Abstract
Multi-layer film packaging (MLF) revolutionized food preservation by combining diverse material layers to optimize barrier properties, mechanical strength, and shelf-life. These materials are essential for transporting perishables across various climates and allow for access to fresh goods in “food deserts”, but they pose significant recycling challenges due to their structural complexity. This perspective examines key structure-property relationships governing barrier performance and highlights innovations in material design. We explore how machine learning can predict performance metrics and propose recyclable alternatives, integrating data-driven approaches with material science insights. By challenging the status quo of MLF design, we advocate for circularity in food packaging, inspiring innovation at the intersection of sustainability, material science, and artificial intelligence.
Data availability
The data that supports the findings presented in this perspective are available in the Supplementary Information. Polymer water vapor permeability data used to train the PolyID model are available at doi.org/10.5281/zenodo.18262440. All data are available from the corresponding authors upon request.
Code availability
The code to run and train the PolyID model and to run DORAnet are available at github.com/NREL/polyid and github.com/wsprague-nu/doranet, respectively. An updated web-based interface that serves the models and makes predictions is available at https://polyid.nrel.gov.
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Acknowledgements
Funding was provided by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Materials and Manufacturing Technologies Office (AMMTO), and Bioenergy Technologies Office (BETO). This work was performed as part of the Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment (BOTTLE) Consortium and was supported by AMMTO and BETO under contract DEAC36-08GO28308 with the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. SK and MM also gratefully acknowledge the support of RePLACE (Redesigning Polymers to Leverage A Circular Economy), funded by the Office of Science of the U.S. Department of Energy via award no. DR-SC0022290. We also thank Scivetica for their help with graphics.
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E.C.Q., L.J.H., J.N.L., B.C.K., and K.M.K. conceived of the idea; E.C.Q., L.J.H., and J.N.L. wrote the original manuscript; E.C.Q., L.J.H., J.N.L., B.C.K., and K.M.K. created the figures; R.W.C., M.M., S.K., R.M.M., and M.J.S. wrote and edited on select sections of the manuscript; J.N.L. built the model and neural network for PolyID; L.J.B., B.C.K., and K.M.K. secured funding; all authors edited the manuscript.
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Quinn, E.C., Hamernik, L.J., Law, J.N. et al. Cracking the code of multi-layer films to promote circularity in single-use plastic packaging. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68936-w
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DOI: https://doi.org/10.1038/s41467-026-68936-w