2025/05/01 by Jianming Cai, Haohao Zhou, Weizhi Luo +6 · 1 voice
Engineering · Materials Science · Medicine · #Elasticity and Material Modeling #Electrospun Nanofibers in Biomedical Applications #Tissue Engineering and Regenerative Medicine
paper · doi:10.1089/ten.tec.2025.0039
openalex publication_date 2025/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/16
Vascular tissue engineering technology uses tubular viscoelastic materials as intermediaries to transmit the mechanical stimuli required for the construction of vascular grafts. However, most existing studies rely on elastic models, which fail to capture the time-dependent nature of viscoelastic materials. Moreover, the long fabrication cycles, high costs, and complex parameter measurements in tissue engineering pose significant challenges to experimental approaches. There is thus an urgent need to develop a viscoelastic mechanical model that combines physical interpretability, computational efficiency, and predictive accuracy, enabling precise characterization of material responses and unified quantification across experimental platforms. Here, we propose an error-corrected linear solid (ECLS) model with an embedded correction term to address the predictive deviations of conventional models in nonlinear viscoelastic scenarios. Instead of expanding the traditional model structure, the ECLS incorporates an error correction method that improves predictive performance while maintaining structural simplicity. Experiments were conducted on three representative viscoelastic materials—silicone rubber, polyurethane, and polytetrafluoroethylene—to acquire time-resolved response data through stress relaxation and creep tests. The fitting performance was quantitatively evaluated using the Euclidean norm and the Akaike information criterion, enabling a systematic comparison between the ECLS model and three classical models (Kelvin–Voigt, Maxwell, and standard linear solid [SLS]). The results show that the ECLS model exhibits higher predictive accuracy over a wide time range, with an average goodness of fit (R2) of 0.99, representing an improvement of ∼6% compared to the SLS model. Furthermore, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the ECLS model are at least one order of magnitude lower than those of the traditional models, significantly improving the description of nonlinear viscoelastic behavior and providing more accurate predictions of material viscoelastic mechanical behavior. Therefore, the ECLS model not only improves the modeling accuracy of viscoelastic behavior but also establishes a unified and scalable framework for predicting and optimizing the mechanical performance of tissue-engineered vessels, expanding the application potential of mechanical modeling in bioreactor design and biomaterials development.