Sensor array for the detection of organic and inorganic contaminants in post-consumer recycled plastics for food contact

Scientometric analysis of the development of plastic packaging considering the circular economy and clean technologies: A review

Plastics are alternatives to enable the distribution of industrialized products, especially food. Packaging is versatile and of great importance for the conservation of products. However, plastic packaging impacts the environment and calls for a clean technology and circular economy approach to mitigate the damage. A scientometric analysis of the relationship between plastic packaging production and the circular economy was reviewed based on the premise that research is intrinsically linked to clean technologies. VosViewer software was used to conduct the analysis, and the revision was conducted for discussion and relationship building. We concluded that there is a gap regarding the connection between the circular economy and clean technologies with plastic packaging. The development of technologies that adapt plastic packaging to the circular economy is rarely discussed. To make plastic packaging more environmentally attractive, technologies based on eco-design are necessary to achieve an alternative scenario associated with a more sustainable circular economy.

Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review

There has been a tremendous increase in demand for virgin and postconsumer recycled (PCR) polymers due to their wide range of chemical and physical characteristics. Despite the numerous potential benefits of using a data-driven approach to polymer design, major hurdles exist in the development of polymer informatics due to the complicated hierarchical polymer structures. In this review, a brief introduction on virgin polymer structure, PCR polymers, compatibilization of polymers to be recycled, and their characterization using sensor array technologies as well as factors affecting the polymer properties are provided. Machine-learning (ML) algorithms are gaining attention as cost-effective scalable solutions to exploit the physical and chemical structures of polymers. The basic steps for applying ML in polymer science such as fingerprinting, algorithms, open-source databases, representations, and polymer design are detailed in this review. Further, a state-of-the-art review of the prediction of various polymer material properties using ML is reviewed. Finally, we discuss open-ended research questions on ML application to PCR polymers as well as potential challenges in the prediction of their properties using artificial intelligence for more efficient and targeted PCR polymer discovery and development.