Stabilization of commercial polyacrylonitrile fibres for fabrication of low-cost medium-strength carbon fibres

Eslami‐Farsani, Reza; Shokuhfar, Ali; Sedghi, Arman

doi:10.1515/epoly.2006.6.1.1

Cited by 8 publications

(9 citation statements)

References 10 publications

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“…A log-sigmoid transfer function is used for both the layers as the outputs are ranging between zero and one. The network was created in MATLAB 6.1 using the function net1Dnewff (minmax (p), (5,4,2), {'logsig','logsig','logsig'}, 'traingda'). Then it was imported to GUI network/data manager for training and testing the results (15-17, 20, 21).…”

Section: Network Experimentationmentioning

confidence: 99%

“…The PAN fiber is first stretched and simultaneously oxidized in a temperature range of 200-300 C (1,4,5). After oxidation, the fibers are carbonized at about 1000 C in inert atmosphere which is usually nitrogen (1,4).…”

Section: Introductionmentioning

confidence: 99%

“…Then, in order to improve the ordering and orientation of the crystallites in the direction of the fiber axis, the fiber must be heated at about 1500-3000 C (4). High temperature process generally leads to higher modulus fibers which expel impurities in the chain as volatile by-products (1,(3)(4)(5)(6)(7). During heating treatment, the fiber shrinks in diameter, builds the structure into a large structure and upgrades the strength by removing the initial nitrogen content of PAN precursor and the timing of nitrogen (1,4,5,8).…”

Section: Introductionmentioning

confidence: 99%

“…According to previous studies, five parameters were selected. It is believed that they have the greatest effects on production process including precursor, temperature oxidation, residence time, LTC and HTC (1,4,5,(9)(10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of High Performance Fibers Strength Using Back Propagation Neural Network

Reisi-Dehkordi

Eslami‐Farsani

2015

Journal of Macromolecular Science, Part A

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Nowadays, quantification of the effects of basic parameters such as precursor, temperature oxidation, residence time, low temperature carbonization (LTC) and high temperature carbonization (HTC) on production process polyacrylonitrile based carbon fibers is not completely understood. In this way, there is not a completely theoretical model that accomplishes to quantitatively describe production process carbon fibers very accurately which needs to be used by engineers in design, simulation and operation of that process. This paper presents the development of a back propagation neural network model for the prediction of carbon fibers produced from PAN fibers. The model is based on experimental data. The precursors, temperature oxidation, residence time, LTC and HTC have been considered as the input parameters and the strength as output parameter to develop the model. The developed model is then compared with experimental results and it is found that the results obtained from the neural network model are accurate in predicting the strength of carbon fibers.

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Section: Network Experimentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of High Performance Fibers Strength Using Back Propagation Neural Network

Reisi-Dehkordi

Eslami‐Farsani

2015

Journal of Macromolecular Science, Part A

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“…The quality of the precursor has to be validated by different characterization methods, e.g., density measurements, cross section—SEM analysis, FT‐IR, tensile, and XRD measurements and adjusted towards the process parameters of wet spinning. 6, …”

Section: Introductionmentioning

confidence: 99%

Characteristics of wet‐spun and thermally treated poly acrylonitrile fibers

Kirsten

Meinl

Schönfeld

et al. 2016

J of Applied Polymer Sci

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The performance of carbon fibers depends on the quality of the precursor and the conditions of the thermal treatment. In detail, for a PAN precursor fiber the viscosity of a spinning dope and the draw ratio during the spinning process needs to be considered. Through wet spinning, different types of PAN precursor fibers with defined spinning parameters, including solid content, solvent content in a bath, and especially draw ratio resulting in defined cross section diameters, were fabricated and analyzed with tensile tests, density investigations, SEM, TGA-MS, FTIR, and XRD. The results show that the mechanical properties of the fibers correlate to crystallinity. The cross section diameter is strongly related to the morphology of the fibers after thermal treatment. By extending the postdrawing of PAN fibers high tenacities were obtained at the cost of the cross section shape. In addition, TGA measurements reveal trapped residues of the wet spinning process as well as show several chemical reactions takes place at the same time at different temperatures

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Polypyrrole/Poly(acrylonitrile‐co‐butyl acrylate) Composite

Unsal¹,

Kalaoğlu²,

Karakas³

et al. 2012

Adv Polym Technol

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Poly(acrylonitrile-co-butyl acrylate) [P(AN-co-BuA)] was synthesized from acrylonitrile (AN) 95% and butyl acrylate (BuA) 5%-molar ratios-by free-radical polymerization in aqueous media, using ceric ammonium nitrate as an initiator. Pyrrole was oxidatively polymerized in dimethylformamide in the presence of P(AN-co-BuA) matrix to produce polypyrrole (PPy) composites. Composite thin films were produced by solution casting onto smooth glass surfaces. Scanning electron microscopy and Fourier transform infrared-attenuated total reflectance analysis of thin films revealed the presence of PPy in the composite structure. Electrical impedance measurements were conducted to reveal the electrical conductivity change due to increasing PPy content. Enhancing the flexibility of PPy structures, as a composite film composed of PPy, could find substantial utility in the application of PPy products. BuA in a copolymer structure with AN improved the mechanical properties of the resulting copolymer by compensating the mechanical disadvantages of PPy. The

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Stabilization of commercial polyacrylonitrile fibres for fabrication of low-cost medium-strength carbon fibres

Cited by 8 publications

References 10 publications

Prediction of High Performance Fibers Strength Using Back Propagation Neural Network

Prediction of High Performance Fibers Strength Using Back Propagation Neural Network

Characteristics of wet‐spun and thermally treated poly acrylonitrile fibers

Polypyrrole/Poly(acrylonitrile‐co‐butyl acrylate) Composite

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