Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

About: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology is a education organization based out in Topi, Pakistan. It is known for research contribution in the topics: Thin film & Quantum efficiency. The organization has 618 authors who have published 940 publications receiving 10674 citations.

The marvelous optical performance of AlGaN-based deep ultraviolet light-emitting diodes with AlInGaN-based last quantum barrier and step electron blocking layer.

Abstract: The optoelectronic characteristics of AlGaN-based deep ultraviolet light-emitting diodes (DUV LEDs) with quaternary last quantum barrier (QLQB) and step-graded electron blocking layer (EBL) are investigated numerically. The results show that the internal quantum efficiency (IQE) and radiative recombination rate are remarkably improved with AlInGaN step-graded EBL and QLQB as compared to conventional or ternary AlGaN EBL and last quantum barrier (LQB). This significant improvement is assigned to the optimal recombination of electron–hole pairs in the multiple quantum wells (MQWs). It is due to the decrease in strain and lattice mismatch between the epi-layers which alleviates the effective potential barrier height of the conduction band and suppressed the electron leakage without affecting the holes transportation to the active region. Moreover, to figure out quantitatively, the electron and hole quantity increased by ~ 25% and ~ 15%, respectively. Additionally, the IQE and radiative recombination rate are enhanced by 48% and 55%, respectively, as compared to conventional LED. So, we believe that our proposed structure is not only a feasible approach for achieving highly efficient DUV LEDs, but the device physics presented in this study establishes a fruitful understanding of III nitride-based optoelectronic devices.

Effect of Copper and Zirconium Addition on Properties of Fe-Co-Si-B-Nb Bulk Metallic Glasses

Abstract: In this research work, iron-based bulk metallic glasses (BMGs) have been fabricated, characterized and compared with Fe-Si alloy BMG alloys of composition ((Fe06Co04)075B020Si005)96Nb4) were synthesized by suction casting technique using chilled copper die Effect of copper and zirconium addition on magnetic, mechanical, thermal and electrochemical behavior of ((Fe06Co04)075B020Si005)96Nb4 BMGs was investigated Furthermore, effect of annealing on nano-crystallization and subsequently on magnetic and mechanical behavior was also analyzed Amorphousness of structure was evidenced by XRD analysis and microscopic visualization, whereas nano-crystallization behavior was identified by peak broadening of XRD patterns Magnetic properties, measured by vibrating sample magnetometer, were found to be improved for as-cast BMG alloys by copper addition and further enhanced by nano-crystallization after annealing Mechanical properties were observed to be increased by zirconium addition while slightly declined by copper addition Potentiodynamic polarization analysis manifested the positive role of zirconium in enhancing corrosion resistance of BMGs in acidic, basic and brine mediums Moreover, mechanical properties and corrosion analysis results affirmed the superiority of BMG alloys over Fe-Si alloy

Development of Prediction Model for Conductive Pattern Lines Generated Through Positive Displacement Microdispensing System Using Artificial Neural Network

Abstract: In the fabrication of electronic devices, uniform and good quality conductive printed lines are highly desirable. The goal of the present study is to develop a predictive model for conductive pattern lines produced by the microdispensing system. For this purpose, an artificial neural network (ANN) based on a feed-forward backpropagation algorithm is adopted. Input process parameters are pressure, feed rate, and standoff distance, while the output performance parameter (response) is the width of pattern lines generated through 200 µm and 500 µm nozzles diameter. The dispensing material is carbon paste having a viscosity of 30 Pa s. Best levels of process parameters are identified to achieve lower width of pattern lines based on the Taguchi signal-to-noise ratios. The identified best levels are found valid in the ranges of printing process parameters after training the neural networks. The prediction ability of ANN models is evaluated based on the leave-one-out cross-validation technique. The results showed that the proposed ANN model accomplished better results in predicting the width of pattern lines. In addition, the proposed approach is extendable to different materials with a variety of viscosities as well as to other similar printing techniques.