Detecting ammonia at low concentrations is crucial in various fields, including environmental monitoring, industrial processes, and healthcare. This study explores the development and performance of an ultra-sensitive ammonia sensor using carboxylic group-functionalized multi-walled carbon nanotubes (f-MWCNTs) overlaid on polyvinyl acetate nanofibers coated on a quartz crystal microbalance (QCM). The sensor demonstrates high responsiveness, with a frequency shift response of over 120 Hz when exposed to 1.5 ppm ammonia, a sensitivity of 23.3 Hz ppm over a concentration range of 1.5-7.5 ppm, and a detection limit of 50 ppb. Additionally, the sensor exhibits a rapid response time of only 14 s, excellent selectivity against other gases, such as acetic acid, formaldehyde, methanol, ethanol, propanol, benzene, toluene, and xylene, and good stability in daily use. These characteristics make the sensor a promising tool for real-time ammonia detection in diverse applications.

Nanofibers made of different materials have been continuously studied and widely used as membranes due to their simple fabrication techniques and tunable surface characteristics. In this work, we developed polyacrylonitrile (PAN) nanofiber membranes by the electrospinning method and blended them with polysulfone (PSU) to obtain superhydrophobic surfaces on the fiber structures. The scanning electron microscopy (SEM) images show that the fabricated nanofibers have smooth and continuous morphology. In addition, to observe the effect of the PSU-based blending material, Fourier-transform infrared (FTIR) spectra of the samples were acquired, providing chemical compositions of the bare and PSU-blended PAN nanofibers. The fabricated PSU/PAN composite nanofibers have a diameter range of 222-392 nm. In terms of the wettability, the measured water contact angle (WCA) value of the PAN nanofibers was improved from (14 ± 1)° to (156 ± 6)°, (160 ± 4)°, (156 ± 6)°, and (158 ± 4)° after being blended with PSU solutions having concentrations of 0.5, 1, 1.5, and 2 wt %, respectively. This result has proven that the PAN nanofiber surfaces can be tuned from hydrophilic to superhydrophobic characteristics simply by introducing PSU into the PAN solution prior to electrospinning, where a small PSU concentration of 0.5% has been sufficient to provide the desired effect. Owing to its low-cost and highly efficient process, this strategy may be further explored for other types of polymer-based nanofibers.

Real-time detection of formaldehyde in the atmosphere remains challenging. The available gaseous formaldehyde sensing methods offer limited sensitivity, selectivity, and robustness. We modified a quartz crystal microbalance (QCM) system for selective detection of formaldehyde in air. The QCM surface was functionalized with polyvinyl acetate (PVAc) nanofibers and doped with 2, 4, and 6 wt% aniline to improve the selectivity and sensitivity of the sensor. The chemical content and morphological structure of PVAc nanofibers doped with aniline were confirmed by Fourier-transform infrared (FTIR) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and scanning electron microscopy (SEM). The results showed that the modified QCM sensor had a sensitivity of 0.056 Hz ppm with a response and recovery times of 200 s and 90 s, respectively. It gave limits of detection (LOD) and limit of quantification (LOQ) of 28 ppm and 96 ppm, respectively. Moreover, the modified QCM was selective towards formaldehyde compared to the other gases. The current workplace exposure limit (WEL) for formaldehyde is 2 ppm, with a time-weighted average over eight hours. Future work will focus on improving the reported QCM sensor to meet the required LOD for formaldehyde detection in the environment and industrial sites.

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

The chemical modification of polymer nanofiber-based ammonia sensors by introducing dopants into the active layers has been proven as one of the low-cost routes to enhance their sensing performance. Herein, we investigate the influence of different citric acid (CA) concentrations on electrospun polyvinyl acetate (PVAc) nanofibers coated on quartz crystal microbalance (QCM) transducers as gravimetric ammonia sensors. The developed CA-doped PVAc nanofiber sensors are tested against various concentrations of ammonia vapors, in which their key sensing performance parameters (, sensitivity, limit of detection (LOD), limit of quantification (LOQ), and repeatability) are studied in detail. The sensitivity and LOD values of 1.34 Hz ppm and 1 ppm, respectively, can be obtained during ammonia exposure assessment. Adding CA dopants with a higher concentration not only increases the sensor sensitivity linearly, but also prolongs both response and recovery times. This finding allows us to better understand the dopant concentration effect, which subsequently can result in an appropriate strategy for manufacturing high-performance portable nanofiber-based sensing devices.

The aim of this study is to determine the performance of a lab-made electronic nose (e-nose) composed of an array of metal oxide semiconductor (MOS) gas sensors in the detection and differentiation of () and () incubated in trypticsoy broth (TSB) media. Conventionally, the detection of and is often performed by enzyme link immunosorbent assay (ELISA) and polymerase chain reaction (PCR). These techniques require trained operators and expert, expensive reagents and specific containment. In this study, three types of samples, namely, TSB media, (serotype 4b American Type Culture Collection (ATCC) 13792), and (ATCC) 10876, were used for this experiment. Prior to measurement using the e-nose, each bacterium was inoculated in TSB at 1 × 10-10 CFU/mL, followed by incubation for 48 h. To evaluate the performance of the e-nose, the measured data were then analyzed with chemometric models, namely linear and quadratic discriminant analysis (LDA and QDA), and support vector machine (SVM). As a result, the e-nose coupled with SVM showeda high accuracy of 98% in discriminating between TSB media and , and between TSB media and . It could be concluded that the lab-made e-nose is able to detect rapidly the presence of bacteria and on TSB media. For the future, it could be used to identify the presence of or contamination in the routine and fast assessment of food products in animal quarantine.

Safrole is the main precursor for producing the amphetamine-type stimulant (ATS) drug, -methyl-3,4-methylenedioxyamphetamine (MDMA), also known as ecstasy. We devise a polyacrylonitrile (PAN) nanofiber-based quartz crystal microbalance (QCM) for detecting safrole. The PAN nanofibers were fabricated by direct electrospinning to modify the QCM chips. The PAN nanofiber on the QCM chips has a diameter of 240 ± 10 nm. The sensing of safrole by QCM modified with PAN nanofiber shows good reversibility and an apparent sensitivity of 4.6 Hz·L/mg. The proposed method is simple, inexpensive, and convenient for detecting safrole, and can be an alternative to conventional instrumental analytical methods for general volatile compounds.