Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm
Abstract The crosslinking characteristics of hydrogels and polymeric films critically influence their swelling capacity, a key factor in wound care applications. This study optimized the swelling behavior of polyvinyl alcohol (PVA)–polyethylene glycol (PEG)–bromothymol blue (BTB) films using three h...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-96953-0 |
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| author | Daeuk Kim Izabelle Nisha Maxine D. Chan Tosha Mae S. Manalastas Ronnie S. Concepcion II Joseph Rey H. Sta. Agueda Rafael Kyle B. Bitangcor |
| author_facet | Daeuk Kim Izabelle Nisha Maxine D. Chan Tosha Mae S. Manalastas Ronnie S. Concepcion II Joseph Rey H. Sta. Agueda Rafael Kyle B. Bitangcor |
| author_sort | Daeuk Kim |
| collection | DOAJ |
| description | Abstract The crosslinking characteristics of hydrogels and polymeric films critically influence their swelling capacity, a key factor in wound care applications. This study optimized the swelling behavior of polyvinyl alcohol (PVA)–polyethylene glycol (PEG)–bromothymol blue (BTB) films using three hybrid metaheuristic algorithms: Hybrid Genetic-Hippopotamus Optimizer (HG-HO), Hybrid Genetic-Walrus Optimization Algorithm (HG-WOA), and Hybrid Genetic-Horned Lizard Optimization Algorithm (HG-HLOA). Among these, HG-HLOA achieved the fastest convergence, while all algorithms reliably identified optimal solutions with comparable fitness values. The optimal glutaraldehyde concentration was determined as 4.9251 wt%, with a predicted maximum swelling ratio of 1217.7311%. Experimental validation yielded an average swelling ratio of 1225.7123%, differing by only 0.65% from the predicted value. An increasing trend in swelling ratio was observed with rising glutaraldehyde concentration up to the optimum point, followed by a decrease due to possible over-crosslinking and formation of acetal linkages. This trend was supported by structural analysis using Fourier Transform Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM). Statistical analysis via Analysis of Variance (ANOVA) confirmed a significant relationship between glutaraldehyde concentration and swelling ratio, with a p-value of 2.1e−161. |
| format | Article |
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| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-d19641bd726a490493ebd2f3778e04152025-08-20T02:12:06ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-96953-0Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithmDaeuk Kim0Izabelle Nisha Maxine D. Chan1Tosha Mae S. Manalastas2Ronnie S. Concepcion II3Joseph Rey H. Sta. Agueda4Rafael Kyle B. Bitangcor5Department of Manufacturing Engineering and Management, De La Salle UniversityDepartment of Manufacturing Engineering and Management, De La Salle UniversityCenter of Engineering and Sustainable Development Research, De La Salle UniversityDepartment of Manufacturing Engineering and Management, De La Salle UniversityDepartment of Manufacturing Engineering and Management, De La Salle UniversityPhilippine Nuclear Research Institute, Department of Science and TechnologyAbstract The crosslinking characteristics of hydrogels and polymeric films critically influence their swelling capacity, a key factor in wound care applications. This study optimized the swelling behavior of polyvinyl alcohol (PVA)–polyethylene glycol (PEG)–bromothymol blue (BTB) films using three hybrid metaheuristic algorithms: Hybrid Genetic-Hippopotamus Optimizer (HG-HO), Hybrid Genetic-Walrus Optimization Algorithm (HG-WOA), and Hybrid Genetic-Horned Lizard Optimization Algorithm (HG-HLOA). Among these, HG-HLOA achieved the fastest convergence, while all algorithms reliably identified optimal solutions with comparable fitness values. The optimal glutaraldehyde concentration was determined as 4.9251 wt%, with a predicted maximum swelling ratio of 1217.7311%. Experimental validation yielded an average swelling ratio of 1225.7123%, differing by only 0.65% from the predicted value. An increasing trend in swelling ratio was observed with rising glutaraldehyde concentration up to the optimum point, followed by a decrease due to possible over-crosslinking and formation of acetal linkages. This trend was supported by structural analysis using Fourier Transform Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM). Statistical analysis via Analysis of Variance (ANOVA) confirmed a significant relationship between glutaraldehyde concentration and swelling ratio, with a p-value of 2.1e−161.https://doi.org/10.1038/s41598-025-96953-0 |
| spellingShingle | Daeuk Kim Izabelle Nisha Maxine D. Chan Tosha Mae S. Manalastas Ronnie S. Concepcion II Joseph Rey H. Sta. Agueda Rafael Kyle B. Bitangcor Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm Scientific Reports |
| title | Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm |
| title_full | Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm |
| title_fullStr | Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm |
| title_full_unstemmed | Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm |
| title_short | Optimization of glutaraldehyde concentration in relation to swelling behavior of PVA-PEG-BTB film using hybrid genetic metaheuristic algorithm |
| title_sort | optimization of glutaraldehyde concentration in relation to swelling behavior of pva peg btb film using hybrid genetic metaheuristic algorithm |
| url | https://doi.org/10.1038/s41598-025-96953-0 |
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