This study reports the development of a high-performance gas sensor for the detection of chlorodifluoromethane (R22) refrigerant gas. The sensor was fabricated using a polypyrrole/tin dioxide (Ppy/SnO2) nanocomposite with varying SnO2 loadings (0–4 wt%). The novelty of this study lies in achieving high R22 sensitivity at low ppm concentrations through a simple Ppy/SnO2 composite design by optimizing the SnO2 loading, enabling efficient detection at a low operating temperature suitable for low-power applications. and the characterizations of these nanocomposites were confirmed by X-ray diffraction (XRD) and FE-SEM. The sensing mechanism is attributed to the formation of p-n heterojunctions at the Ppy/SnO2 interface, which significantly enhances the response. The optimized composite (4 wt% SnO2) exhibited an excellent response, reaching up to 89% at 5 ppm R22. In addition, fast sensing dynamics were obtained; at 70 °C and 5 ppm, the sensor delivered a response of 87.12% with a response time of 1.5 s and a competitive recovery time of 12.94 s. These results demonstrate that Ppy/SnO2 nanocomposite thin films are promising candidates for practical R22 leak detection, combining high sensitivity, rapid response/recovery, and low-temperature operation.

In this contribution, we report our investigations on the stereo-specific styrene polymerization in the presence of a homogeneous catalytic system formed by combining a Ziegler-Natta catalytic system based on vanadium oxychloride (VOCl3) and a methylaluminoxane cocatalyst (MAO). The reaction parameters, including the Al/V molar ratio and polymerization time, were systematically investigated and optimized with respect to polymer conversion, catalytic activity, and tacticity. At an Al/V molar ratio of 100, further increasing the polymerization time resulted in a higher conversion of syndiotactic polystyrene (sPS), reaching 96% with an average molecular weight of 2.50 × 106 g·mol−1. Subsequently, the polymerization reaction was conducted using heterogeneous catalytic systems, with organically modified Algerian clay (OAC) or layered double hydroxides (LDH), as catalytic supports. The influence of the nature of the support on the catalytic performance and the properties of polystyrene was investigated. The catalytic system exhibited syndiospecificity, and the conversion and stereoregularity were strongly dependent on the type of catalyst and the support. Notably, the presence of 5% OAC significantly increased the conversion rate to 85%, indicating superior catalyst activity.” The structure of the obtained polymers was examined by Fourier transform infrared spectroscopy (FTIR), wide angle X-ray scanning (WAXS), and scanning electron microscopy (SEM). Thermal properties were evaluated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Interestingly, the obtained results show that the OAC and LDH supports have both catalytic and nano-filler effects by enhancement of the polystyrene tacticity as well as its thermal degradation behavior.

The application of glass fiber-reinforced plastic (GFRP) has increased substantially, and more attention has been paid to the recycling of the waste GFRP. To provide guidance for the reactor design and optimization for the recycling of waste GFRP, the thermal oxidative degradation kinetics and volatile products of the most widely used polyester GFRP waste were studied by TG-DSC and TG-FTIR-GC/MS in this study. The results indicate that the degradation of polyester GFRP in air can be divided into three stages with the critical conversion rates of 0.33 and 0.85. Four commonly used model-free methods were employed to calculate the kinetic parameters, and then a model-fitting method was used to identify the suitable kinetic model. The average activation energies of the first stage, second stage, and third stage are 109.87 kJ/mol, 125.70 kJ/mol, and 146.34 kJ/mol, respectively. The kinetic model was further reconstructed by the accommodation function, and the modified reaction model f ( α ) = 967.3968 α 1.1977 ( 1 − α ) 10.9725 fits the experimental profile of Stage 2 well. The FTIR spectrum and GC/MS results were analyzed to characterize the volatile products. The volatile products, including H2O, dimethyl phthalate, 1,3-cyclopentadiene, benzaldehyde, and dicyclopentadiene, are produced in the first stage due to the dehydration of aluminum trihydrate and the cleavage of weak ester bonds and side chains in the polyester resin. The major products of the second stage are CO2, H2O, styrene, benzaldehyde, and α-methylstyrene, and the possible reaction mechanisms are discussed. CO2 and CO are produced in the third stage due to the oxidation of char.

This work aimed to compare two chemical separation methods for multilayer films of polyethylene terephthalate (PET), polyethylene (PE), and polyamide (PA). The techniques evaluated and compared were delamination and dissolution-precipitation. Once the polymers were obtained, they were characterized by Fourier transform infrared spectroscopy (FTIR) and thermal analysis by thermogravimetry (TGA) and differential scanning calorimetry (DSC). The combined method of delamination by dissolution-precipitation allows recover of around 90% of the polymeric fractions of the multilayer film. The characterization techniques used were sufficient to recognize contamination or mixtures of components after the evaluated separation treatments.

Gel polymer electrolyte (GPE) films were prepared by combining poly(vinylidene fluoride-haxaflouropropylene) (PVdF-HFP) and lithium triflurometheanesulfonate (LiCF3SO3) with ethylene carbonate (EC), propylene carbonate (PC), and diethyl carbonate (DEC) plasticizers using solution casting. X-ray diffraction (XRD) confirmed structural modifications arising from the interactions among the polymer, salt, and plasticizers. Scanning electron microscopy (SEM) showed interconnected surface microstructures, reflecting improved morphological uniformity. Fourier transform infrared (FTIR) spectroscopy further revealed molecular interactions and salt dissociation within the PVdF-HFP:LiCF3SO3:plasticizer system, confirming the formation of a homogeneous matrix that supports efficient ion transport. The thermal properties of the GPEs were examined using differential scanning calorimetry (DSC). At ambient temperature (303K), the EC-plasticized electrolyte had the greatest ionic conductivity (2.4 × 10-³ S·cm−1) as measured by complex impedance spectroscopy. The ionic transference number is evaluated by DC polarization tests, indicating that ionic transport predominated over electronic contributions, demonstrating the feasibility of these GPEs for high-performance electrochemical applications.

Essential oils (EOs) are promising natural additives due to their antimicrobial properties, hydrophobicity, plasticizing effects, and mechanical enhancements. Hyssop essential oil (HEO), known for its antimicrobial, antifungal, and antiviral activities, shows great potential for diverse applications. This study investigates the impact of HEO incorporation in cassava starch biopolymeric membranes to improve their properties. Additionally, we compared the performance of HEO-supplemented cassava starch biofilms with those incorporating clove (CEO) and oregano (OEO) EOs. The results revealed that the films with EOs were hydrophilic, exhibiting high luminosity (e.g., 91.3 ± 0.2 for HEO films), porosity, and low tensile strength (e.g., 5.2 ± 0.4 MPa for HEO). The films also demonstrated considerable biodegradability, with a degradation time of 35 days for HEO-supplemented films. Infrared spectroscopy analysis indicated no significant changes in the chemical structure of cassava starch after EO incorporation. The antimicrobial tests showed no bacterial proliferation on the films, further supporting their potential for food packaging applications. Notably, HEO-supplemented biofilms exhibited higher solubility values (e.g., 45.2 ± 0.7%) compared to other formulations, confirming the viability of HEO as a natural additive in biopolymers.

Two-dimensional (hBN) nanocomposites based on hexagonal boron nitride (hBN) have high mechanical and thermal performances and are thus very appropriate in advanced electronic applications. The hBN/epoxy nanocomposites in this article were produced with the help of the casting technique with the help of isopropanol and dimethyl ketone as dispersants with a concentration of hBN between 0.25 and 1 wt. The best formulation of 0.5 wt% isopropanol-dispersed hBN revealed excellent improvements, such as a 61 percent growth in tensile strength, a 38.41 percent boost in flexural strength, a 35.80 percent growth in flexural modulus, and an 8 °C rise in thermal stability at 50 percent weight degradation as compared to pure epoxy. SEM and analysis revealed homogeneous filler dispersion, and the Halpin-Tsai analysis model was consistent with the experimental results of the elastic modulus. A MixStyle Neural Network (MSNN) was used to predict the mechanical properties with high accuracy and, in effect, learn domain-invariant feature representations. High predictive performance with R2 values of 0.995–0.999 and low levels of prediction error made the MSNN an influential design and optimization tool of epoxy nanocomposites in the next generation electronic industry.

This study examines the mechanical performance of natural fiber-reinforced epoxy mixtures and enhances their properties using a machine learning (ML) method. Flax fibers were selected as sustainable strengthening due to their high specific strength, stiffness, and biodegradability, though abatron and aluminum liquid compound epoxy resin served as the matrix with high tensile strength (200–300 MPa) and Young’s modulus (70–80 GPa). Combined specimens were created using vacuum-assisted resin transfer molding (VARTM), ensuring uniform resin infusion, low void content, and restrained fiber alignment. Mechanical description exposed tensile strength of 68–72 MPa, flexural strength of 105–110 MPa, Young’s modulus of 17–18.2 GPa, and impact strength of 4–4.1 kJ/m2. Analysis of Variance (ANOVA) documented fiber volume fraction and resin viscosity as the most important factors in mechanical performance. A convolutional neural network (CNN) model was built to predict tensile strength, flexural strength, Young’s modulus, and interfacial shear strength, with success in achieving high accuracy with R2 > 0.95. Sensitivity analysis revealed that raising fiber content and orientation could improve tensile and flexural strengths by up to 12%. The included experimental-ML framework reduces trial-and-error testing and delivers precise guidance for designing high-performance, lightweight, and eco-friendly composites. These materials are capable of automotive, aerospace, and protective applications, emphasizing the potential of relating natural fibers with ML techniques to accelerate material innovation while retaining sustainability.

Sodium alginate, a polysaccharide primarily derived from seaweed, has garnered significant attention due to its exceptional gel-forming capabilities, renewability, biocompatibility, and biodegradability, making it highly versatile for various applications. The molecular weight of sodium alginate is influenced by the type of algae used and the extraction method applied. This study presents a comprehensive investigation of sodium alginate extraction from Cystoseira brown seaweed collected on the north of Algeria’s PHARE beach in Ain Benian, Mediterranean Sea. The primary objective was to evaluate the effects of the acid-to-alkali procedure on the physical, chemical, and thermal properties of the extracted sodium alginate in comparison with commercially available sodium alginate. The findings revealed that the extracted sodium alginate exhibited a higher molecular weight than commercial sodium alginate, along with enhanced thermal stability. FT-IR spectral analysis confirmed that the fundamental structure of sodium alginate remained unchanged regardless of the extraction method. Rheological analysis demonstrated that the extracted sodium alginate behaved as a pseudoplastic fluid with shear-thinning properties and exhibited greater elasticity. Its viscosity followed the Cross model, showing a decrease with increasing temperature. These results highlight a strong correlation between the structural and thermal-rheological properties of the extracted sodium alginate. Given its improved properties, the extracted sodium alginate has potential applications in biomedical, pharmaceutical, and food industries, where enhanced gel strength and thermal stability are advantageous.

This work focuses on the development and characterization of flexible triboelectric nanogenerators (TENGs) using low-cost polymers: high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyurethane (PU), and poly (methyl methacrylate) (PMMA). The materials were synthesized into thin films by a solvent casting method. Scanning Electron Microscopy (SEM) showed different surface morphologies, like ductile and brittle natures. Mechanical testing indicated PMMA has higher tensile strength (35 MPa) and better stiffness properties than TPU, which has better flexibility and elastic properties. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the presence of functional groups (C = O and C-O-C) in PMMA and TPU, which are important for charge transfer. X-ray diffraction showed that HDPE had the highest crystalline content (70%) and a large crystallite size (30.17 Å), which explained its effective tribo-negative material. PMMA and TPU had low crystalline materials (<1%), which enhanced any tribo-positive properties. Differential scanning calorimetry (DSC) suggested that a blend of TPU and PMMA had a melting temperature (Tm) of 175.8 °C and even had better thermal stability than TPU or PMMA polymers. A demonstration of the dielectric study illustrates that PMMA had the largest dielectric constant at high frequencies. A 3D-printed model was fabricated for testing the tribo-model (contact-separation), which produced a peak-to-peak voltage output of 155 V in a TPU/PMMA-HDPE TENG. The results show that intentionally combining the polymers and their various mechanical, thermal, and electrical characteristics was able to achieve a TENG that could be efficient, scalable, and low-cost.