Sintering studies of nano-crystalline zinc oxide

Sintering and grain growth of nano-crystalline undoped ZnO has been studied in detail over a wide range of temperature and holding time. Below 800 °C, sintering of over 70% theoretical density is not observed, irrespective of particle size. At 900 °C for 6 h, the nano-crystalline sample sinters to 99% of theoretical density whereas the density for as received sample is 93% of theoretical density. However, at 1300 °C or higher, the densification is found to be much faster and after a few hours becomes independent of holding time. Grain growth studies reveal a similar feature of attaining saturation over holding time. The average saturated grain size is found to be ∼1.5 and ∼2.2 μm at 800 and 900 °C, respectively, while at 1300 °C or higher, it is in between 12 and 13 μm.     

Anhydrous esterification of myristic acid with propylene: Ion exchange resin and acid-treated clay as catalysts

Anhydrous esterification of myristic acid with propylene was carried out in the temperature range of 110–145°C and pressure from 190–195 psig in the presence of Amberlyst-15 (cation exchange resin) and Filtrol-24 (acid-treated clay) as catalysts. The product ester, isopropyl myristate finds use in cosmetic and topical medicinal preparations where good absorption through the skin is desired. Filtrol-24 is the catalyst of choice, and the recommended operating temperature is 130°C with a pressure of 190 psig.     

Correction to: EDVWDD: Event‑Driven Virtual Wheel‑based Data Dissemination for Mobile Sink‑Enabled Wireless Sensor Networks

The Journal of Supercomputing - A correction to this paper has been published: https://doi.org/10.1007/s11227-021-03790-9     

Prediction of properties of rubber by using artificial neural networks

Different rubber formulations were designed using nitrile rubber and a mixed crosslinking system consisting of sulfur/accelerator and electron beam radiation. Based on the experimental results, an artificial neural network (ANN) was constructed to simulate the mechanical properties and volume fraction of rubber. The ANN could predict accurately the above properties for a series of nitrile rubber compounds. However, the number of training data played a key role in the ANN predictive quality. In addition, the more complex the nonlinear relation between input and output was, the larger was the number of training dataset required. The predicted results were further validated using another mathematical model. The constructed ANN was verified with a completely different styrene butadiene rubber system. The prediction was found to be extremely good. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2227–2237, 2006     

Studies on engineering properties of PVC–PMMA semi‐interpenetrating polymer networks

Semi1 and semi2 interpenetrating polymer networks (IPNs) of PVC and in situ formed PMMA have been synthesized using diallyl phthalate and ethylene glycol dimethacrylate as the crosslinkers of PVC and PMMA, respectively. These two types of IPNs have been compared w.r.t their physical, mechanical, and thermal properties and an endeavor has been made to find a correlation of these properties with the morphology generated in these systems. The semi1 IPNs displayed a decrease in their mechanical parameters and the physical properties as well, while in contrast, the semi2 IPNs exhibited a marginal increase in the corresponding values after an initial drop upto about 15% of crosslinked PMMA incorporation when compared to the crosslinked PVC in the case of semi1 IPN and linear PVC in the case of semi2 IPN. The various samples of semi1 and semi2 IPNs showed a two‐stage degradation typical of PVC, while confirming the increased stability with the samples having higher percentages of PMMA. The influence of crosslinking of the major matrix in semi1 IPN was almost counterbalanced by the influence of crosslinking in the dispersed PMMA phase in the case of semi2 IPN. The softening characteristics as detected by the extent of penetration of the probe, as has been detected by thermomechanical analysis, are in conformity with their mechanicals. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 1725–1735, 2005     

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.