ACS Omega. 2026 Jul 22;11(30):44614-44633. doi: 10.1021/acsomega.5c10279. eCollection 2026 Aug 4.
ABSTRACT
This study presents an in-depth examination of the swelling characteristics of biopolymeric hydrogels developed with sodium alginate, employing both conventional and modern modeling techniques to predict and optimize the process. Hydrogel swelling properties have found critical applications in controlled drug delivery, wound dressing, and food technology applications, and thus have developed a high demand for predictive and tuning strategies. In this case, different concentrations of polymers, cross-linkers, microalgal cells, and media volume are set with different compositional parameters. The measurements of experimental swelling degrees were conducted under controlled conditions to obtain both equilibrium and dynamic measurements. Initial data analysis was performed by response surface methodology (RSM) with a central composite design (CCD), which explained the effects of independent and interactive formulation factors. Then, an artificial neural network (ANN) model was implemented and trained on the same data set to elicit possible complex and nonlinear associations that are not considered in traditional approaches. The results of the two models were evaluated intensively based on the statistical parameters such as R2, RMSE, and the analysis of the residuals. The ANN model appeared more predictive and produced finer nonlinear trends in the swelling response when compared to RSM, which generated understandable equations and response surfaces required to develop mechanistic insight. It is found that the use of hybrid modeling is one of the possible future directions because the complementary strengths were evaluated in terms of comparative analysis. The results show that machine learning and statistical design are synergistic in the study and optimization of hydrogel systems. The article develops the methodological toolkit that can be used to study hydrogels as well as establishes a basis for intelligent material design with data-centric design, which is more likely to be practical with better functionality and performance.
PMID:42569012 | PMC:PMC13448944 | DOI:10.1021/acsomega.5c10279
