Vitrimers are crosslinked polymers containing dynamic covalent linkages. Because of their crosslinked structure, they are stable as thermosets at their service temperatures. At high enough temperatures, dynamic exchange reactions occur and rearrange the polymer network, thus vitrimers become malleable and reprocessable like thermoplastics. The dynamic covalent bonds can also undergo dissociative cleavage reactions under specific conditions, so vitrimers are inherently degradable. To achieve a sustainable future, various biomass resources have been used as raw materials in vitrimer preparation. This review summarizes recent developments in biobased vitrimers and highlights their preparation methods. The limitations of current biobased vitrimers are also discussed.
The rapid development of 3D printing, also known as additive manufacturing, has opened up new opportunities for applying shape memory polymers (SMPs) in various fields. The use of abundant, inexpensive, and easily accessible biomass materials as printing raw materials not only facilitates the creation of more intricate SMPs but also aligns with the principles of low-carbon, green, and sustainable development. Here, we successfully printed a shape memory cross-linked network (NW-MO-TTMP) in a single step by direct-ink-writing printing and an in-situ thiol-ene click reaction with magnolol and trimethylolpropane tris(3-mercaptopropionate) as raw materials. The resulting NW-MO-TTMP network exhibited high mechanical properties and a tensile strength (σ) of up to 2.7 MPa when the thiol-ene ratio was 1.0:1, and the photo-initiator content was 1.5%. To improve printability, ethyl cellulose (EC) derived from biomass was incorporated to enhance the viscosity of the printing precursor fluid, resulting in a significant increase in the σ of the NW-MO-TTMP/EC network, reaching 20.6 MPa. Moreover, the successful printing of intricate models, such as the ‘whale’ and ‘octopus,’ demonstrated excellent shape memory effects. This approach highlights the potential of combining biomass-derived materials with advanced 3D printing techniques to develop sustainable and high-performance SMPs.
The analysis of rheological properties of suspensions requires the use of models such as Einstein’s formulation for viscosity in dilute conditions, but its effectiveness diminishes in the context of concentrated suspensions. This study investigates the rheology of suspensions containing solid particles in aqueous media thickened with starch nanoparticles (SNP). The goal is to model the viscosity of these mixtures across a range of shear rates and varying amounts of SNP and SG hollow spheres (SGHP). Artificial neural networks (ANN) combined with swarm intelligence algorithms were used for viscosity modeling, utilizing 1104 data points. Key features include SNP proportion, SGHP content, log-transformed shear rate (LogSR), and log-transformed viscosity (LogViscosity) as an output. Three swarm algorithms—AntLion Optimizer (ALO), Particle Swarm Optimizer (PSO), and Dragonfly Algorithm (DA)—were evaluated for optimizing ANN hyperparameters. The ALO algorithm proved most effective, demonstrating strong convergence, exploration, and exploitation. Comparative analysis of ANN models revealed the superior performance of ANN-ALO, with an R2 of 0.9861, mean absolute error (MAE) of 0.1013, root mean absolute error (RMSE) of 0.1356, and mean absolute percentage error (MAPE) of 3.198%. While all models showed high predictive accuracy, the ANN-PSO model had more limitations. These findings enhance understanding of starch suspension rheology, offering potential applications in materials science.
