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. 2021 Oct 29;13(21):3745.
doi: 10.3390/polym13213745.

Production and Characterization of Polyethylene Terephthalate Nanoparticles

Affiliations

Production and Characterization of Polyethylene Terephthalate Nanoparticles

Francesca Lionetto et al. Polymers (Basel). .

Abstract

Microplastic (MP) pollution represents one of the biggest environmental problems that is further exacerbated by the continuous degradation in the marine environment of MPs to nanoplastics (NPs). The most diffuse plastics in oceans are commodity polymers, mainly thermoplastics widely used for packaging, such as polyethylene terephthalate (PET). However, the huge interest in the chemical vector role of micro/nanoplastics, their fate and negative effects on the environment and human health is still under discussion and the research is still sparse due also to the difficulties of sampling MPs and NPs from the environment or producing NPs in laboratory. Moreover, the research on MPs and NPs pollution relies on the availability of engineered nanoparticles similar to those present in the marine environment for toxicological, transport and adsorption studies in biological tissues as well as for wastewater remediation studies. This work aims to develop an easy, fast and scalable procedure for the production of representative model nanoplastics from PET pellets. The proposed method, based on a simple and economic milling process, has been optimized considering the peculiarities of the polymer. The results demonstrated the reliability of the method for preparing particle suspensions for aquatic microplastic research, with evident advantages compared to the present literature procedures, such as low cost, the absence of liquid nitrogen, the short production time, the high yield of the process, stability, reproducibility and polydisperse size distribution of the produced water dispersed nanometric PET.

Keywords: DSC; annealing; ball milling; marine plastics; microplastics; model nanoplastics; nanoplastic formation; ocean pollution; poly (ethylene terephthalate) nanoplastics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of the milling process in (a,b) an ultra-centrifugal mill and (c,d) a ball mil (The powder particles are blue, while the milling balls are red).
Figure 2
Figure 2
DSC dynamic scans of PET pellet (before milling) and powders after consecutive milling steps in an ultra centrifugal mill with different mesh sieves. Annealing was performed at 160 °C.
Figure 3
Figure 3
Effect of the number of milling cycles on the apparent crystallinity of PET samples measured by DSC before and after annealing at 160 °C.
Figure 4
Figure 4
(a) Effect of annealing on the XRD spectra of powders milled with the S80 sieve; (b) XRD spectra of annealed powders milled with different sieves.
Figure 5
Figure 5
Size distribution of the powders obtained from ultra-centrifugal milling.
Figure 6
Figure 6
Procedure A: (a) particle size distribution at different milling times and ball diameters and (b) effect of milling time on average size (at 10%, 50% and 90% by volume of the analyzed particles).
Figure 7
Figure 7
Procedure B: (a) size distribution at different milling times and ball diameters and (b) effect of milling time on average size (at 10%, 50% and 90% by volume of the analyzed particles).
Figure 8
Figure 8
Procedure C: (a) size distribution at different milling times and (b) the effect of milling time on average size (at 10%, 50% and 90% by volume of the analyzed particles).
Figure 9
Figure 9
Dispersion stability over time: (a) aqueous dispersion of nanometric PET prepared according to procedure C; (b) aqueous dispersions of nanometric PET sieved before ball milling and then prepared according to procedure C.
Figure 10
Figure 10
(a) Representative SEM images of the sample obtained according to procedure C (scale bar is 10 μm). A zoom on the three different populations founded is also reported. (b) Statistical analysis of the micro/nanoplastics size distribution observed by SEM imaging.
Figure 11
Figure 11
Schematic representation of the optimized Procedure C.

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