Establishment of reversible four-dimensional (4D) printing capability of shape–memory responsive cellulosic composites (RCC) using experimental, theoretical, and computational modeling

Purpose This research aims to provide a innovative class of shape-memory-responsive cellulosic composites (RCC) for 4D printing, enabling self-activated, reversible shape morphing. By integrating experimental, theoretical, and computational modeling, the study optimizes material behavior, offering precise curvature predictions for advanced biomedical and pharmaceutical applications. Design/methodology/approach This study presents an innovative class of shape–memory–responsive cellulosic composites (RCC), with a unique combination of starch and Affnisol TM . RCC-mediated filaments were used to print single-layer strips using fused deposition modeling 3D printing technology. The printed single-layer strip exhibited reversible, contactless and self-activated shape morphing in response to swelling and heat. The programming stage involves the swelling and heating of the composite strip and subsequent shape recovery through heating. The shape deformation during the self-activated programming stage was both estimated and predicted using simple experimental, theoretical and computational tools. The study was conducted at different thicknesses (1.5, 2.0 and 2.5 mm) and temperatures (25°C and 37°C) to validate the performance of the developed model in predicting bending curvature. Findings The developed model showed less than a 13.96 % difference in curvature predicted using theoretical and experimental modeling at studied temperatures. At lower thicknesses, the model can predict the bending curvature with less than a 2.0 % difference in curvature. These RCC materials exhibited potential reversible 4D printing capacity and satisfied the adopted approaches and modeling to forecast the bending curvature for reversible 4D printing. Originality/value This study introduces a new class of composite materials for potential 4D applications and provides simple predictive models to forecast bending curvature in reversible 4D printing.