Production and Characterization of Gelatin Biomaterials Based on Agave Microfibers and Bentonite as Reinforcements

The objective of this work was to obtain biomaterials as gelatin films or biofilms produced by casting, reinforced with a microfiber (MF) from Agave angustifolia Haw bagasse and bentonite (BN) nanoparticles and evaluate the effect of such reinforcements at different concentrations. Agave microfibers were obtained by a non-abrasive chemical method. Three formulations based on gelatin with glycerol were reinforced with microfiber, bentonite and both materials with 1.5, 3.5 and 5.5% w/w solids content. Physicochemical properties were determined using SEM and FTIR, thickness, soluble matter and moisture. The XRD, barrier, mechanical and thermal properties were measured. The films’ micrographs showed agglomerations on the surface. Interactions between its functional groups were found. The solubility increased when the MF concentration increased. The thickness of the films was between 60 and 110 μm. The crystallinity ranged from 23 to 86%. The films with both MF and BN and 3.5% w/w solids had the lowest barrier properties, while the film with 5.5% w/w solids showed the highest mechanical properties, being thermally resistant. Overall, Agave microfibers together with bentonite were able to improve some of the films’ properties, but optimized mixing conditions had to be used to achieve good particle dispersion within the gelatin matrix to improve its final properties. Such materials might have the potential to be used as food packaging.

Predicting mechanical performance of starch-based foam materials

Variations in mixture proportions of plasticizers, additives, and crosslinking agents have significant impacts on mechanical performance of starch-based foam materials. In particular, starch/ethylene-vinyl acetate (EVA) foam materials have been developed with improved mechanical strength by optimizing the formulation. There is a lack of numerical correlations that could help analyze the effects of components and provide a predictive method for future research. In this study, we develop simple and accurate predictions for tensile strength and resilience based on mixture proportions of components for starch-based/EVA foam materials. The models constructed might be used to help design mixture proportions of starch-based foam materials. By combining optimization results from the Taguchi method and machine learning approaches, it is expected that more quantitative data can be extracted from fewer experimental trials at the same time.