Hemocompatible polyurethane/gelatin-heparin nanofibrous scaffolds formed by a bi-layer electrospinning technique as potential artificial blood vessels

In this paper, a scaffold, which mimics the morphology and mechanical properties of a native blood vessel is reported. The scaffold was prepared by sequential bi-layer electrospinning on a rotating mandrel-type collector. The tubular scaffolds (inner diameter 4 mm, length 3 cm) are composed of a polyurethane (PU) fibrous outer-layer and a gelatin-heparin fibrous inner-layer. They were fabricated by electrospinning technology, which enables control of the composition, structure, and mechanical properties of the scaffolds. The microstructure, fiber morphology and mechanical properties of the scaffolds were examined by means of scanning electron microscopy (SEM) and tensile tests. The PU/gelatin-heparin tubular scaffolds have a porous structure. The scaffolds achieved a breaking strength (3.7±0.13 MPa) and an elongation at break (110±8%) that are appropriate for artificial blood vessels. When the scaffolds were immersed in water for 1 h, the breaking strength decreased slightly to 2.2±0.3 MPa, but the elongation at break increased to 145±21%. In platelet adhesion tests the gelatin-heparin fibrous scaffolds showed a significant suppression of platelet adhesion. Heparin was released from the scaffolds at a fairly uniform rate during the period of 2^nd day to 9^th day. The scaffolds are expected to mimic the complex matrix structure of native arteries, and to have good biocompatibility as an artificial blood vessel owing to the heparin release.     

Double‐nozzle air‐jet electrospinning for nanofiber fabrication

A novel double‐nozzle air‐jet electrospinning apparatus was developed to fabricate nanofibers on a large scale. The distribution of the electric field at different nozzle distances was simulated to analyze the jet path, productivity, and deposition area of nanofiber webs and the nanofiber morphology. Our experiments showed that the bubbles usually ruptured intermittently on the top surface of the two nozzles and the jets traveled in a straight path with a high initial velocity. A continuous and even thickness of the nanofiber webs were obtained when the nozzle distances was less than 55 mm. At nozzle distances of 55 mm, the received fibers were thin with the lowest standard deviation. Experimental parameters involving the applied voltage, collecting distance, and air flow rate were also investigated to analyze the nanofiber morphology at a nozzle distance of 55 mm. The results show that the nanofibers presented a finer and thinner diameter at an applied voltage of 36 kV, a collecting distance of 18 cm, and an air flow rate of 800 mL/min. The nanofiber production of this setup increased to nearly 70 times that with a single‐needle electrospinning setup. On the basis of the principle of this air‐jet electrospinning setup, various arrangements of multinozzle electrospinning setups could be designed for higher throughput of nanofibers. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2014 , 131, 40040.     

Fabrication of basalt embedded composite fiber membrane using electrospinning method and response surface methodology

In this study, a novel basalt embedded fiber membranes was prepared by the electrospinning method. The effects of the feed rate, voltage, tip to collector distance, and the basalt content on the prepared composite fiber membranes were investigated and optimized using the response surface method. Four models were built to compare the fibers in terms of deionized water flux (DWF), activated sludge flux, chemical oxygen demand (COD) removal, and porosity of fiber membranes. All the developed models were significant and adequately precise. The maximum flux of DWF was obtained when the voltage was 17.25 kV, the tip to collector distance of 19 cm, and a basalt content in polymer of 1.25%. COD removal decreased at higher voltage values as the feed rate increased. The porosity, pore size, and the contact angle values decreased for basalt embedded fiber membrane. The increases in the basalt percentage in polymer increased the hydrophilicity of the fiber. The flux decline for the basalt embedded fiber membrane was compared with the pristine fiber membrane. The permeate fluxes of pristine and basalt embedded fiber membranes were 42.3 and 59.6 L/m²/h, respectively. The biofouling performances of pristine and basalt embedded fiber membranes were also examined. Irreversible fouling decreased from 42.9% to 8.0%, and reversible fouling increased from 56.5% to 91.1% after modification of the membrane with basalt powder. Scanning electron microscopy with energy dispersive X-ray analysis (SEM–EDX) analysis showed that basalt powder was successfully embedded into polyether sulfone polymer.     

Design of polyurethane fibers: Relation between the spinning technique and the resulting fiber topology

Properties or characteristics of fibers are affected by their topology. In fact, these topologies are found to have significant impacts in the functionalities of many applications. Hence, in this study, the relations between the spinning techniques and the topology of the resulting fibers are studied with the aim to provide a guideline for future reference where fibers with certain topology can be fabricated to suit specific applications. For this purpose, polyurethane is chosen to be the raw material to fabricate the fibers due to its versatility to be applied in various fields. The surface morphology, structures, and alignments of the fibers are studied. It is found that the polymer solution properties largely influence the mechanisms in the spinning process and can significantly affect the topology of the fibers. For instance, viscous solutions enable the spinning of coiled and smooth fibers, whereas conductive solutions encourage the splaying of the solution jet which results in the spinning of straight fibers. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47706.     

The influence of solution parameters on the electrospinning intensity from foamed surface

Electrospinning is an efficient process for producing polymeric and hybrid nanofibers. There is, however, a lack of understanding concerning scalability of the process and in particular the production rate optimization. The electrospinning mass transfer intensity depends predominately on solution parameters, process parameters and the design of the equipment. These parameters influence the deposition intensity of the spinning process differently, but it is not known which factors dominate. The e‐spinning deposition intensity of polyethylene oxide, polyvinyl alcohol and their mixtures was investigated using a bubble foamed polymer solution surface to promote high mass deposition. Based on the measured properties of the solutions, a mathematical criterion was developed which made it possible to predict the electrospinning intensity of a given polymer solution. The proposed formula agrees with the experimental data and confirms that spinning intensity can be predicted from pre‐determined solution parameters. Using computer modeling, the weighting coefficients of the solution parameters have been determined, showing which parameter is the most important for the process intensity. The criterion and the same weighting coefficients were applied to the analysis of published data and it was found that they can be applied not only for electrospinning from the foamed surface but also from the free surface. A physical explanation of the criterion is proposed. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42034.     

Modeling of glycolysis of flexible polyurethane foam wastes by artificial neural network methodology

The glycolysis process as a useful approach to recycling flexible polyurethane foam wastes is modeled in this work. To obtain high quality recycled polyol, the effects of influential processing and material parameters, i.e. process time, process temperature, catalyst‐to‐solvent (Cat/Sol) and solvent‐to‐foam (Sol/Foam) ratios, on the efficiency of the glycolysis reaction were investigated individually and simultaneously. For the continuous prediction of process behavior and interactive effects of parameters, an artificial neural network (ANN) model as an efficient statistical‐mathematical method has been developed. The results of modeling for the criteria that determine the glycolysis process efficiency including the hydroxyl value of the recycled polyol and isocyanate functional group conversion prove that the adopted ANN model successfully anticipates the recycling process responses over the whole range of experimental conditions. The Cat/Sol ratio showed the strongest influence on the quality of the recycled polyol among the studied parameters, where the minimum hydroxyl value was obtained at a medium amount of the assigned ratio. For the consumed polyurethane foam, a higher value of this ratio led to an increase in the hydroxyl value and isocyanate conversion. © 2015 Society of Chemical Industry     

Modeling reaction kinetics of rigid polyurethane foaming process

A theoretical model was developed to simulate the polyurethane foaming process for a rigid foam. In the model, multiple ordinary differential equations were solved by MATLAB and the model was able to predict temperature profiles by inputting foam recipe information. This initial study on foam modeling focusses on reaction kinetic parameters that were fitted to experimental temperature data as a function of time. The modeling was able to accurately model temperature profiles of single‐polyol polyurethane formulations and was able to accurately predict temperature profiles of mixtures based on pure component kinetic parameters. A primary goal of this work is to expedite the ability to develop new foam formulations by simulation—especially for incorporation of new bio‐based polyols into formulations. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 1131‐1138, 2013     

Surface energy evaluation of casting and nanofiber polyurethane films by using different models

The components of the surface free energy (SFE) were determined from static contact angle measurements of five liquids using different methods. The two manufacturing techniques (casting and electrospinning) applied to obtain polyurethane (PU) membranes give surfaces with different wetting properties. The SFE data varied and were strongly dependent on calculations methods and liquids that were used for contact angle measurements. As a whole, the SFE of electrospun PU membrane (PU-N) (~24 mN/m) was slightly higher than that of casting PU membrane (PU-F) (~18 mN/m) with similar chemical compositions. The overall increase in the value of SFE is mainly due to the microstructures with increased surface area and modulations of nanofibers. The results evidence the impact of the PU membrane preparation on the properties of the biomaterial surface. Such structure–properties–function relationship is necessary to lay the groundwork for establishing a set of design criteria to guide the fabrication of synthetic materials.     

Failure mechanism of rigid polyurethane foam under high temperature vibration condition by experimental and finite element method

The failure mechanism of rigid polyurethane foam (RPUF) under room temperature (RT) and high temperature vibration conditions was investigated by experiment and finite element stimulation. Damaged RPUF specimens were prepared at different vibration amplitudes ranging from 0 to 19.879 mm at RT and 150 °C for different vibration times. The tensile test was utilized to evaluate the vibration damage degree of RPUFs, and the results exhibited that tensile strength decreased gradually with the increase of vibration amplitude and time at both RT and 150 °C. Thermogravimetric analysis and Fourier transform infrared spectroscopy illustrated that thermal degradation of RPUF is attributed to the decomposition of carbonyl urethane groups at 150 °C. The scanning electron microscopy analysis of the tensile fracture surfaces revealed that the vibration failure of RPUF mainly resulted from the existence of microcracks in cell structure. A finite element simulation was established by ABAQUS to study stress distribution of RPUF under different vibration loads, which then demonstrated that the microcracks are most likely to exist on the junction of two microcell units, which is due to convergence of stress in the process of vibration. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48343.     

Interjet distance in needleless melt differential electrospinning with umbellate nozzles

Melt electrospinning technique has shown great advantages in numerous areas where polymer dissolving, solvent accumulation, or toxicity is a concern in solution electrospinning. However, conventional capillary spinnerets in electrospinning are less productive. In this article, two needleless umbellate nozzles were used based on melt differential method, and the smallest interjet distance of 1.1 mm was observed. Experimental results indicated that the main factors affecting the interjet distance were the electric field strength and melts viscosity. The produced fiber diameter was related to interjet distance directly. Finite element modeling (FEM) showed that umbellate structure determines the intensity of maximum electric field around the rim of nozzles and the resultant interjet distance. This new method enabled the mass production of ultra‐fine fibers using needleless melt electrospinning method when relatively low voltage (less than 65 kV) was loaded on the receive plate. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40515.     

Bio-inspired acid-sensitive polyurethane woven artificial muscle nanofibers with tunable mechanical properties via electrostatic spinning

Polyurethane (PU) is a traditional chemical known for its chemical stability and mechanical performance. Inspired by the similarity between the formation and breakage of chemical coordination bonds and the energy storage and release of muscle fibers, muscle-like electrostatically spun fibers with acid-responsive energy storage and release were prepared by introducing bio-inspired elastic energy storage groups and bio-active degradation groups (PU-BPY-Fe) in the main chain of PU, taking advantage of the good mechanical properties of PU. The fabricated electrospinning film PU-BPY-Fe can respond to external stimulation, which generated high strain (32 MPa), stretch of 206%, outperforming the nanofiber membrane before stimulation, similar and even higher than the biological muscles. The variable mechanical properties and elastic energy storage capacity of PU-BPY-Fe were attributed to the reversible hydrogen bonding and the destabilization of metal coordination bonds (Fe³⁺ to Fe²⁺) within the material under acidic stimulation. Cytotoxicity testing of the synthesized fibers indicated a degree of biocompatibility, suggesting potential for in vivo applications. This method of storing and releasing elastic energy was demonstrated and has endowed the PU-BPY-Fe with stimuli-responsibility and muscle-like mechanical properties, which may inspire the design of soft muscles materials for robots and tissue engineering applications.     

Simulation of hydrodesulfurization using artificial neural network

Artificial neural network (ANN) is applied to investigate the hydrodesulfurization (HDS) process with light‐cycle oil as feed and NiMo/Al₂O₃ as catalyst. ANN models frequently work as a “black box” which makes the model invisible to users and always need significant data for training. In this work, a new ANN is proposed. The Langmuir–Hinshelwood kinetic mechanism is incorporated into the model so that the proposed ANN model is forced to follow the given reaction mechanisms. Both advantages of self‐learning ability of ANN and the existing knowledge of HDS were taken into account. Lengthy training process is minimised. Effects of operating temperature, pressure, and LHSV on the sulfur removal rate are studied. The inhibition of nitrogen compounds is also investigated. It is shown that the presence of nitrogen can significantly reduce the conversion rate of sulfur components, in particularly, hard sulfur such as 4,6‐DMDBT.     

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand-alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables.     

Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

A four‐layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO‐FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO‐FNN model has been employed to investigate solubility of CO₂ in polystyrene, N₂ in polystyrene, and CO₂ in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO‐FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient (R²) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO‐FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Artificial neural networks modeling of electrospinning of polyethylene oxide from aqueous acid acetic solution

The artificial neural networks (ANNs) were used to provide a model for investigating the relationships of the electrospinning parameters with the diameter of polyethylene oxide (PEO) nanofibers from acid acetic aqueous solution. The effects of four parameters including PEO concentration, acetic acid concentration, applied voltage, and temperature of the electrospinning media on the nanofibers mean diameter were investigated. To train, test, and valid the model, three datasets of the input variables with random values were prepared and the mean diameters obtained were taken as the output for the network. The datasets were analyzed by ANNs software and the correlation coefficient, R-squared (R²), between the predicted values of the nanofibers mean diameter and actual amount were obtained. The results demonstrate the capability of the ANNs model for predicting the nanofibers diameter. The 3-D plots generated from the model show complex and nonlinear relationships between the parameters and nanofibers diameter. From the model, increasing the PEO concentration above a critical point leads to a sharp increase in the nanofibers mean diameter. The effects of applied voltage and temperature are mainly dependent on the PEO concentration. The acetic acid concentration, in general shows a direct relation with the nanofibers mean diameter. The plots also show that to produce nanofibers with the lowest diameter, both the PEO concentration and AcOH concentration should be at lowest values regardless the applied voltage and temperature. In contrast, highest nanofibers diameters are obtained when the PEO concentration and AcOH concentration are at their high values. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Predicting the properties of needlepunched nonwovens using artificial neural network

Needlepunching is a well‐known nonwoven process of converting fibrous webs into self‐locking or coherent structures using barbed needles. In this study, Artificial Neural Network (ANN) modeling technique has been used to predict the bulk density and tensile properties of needlepunched nonwoven structures by relating them with the main process parameters, namely, web area density, punch density, and depth of needle penetration. The simultaneous effect of more than one parameter on bulk density and tensile properties of needlepunched nonwoven structures have been investigated based upon the results of trained ANN models. A comparison is also made between the experimental and predicted values of fabric bulk density and tensile strength in the machine and crossmachine directions in unseen or test data sets. It has been inferred that the ANN models have achieved good level of generalization that is further ascertained by the acceptable level of mean absolute error obtained between predicted and experimental results. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Prediction of water retention capacity of hydrolysed electrospun polyacrylonitrile fibers using statistical model and artificial neural network

Box Behnken design of experiment was used to study the effect of process variables such as alkali concentration, temperature and time on water retention capacity of the alkaline hydrolysed electrospun fibres. The hydrolysis of electrospun polyacrylonitrile fibres was carried out using sodium hydroxide with different processing conditions like concentration of alkali, temperature and time. With the increase in the concentration of alkali, time and temperature, the water retention capacity of membrane was found to increase in the membranes. Water retention capacities of the membranes were modeled and predicted using empirical as well as artificial neural network (ANN model). The fiber diameter and mean flow pore diameter of electrospun polyacrylonitrile fibers and hydrolyzed fibers shown in SEM images were 310 ± 50, 275 ± 75 nm, 0.9258 and 1.12 microns, respectively. The present study indicated that the nanofibrous membranes have potential for the water absorbing applications. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

PVP/PLA乳液静电纺丝成形及纤维毡亲水性能研究

利用乳液静电纺丝可制备一定复合结构的共混纤维,且可通过调控乳液的组成而实现聚合物溶液在低浓度下的静电纺丝成形。以聚乙烯基吡咯烷酮(PVP)水溶液为分散相,聚乳酸(PLA)氯仿溶液为基体相,制备不同水相比例的PVP/PLA乳液,研究了PVP/PLA乳液静电纺丝成形及其纤维毡的亲水性能。结果表明:乳液体系中PVP水相的加入可使PLA乳液在远低于其单独可纺溶液浓度下纺丝成形,所得纳米纤维随着PVP水相比例的提高而表现出纤维直径增加,并发生纤维集结成束,PVP大部分分布在复合纤维毡的表层,纤维毡呈现明显的透水性能;PVP的加入可有效改善PLA纳米纤维毡的亲水性能。     

A study of rheological limitations in rotary jet spinning of polymer nanofibers through modeling and experimentation

The recently popularized method of rotary jet spinning (RJS) or centrifugal spinning is investigated to evaluate the rheological limitations of polymer solutions and melts to optimal spinnability. The influence of Newtonian or non-Newtonian behavior of the polymer on spinnability is discussed. We observe that highly viscous polymers tend to block the die channels within a rotary jet spinneret and therefore suggest the use of relatively low Newtonian viscosities of between 1 and 10 Pa s for optimal fiber production. Computational fluid dynamics simulations are used in conjunction with experimental data to establish important processing parameters, such as typical shear rates in the device and optimal polymer melt or solution viscosities. A theoretical model for RJS is compared to measured fiber diameters. The comparison shows that although fiber diameters can be estimated very roughly in the case of polymer solutions, the prediction of fiber diameter in the case of polymer melts require further modeling work. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48963.     

Ultrafine ibuprofen‐loaded polyvinylpyrrolidone fiber mats using electrospinning

BACKGROUND: Drug‐loaded electrospun ultrafine fibers have the advantages of both nanoscale drug delivery systems and conventional solid dosage forms. To improve the control of drug release, the combined use of electrospinning and pharmaceutical polymers has attracted increasing interest recently. RESULTS: Ultrafine drug‐loaded polyvinylpyrrolidone fibers were successfully prepared using an electrospinning process with ibuprofen as the active pharmaceutical ingredient and polyvinylpyrrolidone K30 as the filament‐forming polymer. The analytical results from scanning electron microscopy, differential scanning calorimetry and Fourier transform infrared spectroscopy indicated that the drug had good compatibility with the polymer and that the drug was well distributed in the ultrafine fibers as an amorphous physical form. In vitro dissolution tests showed that the fiber mats were able to dissolve within 10 s through a polymer‐controlled mechanism. CONCLUSION: The fast dissolution of drug‐loaded fibers may lead to applications that improve dissolution rates of poorly water‐soluble drugs, or that involve the preparation of oral fast‐dissolving drug delivery systems. Copyright © 2009 Society of Chemical Industry