Seyedeh Sara Esnaashari; Majid Naghibzadeh; Mahdi Adabi; Reza Faridi Majidi
Abstract
Objective(s): This paper investigates the validity of Artificial Neural Networks (ANN) model in the prediction of electrospun kefiran nanofibers diameter using 4 effective parameters involved in electrospinning process. Polymer concentration, applied voltage, flow rate and nozzle to collector distance ...
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Objective(s): This paper investigates the validity of Artificial Neural Networks (ANN) model in the prediction of electrospun kefiran nanofibers diameter using 4 effective parameters involved in electrospinning process. Polymer concentration, applied voltage, flow rate and nozzle to collector distance were used as variable parameters to design various sets of electrospinning experiments for production of electrospun kefiran nanofibers. Methods: The Scanning Electron Microscopy (SEM) was used to investigate the morphology and evaluate the size of the nanofiber. Data set was drawn using k fold cross-validation method, which was the most suitable scheme for the volume of the data in this work. Data were partitioned into the five series and trained and tested via ANN method. Results: The Scanning Electron Microscopy (SEM) images of the generated nanofiber samples were confirmed that all of the samples were fine and defect-free. Our results indicated that the network including four input variables, three hidden layers with 10, 18 and 9 nodes in each layer, respectively, and one output layer obtained the highest efficiency in the testing set. The mean squared error (MSE) and linear regression (R) between observed and predicted nanofibers diameter were 0.0452 and 0.950, respectively. Conclusions: The results demonstrated that the proposed neural network was appropriately performed in assessing the input parameters and prediction of nanofibers diameter.
Maryam Jafari; Babak Kaffashi
Abstract
Objective(s): The quantitative calculation of release data is more facil when mathematics come to help. mathematically modeling could aid optimizing and amending the delivery systems design. Aim of this study is to find out the isoniazid release kinetic. Methods: In this work degradable temperature sensitive ...
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Objective(s): The quantitative calculation of release data is more facil when mathematics come to help. mathematically modeling could aid optimizing and amending the delivery systems design. Aim of this study is to find out the isoniazid release kinetic. Methods: In this work degradable temperature sensitive dextran-hydroxy ethyl methacrylate- poly-N-isopropyl acryl amide (Dex-HEMA-PNIPAAm) nanogels which were synthesized by UV polymerization were loaded by Isoniazid. The Isoniazid release amounts taken from in vitro studies at two different temperatures, below and upper lower criticalsolution temperature (LCST) were mathematically modeled to investigate the kinetic of drug release. Mathematically inquiry of release phenomenon of Isoniazid makes it easy to predict and recognize the influence of delivery device laying out parameters on release kinetic formulation. The modeling was performed using model dependent methods, such as zero order, first order, Higuchi, Korsmeyer- Pepas, Hixon and Crowel. Results: The best fitted model showing the highest determination coefficient (R2) was Korsmeyer-Pepas which means predominant release mechanism is controlled by diffusion. Conclusions: The Isoniazid release pattern of most samples was combination of swelling, diffusion and degradation.
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