Validation of an extraction method for microplastics from human materials

Abstract

INTRODUCTION:

Since the beginning of industrial production in 1950, plastic production has continued to grow strongly worldwide and is now at 322 million tonnes in the year 2015. From these very high production volumes ever larger quantities are found in the environment. There the plastics degradate to microplasticity and spread ubiquitously in the world. The present work deals with the possible uptake of microplastic particles in human organisms. For the detection of these plastic particles, an extraction method was developed and validated.

MATERIALS AND METHODS:

Biological materials consist of human blood (healthy volunteers, n = 4) and different tissues of pigs and cattles. Various lysis solutions were tested for degradation efficiency of biological material and for effects on the plastics. The mass loss, surfaces and structure variations as well as the physicochemical spectrum of the material were observed after treatment by atomic force (AFM) and electron microscopy (EM) and Fourier transform infrared spectrometry (FTIR).

RESULTS:

The different plastic types as polyamide (PA), polycarbonate (PC), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyvinyl chloride (PVC) could be clearly differentiated and identified by FTIR. Regarding the surface control, especially PVC showed detectable alterations: After extraction an irregular surface structure caused by protuberances or bubbles could be observed. However, instead of these alterations an equivalent count of plastic particles was found in correlation to the applied plastic amount (recovery rate overall was 99,12±0,67%).

CONCLUSION:

The applied method can be used for plastic extractions from human or animal tissues without remarkable effects on the plastics.

Keywords

Microplastic microparticel extraction method human tissue

1 Introduction

Plastics production has continued to increase worldwide since 1950 when industrial production began, and has now increased to 335 million tonnes in 2016 compared to 322 million tonnes in 2015 [1, 2]. For this mass indication, polyacrylic fibers, PA fibers, PET fibers and PP fibers are not included in this calculation [1]. These very high production volumes, poor waste management and sometimes non-existent environmental concerns worldwide are causing ever larger quantities to be released into the environment. According to more recent estimates, annual pollution levels will double by 2025 [3]. There, the plastics degrade to secondary microplastics through biological, chemical and physical factors [4, 6] and are ubiquitously distributed in the world where they develop their harmful and dangerous potential.

The inclusion of synthetic polymers as nanoparticles is being researched in many disciplines. Here, however, the focus is mainly on the targeting of the nanoparticles. These polymer nanoparticles are expected to act as transporters for drugs to specific sites [7]. The uptake of nanoparticles from polystyrene into human cells such as HeLa cells and Caco-2 cells has been demonstrated [8, 10] It is also known that the uptake rate of polystyrene particle is bound to various factors such as particle size, particle concentration, particle charge, incubation time and any surface modifications [8 –10].

Another finding is that plastics have a high chemical affinity for pollutants such as polychlorinated biphenyls (PCBs), dichlorodiphenyldichloroethylene (DDE) and nonylphenol. These are increasingly bound to the plastics through chemical adsorption and can then be released to the organism over time [11, 13]. Thus, the polymer particles can serve as vectors for organic pollutants into the organism [14, 15] and there lead to negative effects in the organisms [16, 17].

A study by Deng et al. from the year 2017 showed that in the model organism mouse pristine polystyrene microplastics (PS-MPs) in a size of 5 microns and 20 microns can be found in the tissues of the liver, the kidney and the intestinal wall after oral administration. Furthermore, effects on energy and lipid metabolism, the degree of oxidative stress and increased levels of biomarkers for neurotoxicity could be demonstrated [18].

All this combined knowledge points to an increased risk and potential danger of microplastics. However, most of the studies were conducted in oceans, waters and, above all, maritime organisms. Further investigations on the impact of microplastics on mammals, and more specifically on humans, in order to acquire knowledge of the harmful effects and to reduce the emerging risks in a timely manner and in the future through measurements, are required.

Therefore, we have made a modification and combination of the already established method from other publications to develop a stand-alone method for the extraction of microplastics from human tissue or blood. The goal is the development of an effort-reduced and cost-effective extraction. The extraction method is intended to preserve the properties of the plastic such as the mass, surface and structure as well as the physiochemical spectrum of the material after the treatment. For this purpose, each step is validated with the atomic force microscope, light and electron microscope and the Fourier transform infrared spectrometer.

2 Materials and methods

2.1 Reference samples

In these experiments, different types of plastics were tested for their resistance and structural changes due to the chemicals used. These plastics were selected, based on their wider distribution and the increased possibility to occur in the environment. These included polystyrene (PS), polypropylene (PP), polyvinylchloride (PVC), polycarbonate (PC), polyethylene (PE), polyamide (PA) and acrylonitrile-butadiene-styrene copolymer (ABS).

2.2 Contamination prevention

The reduction of the contamination potential is an important task during the experimental course. For the reductions, nitrile gloves were worn over the period of sample manipulation, extraction, and until the transfer to the aluminum oxide membrane. Furthermore, all work surfaces and also preparation materials such as tubes and tweezers (forceps?) had to be washed out with 80% ethanol (diluted from 99.8% ethanol AppliChem GmbH, Darmstadt, Germany) with ultrapure water and dried to be safe from contamination. Sample preparation, extraction and transfer to the aluminum membrane of the FTIR spectrometer were performed under a laminar flow cabinet. The membranes that were too dry were protected from ambient air in a Petri dish and dried in a drying oven.

2.3 Autofluorescence of the polymers

It is known that some plastics have the ability to autofluorescence. It should therefore be determined with some research if the fluorescence can be used practically. The original membranes used for the extraction method were checked for the properties of their fluorescence. The differences were tested for the alumina membranes (Durapore^® membrane Type HVLP, Merck Millipore Ltd, Tullagreen, Carrightwohill Co. Cork, Ireland) and a macroporous silicon lift off membrane (SmartMembranes GmbH, Halle (Saale), Germany). Furthermore, it was examined whether after extraction the contamination of the membrane with the polyamide particles could be estimated by fluorescence. For all these studies the company’s fluorescence microscope (Olympus CKX41) was used.

2.4 Sample material

For the first experiments with human material, the first author’s own blood was used. The blood sample was taken with a citrated blood collection tube on the same day of the experiment. Two milliliters of this citrated blood was filled into Schott bottles and mixed with a defined amount (depending on the experiment) of Polyamide Microplastic.

2.5 Material testing

The extraction method and the chemicals used have been tested for their effect on the plastics. For this reason, visual images were taken with the light microscope and the electron microscope (Type XL 30 ESEM from Philips) after the treatment. Furthermore, fragments of the different types of plastic were incubated for a defined period of 72 hours in 10% potassium hydroxide (AppliChem GmbH, Darmstadt, Germany) and 1% Tween-20 (AppliChem GmbH, Darmstadt, Germany). The mass loss of the fragments was tested every 12 hours.

2.6 Extraction method

The extraction starts with a chemical digestion of the sample material, to which a 10% potassium hydroxide (AppliChem GmbH, Darmstadt, Germany) solution with 1% Tween-20 (AppliChem GmbH, Darmstadt, Germany) is added to the prepared blood sample. The sample is then incubated with agitation in a 50°C water bath (Type SV1422, Memmert GmbH) for 24 to 48 hours (depending on the complexity of the tissue and organism). Thereafter, this liquid is filtered through a filter membrane (Durapore^® membrane Type HVLP, Merck Millipore Ltd, Tullagreen, Carrightwohill Co. Cork, Ireland) with a pore size of 0.45μm and rinsed with a 40°C warm nitric acid (67%, Sigma-Aldrich, St.Louis, USA) solution containing 1% Tween-20 for 5 minutes. Finally, the sample is neutralized with ultrapure water. The particles are then removed from the membrane in ultrasound bursts with an ultrasonic bath (RK 106 S, BANDELIN electronic GmbH & Co. K, Berlin) in ethanol (99.8% ethanol AppliChem GmbH, Darmstadt, Germany) for 15 minutes. Afterwards, this suspension is filtered through an alumina membrane and dried at 40°C overnight. Now the particles on the alumina membrane (Anodisc™ 25, 0,2μm, Diameter 25 mm, GE Healthcare UK limited, Amersham Place, Little Chalfont Buckinghamshire, UK) are ready for further analysis with the FTIR spectrometer (Perkin Elmer Frontier) in combination with a light microscope (Perkin Elmer Spotlight).

2.7 Fourier Transform Infrared Spectrometers (FT-IR)

The detection of plastics and the specific identification of the different plastic types can be achieved by spectroscopic methods. In literature, infrared spectroscopy is most frequently mentioned as a detection method for plastics and represents the state of the art technique in the identification of different types of plastics. The latest development, which not only affects microplastic research, is the combination of spectroscopy with microscopy. This combination increases the information content of the investigation by additional knowledge about the spatial distribution of the particles (chemical imaging). This allows a location-resolved material characterization in the lower micrometer range. In addition, this method makes it possible to determine the type of plastic and at the same time the size and position of the plastic fragments. In 2014 –2015, the methodical detection of plastic fragments up to the lower limit of approximately 500 nm by means of Raman spectroscopy and of approximately 20μm by FT-IR spectroscopy was possible.

3 Results

3.1 Material testing

The various microscopic methods were used for an optical examination and evaluation of the structure and surface of the plastic fragments. These investigations were intended to determine, whether the treatment of the sample material with the 10% potassium hydroxide and 1% Tween 20 solution would change the structure and the surface of the plastics by means of the extraction method.

The PVC sample (Fig. 1) showed a stronger change compared to all other plastics. The surface, which was very smooth and uniform, was then formed with protuberances or bubbles with a very irregular surface (B). Furthermore, an increase and intensification of the mechanical marking (D) were observed. In general, the structure turned out to be slightly swollen.

Fig.1

The left side (A & B) shows the untreated sample and the right side (B & D) shows the same sample after 72 hours in 10% potassium hydroxide solution at 200× magnification. The images (C & D) are in 500× magnification. Here, the polyvinyl chloride (PVC) is shown in each case with a light microscope image in a bright-field setting before its treatment and after its treatment. On the left-hand side is the untreated PVC and, in each case, the PVC, after the treatment for 72 hours at 50°C, with 10% potassium hydroxide and 1% Tween 20. The untreated sample is shown in figure (A) at 200-fold magnification, (B) after treatment with 200-fold magnification, (C) untreated at 500-fold magnification, and (D) treated with 500-fold magnification.

The plastics in powder form had a much larger reactive surface, so any structural changes should be made visible with the scanning electron micrographs. Furthermore, these particles had an approximate size distribution as might be assumed in the organism. In Fig. 2 the polyamide powder is shown at different magnifications. The pictures (A–D) represent untreated samples and (E–H) are the samples treated with the solution. Figure 2 shows the images of the treatment after 72 hours with hydroxide and 1% Tween-20 at a temperature of 50°C.

Fig.2

The left row shows (A–D) the particles before the chemical treatment and in the right row (E–H) the particles after a 72 h treatment with 10% KOH. The image series are each with increasing magnification. Images of the polyamide powder through a scanning electron microscope (SEM) in untreated form in the left image series (A–D), and after 72 hours of chemical treatment at 50°C with 10% potassium hydroxide and 1% Tween 20 (E–H). Also, in ascending magnification of (A–D) and (E–H) at 180, 1000, 2000 and 10000-fold.

In the mass determination experiments it was to be examined whether the various plastic particles were dissolved by the reaction with the chemicals and thus a mass loss occurred. In the run-up, we determined a maximum 5% deviation, which should (was?) not be exceeded. After all the data were collected, we prepared the following result tables for clarification.

In the following experiment, Tables 1 and 2, controls (n = 2) and samples (n = 3) were used. Table 1 then calculated with the mean value from the two measured values of the controls and in Table 2 with the mean values of the three measured sample values. The controls were incubated in water and the samples were incubated in 10% potassium hydroxide +1% Tween- 20 for 72 hours in 50°C. The tables with the individual values are listed in the annex (Tables: Annex a and b) of the publication.

Table 1

Recovery rate in mean of the plastics controls after 72 hours incubation with water at 50°C

Values controle	PA	ABS	PP	PE	PC	PVC	PS
Recovery in%	99,32	100,91	100,00	100,47	99,47	100,00	97,84
Standard deviation	0,03	0,28	0,00	0,47	0,13	0,00	1,60
Variance	0,00	0,08	0,00	0,22	0,02	0,00	2,56

Table 2

Recovery rate in mean of the plastics Samples after 72 hours incubation with 10% potassium hydroxide solution at 50°C

Values sample	PA	ABS	PP	PE	PC	PVC	PS
Recovery in%	98,26	100,20	101,04	99,31	100,13	98,96	100,50
Standard deviation	0,75	0,28	1,90	0,22	0,19	0,44	0,71
Variance	0,57	0,08	3,60	0,05	0,04	0,19	0,50

3.2 Autofluorescence of the polymers

The results of the investigations on the fluorescence of the membranes and the particles are shown below. The alumina membranes show punctiform densifications in their processing (Fig. 3 a & d) in the brightfield setting. These punctiform densifications have the ability to fluoresce upon excitation with light from the blue (Fig. 3b & e) or green (Fig. 3 c & f) spectrum. Figure 4a presents the membrane in the bright field after extraction, showing a large number of particles. In Fig. 4 b/c the fluorescence is activated by excitation with blue and green light. In Fig. 4b & c, it is also noticeable that the particles from Fig. 4a produce luminous fields in reduced intensity. Figure 4 also shows the negative influence of the fluorescing dots of the membrane, distorting the image of the particle distribution.

Fig.3

Microscopic images of the untreated aluminium oxide membrane with its fluorescent properties. In the pictures (A–C) the membrane has been taken with 40× magnification, (A) in brightfield (B) after excitation with blue light, (C) after excitation with green light. For the pictures (D–F) corresponds to the same description, except that the pictures were taken in 100× magnifications.

Fig.4

Microscopic images of the treated alumina membrane after extraction with the extracted polyamide particles. In (A) the membrane in the bright field recording with a large number of polyamide particles, (B) shows the uptake of the particles during the excitation with blue light and (C) the particles during the excitation with green light. At (B & C), the fluorescent dots on the original membrane dampen the contrast of the particles. Nevertheless, the particle distribution and amount of particles can already be guessed at a 40-fold magnification.”

In contrast to that, Fig. 5 shows the images of the silicon oxide membrane. In Fig. 5 (a, b) & (e, f) the membrane is shown in the bright field with its specific structure. In Fig. 5 (c, d) & (g, h), the membrane is excited with blue and green light, revealing the weak intrinsic fluorescence of the membrane.

Fig.5

Microscopic images of the untreated macroporous silicon lift off membrane when examined for their fluorescent properties. The image series (AD) are magnified 40 times, showing (A) the structure of the membrane in bright field setting, (B) the edge area of the membrane in bright field, (C) the edge area under excitation with blue light and (D) den same edge of the membrane under stimulation with green light. In the image series (EH), the images of the membrane were taken in 200× magnification showing (E) the structure of the pores in bright field, (E) the edge in bright field, (G) the edge under excitation of blue light and (H) the edge under stimulation with green light.

3.3 Fourier transform infrared spectrometers (Spectroscopy?)

The complete extraction of microplastic from the blood was tested first of all with the plastic polyamide since this plastic was available as a fine powder. Figure 3 shows a section of a 2 mm×2 mm field on the aluminium oxide membrane of the control, the 2 mg sample and the 0.4 mg sample and their contamination with biological residues and the polyamide particles. Based on such overview images, the putative particles were marked. Subsequently, the markers were automatically analyzed sequentially by the spectrometer and output as a data set. Figure 4 shows a data set for a particle on the membrane. This image shows in the lower part the spectrum of the particle in black and the reference spectrum of the database in red. The area on the top right shows the ten best results of the identification with the associated correlation factors. For this particle, the different results and correlation factors on related nylons were defined. This is contrasted by Fig. 5, in which the plastic polystyrene is defined in all ten results and is given with a correlation factor of almost 88%.

4 Discussion

The aim of this study was to establish a cost-effective and appropriate analytical method for the extraction and identification of microplastics from human material. This study concentrated on the extraction from human blood.

4.1 Material testing

To be sure that the chemicals used did not adversely affect the analysis of the plastics, material tests were carried out. This should check whether, after the chemical treatment, the plastics differ in their appearance, surface, structure and mass from the untreated plastics. The surface and the structure of the plastics show no significant variations after the treatment, as only the plastic PVC (Fig. 1) showed stronger structural changes on the surface (small bubbles, protuberances and slight swellings) after a treatment of 72 h. These stronger changes started after 48 h in 10% potassium hydroxide solution, so we suspect that these will have a very little impact on the methodology. Furthermore, with the electron microscope, it was shown in Fig. 2, that even the polyamide particles used after the treatment show no significant structural variations.

The factor of mass loss is particularly important for the extraction method since too much degradation in the course of time would make the quantitative statements with reality too great a discrepancy. Table 1 shows the mass loss of the controls (n = 2) in water and in Table 2 the mass loss of the samples (n = 3) in 10% KOH at 50°C. The collected data of mass loss after 72 hours under the treatment of 10% potassium hydroxide (Tables 1 and 2) also indicated that there was no significant mass loss. The data in the tables are the mean values of the percentage recovery of the controls and samples used. By the standard deviation, it can be said that there is a slight difference between the controls and the chemically treated samples. The maximum standard deviation of the percent mass loss for the control is 1.6% of the polystyrene control and 1.9% of the polypropylene sample. As a result, there is no significant variation in the mass of the controls and samples after 72 hours of chemical treatment even in this series of experiments.

4.2 Autofluorescence

The investigation of autofluorescence revealed some interesting results, which are to be investigated in the course of further experiments on their most diverse useful applications. In this study, the ability of autofluorescence to assess contamination resp. the number of particles on the alumina membrane were examined. The examination of the original untreated aluminum oxide membrane revealed its own fluorescence at condensed points (Fig. 3). These densified fluorescent dots on the membrane distort the analysis of particle contamination (Fig. 4). The polyamide particles in this state, in comparison, have a reduced intensity. This needs further experiments to amplify. Also, there is evidence that a treatment with a higher temperature can increase fluorescence intensity.

The images in Fig. 5 show that the membranes of silicon oxide only have a barely perceptible fluorescence. Consequently, these silicon membranes could prevent the negative effects of the aluminum oxide membranes (Fig. 3). Thus, an assessment of the particle contamination of the membranes via the autofluorescence of the particles are made possible. Further experiments will investigate the ability of autofluorescence of different types of plastics and how they can be intensified. Finally, useful applications of autofluorescence should also be sought and tested.

4.3 FTIR

The analysis of microplastic with the FTIR is a widely used method and has the advantage of using a connected microscope to determine the localization of the particles on the membrane, size and shape. In the technology used, there is a limit to the particle size to be identified. In the devices used today, the minimum size is about 20 microns in diameter. For particles below this minimum, identification becomes increasingly difficult and the associated correlation factors decrease sharply. Furthermore, the more novel membranes made of silicon oxide should be used for the analyses with the FTIR, since they do not have their own absorption range and thus compensate for the disadvantage of their own absorption of the aluminum oxide membranes in the spectral range below 1800 cm –1 (wavenumber). These novel membranes can reduce the negative impact of spectral analysis, which may also improve the correlation factors of identification. When measuring a high concentration of particles and also samples, the time factor of an FTIR spectrometer with singular measuring unit can increase exorbitantly. It should generally be restructured for large series of measurements and studies with larger particle contamination on devices with parallel measurements. In general, the quality of identification may vary depending on various factors such as biological residues on the membrane, shocks, the effectiveness of cooling during the measurement. As can be seen in the measurement of Fig. 6, correlation factors of about 76% for the polyamide particles could be achieved in the experimental series. The lower correlation factors in the polyamide plastics could be caused by the large number of chemically related polyamides (nylon).

Fig.6

Visual image of a 2 mm×2 mm section of the alumina membrane taken with the FTIR microscope. Obvious here is an increase in the number of particles depending on the mass used.

The identification of the particle in Fig. 7 shows a correlation of almost 88% with the plastic polystyrene. However, this plastic was not used in the test series for the experiments. One explanation for this plastic is an increased likelihood of airborne contamination during processing or analysis under the FTIR spectrometer. This shows how difficult it is to fully prevent microplastic contamination, a fact that is attributed to the now ubiquitous occurrence of microplastic.

Fig.7

FTIR identification of a particle on the alumina membrane with a correlation factor of 76% for Nylon (polyamide).

Fig.8

FTIR identification of a particle on the alumina membrane with a correlation factor of nearly 88% for polystyrene.

Finally, it can be said that the qualitative analysis of microplastics with a diameter of about 20μm is well possible. Furthermore, the spectra confirm that an analysis of the plastics in blood samples is also possible. Further larger scaled experiments with blood products should provide information about the contamination with plastic particles.

Nevertheless, optimizations should be made to further increase the quality of the correlation factors. It should also be checked what technical limitations of the instrument used are and what maximum correlation factors are possible.

4.4 Limitations

The Fourier transform infrared spectrometer has a limitation on the size of the anthropogenic particles to be detected, which affects the quality of the detection. The detection limit of today’s FTIR spectrometer is around 20μm particle size, while the detection of smaller particles has a negative impact on the signal-to-noise ratio and thus reduces the comparison with the database.

In the FTIR spectrometer, which do not allow parallel measurements of different particles, the time required for identification is a negative factor. This problem can be circumvented by an instrument that is suitable for parallel measurement.

The aluminum filter absorbs in the spectral range below 1800 cm–1, which negatively influences the identification. This can be solved using silicon oxide membranes since they do not have their own absorption.

4.5 Conclusion

The extraction method developed in this study also provides a way to extract microplastics from biological material such as blood. Despite the technological limitations, the methods should be further optimized so that a standardized method for extraction can be established soon. In further methodical developments or diagnostic procedures, the property of autofluorescence might be an interesting opportunity. One could use this ability for the detection or possibly also for the differentiation of the plastics against biological material.

5 Annex

Fig.9

Illustration of light microscopic surface control with 10% potassium hydroxide and 1% Tween 20. Here, the various types of plastic are shown before their treatment and after their treatment, respectively, with a light microscope image at 500-fold magnification with the bright-field setting. On the left-hand side are the untreated plastics and, in each case, laterally, the plastics after their 72 hours of treatment at 50°C, with 10% potassium hydroxide and 1% Tween 20. The plastics are (A/B) polyamide, (C/D) polycarbonate, (E/F) polyethylene with a higher density.

Fig.10

Illustration of light microscopic surface control with10% potassium hydroxide and 1% Tween 20. Here, the various types of plastic are shown before their treatment and after their treatment, respectively, with a light microscope image at 500-fold magnification with the bright-field setting. On the left-hand side are the untreated plastics and, in each case, laterally, the plastics after their 72 hours of treatment at 50°C, with 10% potassium hydroxide and 1% Tween 20. The plastics are (A/B) polyethylene with a lower density, (C/D) polypropylene, (E/F) polystyrene.

Table 3

Annex a Tables with the individual values of the controls a and 2

Control 1	PA	ABS	PP	PE	PC	PVC	PS
start weight in [g]	0,0155	0,0168	0,0294	0.0212	0.0304	0.0354	0,0178
after 24 h	0,0155	0,0168	0,0293	0,0214	0.0303	0,035	0,0177
after 48 h	0,0155	0,017	0,0294	0,0209	0,0302	0,0353	0,0177
after 72 h	00154	0,017	0,0294	0,0214	Q0302	0,0354	0,0177
Standard derivation	4,33013E-05	0,0001	4,33013E-05	0,00020463	8,29156E-05	0,000163936	4,33013E-05
% –end value	99,35	101,19	100,00	100,94	99,34	100,00	99,44
% –deviation	–0,65	1,19	0,00	0,94	–0.66	0,00	–0,56
Control 2
start weight in [g]	0,014	0,0157	0,0261	0,0219	0,0255	0,0348	0,0186
after 24 h	0,014	0,0157	0,026	0,0221	0,0254	0,0347	0,0189
after 48 h	0,0139	0,0158	0,026	0,0218	0,0256	0.0348	0,018
after 72 h	0,0139	0,0158	0,0261	0.0219	0,0254	0.0348	0,0179
Standard derivation	5E-05	5E-05	5E-05	0,00010897	8,29156E-05	4,33013E-05	0,000415331
% –end value	99,29	100,64	100,00	100,00	99,61	100,00	96,24
% –deviation	–0,71	0,64	0,00	0,130	–0,39	0,00	–3,76

Table 4

Annex b Tables with the individual values of the Samples 1–3

Sample 1	PA	ABS	PP	FE	PC	PVC	PS
start weight in [g]	0,0145	0,0161	0,0215	0,0235	0,0268	0,0251	0,0138
after 24 h	0,0145	0,016	0,0223	0,037	0,0272	0,025	0,0137
after 48 h	0,0145	0,0163	0,0223	0,0235	0,0263	0,025	0,0137
after 72 h	0,0144	0,0161	00 223	0,0236	0,0263	0,0247	0,0138
Standard derivation	4,33013E-05	0,00010897	0,00034541	0,00578694	0,000173205	0,00015	5E-05
% - end value	99,31	100,00	103,72	99,16	100,00	98,41	100,00
ail –derivation	–0,69	0,00	3,72	–0,34	0,00	–1,59	0,00
Sample 2
start weight in MI	0,0143	0,0166	0,028	0,0237	0,0248	0,0385	0,02
after 24 h	0,014	0,0169	0,0278	0,0239	0,025	0,0385	0,02
after 48 h	0,0141	0,0167	0,0281	0,0238	0,0248	0.0385	0,0194
after 72 h	0,014	0,0167	0,0279	0,0235	0,0249	0,0383	0,0203
Standard derivation	0,00012247	0,00010397	0,000111803	0,0001479	8.29156E-05	8,66025E-05	0,000326917
% –end value	97,90	100,60	99,64	99,16	100,40	99,48	101,60
56 –deviation	–2,10	0,60	–0,36	–0,84	0,40	–0,52	1,50
Sample 3
start weight in [g]	0,0165	0,0168	0,0401	0,026	0,0271	0,0397	0,0163
after 2–4 h	0,0165	0,0169	0,0399	0,0258	0,0274	0,0396	0,0163
after 48 h	0,0162	0,0168	0,0404	0,026	0,0273	0,0395	0,0162
after 72 It	0,0161	0,0168	0,04	0,0259	0,0271	0,0393	0,0163
Standard derivation	0,0013178536	4,3301E-05	0,000187083	8,2916E-05	0000129904	0,000147902	4331313E-05
% –end value	97,58	100,00	99,75	99,62	100,00	98,99	100,00
% –deviation	–2,42	0,00	–0,25	–0,38	0,00	–1,01	0,00

Footnotes

Acknowledgments

This research was done financially independently of other institutes or investors.

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