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Polymers
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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PubMed Central
Other literature type . 2023
License: CC BY
Data sources: PubMed Central
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Microplastic Index—How to Predict Microplastics Formation?

Authors: Arjen Boersma; Kalouda Grigoriadi; Merel G. A. Nooijens; Sieger Henke; Ingeborg M. Kooter; Luke A. Parker; Ardi Dortmans; +1 Authors

Microplastic Index—How to Predict Microplastics Formation?

Abstract

The presence of microplastics in environmental compartments is generally recognized as a (potential) health risk. Many papers have been published on the abundance of microplastics at various locations around the globe, but only limited knowledge is available on possible mitigation routes. One of the mitigation routes is based on the choice of plastic materials used for products that may unintentionally end up in the environment. As a first approach, this paper presents a method to calculate the tendency of polymers to form microplastics, based on their mechanical and physical properties. A MicroPlastic Index (MPI) that correlates the microplastic formation to polymer properties is defined for both impact and wear of polymers via a theoretical particle size and the energy required to form these particles. A first comparison between calculated and experimental particle size is included. The MPI for impact and wear follow the same trend. Finally, these MPIs are correlated to the respective abundance of the microplastics in the environment, corrected for global production of the corresponding polymers: the higher the MPI, the more microplastics are found in the environment. Thus, the MPI can be used as a basis for choice or redesign of polymers to reduce microplastic formation.

Keywords

Impact, Wear, Microplastics, MPI, Polymer properties, Article

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    24
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
24
Top 10%
Average
Top 10%
Green
gold
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