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This study aims to investigate the ammonia-sensing performance of polyaniline/polyethylene oxide (PANI/PEO) and polyaniline/polyethylene oxide/zinc oxide (PANI/PEO-ZnO) composite nanofibers at room temperature.

Gas sensor structures were fabricated using microfabrication techniques. First, onto the SiO₂ wafer, gold electrodes were fabricated via thermal evaporation. PANI/PEO nanofibers were produced by the electrospinning method, and the ZnO layer was deposited by using radio frequency (RF) magnetron sputtering on the electrospun nanofibers as a sensing layer. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction were performed to characterize the analysis of nanofibers. After all, gas sensing analysis of PANI/PEO and PANI/PEO/ZnO nanofibers was conducted using an experimental setup at room temperature conditions. Furthermore, the impact of humidity (17%–90% RH) on the sensor resistance was actively investigated.

FTIR analysis confirms the presence of functional groups of PANI, PEO and ZnO in nanofiber structure. SEM micrographs demonstrate beads-free, thinner and smooth nanofibers with ZnO contribution to electrospun PANI/PEO nanofibers. Moreover, according to the gas sensing results, the PANI/PEO nanofibers exhibit 115 s and 457 s response time and recovery time, respectively. However, the PANI/PEO/ZnO nanofibers exhibit 245 s and 153 s response time and recovery time, respectively. PANI/PEO/MOx composite nanofibers ensure stability to the NH₃ gas owing to the high surface/volume ratio and decrease in the humidity dependence of gas sensors, making gas sensors more stable to the environment.

In this study, ZnO was deposited via RF magnetron sputtering techniques on PANI/PEO nanofibers as a different approach instead of in situ polymerization to investigate and enhance the sensor response and recovery time of the PANI/PEO/ZnO and PANI/PEO composite nanofibers to ammonia. These results indicated that ZnO can enhance the sensing properties of conductive polymer-based resistive sensors.

This paper aims to address the production of biocomposite nanofibers using luffa natural fibers and polyaniline conductive polymer/polyethylene oxides (PANI/PEO).

In this study, luffa natural fibers are extracted by chemical method. After mixing the treated luffa (TL) with the PANI/PEO solution, TL/PANI/PEO nanofibers were produced by electrospinning (ES) method under different ES parameters to examine the optimal conditions for nanofiber production. Then TL/PANI/PEO biocomposite nanofibers prepared in different weight ratios were produced to analyze the effects of luffa in the morphology and thermal properties of the biocomposite nanofibers. The characterization analysis of TL/PANI/PEO biocomposite nanofibers was performed by scanning electron microscopy, Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) analysis methods.

The analysis shows that different weight ratios of TL to PANI/PEO changed the morphology of the membrane. When increasing the weight ratio of TL, the morphological structure of TL/PANI/PEO transformed from nanofiber structure to thin film structure. The appearance of O—H peaks in the FTIR results proved the existence of TL in PANI/PEO nanofibers (membrane). Moreover, an increase in the weight ratio of luffa from 2% to 7.5% leads to an increase in the peak intensity of the O—H group. Regarding DSC analysis, biocomposite nanofibers improved the thermal properties. According to all results, 2%wt TL/PANI/PEO showed optimal morphological properties.

Plant cellulose was extracted from the luffa, one of the natural fibers, by method of alkali treatment. A new type of biocomposite nanofibers was produced using TL blend with PANI via electrospinning method.

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.