No abstract

Three-dimensional (3D) printing has been widely identified as an emerging disruptive technology. This study examines how this technology could enable the circular economy by disrupting the existing materials value chain. Specifically, could this novel technology be used to locally manufacture new goods from local sources of recycled plastic waste, thereby offering benefits for the efficiency and effectiveness of materials cycling? This article uses the London metropolitan area-where system conditions already exist in the form of material flows, technology policy, and facilitiesin order to assess 3D printing's viability as an enabler of a circular economy at the local level. An analysis of stakeholder perceptions identifies economic, technological, social, organizational, and regulatory barriers to mainstream implementation, and their likelihood of being overcome.

This article focuses on agar biopolymer films that offer promise for developing biodegradable packaging, an important solution for reducing plastics pollution. At present there is a lack of data on the mechanical performance of agar biopolymer films using a simple plasticizer. This study takes a Design of Experiments approach to analyze how agar-glycerin biopolymer films perform across a range of ingredients concentrations in terms of their strength, elasticity, and ductility. Our results demonstrate that by systematically varying the quantity of agar and glycerin, tensile properties can be achieved that are comparable to agar-based materials with more complex formulations. Not only does our study significantly broaden the amount of data available on the range of mechanical performance that can be achieved with simple agar biopolymer films, but the data can also be used to guide further optimization efforts that start with a basic formulation that performs well on certain property dimensions. We also find that select formulations have similar tensile properties to thermoplastic starch (TPS), acrylonitrile butadiene styrene (ABS), and polypropylene (PP), indicating potential suitability for select packaging applications. We use our experimental dataset to train a neural network regression model that predicts the Young’s modulus, ultimate tensile strength, and elongation at break of agar biopolymer films given their composition. Our findings support the development of further data-driven design and fabrication workflows.