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.

Stable perovskite based photodetector in impedance and capacitance mode

Abstract: Nowadays perovskite emerges as a promising photosensitive material for next-generation solution-processed devices. Perovskite-based solar cells degrade in ambient conditions up to some extent, after which they are discarded. In our work, we are using reusing the degraded cells as a high-performance stable perovskite-based photodetector. The symmetry of the detector is FTO/PEDOT:PSS/Perovskite/PC61BM/CdS/Ag showing sensitivity to light with respect to (w.r.t) impedance and capacitance. To enhance the excitons generation and absorption of light, the electron transport layer of cadmium sulfide along with PC61BM is selected and PEDOT:PSS layer is used for hole transportation. Atomic force microscopy, X-ray diffraction, and UV-absorption spectrum were obtained to study the surface morphology, composition, and absorption of the perovskite layer. The electric parameters within the frequency range 100 Hz to 200 kHz of samples under the effect of light were investigated. Experimental results showed that with the change in light intensity from dark to 325 W/m2, the highest impedance and capacitance were observed at 100 Hz. The impedance sensitivity is −126.154 kΩ m2/W and the uppermost capacitance sensitivity w.r.t light intensity is 6.77 pF m2/W. Frequency-impedance and capacitance relationships and sensitivities were also measured. The results showed that in the range from 100 Hz to 200 kHz the maximum impedance sensitivity is −343.37 Ω/Hz in dark condition. While the capacitance sensitivity was highest when light intensity was 325 W/m2 (−37.27 pF/kHz), respectively. We attribute these observations due to the generation of electron-hole pairs under light and by the increase of their concentration, by the presence of the built-in capacitance and possible frequency dependence of the mobility of the charges.

Thermodynamic Analysis of Microchannel Heat Sink With Cylindrical Ribs and Cavities

Abstract: Heat transfer improvement in microchannel heat sink (MCHS) has been a challenge, because it increases the power requirements for the fluid flow. In the present study, MCHS with different wall, geometric, and design configurations of cylindrical ribs and cavities are simulated to investigate their effect on thermal and hydrodynamic performance of MCHS using a laminar flow having Reynolds number in the range from 100 to 1000. The wall configurations include; base wall cylindrical ribs (BWCR), side wall cylindrical ribs (SWCR), and all wall cylindrical ribs (AWCR). Moreover, the geometric configurations involve different AWCR cases having rib spacings (Sfr) of 0.4 mm, 0.8 mm, 1.2 mm, and 0.4 mm staggered arrangement. Furthermore, the design configurations include; AWCR, all wall cylindrical cavities (AWCC), and all wall cylindrical ribs and cavities (AWCRC) with constant Sfr = 0.4 mm. The performance of various channels with flow disruptors is analyzed in terms of friction factor (f) and Nusselt number and then compared with smooth channel in terms of thermal enhancement factor (η). Based on the first law of thermodynamics, thermal resistance (Rth) is used to investigate the resistance of any configuration to flow of heat comparing at same pumping power. Moreover, the second law of thermodynamics is applied to quantify the rate of entropy generation (S˙gen) and transport efficiency (ηt) for MCHS. The results show that although the MCHS with all wall ribs has a lower value of η than the base wall and side wall ribs; however, it has the maximum value of ηt and minimum value of Rth and S˙gen; thus, indicating that η is not the only performance criteria for the selection of MCHS.

On Efficient Mining of Frequent Itemsets from Big Uncertain Databases

Abstract: In the current era of information, communication, and technology the data is being generated at an exponential rate. This provides machine learning and data mining algorithms an opportunity to learn from huge data repositories. However, at the same time, the big data poses many challenges. Data uncertainty being the key concern of the modern data mining systems. This work addresses the problem of extracting frequent itemsets from such large uncertain databases to assist the decision makers in understanding the non-trivial data trends. The usual technique utilized to find frequent itemsets from uncertain databases is known as the Possible Word Semantics (PWS). However, as the database size increases, PWS suffers from performance issues. Therefore, there is a need for efficient frequent pattern mining algorithms. This work presents three techniques to address the issue at hand, namely: 3D linked array-based strategy, connected tree technique, and average probability-based setup with the support of a tree data structure. The objective here is to minimize computational cost by traversing the database only once. The 3D linked array-based solution scans the database only once and stores the support information of the item and its association with other items within the 3D array. For the tree-based method, 1D array is associated with each node of the tree, comprising of support information of the database items and their associations with other items. The average probability-based approach computes the average probability factor and utilizes it to map the uncertain database to a tree. The current proposal addresses attribute uncertainty as well as the tuple uncertainty to map large uncertain databases to the proposed data structures. In addition to introducing the three data structures, this work also presents algorithms to extract frequent itemsets. The proposal is compared with four recent works done in this domain for uncertain data, namely, mining threshold-based (MB) technique, frequent itemsets using nodesets (FIN), prepost + , and uncertain apriori (UApriori). Experiments are performed utilizing four benchmark datasets. The results obtained suggest better performance of the three techniques presented here, while consuming 60% less execution time.

Interpreting Surface Degradation of HTV Silicone Rubber Filled with Micro/Nano-Silica Under AC and DC Voltages

Abstract: In this study, high-temperature vulcanized silicone rubber filled with different concentrations of micro- and nano-sized silica were tested under synergistic exposure to multi-stress conditions for a time period of 5000 h separately under alternating current and direct current voltages Different characterization methods were used to evaluate the degradation performance After each aging cycle, leakage current was measured and hydrophobicity classifications were determined by using contact angle measurement and amethod proposed by the Swedish Transmission Research Institute Scanning electron microscopic study and Fourier transform infrared spectroscopy were performed for each test sample Mechanical performance was assessed by measuring hardness, tensile strength and elongation-at-break An outcome of this investigation revealed that samples stressed with DC voltage experienced 20–30% degradation compared to AC-stressed samples under common environmental stresses All hybrid composites were found more resistant to aging but to a different degree depending on the composition The test sample designated as TS2 was found to offer the highest resistance to aging