Synergistic Behavior of Plant Proteins and Biobased Latexes in Bioplastic Food Packaging Materials: Experimental and Machine Learning Study

Abstract

Plant-based proteins are attractive components which may serve as sustainable alternatives to current petrochemical products. Both soy protein and major corn protein, zein, are of interest in food packaging applications due to their sustainability, biodegradation properties, and inherent physicochemical properties. This study discusses the development of bioplastic materials, where it explores the effects of combining zein, soy protein, and plasticizing latexes derived from plant oil-based monomers (POBMs) on properties of resulting bioplastic films. By looking for synergistic effects of soy protein's inherent film formation ability and zein's higher strength, we prepare strong yet flexible soy−zein films as materials, called proteoposites. Incorporation of natural additive POBM-latexes helps to plasticize and hydrophobize the bioplastic films and thus to improve mechanical and barrier properties. Variation of the POBM-latexes' particle size further aims to enhance the performance of resulting bioplastic films. As a result, modified soy−zein proteoposite films with improved moisture resistance, enhanced mechanical behavior, and greater barrier properties were developed. Machine learning-based computational models were utilized in order to find main structural factors affecting the bioplastic's properties and develop a quantitative structure−property relationship model between the physicochemical properties of the film components and the resulted bioplastics' properties and performance. The developed model effectively predicts experimental outcomes with >85% (R 2 : 0.85) accuracy. The newly synthesized proteoposites confirmed the machine learning model predictions. As a result, proteoposite films made of two plant proteins and modified with POBM-latexes can be considered as an attractive and viable replacement for petrochemical food packaging products.

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

“…The authors efficiently visualized these models using 2D contour plots, revealing substantial improvements in lignin yield and β-O-4 content as new data were acquired using the CA strategy. 193 focused on soy and zein for the development of sustainable bioplastic films is not only innovative but timely. The employment of ML to forecast and enhance the properties of proteopposite films shows a commendable intersection of materials science and computational technology.…”

“…The work by Patnode et al focused on soy and zein for the development of sustainable bioplastic films is not only innovative but timely. The employment of ML to forecast and enhance the properties of proteopposite films shows a commendable intersection of materials science and computational technology.…”

Reactive Molecular Dynamics Study of Hygrothermal Degradation of Crosslinked Epoxy Polymers

“…To overcome these challenges, molecular dynamics (MD) simulation and other computational techniques − are often used to offer molecular-level insights to physical and chemical degradation mechanisms in the crosslinked epoxy polymers. Numerous MD simulations about the diffusion behavior of water molecules in the epoxy thermoset and the resulting changes in the structure and properties have been reported. ,− Most of these MD simulations focused on calculating the diffusion coefficient and spatial distribution of water molecules in the epoxy network.…”