Volume 45, Issue 5

The increasing demand for energy systems that are flexible, lightweight, and durable has generated interest in solid polymer electrolytes. Typically, conventional lithium-ion batteries use carbonate-containing liquid electrolytes, which are flammable and pose safety concerns. In contrast, solid electrolytes offer mechanical reliability, environmental sustainability, and enhanced safety. This review examines the mechanical properties of solid polymer electrolytes in recent battery applications. Various electrolyte compositions and processing techniques were examined, including incorporating polymer composites and additives to enhance mechanical properties. The review also discusses the characterization of polymer electrolytes using instruments such as a nanoindenter and a universal testing machine, which enable the evaluation of tensile, flexural, and viscoelastic properties. By understanding and improving the mechanical properties of solid polymer electrolytes, it is possible to develop safer and more efficient energy storage systems.

This study aims to investigate the synergistic effects of graphene, aluminum oxide (Al 2 O 3 ), and silicon oxide (SiO 2 ) nanofillers on the mechanical and thermal properties of epoxy composites. The objectives were to enhance the performance of epoxy resins by incorporating these nanofillers at varying weight fractions (0.5 %, 1 %, and 1.5 %) using ultrasonication for uniform dispersion. Standard ASTM tests, including tensile, flexural, and impact strength tests, along with thermal conductivity measurements, were conducted to evaluate the properties of the nanocomposites. The results revealed that graphene-reinforced composites exhibited the most significant improvements, with tensile strength increasing by 32 %, flexural strength by 21 %, and thermal conductivity by over 300 % compared to neat epoxy. Alumina and silica also enhanced the composite properties but to a lesser extent. Scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) analyses showed key differences in the dispersion of nanoparticles and the interactions between the nanofillers and the epoxy matrix, with graphene demonstrating superior interfacial bonding. These findings highlight graphene’s exceptional potential for enhancing the mechanical and thermal properties of epoxy composites, making them suitable for applications in thermal management and structural components in advanced engineering fields. The study provides valuable insights into optimizing nanocomposite formulations to achieve multifunctional performance.

In this study, we successfully prepared PES/F127/TA hybrid ultrafiltration membranes by reverse thermal phase separation (RTIPS) using Pluronic F127 (F127) and tannic acid (TA) as hydrophilic modifiers. The novelty of this study lies in the synergistic effect of F127 and TA, which significantly enhances the hydrophilicity and anti-fouling properties of PES membranes. The membrane is used to treat organic wastewater. The results showed that the optimal preparation conditions were 0.2 wt% TA and 0.6 wt% F127, with the contact angle decreasing from 65.25° to 44.46°. The water flux of the blend membrane was 1,713.0 L/(m 2  h), which was 5 times that of the pure PES membrane, and it maintained a higher rejection rate of HA (87.02 %) due to its increased porosity. This indicates that the membrane has excellent permeability and strong selectivity for organic wastewater. The blend membrane also exhibited better mechanical properties, prolonging its lifetime.

In this study, a new boron doped carbon dots@chitosan/polyvinyl alcohol (B-CDs@CS/PVA) hydrogel was prepared for methylene blue (MB) removal from aqueous solution. The B-CDs were first synthesized using orange peel with the addition of boric acid by microwave heating process and then the B-CDs were embedded with chitosan and PVA. The structure and characterization of B-CDs and B-CDs@CS/PVA hydrogel were performed by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), differential scanning calorimetry (DSC), thermogravimetric analysis (TG), UV–Vis spectrophotometer and fluorescence spectrophotometer. The results showed that B-CDs solution with an abundance hydroxyl and carbonyl functional groups exhibited UV absorption and fluorescence properties. There is a strong interaction between B-CDs and CS/PVA, which improved the crystallinity and thermal stability of B-CDs@CS/PVA hydrogels. The adsorption performance of B-CDs@CS/PVA hydrogels for MB was studied through batch experiments. The maximum adsorption capacity of the B-CDs/PVA hydrogel for MB was 3.573 mg/g for 12 h. In addition, it was found that the adsorption behavior obeyed the pseudo-second-order kinetic and Freundlich models. The composite hydrogels are promising adsorbent for dye wastewater purification.

Water contamination by heavy metals is a serious ecological problem due to their toxicity to humans, animals and plants. The synthesis of new low-cost adsorbents used in the treatment of polluted water is therefore generating growing interest. In this work, the synthesis of semi-interpenetrating network (SIPN) hydrogels by in-situ free radical polymerization was carried out. Materials based on poly(acrylamide-co- acrylic acid) and different ratios of sodium alginate (Alg-Na) have been prepared to examine their potential use in wastewater treatment. The swelling capabilities of these materials were evaluated observing a general trend of low swelling in acidic pH solutions and more significant swelling in solutions with a pH above 5. The structure and morphology of the hydrogels were investigated using X-ray diffraction, Fourier transform infrared spectroscopy and scanning electron microscopy. The adsorption capacity of the hydrogels for metal ions Pb 2+ , Cd 2+ , and Cr 3+ was studied in aqueous media containing different concentrations of these ions (25–100 ppm). The results showed that the hydrogels have great potential for heavy metal removal from aqueous solutions concluding also that the addition of Alg-Na enhances the metal uptake.

In this study PL fibers were used to analyze the influences of two key parameters in the optimization process aimed at maximizing fiber yield using the centrifugal spinning technique called Forcespinning ® . The optimization process was conducted focusing on rotational velocity (rpm) and precursor polymer concentration. These variables were then correlated with both fiber yield and diameter. Rheological analysis of the precursor polymeric solutions was performed to examine the relationships between fiber production parameters and solution viscosities ( η ) as well as storage ( G′ ) and loss ( G″ ) moduli. Fiber yields were analyzed at intervals from 2,000 to 10,000 rpm. In terms of morphological properties, most fibers exhibited diameters ranging from 0.2 to 1.2 μm, with a gradual decrease observed as the rpm increased, which aligns with expectations. The 18.2 wt% pullulan solution demonstrated an optimal balance of high fiber production yield (60–67 %), low production rpm (4,000), and low viscosity (1,415 cp), facilitating the production process compared with the other systems. Additionally, thermal characterization analysis revealed that the fibers absorbed solvents in the range of 10–11 % by weight, with thermal stability exceeding 270 °C in a nitrogen atmosphere. This optimization study is a processing platform for future research on pullulan-based fibers.

Surrogate models leveraging Neural Networks (NNs) have demonstrated significant potential in accurately modeling the intricate dynamics of the injection molding process, enabling the prediction and optimization of process and material parameters on internal thermodynamics and final product quality. However, the substantial effort required for data collection remains a notable challenge. This study investigates the effectiveness of pretraining methods with varying fidelities for a two-dimensional cooling problem in injection molding. Specifically, NNs are pretrained using low-fidelity (LF) solutions derived from the Finite Element Method (FEM) and Physics-Informed Neural Networks (PINNs). These pretrained models are subsequently fine-tuned with high-fidelity (HF) simulation data to enhance prediction accuracy across previously untrained scenarios. While both FEM and PINNs provide approximations of the cooling process, PINNs offer a continuous solution space, however, at a higher computational cost compared to FEM. Respectively, FEM is more computationally efficient but less proficient at handling highly parameterized input spaces. Comparing different fidelities reveals that prioritizing the capture of main effects over detailed physical implementation during pretraining can be advantageous. The fine-tuned surrogate model demonstrates robust approximation capabilities with limited simulation runs, highlighting its data efficiency.