Tuning a Superhydrophobic Surface on an Electrospun Polyacrylonitrile Nanofiber Membrane by Polysulfone Blending.

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.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication.

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.