000153674 001__ 153674
000153674 005__ 20251017144612.0
000153674 0247_ $$2doi$$a10.1016/j.jmbbm.2025.107005
000153674 0248_ $$2sideral$$a143795
000153674 037__ $$aART-2025-143795
000153674 041__ $$aeng
000153674 100__ $$aDeng, Bincan
000153674 245__ $$aPredicting rheological properties of HAMA/GelMA hybrid hydrogels via machine learning
000153674 260__ $$c2025
000153674 5060_ $$aAccess copy available to the general public$$fUnrestricted
000153674 5203_ $$a- Rheological properties are pivotal in determining the printability of biomaterials, directly impacting the success of 3D bioprinted constructs. Understanding the intricate relationship between biomaterial formulations, rheological behavior and printability can facilitate the advancement and rapid development of biomaterials. Herein, we critically measured the rheological properties of hyaluronic acid methacrylate (HAMA)/gelatin methacrylate (GelMA) hybrid hydrogels with varied formulations and generated a dataset to train a machine learning (ML) model. By utilizing four well-known algorithms, we developed the ML model for the viscosity and shear stress of HAMA/GelMA hydrogel mixtures. To improve model interpretability, we further created a multilayer perceptron framed model, known as HydroThermoMLP, by incorporating the Redlich-Kister polynomial as the thermodynamic representation of viscosity of mixtures. To accomplish the MLP learning on limited data, the shared loss function was formulated on the basis of the R-K presentation to guide the joint training process. The established HydroThermoMLP model, while maintaining the same accuracy as Random Forest, produces outputs that adhere to thermodynamic constraints and instill confidence in generalization applications with a simple algorithm informed by the R-K polynomial. It presents a robust predictive ML tool to forecast the viscosity of hybrid hydrogels and direct the design of biomaterials while appropriately abiding by thermodynamic constraints as essential guidelines.
000153674 540__ $$9info:eu-repo/semantics/embargoedAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000153674 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000153674 700__ $$aChen, Sibai
000153674 700__ $$aLasaosa, Fernando López
000153674 700__ $$aXue, Xuan
000153674 700__ $$aXuan, Chen
000153674 700__ $$aMao, Hongli
000153674 700__ $$aCui, Yuwen
000153674 700__ $$aGu, Zhongwei
000153674 700__ $$0(orcid)0000-0001-8741-6452$$aDoblare, Manuel$$uUniversidad de Zaragoza
000153674 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000153674 773__ $$g168 (2025), 107005 [11 pp.]$$pJ. mech. behav. boomed. mater.$$tJournal of the Mechanical Behavior of Biomedical Materials$$x1751-6161
000153674 8564_ $$s5344739$$uhttps://zaguan.unizar.es/record/153674/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-04-11
000153674 8564_ $$s1595544$$uhttps://zaguan.unizar.es/record/153674/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-04-11
000153674 909CO $$ooai:zaguan.unizar.es:153674$$particulos$$pdriver
000153674 951__ $$a2025-10-17-14:17:54
000153674 980__ $$aARTICLE