Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies
Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amoun...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/14/3/527 |
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| author | Jing Wang Mohamed Arselene Ayari Amith Khandakar Muhammad E. H. Chowdhury Sm Ashfaq Uz Zaman Tawsifur Rahman Behzad Vaferi |
| author_facet | Jing Wang Mohamed Arselene Ayari Amith Khandakar Muhammad E. H. Chowdhury Sm Ashfaq Uz Zaman Tawsifur Rahman Behzad Vaferi |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector regressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R<sup>2</sup>) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained. |
| first_indexed | 2024-03-09T23:16:49Z |
| format | Article |
| id | doaj.art-9d712010258744d396e5d367cb19adc6 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-09T23:16:49Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-9d712010258744d396e5d367cb19adc62023-11-23T17:35:19ZengMDPI AGPolymers2073-43602022-01-0114352710.3390/polym14030527Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning MethodologiesJing Wang0Mohamed Arselene Ayari1Amith Khandakar2Muhammad E. H. Chowdhury3Sm Ashfaq Uz Zaman4Tawsifur Rahman5Behzad Vaferi6College of Energy Engineering, Yulin University, Yulin 719000, ChinaDepartment of Civil and Architectural Engineering, College of Engineering, Qatar University, Doha 2713, QatarElectrical Engineering Department, College of Engineering, Qatar University, Doha 2713, QatarElectrical Engineering Department, College of Engineering, Qatar University, Doha 2713, QatarDepartment of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaElectrical Engineering Department, College of Engineering, Qatar University, Doha 2713, QatarDepartment of Chemical Engineering, Shiraz Branch, Islamic Azad University, Shiraz 7473171987, IranBiodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector regressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R<sup>2</sup>) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained.https://www.mdpi.com/2073-4360/14/3/527polylactic acidpolyglycolidebiodegradable compositerelative crystallinitymachine learning methods |
| spellingShingle | Jing Wang Mohamed Arselene Ayari Amith Khandakar Muhammad E. H. Chowdhury Sm Ashfaq Uz Zaman Tawsifur Rahman Behzad Vaferi Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies Polymers polylactic acid polyglycolide biodegradable composite relative crystallinity machine learning methods |
| title | Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies |
| title_full | Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies |
| title_fullStr | Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies |
| title_full_unstemmed | Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies |
| title_short | Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies |
| title_sort | estimating the relative crystallinity of biodegradable polylactic acid and polyglycolide polymer composites by machine learning methodologies |
| topic | polylactic acid polyglycolide biodegradable composite relative crystallinity machine learning methods |
| url | https://www.mdpi.com/2073-4360/14/3/527 |
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