Sounak Kumar Choudhury

This work attempts to induce superhydrophobicity on a hydrophilic D2 steel surface by microtexturing, using the Electro-discharge machining process, as hydrophobic surfaces offer enhanced self-cleaning and corrosion resistance. The laser machining process is praiseworthy for surface texturing on nonmetals but not widely recommended for conductive metals. Electro-discharge machining is very effective for micro fabrications on metals and its potential in surface texturing should be explored. Texturing of the D2 steel surface is done by fabricating microchannels in mutually perpendicular directions using a thin Cu foil as a tool in the EDM process. Texture dimensions in relation to the microchannel width are calculated from Cassie-Baxter equations. It has been observed that surface texturing improves the contact angle of water droplets on a metallic surface. The static contact angle of a water droplet on a hydrophilic D2 steel surface was increased from 78.6° to 158.7°, resulting in superhydrophobicity.

This paper presents the comparison of simulation and experimental output with the neural network results of different materials like Si and GaAs-based IMPATT diode at 94 GHz window frequency. The experimental results are nearly equal to simulation results and show a quantitative agreement between theoretical values and experimental output. The experimental results in terms of efficiency and RF power are almost 80-85% of the simulation results. Rest has 15-20% discrepancy between theory and experiment due to several causes like temperature, small-signal approach, parasitic effects, impedance matching problem and proper heat-sink arrangements. The developed neural network model is used to optimise performance of the diode by taking other wide band gap semiconductor material-based IMPATT diode. The model and the results will be very useful for applications in radar and guided missile technology.