Atmospheric pressure plasma chemical vapor deposition reactor for 100 mm wafers, optimized for minimum contamination at low gas flow rates

Gas discharge plasmas used for thinfilm deposition by plasma-enhanced chemical vapor deposition (PECVD) must be devoid of contaminants, like dust or active species which disturb the intended chemical reaction. In atmospheric pressure plasma systems employing an inert gas, the main source of such contamination is the residual air inside the system. To enable the construction of an atmospheric pressure plasma (APP) system with minimal contamination, we have carried out fluid dynamic simulation of the APP chamber into which an inert gas is injected at different mass flow rates. On the basis of the simulation results, we have designed and built a simple, scaled APP system, which is capable of holding a 100 mm substrate wafer, so that the presence of air (contamination) in the APP chamber is minimized with as low a flow rate of argon as possible. This is examined systematically by examining optical emission from the plasma as a function of inert gas flow rate. It is found that optical emission from the plasma shows the presence of atmospheric air, if the inlet argon flow rate is lowered below 300 sccm. That there is minimal contamination of the APP reactor built here, was verified by conducting an atmospheric pressure PECVD process under acetylene flow, combined with argon flow at 100 sccm and 500 sccm. The deposition of a polymer coating is confirmed by infrared spectroscopy. X-ray photoelectron spectroscopy shows that the polymer coating contains only 5% of oxygen, which is comparable to the oxygen content in polymer deposits obtained in low-pressure PECVD systems. (C) 2015 AIP Publishing LLC.

DNA methylation analysis of phenotype specific stratified Indian population

Background: DNA methylation and its perturbations are an established attribute to a wide spectrum of phenotypic variations and disease conditions. Indian traditional system practices personalized medicine through indigenous concept of distinctly descriptive physiological, psychological and anatomical features known as prakriti. Here we attempted to establish DNA methylation differences in these three prakriti phenotypes. Methods: Following structured and objective measurement of 3416 subjects, whole blood DNA of 147 healthy male individuals belonging to defined prakriti (Vata, Pitta and Kapha) between the age group of 20-30years were subjected to methylated DNA immunoprecipitation (MeDIP) and microarray analysis. After data analysis, prakriti specific signatures were validated through bisulfite DNA sequencing. Results: Differentially methylated regions in CpG islands and shores were significantly enriched in promoters/UTRs and gene body regions. Phenotypes characterized by higher metabolism (Pitta prakriti) in individuals showed distinct promoter (34) and gene body methylation (204), followed by Vata prakriti which correlates to motion showed DNA methylation in 52 promoters and 139 CpG islands and finally individuals with structural attributes (Kapha prakriti) with 23 and 19 promoters and CpG islands respectively. Bisulfite DNA sequencing of prakriti specific multiple CpG sites in promoters and 5'-UTR such as; LHX1 (Vata prakriti), SOX11 (Pitta prakriti) and CDH22 (Kapha prakriti) were validated. Kapha prakriti specific CDH22 5'-UTR CpG methylation was also found to be associated with higher body mass index (BMI). Conclusion: Differential DNA methylation signatures in three distinct prakriti phenotypes demonstrate the epigenetic basis of Indian traditional human classification which may have relevance to personalized medicine.

Optimal Guidance for Accurate Lunar Soft Landing with Minimum Fuel Consumption using Model Predictive Static Programming

In this paper the soft lunar landing with minimum fuel expenditure is formulated as a nonlinear optimal guidance problem. The realization of pinpoint soft landing with terminal velocity and position constraints is achieved using Model Predictive Static Programming (MPSP). The high accuracy of the terminal conditions is ensured as the formulation of the MPSP inherently poses final conditions as a set of hard constraints. The computational efficiency and fast convergence make the MPSP preferable for fixed final time onboard optimal guidance algorithm. It has also been observed that the minimum fuel requirement strongly depends on the choice of the final time (a critical point that is not given due importance in many literature). Hence, to optimally select the final time, a neural network is used to learn the mapping between various initial conditions in the domain of interest and the corresponding optimal flight time. To generate the training data set, the optimal final time is computed offline using a gradient based optimization technique. The effectiveness of the proposed method is demonstrated with rigorous simulation results.