The increasing presence of pharmaceutical pollutants, such as acetaminophen, in water bodies poses a significant environmental challenge due to their persistence and potential toxicity. This study investigated the enhanced photodegradation of acetaminophen using ZnO-NiO nanofibers as superior photocatalysts. The nanofibers synthesized with varying NiO contents (designated as ZN0.5, ZN1, ZN1.5, and ZN2), were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman, FTIR, Brunauer–Emmett–Teller (BET) analysis, and diffuse reflectance spectroscopy (DRS) to elucidate their structural, morphological, and optical properties. Thermogravimetric analysis (TGA) indicated that the nanofibers exhibit high thermal stability, with major weight loss attributed to the decomposition of the polymer matrix and residual organics. The BET analysis revealed that the specific surface area remains stable after increasing the NiO content up to a certain ratio. This stability correlates with the enhanced photocatalytic performance due to increased light absorption and improved charge separation. The diffuse reflectance spectra and Kubelka–Munk plots demonstrated a reduction in bandgap energy with higher NiO content, facilitating greater visible light absorption. Photocatalytic experiments under visible light irradiation, in the presence of peroxymonosulfate (PMS), showed that the ZN1.5 nanofibers achieved the highest acetaminophen degradation rate, i.e., 92%, within 3 h. Mechanistic studies, supported by radical trapping experiments, revealed that the improved photocatalytic efficiency is due to the synergistic effects of ZnO and NiO heterojunctions, which enhance charge separation and reactive oxygen species (ROS) generation. This research highlights the potential of ZnO-NiO nanofibers as effective photocatalysts for the degradation of pharmaceutical pollutants. The findings demonstrate that optimizing the composition and structure of nanofibers can significantly improve their environmental remediation capabilities, providing a promising solution for sustainable water treatment.

Quartz crystal microbalance (QCM) electrodes were coated from a mixture of hexamethyldisiloxane (HMDSO) and O ₂ for the detection of volatile organic compounds (VOCs). The sensitivity of the coated QCM-based sensors was evaluated by monitoring the frequency shifts of the quartz exposed to different concentrations of volatile organic compounds (VOCs), such as; ethanol, benzene and chloroform. The sensors responses data have been used in subsequent data processing step for quantitative gas identification. It was shown that, with varying oxygen proportion in the mixture, the sensitivity and the selectivity of the QCM sensor towards on a given analyte change. Several multivariable analysis techniques, with varying degrees of success for automated identification of VOCs such as; linear method: principal component analysis (PCA) and nonlinear method: Artificial neural networks multilayer perceptions (ANNMLP) have been used to explore the data in this study. The collected data processed by principal component analysis has identified ethanol compared to the other two gases. The artificial neural networks multilayer perception after training has allowed the identification of 100% of these three types of VOCs and estimated their concentration, which demonstrates the success of nonlinear methods for identification and quantification of gas using this type of sensors.

Aerosol assisted plasma deposition is an innovative method to prepare thin films from liquid solutions independently on their composition and their physico‐chemical properties. This paper aims to discuss how the characteristics of a liquid precursor impacts the thin film deposition process using four different alkanes classically used as solvents, from n‐pentane to n‐octane. While the liquid injection rate does not significantly vary, the droplet size and the vapor phase are highly affected by the liquid. However, as the film composition does not significantly vary, it is shown that balance of matter is more efficient with n‐octane than with the other smaller molecules. Hence, the optimization of the processes needs to fully characterize the process from aerosols to final coatings.