Deadline for manuscript submissions: 30 October 2024.
The analysis of rheological properties of suspensions requires the use of models such as Einstein’s formulation for viscosity in dilute conditions, but its effectiveness diminishes in the context of concentrated suspensions. This study investigates the rheology of suspensions containing solid particles in aqueous media thickened with starch nanoparticles (SNP). The goal is to model the viscosity of these mixtures across a range of shear rates and varying amounts of SNP and SG hollow spheres (SGHP). Artificial neural networks (ANN) combined with swarm intelligence algorithms were used for viscosity modeling, utilizing 1104 data points. Key features include SNP proportion, SGHP content, log-transformed shear rate (LogSR), and log-transformed viscosity (LogViscosity) as an output. Three swarm algorithms—AntLion Optimizer (ALO), Particle Swarm Optimizer (PSO), and Dragonfly Algorithm (DA)—were evaluated for optimizing ANN hyperparameters. The ALO algorithm proved most effective, demonstrating strong convergence, exploration, and exploitation. Comparative analysis of ANN models revealed the superior performance of ANN-ALO, with an R2 of 0.9861, mean absolute error (MAE) of 0.1013, root mean absolute error (RMSE) of 0.1356, and mean absolute percentage error (MAPE) of 3.198%. While all models showed high predictive accuracy, the ANN-PSO model had more limitations. These findings enhance understanding of starch suspension rheology, offering potential applications in materials science.
Multi-objective optimization (MOO) techniques are crucial in addressing complex engineering problems with conflicting objectives, particularly in pharmaceutical applications. This study focuses on optimizing a biodegradable micro-polymeric carrier system for drug delivery, specifically maximizing the encapsulation efficiency and drug release of Candesartan Cilexetil antihypertensive drug. Achieving a balance between these two goals is essential, as higher encapsulation efficiency ensures adequate drug loading. In contrast, optimal drug release rates are critical for maintaining bioavailability and achieving therapeutic efficacy. Using response surface models to formulate the problem definition, five prominent MOO algorithms were employed: NSGA-III, MOEAD, RVEA, C-TAEA, and AGE-MOEA. The optimization process aimed to generate Pareto fronts representing compromise solutions between encapsulation efficiency and drug release. The results revealed inherent conflicts between objectives: increasing encapsulation efficiency often came at the cost of reducing the drug release rate. Evaluation of MOO algorithms using performance metrics such as hypervolume, generational distance, inverted generational distance, spacing, maximum spread, and spread metric provided insights into their strengths and weaknesses. Among the evaluated algorithms, NSGA-III emerged as the top performer, achieving a Weighted Sum Method (WSM) score of 82.0776, followed closely by MOEAD with a WSM score of 80.8869. RVEA, C-TAEA, and AGE-MOEA also demonstrated competitive formulation quality, albeit with slightly lower WSM scores. In conclusion, the study underscores the importance of MOO techniques in optimizing pharmaceutical formulations, providing valuable insights for decision-makers in selecting optimal formulations.
In response to the growing environmental threats and pollution linked to synthetic plastics, current scientific inquiry is prioritizing the advancement of biodegradable materials. In this context, this study investigates the possibility of developing fully biodegradable materials using plant fibers extracted from the Diss plant (Ampelodesmos mauritanicus) as reinforcement in poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)-based biocomposites. The biocomposites were prepared by melt blending in the following weight ratio: PHBV/Diss fibers 80/20. The chemical structure of Diss fibers was characterized by Fourier transform infrared spectroscopy (FTIR) and X-ray fluorescence spectrometry (XRF). The impact of Diss fibers on the mechanical properties of biocomposites has also been investigated in comparison to neat PHBV. FTIR and XRF analyses identified cellulose, hemicellulose, and lignin as the main components of Diss fibers. On the other hand, the results showed a significant enhancement of Young’s modulus (⁓21%) of PHBV/DF biocomposites in comparison to neat PHBV due to a better dispersion of the fibers in the matrix, as confirmed by atomic force microscopy (AFM) images.
In this work, Cobalt oxide nanoparticles (Co3O4·NPs) were synthesized via a simple sonochemical reaction by using polyethylene glycol (PEG) as a surfactant. Structural, morphological and spectroscopic analysis of obtained powder (Co3O4·NPs) was investigated by X-ray diffraction, FTIR spectroscopy and scanning electron microscope (SEM). The nanocrystalline nature of the sample was confirmed by XRD, which exhibits the cubic face-centered normal spinel structure of (Co3O4·NPs) and the space group of Fd-3m with the average crystallite size around 15 nm. FTIR spectrum shows two strong absorption bands of (Co+2–O) and (Co+3–O) which confirm the spinel structure of Co3O4·NPs. Moreover, SEM micrographs showed that the agglomeration of the nanoparticles was reduced by the addition of (PEG) surfactant and UV-Vis was used to study the synthesized material’s optical properties. The Co3O4 band gap ranged around 2.2 and 3.5 eV.
The structure and thermophysical properties of polymer blends polyamide 6/high-density polyethylene with component ratios of 70:30, 45:55 and 30:70, which not only provide phase inversion of the blended polymers, but also the formation of an interpenetrating network, have been investigated by differential scanning calorimetry, scanning electron microscopy and light flash method. The data on the influence of blends composition on their mechanical properties, density, structure, temperature, as well as melting and crystallization heats of polymer components have been obtained. The regularities of changes in thermal diffusion, heat capacities and thermal conductivity coefficients of polyamide 6 and high-density polyethylene individually and as part of the blends in dependence on their composition and temperature have been established. A nonlinear increase of the thermal conductivity coefficient from temperature was revealed when melting a more easily melting component of the blend. It was found that the maximum increase in thermal conductivity occurs in the blend forming an interpenetrating network. A possible way of creating composites with adaptive thermal conductivity by melting one of the components of the blend is proposed.
The present work aims to examine the influence of designing mini channel heat sinks using Stereolithography (SLA) 3D printing. Stereolithography (SLA) is a common additive manufacturing technique. The internal mini channels of the heat sink are made of aluminium materials and the outer cover is made of commercial polymer. Three models of the mini channel heat sinks are considered. A constant heat flow is applied to the bottom wall of the heat sink, and water is used as a coolant. The flow and heat transfer were studied for different cooling speeds. The physical properties of the fluid provided good thermal performance for the heat sink, especially at increased flow rates. The acrylonitrile butadiene styrene (ABS) copolymer resin has shown its good insulator for the heat sink and has improved the performance of the heat sink. This study demonstrates that the ABS copolymer resin enhances the cooling of electronic components.
Along with the development of electric vehicles and electronic devices, all-solid-state batteries (ASSBs) have become the next-generation energy storage batteries, owing to their safety and chemical stability. Sulfide Solid Electrolytes (SSEs) are deemed to be crucial materials for ASSBs because of their ultrahigh ionic conductivity (10−3–10−2 S cm−1), but are still plagued by the narrow electrochemical window and poor interfacial stability. In this paper, we summarize our systematic research progress on sulfide SSEs from the view of how theoretical calculations and simulations play a crucial role in material design. First-principles calculation gives evidence of the structure’s stability and ion migration mechanism for electrolytes, MD and AIMD simulations provide insights for the dynamic diffusion behavior and the interface reaction mechanism. High-throughput screening and machine learning have accelerated new electrolyte designs. Scientists discovered Li10GeP2S12 and explored ion dynamics in a crystal lattice of that material. There are also material interface phenomena such as space charge layers and chemical breakdown. These problems can be managed by developing and tuning appropriate computational models to steer material doping and protective layer design. In this paper, we demonstrate that the combination of computer simulations and real experiments is valuable.
Among the known chalcones, dibenzyldieneacetone is an organic molecule that was synthesized in this study and encapsulated into the Ethyl cellulose matrix by solvent evaporation technique. Microencapsulation aims to shield the core material from environmental influences (like light, humidity, temperature, and oxygen), extend its shelf life, and enhance the product’s quality. The microsphere size distribution was determined using an optical microscope. The synthesis product, as well as the particles, were characterized by ultraviolet-visible, infrared, and XRD. This study allowed us to identify particle morphology, encapsulation rate, and particle size distribution.