Optimization and predictive modelling for the diameter of nylon-6,6 nanofibers via electrospinning for coronavirus face masks

Melt Spinnability Comparison of Mechanically and Chemically Recycled Polyamide 6 for Plastic Waste Reuse

With the increasing volume of synthetic fiber waste, interest in plastic reuse technologies has grown. To address this issue, physical and chemical recycling techniques for polyamide, a major component of textile waste, have been developed. This study investigates the remelting and reforming properties of four types of pristine and recycled polyamide 6, focusing on how the microstructural arrangement of recycled polyamides affects polymer fiber formation. DSC and FT-IR were used to determine the thermal properties and chemical composition of the reformed thin films. Differences in the elongation behavior of molten fibers during the spinning process were also observed, and the morphology of the resulting fibers was examined via SEM. Birefringence analysis revealed that the uniformity of the molecular structure greatly influenced differences in the re-fiberization process, suggesting that chemically recycled polyamide is the most suitable material for re-fiberization with its high structural similarity to pristine polyamide.

Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils

In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions. In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.

Nanotechnology Transition Roadmap toward Multifunctional Stimuli-Responsive Face Masks

In recent times, the use of personal protective equipment, such as face masks or respirators, is becoming more and more critically important because of common pollution; furthermore, face masks have become a necessary element in the global fight against the COVID-19 pandemic. For this reason, the main mission of scientists has become the development of face masks with exceptional properties that will enhance their performance. The versatility of electrospun polymer nanofibers has determined their suitability as a material for constructing "smart" filter media. This paper provides an overview of the research carried out on nanofibrous filters obtained by electrospinning. The progressive development of the next generation of face masks whose unique properties can be activated in response to a specific external stimulus is highlighted. Thanks to additional components incorporated into the fiber structure, filters can, for example, acquire antibacterial or antiviral properties, self-sterilize the structure, and store the energy generated by users. Despite the discovery of several fascinating possibilities, some of them remain unexplored. Stimuli-responsive filters have the potential to become products of large-scale availability and great importance to society as a whole.

Symptom-based analysis of COVID-19 cases using supervised machine learning approaches to improve medical decision-making

The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients’ recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors to improve health services and medical decision-making for different groups of COVID-19 patients.

Fabrication of Ceftriaxone-Loaded Cellulose Acetate and Polyvinyl Alcohol Nanofibers and Their Antibacterial Evaluation

Nanotechnology provides solutions by combining the fields of textiles and medicine to prevent infectious microbial spread. Our study aimed to evaluate the antimicrobial activity of nanofiber sheets incorporated with a well-known antibiotic, ceftriaxone. It is a third-generation antibiotic that belongs to the cephalosporin group. Different percentages (0, 5%, 10%, 15%, and 20%; based on polymer wt%) of ceftriaxone were incorporated with a polymer such as polyvinyl alcohol (PVA) via electrospinning to fabricate nanofiber sheets. The Kirby-Bauer method was used to evaluate the antimicrobial susceptibility of the nanofiber sheets using Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). For the characterization of the nanofiber sheets incorporated with the drug, several techniques were used, such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). Our results showed that the nanofiber sheets containing ceftriaxone had potential inhibitory activity against E. coli and S. aureus as they had inhibition zones of approximately 20–25 mm on Mueller-Hinton-agar-containing plates. In conclusion, our nanofiber sheets fabricated with ceftriaxone have potential inhibitory effects against bacteria and can be used as a dressing to treat wounds in hospitals and for other biomedical applications.

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID-19 Detection

Image medical semantic segmentation has been employed in various areas, including medical imaging, computer vision, and intelligent transportation. In this study, the method of semantic segmenting images is split into two sections: the method of the deep neural network and previous traditional method. The traditional method and the published dataset for segmentation are reviewed in the first step. The presented aspects, including all-convolution network, sampling methods, FCN connector with CRF methods, extended convolutional neural network methods, improvements in network structure, pyramid methods, multistage and multifeature methods, supervised methods, semiregulatory methods, and nonregulatory methods, are then thoroughly explored in current methods based on the deep neural network. Finally, a general conclusion on the use of developed advances based on deep neural network concepts in semantic segmentation is presented.