch uniformity is not good due to the presence of a magnetic field. An inductive coupled plasma (ICP) tool provides the same advantage as ECR, that is, low pressure (1 mTorr), and higher etch rate because of high ion density. In ICP, the ion flux is controlled by the ICP power and the gas pressure while the ion energy is controlled by the RF power of the substrate holder.
A constrained localization algorithm for improving the efficiency and accuracy of forming curved hull plates in shipbuilding
Published in International Journal of Computer Integrated Manufacturing, 2019
Bao Zhao, Zhouqi Wu, Xijin Zhen, Juntong Xi
The algorithm of localization or registration is a popular topic, and it is widely studied in literature. For the details on 3D localization, the readers can refer to two comprehensive surveys (Salvi et al. 2007; Tam et al. 2013). The process of localization usually involves two steps: coarse and fine localization (Yang, Zhang, and Cao 2017). The aim of coarse localization is to estimate an initial transformation between two-point clouds, and the goal of the fine localization algorithm s to further refine the obtained initial transformation. Since the hull curved plates have obvious information of edges and corners, the coarse localization of the curved plates can be implemented easily based on these edges and corners. Thus, the state-of-the-art algorithms of coarse localization, such as the techniques proposed in (Albarelli, Rodolà, and Torsello 2015; Guo et al. 2013; Salti, Tombari, and Stefano 2014), are not necessary in this work. For the details of coarse localization, the readers can refer to two comprehensive surveys (Díez et al. 2015; Guo et al. 2016). Once the coarse localization is finished, a fine localization algorithm is performed to produce a more accurate result. The most popular algorithm of fine localization is the iterative closest point (ICP) algorithm (Chen and Medioni 1992; Besl and McKay 1992), which was introduced in the early 1990s. ICP is an iterative process that alternately performs two key steps: searching correspondences and estimating transformation. Later, a set of its variants (Mavridis, Andreadis, and Papaioannou 2015; Rusinkiewicz and Levoy 2001; Kwok and Tang 2016) were proposed to improve the performance of the original ICP algorithm. Due to conceptual simplicity, high stability and good performance in practice, ICP and its variants are very popular and practical. Another way to achieve point clouds localization is based on the probabilistic methods (Jian and Vemuri 2011; Myronenko and Song 2010; Papazov and Burschka 2011). These methods use probability distributions to model point sets, and then achieve the localization by seeking the transformation so that the two points set exhibit similar GMM responses. Experimental results showed that the computational complexity of these probabilistic methods is high, especially for large-scale point clouds (Lu et al. 2016). Recently, a globally optimal method called Go-ICP was proposed (Yang et al. 2016; Hartley and Kahl 2009). Go-ICP is based on a branch-and-bound (BnB) scheme in which ICP is integrated. Since Go-ICP needs to search the whole six-dimensional SE(3) space, it is also inefficient compared with the ICP.