Analysis of effective mechanical properties of thin films used in microelectromechanical systems

This research aims at analyzing the effective mechanical properties of thin film materials that are used in MEMS. Using the effective mechanical properties, reliable simulations of new or slightly altered designs can be performed successfully. The main reason for investigating effective material properties of MEMS devices is that the existing techniques can not provide consistent prediction of the mechanical properties without time-consuming and costly physical prototyping if the device or the fabrication recipe is slightly altered. To achieve this goal, two approaches were investigated: soft computing and analytical. In the soft computing approach, the effective material properties are empirically modeled and estimated based on experimental data and the relationships between the parameters affecting the mechanical properties of devices are discovered. In this approach, 2D-search, Micro Genetic Algorithms, Neural networks, and Radial Basis Functions Networks were explored for the search of the effective material properties of the thin films with the help of a Finite Element Analysis (FEA) and modeling the mechanical behavior such that the effective material properties can be estimated for a new device. In the analytical approach, the physical behavior of the thin films is modeled analytically using standard elastic theories such as Stoney’s formulae. As a case study, bilayer cantilevers of various dimensions were fabricated for extracting the effective Young’s modulus of thin film materials: Aluminum, TetraEthylOrthoSilicate (TEOS)-based SiO2, and Polyimide. In addition, a Matlab® graphical user interface (GUI), STEAM, is developed which interfaces with Ansys®. In STEAM, a fuzzy confidence factor is also developed to validate the reliability of the estimates based on factors such as facility and recipe-dependent variables. The results obtained from both approaches generated comparable effective material properties which are in accord with the experimental measurements. The results show that effective material properties of thin films can be estimated so that reliable MEMS devices can be designed without timely and costly physical prototyping

Application of soft computing techniques to estimate the effective Young\u27s Modulus of thin film materials

This research aims at characterizing and predicting the Young’s Modulus of thin film materials that are utilized in Microelectromechanical systems (MEMS). Recent studies indicate that the mechanical properties such as Young’s Modulus of thin films are significantly different from the bulk values. Due to the lack of proper understanding of the physics in the micro-scale domain the state-of-art estimation techniques are unreliable and often unfit for use for predicating the mechanical behavior of slight modifications of existing designs as well as new designs. This disadvantage limits the MEMS designers to physical prototyping which is cost ineffective and time consuming. As a result there is an immediate need for alternative techniques that can learn the complex relationship between the various parameters and predict the effective Young’s Modulus of the thin films materials. The proposed technique attempts to solve this problem using empirical estimation techniques that utilize soft computing techniques for the estimation as well as the prediction of the effective Young’s Modulus. As a proof of concept, effective Young’s Modulus of Aluminum and TetrathylOrthoSilicate (TEOS) thin films were computed by fabricating and analyzing self-deformed micromachined bilayer cantilevers. In the estimation phase, 2D search and micro Genetic algorithm were studied and in the prediction phase, back propagation based Neural networks and One Dimensional Radial Basis Function Networks (1D-RBFN) were studied. The performance of all combinations of these soft computing techniques is studied. Based on the results, we conclude that performance of the soft computing techniques is superior to the existing methods. In addition, the effective values generated using this methodology is comparable to the values reported in the literature. Given a finite number of data samples, the combination of 1D-RBFN (prediction phase) and GA (estimation phase) presented the best results. Due to these advantages, this methodology is foreseen to be an essential tool for developing accurate models that can estimate the mechanical behavior of thin films

Generalized Design of Diffractive Optical Elements Using Neural Networks

Diffractive optical elements (DOE) utilize diffraction to manipulate light in optical systems. These elements have a wide range of applications including optical interconnects, coherent beam addition, laser beam shaping and refractive optics aberration correction. Due to the wide range of applications, optimal design of DOE has become an important research problem. In the design of the DOEs, existing techniques utilize the Fresnel diffraction theory to compute the phase at the desired location at the output plane. This process involves solving nonlinear integral equations for which various numerical methods along with robust optimization algorithms exist in literature. However all the algorithms proposed so far assume that the size and the spacing of the elements as independent variables in the design of optimal diffractive gratings. Therefore search algorithms need to be called every time the required geometry of the elements changes, resulting in a computationally expensive design procedure for systems utilizing a large number of DOEs. In this work we have developed a novel algorithm that uses neural networks with possibly multiple hidden layers to overcome this limitation and arrives at a general solution for the design of the DOEs for a given application. Inputs to this network are the spacing between the elements and the input/output planes. The network outputs the phase gratings that are required to obtain the desired intensity at the specified location in the output plane. The network was trained using the back-propagation technique. The training set was generated by using GS algorithm approach as described in literature. The mean square error obtained is comparable to conventional techniques but with much lower computational costs

A synthetic antioxidant molecule, GP13 derived from cysteine desulfurase of spirulina, Arthrospira platensis exhibited anti-diabetic activity on L6 rat skeletal muscle cells through GLUT-4 pathway

Objectives: Diabetes creates oxidative stress, which damages several organs and causes various problems including hyperglycemia, hyperlipidemia, hypertension, and maybe iron dyshomeostasis. Consequently, antioxidant therapy may be a promising strategy to avoid diabetes and diabetic complications. In the current study, we investigated the activity of the antioxidant GP13 peptide in an in-vitro diabetic model. Methods: All anti-diabetic and antioxidant in-vitro tests were performed on differentiated L6 myotubes cells. MTT assay was used to analyze the cytotoxic effect of the GP13 at different concentrations (10 μM to 80 μM) in the L6 cells. The DCFDA fluorescence was performed to confirm the radical scavenging effect of GP13 in the myotubule cells. The cells were treated with different concentrations of GP13 peptide before the enzyme assay was conducted. The differentiated L6 myotubes were kept for serum deprivation for 8 h before being treated with GP13 peptide. The RNA extraction from the L6 myotubes was performed using the Trizol reagent. Results: Cell viability analysis exhibited the non-toxic nature of GP13 in a dose-dependent manner (10 μM to 80 μM). Antioxidant enzyme, superoxide dismutase activity was 23.25 U/mL in the untreated group, whereas it was only 11.75 U/mL in the group that was exposed to GP13 at 80 μM. The catalase activity at 40 μM was slightly altered in the cells, while the hydrogen peroxide inhibition activity was higher (91.2%) compared to the control group. Additionally, GP13 showed anti-diabetic effects through a dose-dependent increase in glycogen storage (6.1 mM). It was discovered that 40 μM was the ideal concentration for the highest level of activity. Additionally, the genes involved in diabetes-related to antioxidants and the insulin signalling system were investigated. Conclusion: It is concluded that the GP13 peptide from A. platensis is a promising agent for anti-diabetic and antioxidant activities. To treat diabetes and its consequences, we thus propose that GP13 be regarded as a natural lead. The animal model design of this study has limitations, and further research is needed to draw conclusions about its therapeutic relevance to people

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Not AvailablePeanut plays a key role to the livelihood of millions in the world especially in Arid and Semi-Arid regions. Peanut with high oleic acid content aids to increase shelf-life of peanut oil as well as food products and extends major health benefits to the consumers. In peanut, ahFAD2 gene controls quantity of two major fatty acids viz, oleic and linoleic acids. These two fatty acids together with palmitic acid constitute 90% fat composition in peanut and regulate the quality of peanut oil. Here, two ahfad2 alleles from SunOleic 95R were introgressed into ICGV 05141 using marker-assisted selection. Marker-assisted breeding effectively increased oleic acid and oleic to linoleic acid ratio in recombinant lines up to 44% and 30%, respectively as compared to ICGV 05141. In addition to improved oil quality, the recombinant lines also had superiority in pod yield together with desired pod/seed attributes. Realizing the health benefits and ever increasing demand in domestic and international market, the high oleic peanut recombinant lines will certainly boost the economical benefits to the Indian farmers in addition to ensuring availability of high oleic peanuts to the traders and industry.Not Availabl

Steady expression of high oleic acid in peanut bred by marker-assisted backcrossing for fatty acid desaturase mutant alleles and its effect on seed germination along with other seedling traits.

Peanut (Arachis hypogaea L.) is an important nutrient-rich food legume and valued for its good quality cooking oil. The fatty acid content is the major determinant of the quality of the edible oil. The oils containing higher monounsaturated fatty acid are preferred for improved shelf life and potential health benefits. Therefore, a high oleic/linoleic fatty acid ratio is the target trait in an advanced breeding program. The two mutant alleles, ahFAD2A (on linkage group a09) and ahFAD2B (on linkage group b09) control fatty acid composition for higher oleic/linoleic ratio in peanut. In the present study, marker-assisted backcrossing was employed for the introgression of two FAD2 mutant alleles from SunOleic95R into the chromosome of ICGV06100, a high oil content peanut breeding line. In the marker-assisted backcrossing-introgression lines, a 97% increase in oleic acid, and a 92% reduction in linoleic acid content was observed in comparison to the recurrent parent. Besides, the oleic/linoleic ratio was increased to 25 with respect to the recurrent parent, which was only 1.2. The most significant outcome was the stable expression of oil-content, oleic acid, linoleic acid, and palmitic acid in the marker-assisted backcrossing-introgression lines over the locations. No significant difference was observed between high oleic and normal oleic in peanuts for seedling traits except germination percentage. In addition, marker-assisted backcrossing-introgression lines exhibited higher yield and resistance to foliar fungal diseases, i.e., late leaf spot and rust

Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update (vol 13, pg 353, 2019)

10.1007/s12072-019-09980-1HEPATOLOGY INTERNATIONAL136826-82