Towards predicting the piezoelectricity and physiochemical properties of the electrospun P(VDF-TrFE) nanogenrators using an artificial neural network

Abolhasani, Mohammad Mahdi; Shirvanimoghaddam, Kamyar; Khayyam, Hamid; Moosavi, Seyed Masoud; Zohdi, Nima; Naebe, Minoo

doi:10.1016/j.polymertesting.2018.01.010

Cited by 32 publications

(14 citation statements)

References 56 publications

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“…17,24 ANN models have been applied previously as a predictive modeling tool for electrospun nanobers. 52 For example, Ketabchi et al developed and tested the accuracy of the ANN model for predicting the diameter of chitosan/PEO nanobers in trials and studies and for analysis of the interactions between the involved electrospinning parameters and the diameter of the chitosan/PEO nanobers with sufficient sensitivity and specicity. 53 In this research, we attempted to validate the accuracy of data mining models such as MLP, RBFNN, and SVM, on the effects of processing parameters including the polymer weight ratios, nozzle-collector distance, applied voltage, and the injection rate on the average diameter of the electrospun PCL/Gt nanobers.…”

Section: Prediction Performance Of Svmmentioning

confidence: 99%

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Kalantary

Jahani²,

Pourbabaki

et al. 2019

RSC Adv.

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Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(3-caprolactone) (PCL)/gelatin (Gt) nanofibers.Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFM MLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving.According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFM MLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.

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Section: Prediction Performance Of Svmmentioning

confidence: 99%

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Kalantary

Jahani²,

Pourbabaki

et al. 2019

RSC Adv.

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“…Among various polymer fiber fabrication techniques, wet spinning and electrospinning have been the most frequently applied methods in the recent years [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Known as a primary method, wet spinning has been employed to prepare a significant majority of PAN fibers [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Development of Porous Polyacrylonitrile Composite Fibers: New Precursor Fibers with High Thermal Stability

Samimi-Sohrforozani

Azimi

Abolhasani

et al. 2021

Electronic Materials

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Polyacrylonitrile (PAN) fibers with unique properties are becoming increasingly important as precursors for the fabrication of carbon fibers. Here, we suggest the preparation of porous PAN composite fibers to increase the homogeneity and thermal stability of the fibers. Based on the thermodynamics of polymer solutions, the ternary phase diagram of the PAN/H2O/Dimethylformamide (DMF) system has been modeled to introduce porosity in the fibers. Adding a conscious amount of water (4.1 wt.%) as a non-solvent to the PAN solution containing 1 wt.% of graphene oxide (GO), followed by wet spinning, has led to the preparation of porous composite fibers with high thermal stability and unique physicochemical properties. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) results elucidate that PAN/GO/H2O porous composite fibers have a higher thermal decomposition temperature, increased residual weight, reduced heat release rate, and higher crystallinity in comparison with the pristine PAN fibers, being a promising precursor for the development of high-performance carbon fibers. The results show a promising application window of the synthesized PAN fibers in electronic and electrochemical devices.

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“…[4] Nanogenerators have traditionally [5] been demonstrated with inorganic materials, such as zinc oxide (ZnO) [6,7] and subsequently, gallium nitride (GaN), [8] barium titanate (BaTiO 3 ), [9] and PbZr x Ti 1−x O 3 (PZT). [10] More recently, owing to their flexibility and ease of processing, polymeric ferro/piezoelectric materials, such as nylon, [11,12] poly(vinylidene fluoride) (PVDF), [13,14] and poly(vinylidene fluoridecotrifluoroethylene) (P(VDFTrFE)), [15][16][17][18][19] as well as hybrid nanocomposites [20] have gained significant interest, not only for nanogenerators, but for printable memory devices.…”

Section: Introductionmentioning

confidence: 99%

Self‐Poled Sausage‐Like PVDF Nanowires Produced by Confined Phase Inversion as Novel Piezoelectric Nanogenerators

Soleymani

Noormohammadi

Kashi

et al. 2021

Adv Materials Inter

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Piezoelectric poly(vinylidene fluoride) (PVDF) nanowires are of particular interest for energy harvesting as they are ultra‐sensitive to small vibrations. Here, a new, cost‐effective, and scalable approach to producing PVDF nanowires with strongly enhanced power output is presented. The method combines template‐wetting in cylindrical nano‐confinement with anisotropic solvent‐nonsolvent phase inversion to yield a fully novel nanowire morphology consisting of “sausage‐like” strings of nano‐domains. Dynamic numerical simulations of the phase inversion reveal the formation of these structures to be subject to a very rich and complex phenomenology. The simulated dependence of the feature size on the degree of confinement agrees with the experimentally observed trend. It is unambiguously demonstrated that the sausage‐like nano‐generators upsurge the power density to 280% compared to normal nanowires. Finite element modeling explains how the higher deformability of the sausage‐like nanostructures gives rise to this significant enhancement in piezoelectric performance.

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Towards predicting the piezoelectricity and physiochemical properties of the electrospun P(VDF-TrFE) nanogenrators using an artificial neural network

Cited by 32 publications

References 56 publications

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Development of Porous Polyacrylonitrile Composite Fibers: New Precursor Fibers with High Thermal Stability

Self‐Poled Sausage‐Like PVDF Nanowires Produced by Confined Phase Inversion as Novel Piezoelectric Nanogenerators

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