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Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils
In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions....
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Published in: | Materials 2023-08, Vol.16 (16), p.5720 |
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description | In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions. In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers. |
doi_str_mv | 10.3390/ma16165720 |
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In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.</description><identifier>ISSN: 1996-1944</identifier><identifier>EISSN: 1996-1944</identifier><identifier>DOI: 10.3390/ma16165720</identifier><identifier>PMID: 37630012</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Artificial neural networks ; Back propagation ; Biopolymers ; Chitosan ; Configurations ; Correlation coefficients ; Electrospinning ; Emulsions ; Essential oils ; Gelatin ; Mathematical analysis ; Mathematical models ; Nanofibers ; Neural networks ; Neurons ; Oils & fats ; Olive oil ; Physical properties ; Polyvinyl alcohol ; Scanning electron microscopy ; Software ; Synthesis ; Tocopherol ; Variables ; Viscosity</subject><ispartof>Materials, 2023-08, Vol.16 (16), p.5720</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 by the authors. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c382t-ebcf3d9c1e8fb0f0d8b288845c6d7077b5b35ace7921d0cb257208a3e5ac4b0c3</cites><orcidid>0000-0003-1716-7707 ; 0000-0003-0918-5008 ; 0000-0003-0527-5815 ; 0000-0002-5796-0649</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2857404016/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2857404016?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,38516,43895,44590,53791,53793,74412,75126</link.rule.ids></links><search><creatorcontrib>Cuahuizo-Huitzil, Guadalupe</creatorcontrib><creatorcontrib>Olivares-Xometl, Octavio</creatorcontrib><creatorcontrib>Eugenia Castro, María</creatorcontrib><creatorcontrib>Arellanes-Lozada, Paulina</creatorcontrib><creatorcontrib>Meléndez-Bustamante, Francisco J.</creatorcontrib><creatorcontrib>Pineda Torres, Ivo Humberto</creatorcontrib><creatorcontrib>Santacruz-Vázquez, Claudia</creatorcontrib><creatorcontrib>Santacruz-Vázquez, Verónica</creatorcontrib><title>Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils</title><title>Materials</title><description>In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions. In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Back propagation</subject><subject>Biopolymers</subject><subject>Chitosan</subject><subject>Configurations</subject><subject>Correlation coefficients</subject><subject>Electrospinning</subject><subject>Emulsions</subject><subject>Essential oils</subject><subject>Gelatin</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Nanofibers</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Oils & fats</subject><subject>Olive oil</subject><subject>Physical properties</subject><subject>Polyvinyl alcohol</subject><subject>Scanning electron microscopy</subject><subject>Software</subject><subject>Synthesis</subject><subject>Tocopherol</subject><subject>Variables</subject><subject>Viscosity</subject><issn>1996-1944</issn><issn>1996-1944</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNpdkt9PFDEQxzdGIwR58S9o4osxOeiv3e0-mRMOJSFggj433e70KHbbs-1qjv_B_9kuRxBsH2Yy_XxnOu1U1VuCjxjr8PGoSEOauqX4RbVPuq5ZkI7zl0_8veowpVtcFmNE0O51tcfahmFM6H71ZxmzNVZb5dAlTPHe5N8h_kjIhIi-RhisztavUb4BdGrVCBkiCgatHOgcQ9pMHl0qH4ztISZ0vfWFTPYOBmRiGNF1cFO2wafj1Ti5NHuz_JMNm-C246xRfkBX1qU31SujXILDB3tQfT9bfTv5sri4-nx-srxYaCZoXkCvDRs6TUCYHhs8iJ4KIXitm6HFbdvXPauVhrajZMC6p_PzCMWgBHmPNTuoPu7ybqZ-hEGDz6VzuYl2VHErg7Ly-Ym3N3IdfkmCed3UFJcM7x8yxPBzgpTlaJMG55SHMCVJRd0KThreFfTdf-htmKIv_d1THHNMmkId7ai1ciCtN6EU1mUPMFodPBhb4su2oVxQxnkRfNgJdPmDFME8Xp9gOY-G_Dca7C93yq06</recordid><startdate>20230821</startdate><enddate>20230821</enddate><creator>Cuahuizo-Huitzil, Guadalupe</creator><creator>Olivares-Xometl, Octavio</creator><creator>Eugenia Castro, María</creator><creator>Arellanes-Lozada, Paulina</creator><creator>Meléndez-Bustamante, Francisco J.</creator><creator>Pineda Torres, Ivo Humberto</creator><creator>Santacruz-Vázquez, Claudia</creator><creator>Santacruz-Vázquez, Verónica</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1716-7707</orcidid><orcidid>https://orcid.org/0000-0003-0918-5008</orcidid><orcidid>https://orcid.org/0000-0003-0527-5815</orcidid><orcidid>https://orcid.org/0000-0002-5796-0649</orcidid></search><sort><creationdate>20230821</creationdate><title>Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils</title><author>Cuahuizo-Huitzil, Guadalupe ; 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In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>37630012</pmid><doi>10.3390/ma16165720</doi><orcidid>https://orcid.org/0000-0003-1716-7707</orcidid><orcidid>https://orcid.org/0000-0003-0918-5008</orcidid><orcidid>https://orcid.org/0000-0003-0527-5815</orcidid><orcidid>https://orcid.org/0000-0002-5796-0649</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Artificial neural networks Back propagation Biopolymers Chitosan Configurations Correlation coefficients Electrospinning Emulsions Essential oils Gelatin Mathematical analysis Mathematical models Nanofibers Neural networks Neurons Oils & fats Olive oil Physical properties Polyvinyl alcohol Scanning electron microscopy Software Synthesis Tocopherol Variables Viscosity |
title | Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils |
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