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.

Performance of Oriented Strand Board Made of Heat-Treated Bamboo (Dendrocalamus asper (Schult.) Backer) Strands.

This study aimed to analyze the effect of pre-heat treatment on bamboo strand properties and its impact on the properties of the resulting bamboo-oriented strand board (BOSB). Giant bamboo (Dendrocalamus asper (Schult.) Backer) with a density of 0.53 g cm-3 was converted into bamboo strands. These strands were pre-heat-treated at 140 and 160 °C for a duration of 1, 2, and 3 h. Changes in the chemical composition of the strand due to subsequent treatment were assessed. Fourier-transform infrared spectroscopy (FTIR) and X-Ray diffraction analysis (XRD) were used to determine the changes in the chemical composition of bamboo strands. The BOSB panels were produced with a target density of 0.7 g cm-3. The manufacturing of the BOSB was conducted in three layers with a ratio of 25:50:25, bonded with phenol-formaldehyde resin. The physical and mechanical properties of the laboratory-fabricated BOSB were tested in compliance with the criteria given in JIS A 5908 standards. Comparisons were made against OSB CSA 0437.0 Grade O-1 commercial standard. The pre-heat treatment led to chemical alterations within the material when set at 140 and 160 °C for 1 to 3 hours (h). FTIR spectral analysis demonstrated that longer exposure and higher temperatures resulted in fewer functional groups within the bamboo strands. The increased temperature and duration of pre-heat treatment enhanced the crystallinity index (CI). The dimensional stability and mechanical properties of the composites were improved significantly as hemicellulose and extractive content were reduced. This study demonstrated that the pre-heat treatment of bamboo strands at a temperature of 160 °C for a duration of 1 h was an adequate approach for heat modification and fabrication of BOSB panels with acceptable properties according to OSB CSA 0437.0 Grade O-1 commercial standard.

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.

Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers.

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 (i.e., 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-1 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.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Possibility Routes for Textile Recycling Technology.

The fashion industry contributes to a significant environmental issue due to the increasing production and needs of the industry. The proactive efforts toward developing a more sustainable process via textile recycling has become the preferable solution. This urgent and important need to develop cheap and efficient recycling methods for textile waste has led to the research community's development of various recycling methods. The textile waste recycling process can be categorized into chemical and mechanical recycling methods. This paper provides an overview of the state of the art regarding different types of textile recycling technologies along with their current challenges and limitations. The critical parameters determining recycling performance are summarized and discussed and focus on the current challenges in mechanical and chemical recycling (pyrolysis, enzymatic hydrolysis, hydrothermal, ammonolysis, and glycolysis). Textile waste has been demonstrated to be re-spun into yarn (re-woven or knitted) by spinning carded yarn and mixed shoddy through mechanical recycling. On the other hand, it is difficult to recycle some textiles by means of enzymatic hydrolysis; high product yield has been shown under mild temperatures. Furthermore, the emergence of existing technology such as the internet of things (IoT) being implemented to enable efficient textile waste sorting and identification is also discussed. Moreover, we provide an outlook as to upcoming technological developments that will contribute to facilitating the circular economy, allowing for a more sustainable textile recycling process.

Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance Sensor Array.

We devised a low-cost mobile electronic nose (e-nose) system using a quartz crystal microbalance (QCM) sensor array functionalized with various polymer-based thin active films (i.e., polyacrylonitrile, poly(vinylidene fluoride), poly(vinyl pyrrolidone), and poly(vinyl acetate)). It works based on the gravimetric detection principle, where the additional mass of the adsorbed molecules on the polymer surface can induce QCM resonance frequency shifts. To collect and process the obtained sensing data sets, a multichannel data acquisition (DAQ) circuitry was developed and calibrated using a function generator resulting in a device frequency resolution of 0.5 Hz. Four prepared QCM sensors demonstrated various sensitivity levels with high reproducibility and consistency under exposure to seven different volatile organic compounds (VOCs). Moreover, two types of machine learning algorithms (i.e., linear discriminant analysis and support vector machine models) were employed to differentiate and classify those tested analytes, in which classification accuracies of up to 98 and 99% could be obtained, respectively. This high-performance e-nose system is expected to be used as a versatile sensing platform for performing reliable qualitative and quantitative analyses in complex gaseous mixtures containing numerous VOCs for early disease diagnosis and environmental quality monitoring.

A highly sensitive safrole sensor based on polyvinyl acetate (PVAc) nanofiber-coated QCM.

A novel, highly sensitive and selective safrole sensor has been developed using quartz crystal microbalance (QCM) coated with polyvinyl acetate (PVAc) nanofibers. The nanofibers were collected on the QCM sensing surface using an electrospinning method with an average diameter ranging from 612 nm to 698 nm and relatively high Q-factors (rigid coating). Scanning electron microscopy (SEM) and atomic force microscopy (AFM) were used to analyze the PVAc nanofiber surface morphology, confirming its high surface area and roughness, which are beneficial in improving the sensor sensitivity compared to its thin-film counterpart. The as-spun PVAc nanofiber sensor could demonstrate a safrole limit of detection (LOD) of down to 0.7 ppm with a response time of 171 s and a sensitivity of 1.866 Hz/ppm. It also showed good reproducibility, rapid response time, and excellent recovery. Moreover, cross-interference of the QCM sensor response to non-target gases was investigated, yielding very low cross-sensitivity and high selectivity of the safrole sensor. Owing to its high robustness and low fabrication cost, this proposed sensing device is expected to be a promising alternative to classical instrumental analytical methods for monitoring safrole-based drug precursors.

Solvent vapor treatment improves mechanical strength of electrospun polyvinyl alcohol nanofibers.

Electrospun nanofibers of polyvinyl alcohol (PVA) have poor mechanical strength. As such their use has often been avoided, particularly in applications that require high mechanical properties. The objective of this study is to increase the mechanical properties of PVA nanofiber mats via physical crosslinking with solvent vapor treatment using organic solvents, dimethyl sulfoxide (DMSO), N, N-dimethyl formamide (DMF), and methanol. The effect of solvent vapor treatment on PVA nanofibers is clearly observed by scanning electron microscope (SEM). The tensile strength increased by over 60%, 90%, and 115% after solvent vapor treatment with DMF at a temperature of 40 °C for 2 h, 4 h, and 8 h, respectively, compared to untreated PVA nanofibers. In addition, Young's modulus of PVA nanofiber mats also increased after DMF treatment. As a comparison, DMSO and methanol were also used in solvent vapor treatment because of differences in their polymer-solvent affinity. Results showed that the highest improvement (100%) in mechanical strength was obtained using DMF. This study shows that solvent vapor treatment offers a simple and inexpensive method that provides excellent results and is a promising alternative treatment for use in increasing the mechanical properties of electrospun nanofibers.

Polyacrylonitrile Nanofiber-Based Quartz Crystal Microbalance for Sensitive Detection of Safrole.

Safrole is the main precursor for producing the amphetamine-type stimulant (ATS) drug, N-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.