The hydrogels responding to pH synthesized by graft copolymerization only and then concurrent grafting and crosslinking of monomer N-isopropyl acrylamide (NIPAAM) and binary comonomers acrylamide, acrylic acid and acrylonitrile (AAm, AA and AN) onto chitosan support were explored for the percent upload and release study for anti-inflammatory diclofenac sodium drug (DS), w.r.t. time and pH. Diclofenac sodium DS was seized in polymeric matrices by the equilibration process. The crosslinked-graft copolymers showed the highest percent uptake than graft copolymers (without crosslinker) and chitosan itself. The sustainable release of the loaded drug was studied with respect to time at pH 2.2, 7.0, 7.4 and 9.4. Among graft copolymers (without crosslinking), Chit-g-polymer (NIPAAM-co-AA) and Chit-g-polymer (NIPAAM-co-AN) exhibited worthy results for sustainable drug deliverance, whereas Crosslink-Chit-g-polymer (NIPAAM-co-AA) and Crosslink-Chit-g-polymer (NIPAAM-co-AAm) presented the best results for controlled/sustained release of diclofenac sodium DS with 93.86 % and 96.30 % percent release, respectively, in 6 h contact time. Therefore, the grafted and the crosslinked graft copolymers of the chitosan showed excellent delivery devices for the DS with sustainable/prolonged release in response to pH. Drug release kinetics was studied using Fick’s law. The kinetic study revealed that polymeric matrices showed the value of n as n > 1.0, hence drug release took place by non-Fickian diffusion. Hence, the present novel findings showed the multidirectional drug release rate. The morphological changes due to interwoven network structure of the crosslinked are evident by the Scanning electron microscopy (SEM) analysis.
In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially tested MOGNDO on standard multi-objective benchmark functions, where it showed strong performance. When applied to the BLDC motor design with the objectives of either maximizing operational efficiency or minimizing motor mass, the MOGNDO algorithm consistently outperformed other techniques like Ant Lion Optimizer (ALO), Ion Motion Optimization (IMO), and Sine Cosine Algorithm (SCA). Specifically, MOGNDO yielded the most optimal values across efficiency and mass metrics, providing practical solutions for real-world BLDC motor design. The MOGNDO source code is available at: https://github.com/kanak02/MOGNDO.
Fused filament fabrication (FFF) has numerous process parameters that influence the mechanical strength of parts. Hence, many optimization studies are performed using conventional tools and algorithms. Although studies have also been performed using advanced algorithms, limited research has been reported in which variants of the naked mole-rat algorithm (NMRA) are implemented for solving the optimization issues of manufacturing processes. This study was performed to scrutinize optimum parameters and their levels to attain maximum impact strength, flexural strength and tensile strength based on five different FFF process parameters. The algorithm yielded better results than other studies and successfully achieved a maximum response, which may be helpful to enhance the mechanical strength of FFF parts. The study opens a plethora of research prospects for implementing NMRA in manufacturing. Moreover, the findings may help identify critical parametric levels for the fabrication of customized products at the commercial level and help to attain the objectives of Industry 4.0.
The findings of an extensive experimental research study on the usage of nano-sized cement powder and other additives combined to form cement-fine-aggregate matrices are discussed in this work. In the laboratory, dry and wet methods were used to create nano-sized cements. The influence of these nano-sized cements, nano-silica fumes, and nano-fly ash in different proportions was studied to the evaluate the engineering properties of the cement-fine-aggregate matrices concerning normal-sized, commercially available cement. The composites produced with modified cement-fine-aggregate matrices were subjected to microscopic-scale analyses using a petrographic microscope, a Scanning Electron Microscope (SEM), and a Transmission Electron Microscope (TEM). These studies unravelled the placement and behaviour of additives in controlling the engineering properties of the mix. The test results indicated that nano-cement and nano-sized particles improved the engineering properties of the hardened cement matrix. The wet-ground nano-cement showed the best result, 40 MPa 28th-day compressive strength, without mixing any additive compared with ordinary and dry-ground cements. The mix containing 50:50 normal and wet-ground cement exhibited 37.20 MPa 28th-day compressive strength. All other mixes with nano-sized dry cement, silica fume, and fly ash with different permutations and combinations gave better results than the normal-cement-fine-aggregate mix. The petrographic studies and the Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) analyses further validated the above findings. Statistical analyses and techniques such as correlation and stepwise multiple regression analysis were conducted to compose a predictive equation to calculate the 28th-day compressive strength. In addition to these methods, a repeated measures Analysis of Variance (ANOVA) was also implemented to analyse the statistically significant differences among three differently timed strength readings.