Preparation and characterization of nylon-6/gelatin composite nanofibers via electrospinning for biomedical applications

Easy fabrication, porosity, good mechanical properties, and composition controllable of the electrospun nanofiber mat make this material a promising candidate for wound dressing applications. In the present study, nylon6/gelatin electrospun nanofiber mats are introduced as novel wound dressing materials. The introduced mats were synthesized by electrospinning of nylon6 and gelatin mixtures, three mats containing different gelatin content were prepared; 10, 20 and 30 wt%. Interestingly, addition of the gelatin did not affect the mechanical properties of the nylon 6, moreover the mat containing 10 wt% gelatin revealed higher mechanical properties due to formation of spider-net like structure from very thin nanofibers (～10 nm diameter) bonding the main nanofibers. Biologically study indicates that gelatin incorporation strongly enhances the bioactivity performance as increasing the gelatin content linearly increases the MC3T3-E1 cell attachment. Overall, the obtained results recommend exploiting the introduced mats as wound dressing material.     

Electrical properties of ultrafine nylon-6 nanofibers prepared via electrospinning

We report on the preparation and electrical characterization of nylon-6 nanofibers via electrospinning technique. During electrospinning, the polymer solution became highly ionized and emerging out of the micro-tip syringe by forming mesh-like ultrafine nanofibers structure in between the main fibers. The resultant nylon-6 nanofibers were well-oriented with uniform structure. The diameter of the ultrafine nanofibers (7 to 40 nm) is one order less than those of main fibers (100 to 200 nm). The current-voltage (I-V) measurements revealed a linear curve with an occurrence of negative differential resistance (NDR) behavior. The existence of NDR region in the nylon-6 nanofibers can be attributed to the tunneling current through the ultrafine structures. The fabrication of nanofibers, in the form of ultrafine mesh-like form, is relatively fast and inexpensive, and it paves the way to build up of new dimension for nano device applications.     

Mapping the influence of electrospinning parameters on the morphology transition of short and continuous nanofibers

A theoretical model for the morphology transition of short and continuous nanofibers by electrospinning has been proposed. The influences of polymer concentration, applied voltage, and flow rate on the fabrication of short and continuous nanofibers were mapped for use as a reference in the design and construction of the theoretical model. The morphology transition of short and continuous nanofibers occurred mainly due to changes in the flow rate and voltage. According to the concentration of the polymer in the solution, the map of the short nanofiber region was narrowed as the polymer concentration increased. The theoretical model derived from the conservation of kinetic energy and potential energy experienced by the polymer solution resulted in an equation that could be used to calculate the voltage and flow rates under certain boundary conditions when cutting nanofibers. The boundary conditions for voltage were 4.7-4.9 kV, and the boundary conditions for flow rate were 0.1-1.1 µl/min.     

Fabrication of PVA-BaSO4 hybrid nanofibers and dispersion of BaSO4 particles via ultrasonic electrospinning

We reported the preparation and characterization of the poly(vinyl alcohol) (PVA)/BaSO₄ hybrid nanofibers prepared by normal and ultrasonic electrospinning, respectively. Compared to normal electrospinning, BaSO₄ particles in the resultant PVA/BaSO₄ hybrid nanofibers prepared by ultrasonic electrospinning were well-dispersed without severe agglomerations, as confirmed by scanning electron microscopy (SEM) analysis. X-ray diffraction (XRD) analysis indicated that typical crystalline peaks of PVA and BaSO₄ particles were dramatically decreased by ultrasonication during electrospinning. Moreover, the size of BaSO₄ aggregates became smaller.     

Predicting bending rigidity of woven fabrics using artificial neural networks

This paper reports an investigation on the predictability of bending property of woven fabrics from their constructional parameters using artificial neural network (ANN) approach. Number of cotton grey fabrics made of plain and satin weave designs were desized, scoured, and relaxed. The fabrics were then conditioned and tested for bending properties. Thread density in fabric, yarn linear density, twist in yarn, and weave design were accounted as input parameters for the model whereas bending rigidity in warp and weft directions of fabric formed the outputs. Gradient descent with momentum and an adaptive learning rate back-propagation was employed as learning algorithm to train the network. A sensitivity analysis was carried out to study the robustness of the model.     

Prediction of various functional finishing treatments using artificial neural networks

In this study, in order to produce different water-oil repellent and wrinkle resistant fabrics, 21 different kinds of blended woven fabrics were treated (padded and transfered) with both classic and nano chemicals according to 4 different levels of concentrations. Afterwards, water, oil repellency and wrinkle angle recovery properties of the fabrics were measured. The purpose of this study is to predict these aforementioned functional properties of the fabrics before manufacturing based on the fabric blend, treatment method, used chemicals and chemical concentrations with the help of multi layer perceptron, one of the most popular network architecture. As a result of the study, it can be concluded that multi layer perceptron method can also be used for the classification problems successfully.     

Preparation of poly(vinyl alcohol)/ZrO2 composite nanofibers via co-axial electrospinning with higher ZrO2 particle content

Poly(vinyl alcohol) (PVA)/zirconium oxide (ZrO₂) composite nanofibers with a skin-core structure were prepared and the effect of ZrO₂ particle content on uniform web formation was investigated. The optimized polymer concentration, tip to collector distance, and applied voltage for electrospinning were 11 wt%, 12 cm, and 20 kV, respectively. Skin-core PVA/ZrO₂ composite nanofibers containing up to 12 wt% ZrO₂ were successfully prepared, but it was difficult to obtain PVA/ZrO₂ composite nanofiber webs via conventional electrospinning. Increasing the amount of ZrO₂ caused the morphology of the PVA/ZrO₂ composite nanofibers to become a non-uniform nanoweb with irregular nanofiber diameters. While it was difficult to obtain a uniform nanofiber web containing a content of ZrO₂ over 6 wt% for conventional electrospinning, a more uniform nanofiber web could be obtained at up to 9 wt% ZrO₂ using a skin-core dual nozzle. More uniform webs could also be obtained when ZrO₂ was in the skin rather than the core.     

Development of prediction system using artificial neural networks for the optimization of spinning process

This article correlates draw frame settings with quality characteristics of sliver and ring spun yarn using artificial neural networks. Considering the importance of draw frame as the last quality improvement machine in the spinning process, the quality influencing parameters of the draw frame were used as input for artificial neural networks. The neural networks were trained using a combination of Levenberg-Marquardt algorithm and Bayesian regularization for better generalization of the networks. Cross validation was performed for each trained network to test the performance of networks. The promising results achieved by this research work emphasize the ability of neural networks to predict the quality characteristics of sliver and yarn using the artificial neural networks. Therefore, draw frame parameters can be adjusted on the basis of required sliver and yarn quality. Furthermore, machines can be involved in the decision making process in spinning mills.     

Prediction of water quality effect on saturated hydraulic conductivity of soil by artificial neural networks

This study was conducted to investigate the impact of water salinity (EC_w) and sodicity (SAR_w) on saturated (K_s) and relative (K_r) hydraulic conductivities in two clay (C) and sandy clay loam (SCL) soils. The results showed that the K_s decreased with increasing SAR_w, and in all of water quality treatments, the K_s of SCL soil was higher than that of the C soil. Sodicity effect (even at high SAR_w) on the K_r of clay soil was minimized by high salinity. Although K_r of both soils similarly responded to EC_w and SAR_w, microstructure of clay soil was more sensitive to water quality. Effect of EC_w on soil structure was greater than that of SAR_w. In order to assess the applicability of artificial neural networks (ANNs) in estimating K_s and K_r, two types of FFBP and CFBP ANNs and two training algorithms, namely Levenberg–Marquardt (LM) and Bayesian regulation, were employed with two strategies of uniform threshold and different threshold functions. Multiple linear regressions were also used for K_s and K_r prediction. Based on the ANN results of second strategy, best topology (4–5–4–1) was belonged to CFBP network with LM algorithm, LOGSIG–LOGSIG–TANSIG threshold functions, and values of MAE and R² are equal to 0.1761 and 0.9945, respectively. Overall, the efficacy of ANNs is much greater than regression method for K_s prediction.     

Comparison between artificial neural network and response surface methodology in the prediction of the production rate of polyacrylonitrile electrospun nanofibers

This paper focused on using response surface methodology (RSM) and artificial neural network (ANN) to analyze production rate of electrospun nanofibers. The three important electrospinning factors were studied including polymer concentration (wt %), applied voltage (kV) and the nozzle-collector distance (cm). The predicted production rates were in agreement with the experimental results in both ANN and RSM techniques. High regression coefficient between the variables and the response (R ²=0.975) indicates excellent evaluation of experimental data by second-order polynomial regression model. The regression coefficient was 0.988, which indicates that the ANN model was shows good fitting with experimental data. The obtained results indicate that the performance of ANN was better than RSM. It was concluded that applied voltage plays an important role (relative importance of 42.8 %) against production rate of electrospun nanofibers. The RSM model predicted the 2802.3 m/min value of the highest production rate at conditions of 15 wt % polymer concentration, 16 kV of the applied voltage, and 15 cm of nozzle-collector distance. The predicted value showed only 4.4 % difference with experimental results in which 2931.0 m/min at the same setting was observed.     

Prediction of milled rice grades using Fourier transform near-infrared spectroscopy and artificial neural networks

This paper describes a method to rapidly and objectively predict the grades of milled rice according to the surface lipid content (SLC), which was determined by using near-infrared (NIR) spectroscopy. Sixty-six rice varieties were milled to different degrees. Then each sample was graded by a three-member panel. After the NIR spectra for each sample were collected over the wavenumber range of 11,000–4000 cm⁻¹, the SLC of each sample was measured according to the official method. The calibration equations relating the Fourier Transform Near-infrared (FT-NIR) spectra to the measured SLC were developed based on the partial least square (PLS) regression. The best model gave the root mean square error of the prediction (RMSEP) of 0.0248% and the determination coefficients of 0.9905. If the relationships between the grades and the SLC predicted by the developed NIR model were described with the linear and the logarithmic regression equations, the correct prediction percents (CCP) were 75.76% and 83.33%, respectively. When the back propagation artificial neural network (BP-ANN) model was developed to estimate the grades according to SLC, the resultant CCP was 95.45%, indicating that the milled rice grades could be predicted by the proposed BP-ANN model with satisfactory accuracy.     

Predicting the tensile strength of polyester/cotton blended woven fabrics using feed forward back propagation artificial neural networks

Tensile strength plays a vital role in determining the mechanical behavior of woven fabrics. In this study, two artificial neural networks have been designed to predict the warp and weft wise tensile strength of polyester cotton blended fabrics. Various process and material related parameters have been considered for selection of vital few input parameters that significantly affect fabric tensile strength. A total of 270 fabric samples are woven with varying constructions. Application of nonlinear modeling technique and appreciable volume of data sets for training, testing and validating both prediction models resulted in best fitting of data and minimization of prediction error. Sensitivity analysis has been carried out for both models to determine the contribution percentage of input parameters and evaluating the most impacting variable on fabric strength.     

Investigation on air permeability of finished stretch plain knitted fabrics. I. Predicting air permeability using artificial neural networks

Air permeability is one of the most important utility properties of textile materials as it influences air flow through textile material. Air permeability plays a significant role in well-being due to its influence on physiological comfort. The air permeability of textile materials depends on their porosity. There are a lot of structural properties of textile materials also operating parameters (knitting+finishing) influencing air permeability and there are also statistically significant interactions between the main factors influencing the air permeability of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. Two types of artificial neural networks (ANNs) model have been set up before modeling procedure by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. Moreover this study exhibited that air permeability could be predicted with high accuracy for stretch plain knitted fabrics treated with different finishing processes. Within the framework of the work presented, ANNs were applied to help industry to adjust the operating parameter before the actual manufacturing to reach the desired air permeability and satisfy their consumers.