Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids
Source abstract
Abstract
Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers. The characterization of transport properties, including diffusion and leaching of these molecules, is largely hampered by the long timescales involved in shelf life experiments. In this work, we employ atomistic molecular simulation techniques to explore the main mechanisms involved in the bulk and interfacial transport of monomer molecules from three polymers commonly employed as packaging materials: polyamide-6, polycarbonate, and poly(methyl methacrylate). Our simulations showed that both hopping and continuous diffusion play important roles in inbound monomer diffusion and that solvent-polymer compatibility significantly affects monomer leaching. These results provide rationalization for monomer leaching in model food formulations as well as bulky industry-relevant molecules. Through this molecular-scale characterization, we offer insights to aid in the design of polymer/consumer product interfaces with reduced risk of contamination and longer shelf life.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Effects and risks of endocrine disrupting chemicals · Recycling and Waste Management Techniques · Microplastics and Plastic Pollution
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Schrödinger, GmbH, Glücksteinallee 25, Mannheim 68159, Germany
Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
Schrödinger, Inc., 01 SW Main St. #1300, Portland, Oregon 97204, United States
Schrödinger, Inc., 5820 Oberlin Dr., San Diego, California 92121, United States
References (72)
Show source-provided references
1. 10.1016/j.jhazmat.2022.12916710.1016/j.jhazmat.2022.129167 ↗
2. 10.1002/pi.316110.1002/pi.3161 ↗
3. 10.1016/j.mattod.2022.01.02210.1016/j.mattod.2022.01.022 ↗
4. 10.1016/j.fpsl.2022.10102010.1016/j.fpsl.2022.101020 ↗
5. 10.1017/plc.2023.710.1017/plc.2023.7 ↗
6. 10.1007/s10068-018-0403-410.1007/s10068-018-0403-4 ↗
7. 10.1088/1755-1315/657/1/01203610.1088/1755-1315/657/1/012036 ↗
8. 10.1021/acs.langmuir.1c0156310.1021/acs.langmuir.1c01563 ↗
9. 10.1021/acs.langmuir.6b0082410.1021/acs.langmuir.6b00824 ↗
10. 10.1021/la500476u10.1021/la500476u ↗
11. Hoekstra, E. J.; Brandsch, R.; Dequatre, C.; Mercea, P.; Milana, M.R.; Störmer, A.; Trier, X.; Vitrac, O.; Schäafer, A.; Simoneau, C. Practical Guidelines on the Application of Migration Modelling for the Estimation of Specific Migration, 2015 (accessed 2023-12-17). 10.2788/04517.10.2788/04517 ↗
12. 10.1080/0265203040002803510.1080/02652030400028035 ↗
13. Wang T. · 2000 · 9
14. 10.1063/1.46196310.1063/1.461963 ↗
15. 10.1063/1.46280610.1063/1.462806 ↗
16. 10.1063/1.45849610.1063/1.458496 ↗
17. 10.1063/1.45919410.1063/1.459194 ↗
18. 10.1002/app.209310.1002/app.2093 ↗
19. 10.3390/ma1221351510.3390/ma12213515 ↗
20. 10.1063/1.47990410.1063/1.479904 ↗
21. 10.1016/j.colsurfa.2017.02.07510.1016/j.colsurfa.2017.02.075 ↗
22. 10.1016/j.cplett.2020.13712510.1016/j.cplett.2020.137125 ↗
23. Kohan M. I. · 1995
24. 10.1007/s10068-018-0403-410.1007/s10068-018-0403-4 ↗
25. 10.1016/j.tifs.2006.04.00910.1016/j.tifs.2006.04.009 ↗
26. 10.3390/coatings1304076210.3390/coatings13040762 ↗
27. Kim S. · 2021 · 209310.1002/9781119468769.hpcbm026 ↗
28. 10.1088/1742-6596/1973/1/01200110.1088/1742-6596/1973/1/012001 ↗
29. 10.1016/0009-2509(86)80025-210.1016/0009-2509(86)80025-2 ↗
30. 10.1007/s00217-014-2333-610.1007/s00217-014-2333-6 ↗
31. 10.1002/jssc.20090034910.1002/jssc.200900349 ↗
32. Goseki R. · 2015 · 170210.1007/978-3-642-29648-2_244 ↗
33. 10.1088/1755-1315/657/1/01203610.1088/1755-1315/657/1/012036 ↗
34. 10.1111/j.1875-595X.2003.tb00736.x10.1111/j.1875-595X.2003.tb00736.x ↗
35. 10.1080/1933824090324129110.1080/19338240903241291 ↗
36. 2021
37. 10.1021/acs.jctc.1c0030210.1021/acs.jctc.1c00302 ↗
38. 10.1021/acsapm.0c0052410.1021/acsapm.0c00524 ↗
39. 10.1016/j.carbpol.2020.11716110.1016/j.carbpol.2020.117161 ↗
40. 2021
41. Bowers, K. J.; Chow, D. E.; Xu, H.; Dror, R. O.; Eastwood, M. P.; Gregersen, B. A.; Klepeis, J. L.; Kolossvary, I.; Moraes, M. A.; Sacerdoti, F. D.; Salmon, J. K.; Shan, Y.; Shaw, D. E. Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. InSC ’06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing; 2006; p 43.10.1109/SC.2006.54 ↗
42. Shaw, D. E.; Grossman, J. P.; Bank, J. A.; Batson, B.; Butts, J. A.; Chao, J. C.; Deneroff, M. M.; Dror, R. O.; Even, A.; Fenton, C. H.; Forte, A.; Gagliardo, J.; Gill, G.; Greskamp, B.; Ho, C. R.; Ierardi, D. J.; Iserovich, L.; Kuskin, J. S.; Larson, R. H.; Layman, T.; Lee, L.S.; Lerer, A. K.; Li, C.; Killebrew, D.; Mackenzie, K. M.; Mok, S. Y.H.; Moraes, M. A.; Mueller, R.; Nociolo, L. J.; Peticolas, J. L.; Quan, T.; Ramot, D.; Salmon, J. K.; Scarpazza, D. P.; Schafer, U. Ben.; Siddique, N.; Snyder, C. W.; Spengler, J.; Tang, P. T. P.; Theobald, M.; Toma, H.; Towles, B.; Vitale, B.; Wang, S. C.; Young, C. Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer. InSC ’14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis; 2014; pp 41–53.10.1109/SC.2014.9 ↗
43. Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W F.; Hermans, J. Interaction Models for Water in Relation to Protein Hydration. InIntermolecular Forces: Proceedings of the Fourteenth Jerusalem Symposium on Quantum Chemistry and Biochemistry Held in Jerusalem, Israel, April 13–16, 1981; Pullman, B., Ed. Springer Netherlands: Dordrecht, 1981; pp 331–342.
44. 10.1021/ma200345v10.1021/ma200345v ↗
45. 10.1063/1.44733410.1063/1.447334 ↗
46. 10.1103/PhysRevA.31.169510.1103/PhysRevA.31.1695 ↗
47. 10.1063/1.46746810.1063/1.467468 ↗
48. 10.1103/PhysRevE.74.06180310.1103/PhysRevE.74.061803 ↗
49. 10.1039/C5RA21115H10.1039/C5RA21115H ↗
50. 10.1021/ma00104a03610.1021/ma00104a036 ↗
51. 10.1039/D0CP03163A10.1039/D0CP03163A ↗
52. 10.1016/j.jmgm.2015.01.01110.1016/j.jmgm.2015.01.011 ↗
53. 10.1021/acsapm.3c0304010.1021/acsapm.3c03040 ↗
54. 10.1038/s41524-021-00647-w10.1038/s41524-021-00647-w ↗
55. 10.1016/j.polymer.2016.01.07410.1016/j.polymer.2016.01.074 ↗
56. 10.1021/acs.jctc.5b0086410.1021/acs.jctc.5b00864 ↗
57. 10.1021/jp003919d10.1021/jp003919d ↗
58. 10.1021/ja962176010.1021/ja9621760 ↗
59. 10.1039/D0CP03163A10.1039/D0CP03163A ↗
60. Bicerano J. · 200210.1201/9780203910115 ↗
61. 10.1002/app.2942110.1002/app.29421 ↗
62. 10.1007/s10965-006-0055-610.1007/s10965-006-0055-6 ↗
63. 10.1016/0021-9673(92)87066-H10.1016/0021-9673(92)87066-H ↗
64. 10.1002/app.1975.07019091110.1002/app.1975.070190911 ↗
65. 10.1002/polb.2118910.1002/polb.21189 ↗
66. Slovensky Grob. Molinspiration Cheminformatics free web services. https://www.molinspiration.com, 2024.
67. 10.1002/1521-3919(20001201)9:9<687::AID-MATS687>3.0.CO10.1002/1521-3919(20001201)9:9<687::AID-MATS687>3.0.CO ↗
68. 10.1021/jp309997310.1021/jp3099973 ↗
69. 10.1039/C8CP02829J10.1039/C8CP02829J ↗
70. 10.1080/0265203880937368010.1080/02652038809373680 ↗
71. 10.1080/0265203920937405610.1080/02652039209374056 ↗
72. 10.1002/app.1990.07039040410.1002/app.1990.070390404 ↗