000170075 001__ 170075
000170075 005__ 20260316092630.0
000170075 0247_ $$2doi$$a10.1016/j.polymertesting.2026.109132
000170075 0248_ $$2sideral$$a148629
000170075 037__ $$aART-2026-148629
000170075 041__ $$aeng
000170075 100__ $$aDeng, Bincan
000170075 245__ $$aAn interactive Bayesian optimization framework for intelligent design of HAMA/GelMA hybrid hydrogels
000170075 260__ $$c2026
000170075 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170075 5203_ $$a- Hyaluronic acid methacrylate (HAMA)/gelatin methacrylate (GelMA) hybrid hydrogels are extensively utilized in biomanufacturing and tissue engineering, where their rheological properties are determinants of bioprintability and functional performance. However, optimizing these behaviors remains challenging due to the complex nonlinearity and high-dimensional design space defined by hydrogel concentration and temperature. Compared with previous machine-learning studies on hydrogel systems that primarily perform forward prediction of rheological or mechanical properties, here we introduce an interactive Bayesian optimization (IBO) framework that integrates Bayesian optimization with both an environment model and a discriminative model to optimize concentration–temperature values to achieve a target viscosity. The multilayer perceptron–based environment model here proposed exhibits high predictive performance (R2 ≥ 0.994, RMSE = 4.68), while the support vector machine–based discriminator achieved F1 > 0.95 and AUC >0.998 in distinguishing thermosensitive regions. Through feedback-driven iterations, IBO improved efficiency and robustness in targeting viscosity, with its mean value converging from 66.01 ± 8.76 Pa s to 51.81 ± 4.38 Pa s across three rounds, reaching a qualified rate of 80%. Even under a constrained HAMA content of 0.40% (w/v), IBO generated near-target viscosities (47.64–49.64 Pa s). These results collectively demonstrate that IBO can efficiently navigate complex, nonlinear rheological landscapes and reliably converge toward user-defined performance targets with low experimental data cost, while maintaining robustness under practical formulation constraints, thereby enabling efficient and directed formulation design. Overall, IBO provides an efficient, reliable, and scalable paradigm for viscosity-guided formulation design of HAMA/GelMA hybrid hydrogels, with potential applicability to soft matter and polymer systems. These findings can further assist in developing hydrogel formulations with improved printability and performance in biomanufacturing and related biomedical applications.
000170075 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000170075 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170075 700__ $$aLópez Lasaosa, Fernando
000170075 700__ $$aChen, Dingding
000170075 700__ $$aZheng, Caimiao
000170075 700__ $$aHe, Yiyan
000170075 700__ $$aXuan, Chen
000170075 700__ $$aCui, Yuwen
000170075 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, Manuel$$uUniversidad de Zaragoza
000170075 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000170075 773__ $$g156 (2026), 109132 [11 pp.]$$pPolym. test.$$tPOLYMER TESTING$$x0142-9418
000170075 8564_ $$s7311511$$uhttps://zaguan.unizar.es/record/170075/files/texto_completo.pdf$$yVersión publicada
000170075 8564_ $$s2143855$$uhttps://zaguan.unizar.es/record/170075/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170075 909CO $$ooai:zaguan.unizar.es:170075$$particulos$$pdriver
000170075 951__ $$a2026-03-16-08:18:09
000170075 980__ $$aARTICLE