Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites
This study explores the use of citric acid and L-ascorbic acid as reducing agents in CNC/CNF/rGO nanocomposite fabrication, focusing on their effects on electrical conductivity and mechanical properties. Through comprehensive analysis, L-ascorbic acid showed superior reduction efficiency, producing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/16/4/393 |
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| author | Ghazaleh Ramezani Ixchel Ocampo Silva Ion Stiharu Theo G. M. van de Ven Vahe Nerguizian |
| author_facet | Ghazaleh Ramezani Ixchel Ocampo Silva Ion Stiharu Theo G. M. van de Ven Vahe Nerguizian |
| author_sort | Ghazaleh Ramezani |
| collection | DOAJ |
| description | This study explores the use of citric acid and L-ascorbic acid as reducing agents in CNC/CNF/rGO nanocomposite fabrication, focusing on their effects on electrical conductivity and mechanical properties. Through comprehensive analysis, L-ascorbic acid showed superior reduction efficiency, producing rGO with enhanced electrical conductivity up to 2.5 S/m, while citric acid offered better CNC and CNF dispersion, leading to higher mechanical stability. The research employs an advanced optimization framework, integrating regression models and a neural network with 30 hidden layers, to provide insights into composition–property relationships and enable precise material tailoring. The neural network model, trained on various input variables, demonstrated excellent predictive performance, with R<sup>2</sup> values exceeding 0.998. A LASSO model was also implemented to analyze variable impacts on material properties. The findings, supported by machine learning optimization, have significant implications for flexible electronics, smart packaging, and biomedical applications, paving the way for future research on scalability, long-term stability, and advanced modeling techniques for these sustainable, multifunctional materials. |
| format | Article |
| id | doaj-art-83315c039d0c4cdd952dafe5eea640b5 |
| institution | DOAJ |
| issn | 2072-666X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj-art-83315c039d0c4cdd952dafe5eea640b52025-08-20T03:13:55ZengMDPI AGMicromachines2072-666X2025-03-0116439310.3390/mi16040393Lasso Model-Based Optimization of CNC/CNF/rGO NanocompositesGhazaleh Ramezani0Ixchel Ocampo Silva1Ion Stiharu2Theo G. M. van de Ven3Vahe Nerguizian4Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC H3G 1M8, CanadaSchool of Engineering and Sciences, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, MexicoDepartment of Mechanical and Industrial Engineering, Concordia University, Montreal, QC H3G 1M8, CanadaDepartment of Chemistry, McGill University, Montreal, QC H4A 3J1, CanadaDépartement de Génie Électrique, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaThis study explores the use of citric acid and L-ascorbic acid as reducing agents in CNC/CNF/rGO nanocomposite fabrication, focusing on their effects on electrical conductivity and mechanical properties. Through comprehensive analysis, L-ascorbic acid showed superior reduction efficiency, producing rGO with enhanced electrical conductivity up to 2.5 S/m, while citric acid offered better CNC and CNF dispersion, leading to higher mechanical stability. The research employs an advanced optimization framework, integrating regression models and a neural network with 30 hidden layers, to provide insights into composition–property relationships and enable precise material tailoring. The neural network model, trained on various input variables, demonstrated excellent predictive performance, with R<sup>2</sup> values exceeding 0.998. A LASSO model was also implemented to analyze variable impacts on material properties. The findings, supported by machine learning optimization, have significant implications for flexible electronics, smart packaging, and biomedical applications, paving the way for future research on scalability, long-term stability, and advanced modeling techniques for these sustainable, multifunctional materials.https://www.mdpi.com/2072-666X/16/4/393CNC/CNF/rGO nanocompositesgraphene oxide reductioncitric acidL-ascorbic acidelectrical conductivitytensile strength |
| spellingShingle | Ghazaleh Ramezani Ixchel Ocampo Silva Ion Stiharu Theo G. M. van de Ven Vahe Nerguizian Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites Micromachines CNC/CNF/rGO nanocomposites graphene oxide reduction citric acid L-ascorbic acid electrical conductivity tensile strength |
| title | Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites |
| title_full | Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites |
| title_fullStr | Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites |
| title_full_unstemmed | Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites |
| title_short | Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites |
| title_sort | lasso model based optimization of cnc cnf rgo nanocomposites |
| topic | CNC/CNF/rGO nanocomposites graphene oxide reduction citric acid L-ascorbic acid electrical conductivity tensile strength |
| url | https://www.mdpi.com/2072-666X/16/4/393 |
| work_keys_str_mv | AT ghazalehramezani lassomodelbasedoptimizationofcnccnfrgonanocomposites AT ixchelocamposilva lassomodelbasedoptimizationofcnccnfrgonanocomposites AT ionstiharu lassomodelbasedoptimizationofcnccnfrgonanocomposites AT theogmvandeven lassomodelbasedoptimizationofcnccnfrgonanocomposites AT vahenerguizian lassomodelbasedoptimizationofcnccnfrgonanocomposites |