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

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% (R2: 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.

Enhancing Enzyme Immobilization on Carbon Nanotubes via Metal-Organic Frameworks for Large-Substrate Biocatalysis.

Biocatalysis of large-sized substrates finds wide applications. Immobilizing the involved enzymes on solid supports improves biocatalysis yet faces challenges such as enzyme structural perturbation, leaching, and low cost-efficiencies, depending on immobilization strategies/matrices. Carbon nanotubes (CNTs) are attractive matrices but challenged by enzyme leaching (physical adsorption) or perturbation (covalent linking). Zeolitic imidazolate frameworks (ZIFs) overcome these issues. However, our recent study [ J. Am. Chem. Soc., 2018, 140, 16032-16036] showed reduced cost-efficiency as enzymes trapped below the ZIF surfaces cannot participate in biocatalysis; the enzyme-ZIF composites are also unstable under acidic conditions. In this work, we demonstrate the feasibility of using ZIFs to immobilize enzymes on CNT surfaces on two model enzymes, T4 lysozyme and amylase, both of which showed negligible leaching and retained catalytic activity under neutral and acidic conditions. To better understand the behavior of enzymes on CNTs and CNT-ZIF, we characterized enzyme orientation on both matrices using site-directed spin-labeling (SDSL)-electron paramagnetic resonance (EPR), which is immune to the complexities caused by CNT and ZIF background signals and enzyme-matrix interactions. Our structural investigations showed enhanced enzyme exposure to the solvent compared to enzymes in ZIFs alone; orientation of enzymes in matrices itself is directly related to substrate accessibility and, therefore, essential for understanding and improving catalytic efficiency. To the best of our knowledge, this is the first time ZIFs and one-pot synthesis are employed to anchor large-substrate enzymes on CNT surfaces for biocatalysis. This is also the first report of enzyme orientation on the CNT surface and upon trapping in CNT-ZIF composites. Our results are essential for guiding the rational design of CNT-ZIF combinations to improve enzyme stabilization, loading capacity, and catalytic efficiency.