Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels
This study explores the application of machine learning techniques, specifically principal component analysis (PCA), to analyze the influence of silica content on the physical and chemical properties of aerogels. Silica aerogels are renowned for their exceptional properties, including high porosity,...
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MDPI AG
2024-08-01
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| Series: | Gels |
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| Online Access: | https://www.mdpi.com/2310-2861/10/9/554 |
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| author | Hamdi Chaouk Emil Obeid Jalal Halwani Jack Arayro Rabih Mezher Omar Mouhtady Eddie Gazo-Hanna Semaan Amine Khaled Younes |
| author_facet | Hamdi Chaouk Emil Obeid Jalal Halwani Jack Arayro Rabih Mezher Omar Mouhtady Eddie Gazo-Hanna Semaan Amine Khaled Younes |
| author_sort | Hamdi Chaouk |
| collection | DOAJ |
| description | This study explores the application of machine learning techniques, specifically principal component analysis (PCA), to analyze the influence of silica content on the physical and chemical properties of aerogels. Silica aerogels are renowned for their exceptional properties, including high porosity, large surface area, and low thermal conductivity, but their mechanical brittleness poses significant challenges. The study initially utilized cross-correlation analysis to examine the relationships between key properties such as the Brunauer–Emmett–Teller (BET) surface area, pore volume, density, and thermal conductivity. However, weak correlations prompted the application of PCA to uncover deeper insights into the data. The PCA results demonstrated that silica content has a significant impact on aerogel properties, with the first principal component (PC1) showing a strong positive correlation (R<sup>2</sup> = 94%) with silica content. This suggests that higher silica levels correspond to lower thermal conductivity, porosity, and BET surface area, while increasing the density and elastic modulus. Additionally, the analysis identified the critical role of thermal conductivity in the second principal component (PC2), particularly in samples with moderate to high silica content. Overall, this study highlights the effectiveness of machine learning techniques like PCA in optimizing and understanding the complex inter-relationships among the physico-chemical properties of silica aerogels. |
| format | Article |
| id | doaj-art-611516ff593d41dda876588180b0bf05 |
| institution | Kabale University |
| issn | 2310-2861 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Gels |
| spelling | doaj-art-611516ff593d41dda876588180b0bf052025-01-29T10:32:17ZengMDPI AGGels2310-28612024-08-0110955410.3390/gels10090554Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of AerogelsHamdi Chaouk0Emil Obeid1Jalal Halwani2Jack Arayro3Rabih Mezher4Omar Mouhtady5Eddie Gazo-Hanna6Semaan Amine7Khaled Younes8College of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitWater and Environment Sciences Laboratory, Lebanese University, Tripoli P.O. Box 6573/14, LebanonCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitThis study explores the application of machine learning techniques, specifically principal component analysis (PCA), to analyze the influence of silica content on the physical and chemical properties of aerogels. Silica aerogels are renowned for their exceptional properties, including high porosity, large surface area, and low thermal conductivity, but their mechanical brittleness poses significant challenges. The study initially utilized cross-correlation analysis to examine the relationships between key properties such as the Brunauer–Emmett–Teller (BET) surface area, pore volume, density, and thermal conductivity. However, weak correlations prompted the application of PCA to uncover deeper insights into the data. The PCA results demonstrated that silica content has a significant impact on aerogel properties, with the first principal component (PC1) showing a strong positive correlation (R<sup>2</sup> = 94%) with silica content. This suggests that higher silica levels correspond to lower thermal conductivity, porosity, and BET surface area, while increasing the density and elastic modulus. Additionally, the analysis identified the critical role of thermal conductivity in the second principal component (PC2), particularly in samples with moderate to high silica content. Overall, this study highlights the effectiveness of machine learning techniques like PCA in optimizing and understanding the complex inter-relationships among the physico-chemical properties of silica aerogels.https://www.mdpi.com/2310-2861/10/9/554silica aerogelscompositeprincipal component analysisphysical properties |
| spellingShingle | Hamdi Chaouk Emil Obeid Jalal Halwani Jack Arayro Rabih Mezher Omar Mouhtady Eddie Gazo-Hanna Semaan Amine Khaled Younes Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels Gels silica aerogels composite principal component analysis physical properties |
| title | Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels |
| title_full | Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels |
| title_fullStr | Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels |
| title_full_unstemmed | Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels |
| title_short | Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels |
| title_sort | machine learning techniques to analyze the influence of silica on the physico chemical properties of aerogels |
| topic | silica aerogels composite principal component analysis physical properties |
| url | https://www.mdpi.com/2310-2861/10/9/554 |
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