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.
Biodegradable plastics are a potential sustainable alternative to conventional petrochemical-based non-degradable plastics. Due to their lightweight, flexibility, durability, versatile applications, chemical inertness, electrical and heat insulation, and conductivity, plastics have become an essential material for many industries, with annual production currently exceeding 450 million tons. However, these materials are non-biodegradable, leading to detrimental consequences such as the formation of microplastics from improper disposal and the generation of toxic gases, including furans, dioxins, mercury, and polychlorinated biphenyls, from burning plastic waste. This results in environmental pollution, affecting land, water bodies, and the atmosphere. In response, studies where the focus has been on creating bio-degradable polymers such as polylactic acid, polyhydroxy alkanoates, Polycaprolactone, Poly(butylene adipate-co-terephthalate), and Polybutylene succinate, which were extracted from renewable resources or chemically modified as biodegradable polymers. Biodegradable polymers exhibit a wide range of properties and can now be modified to be used in various applications suitable for substituting some conventional plastic products. Thus, the article highlights the critical issue of environmental pollution caused by non-biodegradable plastics and provides a comprehensive overview of the synthesis processes, properties, novel applications, and challenges associated with the use of biodegradable plastics.
Phase change materials (PCMs) face challenges such as low thermal conductivity and leakage, often addressed through attempts at encapsulation or integration into polymer matrices or porous materials. This study uses expanded perlite to prepare a PCM composite. The perlite is treated with hydrochloric acid to remove impurities and improve its absorption, then impregnated with paraffin at 65 °C, with the addition of copper to enhance thermal conductivity. After drying, the material was coated with epoxy resin to prevent leakage and mixed with high-density polyethylene (HDPE) to improve its mechanical strength and facilitate integration with other materials. Characterization techniques, including differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM), evaluate the structure and properties of the composite. TGA results show that acid treatment increases paraffin absorption to 80% by weight, while weight loss tests confirm the effectiveness of the epoxy coating against leaks. A decrease in melting temperatures was observed in all HDPE blends, ranging from 4.72 °C to 9.58 °C, likely due to the integrated elements interfering with the reorganization of the molecular chains of HDPE. Although the preparation improved thermal conductivity, thermal tests revealed that increasing the (perlite/PCM) phase in HDPE is essential for further optimization, highlighting the potential of the composite as an effective energy storage solution for sustainable systems.
