Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM
Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Khatti, Tahere [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Fibers and polymers - Seoul : The Korean Fiber Society, 2000, 18(2017), 12 vom: Dez., Seite 2368-2378 |
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| Übergeordnetes Werk: |
volume:18 ; year:2017 ; number:12 ; month:12 ; pages:2368-2378 |
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| DOI / URN: |
10.1007/s12221-017-7631-8 |
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| Katalog-ID: |
SPR025438263 |
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| 520 | |a Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. | ||
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| 650 | 4 | |a Response surface methodology |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Polycaprolactone |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Gelatin |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Naderi-Manesh, Hossein |4 aut | |
| 700 | 1 | |a Kalantar, Seyed Mehdi |4 aut | |
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10.1007/s12221-017-7631-8 doi (DE-627)SPR025438263 (SPR)s12221-017-7631-8-e DE-627 ger DE-627 rakwb eng Khatti, Tahere verfasserin aut Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. Electrospinning (dpeaa)DE-He213 Nanofiber (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Polycaprolactone (dpeaa)DE-He213 Gelatin (dpeaa)DE-He213 Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 18(2017), 12 vom: Dez., Seite 2368-2378 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:18 year:2017 number:12 month:12 pages:2368-2378 https://dx.doi.org/10.1007/s12221-017-7631-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2017 12 12 2368-2378 |
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10.1007/s12221-017-7631-8 doi (DE-627)SPR025438263 (SPR)s12221-017-7631-8-e DE-627 ger DE-627 rakwb eng Khatti, Tahere verfasserin aut Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. Electrospinning (dpeaa)DE-He213 Nanofiber (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Polycaprolactone (dpeaa)DE-He213 Gelatin (dpeaa)DE-He213 Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 18(2017), 12 vom: Dez., Seite 2368-2378 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:18 year:2017 number:12 month:12 pages:2368-2378 https://dx.doi.org/10.1007/s12221-017-7631-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2017 12 12 2368-2378 |
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10.1007/s12221-017-7631-8 doi (DE-627)SPR025438263 (SPR)s12221-017-7631-8-e DE-627 ger DE-627 rakwb eng Khatti, Tahere verfasserin aut Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. Electrospinning (dpeaa)DE-He213 Nanofiber (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Polycaprolactone (dpeaa)DE-He213 Gelatin (dpeaa)DE-He213 Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 18(2017), 12 vom: Dez., Seite 2368-2378 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:18 year:2017 number:12 month:12 pages:2368-2378 https://dx.doi.org/10.1007/s12221-017-7631-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2017 12 12 2368-2378 |
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10.1007/s12221-017-7631-8 doi (DE-627)SPR025438263 (SPR)s12221-017-7631-8-e DE-627 ger DE-627 rakwb eng Khatti, Tahere verfasserin aut Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. Electrospinning (dpeaa)DE-He213 Nanofiber (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Polycaprolactone (dpeaa)DE-He213 Gelatin (dpeaa)DE-He213 Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 18(2017), 12 vom: Dez., Seite 2368-2378 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:18 year:2017 number:12 month:12 pages:2368-2378 https://dx.doi.org/10.1007/s12221-017-7631-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2017 12 12 2368-2378 |
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10.1007/s12221-017-7631-8 doi (DE-627)SPR025438263 (SPR)s12221-017-7631-8-e DE-627 ger DE-627 rakwb eng Khatti, Tahere verfasserin aut Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. Electrospinning (dpeaa)DE-He213 Nanofiber (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Polycaprolactone (dpeaa)DE-He213 Gelatin (dpeaa)DE-He213 Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 18(2017), 12 vom: Dez., Seite 2368-2378 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:18 year:2017 number:12 month:12 pages:2368-2378 https://dx.doi.org/10.1007/s12221-017-7631-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2017 12 12 2368-2378 |
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Enthalten in Fibers and polymers 18(2017), 12 vom: Dez., Seite 2368-2378 volume:18 year:2017 number:12 month:12 pages:2368-2378 |
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Enthalten in Fibers and polymers 18(2017), 12 vom: Dez., Seite 2368-2378 volume:18 year:2017 number:12 month:12 pages:2368-2378 |
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Electrospinning Nanofiber Artificial neural networks Response surface methodology Polycaprolactone Gelatin |
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Fibers and polymers |
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Khatti, Tahere @@aut@@ Naderi-Manesh, Hossein @@aut@@ Kalantar, Seyed Mehdi @@aut@@ |
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The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). 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Khatti, Tahere |
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Khatti, Tahere misc Electrospinning misc Nanofiber misc Artificial neural networks misc Response surface methodology misc Polycaprolactone misc Gelatin Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM |
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Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM Electrospinning (dpeaa)DE-He213 Nanofiber (dpeaa)DE-He213 Artificial neural networks (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Polycaprolactone (dpeaa)DE-He213 Gelatin (dpeaa)DE-He213 |
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misc Electrospinning misc Nanofiber misc Artificial neural networks misc Response surface methodology misc Polycaprolactone misc Gelatin |
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Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM |
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Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM |
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Khatti, Tahere Naderi-Manesh, Hossein Kalantar, Seyed Mehdi |
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prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ann and rsm |
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Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM |
| abstract |
Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 |
| abstractGer |
Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 |
| abstract_unstemmed |
Abstract Fabrication of nanofibers with a defined diameter is a primary purpose of the electrospinning process. The diameter of nanofiber is directly related to its individual features, such as mechanical property and porosity. The motivation to conduct the current study was to explore the diameter of hybrid nanofibers of polycaprolactone-gelatin (PCL-GT) as one of the most attractive scaffolds employed in various research fields, such as tissue engineering and industrial fields. We have developed two predictive models describing the electrospinning process of PCL-GT using response surface methodology (RSM) and artificial neural network (ANN). The effect of 4 variables on diameter was analyzed, including total polymer concentration, ratio of PCL to Gel, voltage, and tip-to-collector distance. The individual and interactive effects of the mentioned factors were analyzed using RSM. The total polymer concentration had the most significant individual effect on the diameter of PCL-Gel nanofiber, whereas the other three factors showed less strong individual effects, although, the interactive effects of these factors were more remarkable. It was demonstrated that both models, especially the ANN model, could accurately predict the diameter of PCL-GT nanofiber (regression coefficient > 0.92, mean absolute percentage error < 5.7). The represented predictive models could facilitate construction of electrospun nanofibers from PCL-Gel with wellcontrolled diameter required for any intended purpose. © The Korean Fiber Society and Springer Science+Business Media B.V., part of Springer Nature 2017 |
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Prediction of diameter in blended nanofibers of polycaprolactone-gelatin using ANN and RSM |
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https://dx.doi.org/10.1007/s12221-017-7631-8 |
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Naderi-Manesh, Hossein Kalantar, Seyed Mehdi |
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10.1007/s12221-017-7631-8 |
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2024-07-03T15:59:52.089Z |
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| score |
7.399276 |