# EasyMP: Diverse and Environmentally Relevant Microplastic Reference Materials Encompassing Fragments and Fibers

Oskar Hagelskjær, Frederik Hagelskjær, Henar Margenat, Jeroen E Sonke, Gaël Le Roux

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Oskar Hagelskjær, Frederik Hagelskjær, Henar Margenat, Jeroen E Sonke, Gaël Le Roux. EasyMP: Diverse and Environmentally Relevant Microplastic Reference Materials Encompassing Fragments and Fibers. Nano Select, 2025, 6 (10), pp.e70004. 10.1002/nano.70004. hal-05537546

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RESEARCH ARTICLE

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# EasyMP: Diverse and Environmentally Relevant Microplastic Reference Materials Encompassing Fragments and Fibers

Oskar Hagelskjær¹,² ID | Frederik Hagelskjær³ ID | Henar Margenat² ID | Jeroen E. Sonke⁴ ID | Gaël Le Roux² ID

¹Microplastic Solution, Castanet-Tolosan, France | ²Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France | ³SDU Robotics, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense M, Denmark | ⁴Géosciences Environnement Toulouse, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France

**Correspondence:** Oskar Hagelskjær (oskar@microplasticsolution.com)

**Received:** 26 July 2024 | **Revised:** 30 October 2024 | **Accepted:** 25 February 2025

**Funding:** This work is funded by an 80Prime CNRS grant - 4DµPlast (G.L.R., J.E.S.). This publication was supported by ATMO-PLASTIC - ANR-20-CE34-0014 (G.L.R., J.E.S.) and the Plasticopyr project within the Interreg V-A Spain-France-Andorra program (G.L.R.) as well as observatoire Homme-Milieu Pyrénées Haut Vicdessos - LABEX DRIIHM ANR-11-LABX0010 (G.L.R.).

**Keywords:** analytical precision | measurement repeatability | quality assurance | standards | synthetic particles | validation study

## ABSTRACT

The field of microplastic (MP) research has expanded significantly since the terminology's inception in 2004. However, despite growing interest in MP research, the availability of environmentally relevant MP reference materials (RMs) remains limited. This study introduces data to support the validation of "EasyMP"; a diverse range of commercially available MP RMs. The current study presents data on fragments (10–100 µm) and fibers (50–1000 µm) suspended in 95 vol.% ethanol solution at various MP concentrations (n/mL). A total of 10 EasyMP MP RM samples containing either fragments or fibers were evaluated for repeatability. Relative standard deviation (RSD) of particle concentration irrespective of particle size, was determined at 10% (n = 5) for fragments (10–100 µm) and 9% (n = 5) for fibers (50–1000 µm). In addition, size group-specific RSD was evaluated to determine the consistency of the particle size distribution between four individual subsamples. For fragments (10–100 µm), size-group specific RSD on the order of 10 µm was consistently below 20%, while RSD for fibers on the order of 50 µm varied between 17% and 51%. EasyMP MP RMs are available for purchase on www.microplastic.store, and are customizable to meet user-specific requirements, addressing a current gap in MP research.

## 1 | Introduction

The field of microplastic (MP) research has experienced considerable growth since the term was first introduced in 2004 [1, 2]. Between 2010 to 2021, the number of published MP studies has increased 40% annually, currently amounting to thousands of publications per year [3]. However, despite the growing interest in

MP research, the access to environmentally relevant MP reference materials (RMs) has been limited [4–6]. To this date, no certified MP RMs exist and the lack of commercially available RMs hinders the harmonization of analytical methods and the generation of comparable data [7]. In the absence of commercially available MP RMs, researchers have often been compelled to produce their own RMs [8, 9]. This can lead to experimental setups that do not

<footnote>**Abbreviations:** COA, certificate of analysis; MP, microplastic; PA6.6, polyamide 6.6; PAN, polyacrylonitrile; PE, polyethylene; PET, polyethylene terephthalate; PP, polypropylene; PSD, particle size distribution; RM, reference material; RSD, relative standard deviation; SD, standard deviation; SDS, safety data sheet.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). *Nano Select* published by Wiley-VCH GmbH.</footnote>
accurately reflect natural conditions, as researchers sometimes use MP RMs with morphological features less relevant to the environment being studied.

While most MP recovery experiments, toxicological studies, and method validation approaches have relied on surrogate microbeads [10], the majority of MPs present in the environment are either of fragment- or fiber-type morphology [11–15]. Depending on the environmental setting (e.g., indoor vs. outdoor, anthropogenic vs. remote) and analytical target MP size range, the proportion between fibers and fragments in environmental samples may diverge significantly [16, 17]. To replicate particle behavior in experimental setups, MP RMs should accurately reflect those found in the relevant environment. It is therefore important that both accurate fragment- and fiber-type MP RMs are available to researchers.

In addition to being environmentally relevant, MP RMs should also be practical to use. Here, dosing quantity and flexibility are important aspects to consider. For example, dosing by weighing dry MP fragment (or bead) powders can be impractical in the size range below 100 µm, and may contribute to laboratory experiment dosages orders of magnitude above environmental concentrations and/or lead to considerable error between replicates [9, 18]. Consequently, diluting MPs into another medium, whether solid or liquid, appears to be the most suitable approach [4, 19, 20]. For comprehensive validation of methods used in MP analysis and experiments, it is therefore important that MP RMs of both fragment- and fiber-type morphology are made available in environmentally relevant concentrations. RMs of different polymer types should also be available due to variation in density, polarity, and chemical resistance [21]. Furthermore, for ease of identification without the need for chemical identification, the application of pigmented MP RMs may, under certain conditions, be advantageous [22].

To address the need for environmentally relevant MP RMs, EasyMP was developed with the aim of democratizing MP research by being affordable and globally available. EasyMP MP RMs are manufactured upon request to meet user-specific requirements including particle concentration, morphology, polymer type, and color. This study presents experimental data to self-validate EasyMP MP RMs of the current morphological types: fragments measuring between 10 and 100 µm, and fibers from a length of 50 to 1000 µm, suspended in 95 vol.% ethanol solution at varying MP concentrations (n/mL). For fragments and fibers respectively, five samples, each based on four subsamples, were prepared and evaluated for repeatability. Mean relative standard deviation (RSD) was determined for particle count (n/mL) and calculated mass (µg/mL), irrespective of particle size, as well as within incremental size groups on the order of 10 and 50 µm for fragments and fibers, respectively.

## 2 | Methods and Materials

### 2.1 | Fragment and Fiber Manufacturing

MP fragments were manufactured through the process of cryomilling of larger plastic items, using liquid nitrogen, in concordance with best current practices [23]. By vacuum filtration, the

incident fragments were cascade sieved through 500, 100, and 5 µm meshes. Note that although the cut-off value of evaluated fragments is 10 µm, the utilization of a 5 µm mesh allows for the inclusion of fragments measuring 10 µm on their largest axis, with aspect ratios up to 2. Fiber filaments were cut in varying lengths from 1 mm down to 50 µm, and mixed. The resulting particles, both fragments and fibers, were transferred into 100-mL glass vials with integrated pipette screw caps and were resuspended in prefiltered (0.20 µm) 95 vol.% ethanol solution. The polymeric composition of the mother plastics used in the manufacturing of the MP RMs was confirmed through Raman microspectroscopy, following the instrumental settings described by Hagelskjær et al. [24].

### 2.2 | Data Acquisition

A total of ten 100 mL EasyMP samples were prepared; five fragment samples (10–100 µm) and five fiber samples (50–1000 µm) of different polymer types (listed in Section 3.1). For each individual sample, four subsamples between 0.5 and 1.0 mL, depending on the particle concentration in the relevant sample, were pipetted directly from the sample vial using the integrated pipette, onto 5 µm, 25 mm membranes. For each subsample, the total number of particles and their individual morphological features, were registered using critical angle darkfield illumination (CADFLI) microscopy (ColSpec MK2, LightForm inc.). CADFLI enables high visual contrast between MP particles and the filter membrane, while minimizing particle glare that can otherwise lead to visual agglomeration of neighboring particles. Multiple micrographs were stitched together to form coherent mosaics, at a spatial resolution in the order of 2 µm/pixel, from which the data was extracted (Figure 1).

CADFLI microscopy is capable of visually eliminating the filter background, allowing for automated identification and extraction of morphological data of particles measuring down to 10 µm on their largest dimension, including both fragments and fibers. By ensuring that particle area coverage (A%) does not surpass 5%, the protocol enables a homogeneous particle distribution, resulting in sufficient particle spread with minimal overlap [22]. Additionally, each mosaic was manually inspected and modified to correct for potential particle partitioning or agglomeration which would otherwise lead to particle misidentification.

The extracted data included: area (µm²), Feret diameter (µm), minimum Feret diameter (µm), circularity, and aspect ratio. The volume of a fragment ($V_{fragment}$) [µm³] is calculated as a function of area (A) and height (h) [µm], where h is assumed to be 0.3 times the minor axis (Feret$_{minimum}$) [µm] of the fragment in question (Equation 1).

$$V_{fragment} = A \cdot h = A \cdot (0.3 \cdot \text{Feret}_{\text{minimum}}) \quad (1)$$

The volume of a fiber ($V_{fiber}$) is calculated based on a cylindrical model (Equation 2).

$$V_{fiber} = \pi \cdot r^2 \cdot L \quad (2)$$

where r is the mean radius (n = 10), and L is the length of the fiber filament in question. For both fragments and fibers,
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** | Excerpt of micrograph mosaics captured under darkfield illumination, depicting fragments (top) and fibers (bottom). "Micrograph mosaic" refers to composites of the original images, while "Mask overlay" uses distinct colors to highlight the defined individual particles.</figcaption></figure>

mass is calculated by multiplying volume by the specific gravity
[µg/µm³] of the relevant polymer type. MP concentration, particle
size distribution (PSD), and mass distribution are based on the
mean of the four investigated subsamples and are unique to
each individual sample. Each certificate of analysis (COA) and
the relevant safety data sheet (SDS) are available in a dedicated
“Google Drive” folder, only accessible to the user by weblink or
by scanning the QR code on the sample label (Figure 2).

3 | Results and Discussion

3.1 | Repeatability

To determine the repeatability of EasyMP RMs, RSD was evalu-
ated on the basis of five individual fragment and fiber samples,
respectively. For each individual sample, mean MP concentration
and standard deviation (SD) were calculated from four subsam-
ples. Samples were labeled using the prefix “fragment” or “fiber”
followed by “sample #1,” “sample #2,” and so forth. To provide
an example of the dataset that accompanies an EasyMP sample,
the partial COAs of “fragment sample #1” and “fiber sample #1”
are presented in Table 1. Irrespective of particle size, the samples
demonstrated 7% and 9% RSD (n = 5) in MP particle count and 10%
and 8% RSD (n = 5) in calculated mass for “fragment sample #1”
and “fiber sample #1,” respectively. The table includes mean MP
particle count, mass and PSD, as well as SD within the respective
size groups. The same data is illustrated in Figure 3. The first
page of the COA lists, from left to right, the number and mass of

MPs within each of the four subsamples (named “SS_1,” “SS_2,”
etc.), the mean values of these, and the PSD. Each row represents
a size group, with incremental growth in particle size, from top
to bottom. Accurate PSD data helps researchers to better assess
size-specific MP behavior. All data are available in the Supporting
Information S1.

Between five individual 100 mL EasyMP fragment-samples (10–
100 µm) with concentrations ranging from hundreds to thousands
of MPs (n/mL), including polypropylene (PP), polyurethane (PU),
and polyamide 6,6 (PA6,6), the mean RSD of count and mass of
fragments irrespective of size, was estimated at 9% and 13% (n =
5), respectively. Within size groups on the order of 10 µm, from
10 to 100 µm, the mean RSD of particle counts ranged from 11%
up to 19% (n = 5) (Table 2). Mean RSD increased with decreasing
particle count.

Between five individual 100 mL EasyMP fiber-samples (50–
1000 µm) with concentrations in the range of hundreds of
fibers (n/mL), including polyethylene terephthalate (PET), PA6,6,
polyacrylonitrile (PAN), and cotton (cellulose), the mean RSD of
count and mass of fibers irrespective of length, was estimated at
9% and 10% (n = 5), respectively. Within size groups on the order
of 50 µm, from 50 to 500 µm, the mean RSD of fiber particle counts
ranged from 17% to 51% (n = 5). Similarly for fragments, the mean
RSD increased as particle count decreased.

For both fragment and fiber samples, mean RSD increased with
decreasing PSD (Figure 4). Mean RSD irrespective of particle
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | Example of a 100 mL EasyMP sample with explanation of the label information. The included QR code provides access to a dedicated cloud folder containing the certificate of analysis (COA) and the safety data sheet (SDS).</figcaption></figure>

size was estimated at 10% and 9% (n = 5) for fragments and fibers, respectively; well below the 20% threshold for acceptable error suggested by the EUROqCHARM project [4]. In addition, mean RSD of fragments within size groups on the order of 10 µm, from 10 to 100 µm, was consistently below 20%. For fibers, mean RSD was only above 30% within size groups that constituted less than 10% of the PSD. However, there are currently no established guidelines for RSD within size groups, as this approach has not been previously implemented for MP RMs [22]. Therefore, this data sets a benchmark for RSD within size groups for fragments from 10 to 100 µm and fibers from 50 to 1000 µm.

## 3.2 | Quality Control

To prevent external contamination during sample preparation, rigorous quality control measures were adhered to. Sample preparation took place in a laminar flow cabinet situated in a dedicated MP laboratory with restricted access and constant air filtration. Surfaces were thoroughly cleaned with a prefiltered (0.20 µm) 50 vol.% ethanol solution. All utilized glassware was kiln sterilized at 500°C for 1 h, after which they were flushed with prefiltered (0.20 µm) 95 vol.% ethanol prior to use.

Procedural blanks were prepared by sonicating the filter membranes, which served as substrates for RMs during micrograph acquisition, in prefiltered (0.20 µm) 95 vol.% ethanol for 10 s prior to sample-spiking using the same 100 mL glass bottles, as per the protocol. Microscopic examination of 1 mL of the prefiltered (0.20 µm) 95 vol.% ethanol solution revealed none or negligible numbers of particles on three individual filter membranes. Additionally, all micrograph mosaics were manually inspected and corrected for artifacts to prevent visual partitioning or agglomeration of particles, which could lead to under- or overestimation of the particle count.

## 4 | Perspectives

All EasyMP MP RMs are accompanied by a unique COA that contains both sorted and raw data, as well as the micrographs from which the data were extracted (see *Supporting Information S1*). EasyMP MP RMs are manufactured upon order according to user-specific requirements. Customization accommodates experiments that simulate specific environmental conditions, thus requiring MPs in different concentrations and of different morphological types, sizes, and polymer compositions [25]. Using colored fragments that retain their hue at the microscopic scale offers a cost-effective and efficient approach for spike-particle
<table><caption>**TABLE 1** | Depiction of EasyMP COA datasets for the two MP RM samples "fragment sample #1" (top) and "fiber sample #1" (bottom), demonstrating mean particle count, mass, and PSD within size groups in the order of 10 and 50 µm, respectively.</caption><caption>EasyMP | Certificate of analysis (COA) | Fragment sample #1</caption><thead><tr><th rowspan="2">Diameter<br>(µm)</th><th colspan="3">SS_1</th><th colspan="3">SS_2</th><th colspan="3">SS_3</th><th colspan="3">SS_4</th><th colspan="3">Number of particles<br>(n/mL)</th><th colspan="3">Mass of particles<br>(µg/mL)</th><th colspan="3">Particle size distribution<br>(PSD)</th></tr><tr><th>n/mL</th><th>µg/mL</th><th>n/mL</th><th>µg/mL</th><th>n/mL</th><th>µg/mL</th><th>n/mL</th><th>µg/mL</th><th>n/mL</th><th>µg/mL</th><th>Mean</th><th>SD<br>(n)</th><th>RSD<br>(%)</th><th>Mean</th><th>SD<br>(µg)</th><th>RSD<br>(%)</th><th>Mean<br>diameter<br>(µm)</th><th>PSD<br>(%)</th><th>Cum.<br>PSD<br>(%)</th></tr></thead><tbody><tr><td>10-20</td><td>1524</td><td>0.83</td><td>1340</td><td>0.75</td><td>1984</td><td>1.03</td><td>1508</td><td>0.87</td><td>1589</td><td>1.79</td><td>904</td><td>114</td><td>13</td><td>0.87</td><td>0.10</td><td>12</td><td>10</td><td>N/A</td><td>1</td></tr><tr><td>20-30</td><td>892</td><td>2.09</td><td>978</td><td>2.44</td><td>1022</td><td>2.23</td><td>724</td><td>1.79</td><td>574</td><td>3.35</td><td>574</td><td>39</td><td>7</td><td>2.14</td><td>0.24</td><td>11</td><td>15</td><td>37.9</td><td>37.9</td></tr><tr><td>30-40</td><td>632</td><td>3.97</td><td>564</td><td>3.38</td><td>578</td><td>3.31</td><td>522</td><td>3.35</td><td>342</td><td>4.42</td><td>321</td><td>12</td><td>4</td><td>3.50</td><td>0.27</td><td>8</td><td>25</td><td>21.6</td><td>59.5</td></tr><tr><td>40-50</td><td>318</td><td>4.18</td><td>314</td><td>4.01</td><td>310</td><td>3.57</td><td>342</td><td>4.42</td><td>320</td><td>4.39</td><td>202</td><td>13</td><td>6</td><td>4.04</td><td>0.31</td><td>8</td><td>35</td><td>13.7</td><td>73.1</td></tr><tr><td>50-60</td><td>220</td><td>4.96</td><td>208</td><td>4.76</td><td>186</td><td>3.88</td><td>194</td><td>4.39</td><td>202</td><td>4.39</td><td>202</td><td>13</td><td>6</td><td>4.50</td><td>0.41</td><td>9</td><td>45</td><td>7.7</td><td>80.8</td></tr><tr><td>60-70</td><td>188</td><td>6.56</td><td>140</td><td>4.70</td><td>120</td><td>3.59</td><td>156</td><td>5.38</td><td>151</td><td>5.38</td><td>151</td><td>25</td><td>16</td><td>5.06</td><td>1.08</td><td>21</td><td>55</td><td>4.8</td><td>85.6</td></tr><tr><td>70-80</td><td>132</td><td>6.60</td><td>102</td><td>5.06</td><td>100</td><td>5.44</td><td>100</td><td>5.14</td><td>109</td><td>5.14</td><td>109</td><td>14</td><td>13</td><td>5.56</td><td>0.62</td><td>11</td><td>65</td><td>3.6</td><td>89.2</td></tr><tr><td>80-90</td><td>68</td><td>5.54</td><td>82</td><td>6.08</td><td>78</td><td>5.46</td><td>92</td><td>7.86</td><td>80</td><td>7.86</td><td>9</td><td>11</td><td>6.23</td><td>0.97</td><td>16</td><td>75</td><td>2.6</td><td>91.8</td><td></td></tr><tr><td>90-100</td><td>60</td><td>5.83</td><td>54</td><td>6.38</td><td>52</td><td>5.00</td><td>68</td><td>8.36</td><td>59</td><td>8.36</td><td>6</td><td>11</td><td>6.39</td><td>1.24</td><td>19</td><td>85</td><td>1.9</td><td>93.7</td><td></td></tr><tr><td>100-110</td><td>44</td><td>5.96</td><td>52</td><td>7.70</td><td>36</td><td>6.19</td><td>34</td><td>5.22</td><td>42</td><td>5.22</td><td>42</td><td>7</td><td>17</td><td>6.27</td><td>0.90</td><td>14</td><td>95</td><td>1.4</td><td>95.1</td></tr><tr><td>110-120</td><td>46</td><td>8.70</td><td>38</td><td>6.98</td><td>28</td><td>4.94</td><td>30</td><td>7.12</td><td>36</td><td>7.12</td><td>36</td><td>7</td><td>20</td><td>6.94</td><td>1.34</td><td>19</td><td>105</td><td>1.0</td><td>96.1</td></tr><tr><td>120-130</td><td>32</td><td>7.18</td><td>38</td><td>9.54</td><td>28</td><td>6.05</td><td>26</td><td>6.57</td><td>31</td><td>6.57</td><td>25</td><td>5</td><td>21</td><td>7.34</td><td>1.33</td><td>18</td><td>115</td><td>0.8</td><td>96.9</td></tr><tr><td>130-140</td><td>32</td><td>9.31</td><td>26</td><td>6.08</td><td>18</td><td>5.34</td><td>22</td><td>7.36</td><td>25</td><td>7.36</td><td>22</td><td>6</td><td>39</td><td>5.07</td><td>2.89</td><td>57</td><td>135</td><td>0.6</td><td>98.3</td></tr><tr><td>140-150</td><td>20</td><td>8.35</td><td>16</td><td>3.40</td><td>6</td><td>1.22</td><td>22</td><td>7.30</td><td>16</td><td>7.30</td><td>16</td><td>6</td><td>39</td><td>5.07</td><td>2.89</td><td>57</td><td>135</td><td>0.6</td><td>98.7</td></tr><tr><td>150-160</td><td>22</td><td>10.82</td><td>6</td><td>3.05</td><td>16</td><td>8.09</td><td>12</td><td>6.34</td><td>14</td><td>6.34</td><td>14</td><td>6</td><td>42</td><td>7.08</td><td>2.82</td><td>40</td><td>145</td><td>0.4</td><td>98.7</td></tr><tr><td>160-170</td><td>10</td><td>4.62</td><td>14</td><td>5.12</td><td>10</td><td>4.29</td><td>12</td><td>5.86</td><td>12</td><td>5.86</td><td>12</td><td>2</td><td>14</td><td>4.97</td><td>0.59</td><td>12</td><td>155</td><td>0.3</td><td>99.0</td></tr><tr><td>170-180</td><td>4</td><td>2.92</td><td>12</td><td>4.77</td><td>10</td><td>7.02</td><td>8</td><td>2.76</td><td>9</td><td>2.76</td><td>9</td><td>3</td><td>35</td><td>4.36</td><td>1.72</td><td>39</td><td>165</td><td>0.3</td><td>99.3</td></tr><tr><td>180-190</td><td>8</td><td>4.85</td><td>4</td><td>2.96</td><td>2</td><td>1.89</td><td>6</td><td>3.43</td><td>5</td><td>3.43</td><td>5</td><td>2</td><td>45</td><td>3.28</td><td>1.06</td><td>32</td><td>175</td><td>0.2</td><td>99.5</td></tr><tr><td>190-200</td><td>4</td><td>1.28</td><td>4</td><td>2.75</td><td>4</td><td>1.81</td><td>4</td><td>3.42</td><td>4</td><td>3.42</td><td>4</td><td>0</td><td>0</td><td>2.32</td><td>0.83</td><td>36</td><td>185</td><td>0.1</td><td>99.6</td></tr><tr><td>&gt; 200</td><td>16</td><td>10.85</td><td>8</td><td>4.07</td><td>14</td><td>13.58</td><td>16</td><td>18.70</td><td>14</td><td>18.70</td><td>14</td><td>3</td><td>24</td><td>11.80</td><td>5.28</td><td>45</td><td>195</td><td>0.1</td><td>99.7</td></tr><tr><td>Total</td><td>4272</td><td>115.42</td><td>4000</td><td>93.98</td><td>4602</td><td>93.92</td><td>3898</td><td>115.64</td><td>4193</td><td>7</td><td>104.74</td><td>10.8</td><td>10</td><td></td><td></td><td></td><td>&gt; 200</td><td>0.3</td><td>100.0</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 | (Continued)</caption><caption>EasyMP | Certificate of analysis (COA) | Fiber sample #1</caption><thead><tr><th rowspan="2">Length<br>(µm)</th><th colspan="2">SS_1</th><th colspan="2">SS_2</th><th colspan="2">SS_3</th><th colspan="2">SS_4</th><th colspan="2">Number of particles<br>(n/mL)</th><th colspan="2">Mass of particles<br>(µg/mL)</th><th colspan="2">Particle size distribution<br>(PSD)</th></tr><tr><th>n/mL</th><th>µg/mL</th><th>n/mL</th><th>µg/mL</th><th>n/mL</th><th>µg/mL</th><th>n/mL</th><th>µg/mL</th><th>Mean</th><th>SD<br>(n)</th><th>RSD<br>(%)</th><th>Mean</th><th>SD<br>(µg)</th><th>RSD<br>(%)</th><th>Mean<br>length<br>(µm)</th><th>PSD<br>(%)</th><th>Cum.<br>PSD<br>(%)</th></tr></thead><tbody><tr><td>50–100</td><td>42</td><td>0.75</td><td>20</td><td>0.39</td><td>40</td><td>0.74</td><td>26</td><td>0.51</td><td>32</td><td>9</td><td>29</td><td>0.60</td><td>0.15</td><td>26</td><td>75</td><td>8.3</td><td>8.3</td></tr><tr><td>100–150</td><td>66</td><td>1.91</td><td>54</td><td>1.57</td><td>62</td><td>1.76</td><td>48</td><td>1.37</td><td>58</td><td>7</td><td>12</td><td>1.66</td><td>0.20</td><td>12</td><td>125</td><td>15.0</td><td>23.3</td></tr><tr><td>150–200</td><td>72</td><td>2.94</td><td>72</td><td>2.87</td><td>54</td><td>2.16</td><td>60</td><td>2.36</td><td>65</td><td>8</td><td>12</td><td>2.58</td><td>0.33</td><td>13</td><td>175</td><td>16.8</td><td>40.2</td></tr><tr><td>200–250</td><td>78</td><td>4.00</td><td>76</td><td>3.80</td><td>78</td><td>3.98</td><td>58</td><td>2.98</td><td>73</td><td>8</td><td>12</td><td>3.69</td><td>0.42</td><td>11</td><td>225</td><td>18.9</td><td>59.1</td></tr><tr><td>250–300</td><td>60</td><td>3.72</td><td>58</td><td>3.62</td><td>52</td><td>3.25</td><td>50</td><td>3.15</td><td>55</td><td>4</td><td>7</td><td>3.44</td><td>0.24</td><td>7</td><td>275</td><td>14.3</td><td>73.4</td></tr><tr><td>300–350</td><td>46</td><td>3.37</td><td>28</td><td>2.07</td><td>28</td><td>2.04</td><td>18</td><td>1.33</td><td>30</td><td>10</td><td>34</td><td>2.20</td><td>0.74</td><td>33</td><td>325</td><td>7.8</td><td>81.2</td></tr><tr><td>350–400</td><td>26</td><td>2.23</td><td>18</td><td>1.52</td><td>26</td><td>2.22</td><td>14</td><td>1.19</td><td>21</td><td>5</td><td>25</td><td>1.79</td><td>0.45</td><td>25</td><td>375</td><td>5.5</td><td>86.7</td></tr><tr><td>400–450</td><td>10</td><td>0.97</td><td>18</td><td>1.73</td><td>12</td><td>1.17</td><td>20</td><td>1.91</td><td>15</td><td>4</td><td>27</td><td>1.45</td><td>0.38</td><td>27</td><td>425</td><td>3.9</td><td>90.6</td></tr><tr><td>450–500</td><td>2</td><td>0.21</td><td>16</td><td>1.75</td><td>16</td><td>1.72</td><td>18</td><td>1.95</td><td>13</td><td>6</td><td>49</td><td>1.41</td><td>0.70</td><td>50</td><td>475</td><td>3.4</td><td>94.0</td></tr><tr><td>500–550</td><td>16</td><td>1.90</td><td>8</td><td>0.97</td><td>2</td><td>0.24</td><td>12</td><td>1.48</td><td>10</td><td>5</td><td>54</td><td>1.15</td><td>0.62</td><td>54</td><td>525</td><td>2.5</td><td>96.5</td></tr><tr><td>550–600</td><td>14</td><td>1.84</td><td>2</td><td>0.26</td><td>6</td><td>0.78</td><td>12</td><td>1.58</td><td>9</td><td>5</td><td>56</td><td>1.11</td><td>0.63</td><td>56</td><td>575</td><td>2.2</td><td>98.7</td></tr><tr><td>600–650</td><td>0</td><td>0.00</td><td>4</td><td>0.57</td><td>0</td><td>0.00</td><td>4</td><td>0.56</td><td>2</td><td>2</td><td>100</td><td>0.28</td><td>0.28</td><td>100</td><td>625</td><td>0.5</td><td>99.2</td></tr><tr><td>650–700</td><td>2</td><td>0.31</td><td>2</td><td>0.32</td><td>2</td><td>0.31</td><td>0</td><td>0.00</td><td>2</td><td>1</td><td>58</td><td>0.23</td><td>0.14</td><td>58</td><td>675</td><td>0.4</td><td>99.6</td></tr><tr><td>700–750</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>2</td><td>0.32</td><td>1</td><td>1</td><td>173</td><td>0.08</td><td>0.14</td><td>173</td><td>725</td><td>0.1</td><td>99.7</td></tr><tr><td>750–800</td><td>0</td><td>0.00</td><td>2</td><td>0.35</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>1</td><td>1</td><td>173</td><td>0.09</td><td>0.15</td><td>173</td><td>775</td><td>0.1</td><td>99.9</td></tr><tr><td>800–850</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0</td><td>N/A</td><td>0.00</td><td>0.00</td><td>N/A</td><td>825</td><td>0.0</td><td>99.9</td></tr><tr><td>850–900</td><td>2</td><td>0.40</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>1</td><td>1</td><td>173</td><td>0.10</td><td>0.17</td><td>173</td><td>875</td><td>0.1</td><td>100.0</td></tr></tbody><tfoot><tr><th>Total</th><td>436</td><td>24.57</td><td>378</td><td>21.81</td><td>378</td><td>20.38</td><td>342</td><td>20.69</td><td>384</td><td>34</td><td>9</td><td>21.86</td><td>1.65</td><td>8</td><td></td><td></td><td></td></tr></tfoot></table>

Note: The MP count in the two samples exhibited RSD values of 7% and 9%, respectively. N/A = not applicable.
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Graphical illustration of particle concentration in samples "fragment sample #1" (top) and "fiber sample #1" (bottom). Histogram bars illustrate the number of particles (left y-axis) within each size group with associated SD (n = 4), while the line graph represents the mass of particles within that group (SD not displayed) (right y-axis). The diagrams on the right-hand side illustrate the cumulated PSD of the relevant sample.</figcaption></figure>

identification, eliminating the need for vibrational microspec-
troscopy or other chemical identification methods (Figure 5).

In the current study, MPs were suspended in ethanol solution; however, for biological studies, ultraviolet (UV)-C sterilized Milli-Q water w. 0.1 vol.% (1 mg/mL) Tween 20 can also be used as a suspension medium [26]. Tween 20 is non-cytotoxic in water solutions below 3.0 vol.% (30 mg/mL) [27]. The ten MP RM samples evaluated in this study were donated to academic and industrial partners and were initially manufactured to meet their required specifications, including morphology, polymer type and concentration.

The current study presents internal, self-validated results only. Therefore, to further enhance the credibility of EasyMP MP RMs, the next step will involve validation through an interlaboratory comparison (ILC) study. Additionally, to enhance the quality of data processing while reducing time consumption, Microplastic Solution is currently developing a deep learning algorithm capable of autonomously identifying and indexing individual particles, even in cases of overlap and agglomeration.

**5** | **Conclusions**

EasyMP MP RMs encompass both fragments from 10 µm in
diameter and fibers from 50 µm in length, in known quantities

suspended in 95 vol.% ethanol solution. In the current study,
five fragment-samples (10–100 µm) and five fiber-samples (50–
1000 µm) were evaluated for particle concentration repeatability
(all suspended in 95 vol.% ethanol solution). For each individual
sample, four subsamples were prepared to determine the RSD
of the MP concentration in both particle count and calculated
mass. The mean RSD for particle count irrespective of particle
size, was 10% and 9% (n = 5) for fragments and fibers, respectively;
well below the 20% threshold for acceptable error for MP RMs
suggested by the EUROqCHARM project.

In addition, RSD within incremental size groups was evaluated
to determine the consistency of the PSD between four individual
subsamples. For fragments in size groups between 10 and 100 µm
on the order of 10 µm, mean RSD remained consistently below
20% (n = 5), increasing with decreasing particle count within the
respective size groups. For fibers in size groups from 50 to 500 µm
on the order of 50 µm, mean RSD was mostly above 20%. However,
mean RSD was only above 30% within size groups that constituted
less than 10% of the PSD. Because no established guidelines or
comparable studies currently exist for RSD within size groups,
this data sets a benchmark for fragments from 10 to 100 µm and
fibers from 50 to 500 µm.

EasyMP MP RM samples are uniquely prepared to meet user-
specific requirements. The user defines the particle concentra-
tion, particle morphology, polymer type, and color. This approach
<table><caption>TABLE 2 | From left to right, the columns list the sample number, MP polymer type, and RSD (n/mL and µg/mL).</caption><thead><tr><th rowspan="2">Sample</th><th rowspan="2">Polymer type</th><th rowspan="2">Concentration (n/mL)</th><th rowspan="2">Total RSD% (count)</th><th rowspan="2">Total RSD% (mass)</th><th colspan="8">EasyMP | Fragments</th></tr><tr><th>10-20</th><th>20-30</th><th>30-40</th><th>40-50</th><th>50-60</th><th>60-70</th><th>70-80</th><th>80-90</th><th>90-100</th></tr></thead><tbody><tr><td>#1</td><td>PA6,6</td><td>4139</td><td>7</td><td>10</td><td>15</td><td>13</td><td>7</td><td>4</td><td>6</td><td>16</td><td>13</td><td>11</td><td>11</td></tr><tr><td>#2</td><td>PP</td><td>12 793</td><td>9</td><td>4</td><td>9</td><td>11</td><td>10</td><td>15</td><td>12</td><td>7</td><td>10</td><td>13</td><td>12</td></tr><tr><td>#3</td><td>PU</td><td>340</td><td>9</td><td>8</td><td>12</td><td>17</td><td>7</td><td>20</td><td>23</td><td>13</td><td>0</td><td>26</td><td>38</td></tr><tr><td>#4</td><td>PA6,6</td><td>2893</td><td>15</td><td>13</td><td>13</td><td>14</td><td>26</td><td>22</td><td>22</td><td>21</td><td>25</td><td>18</td><td>18</td></tr><tr><td>#5</td><td>PP</td><td>4469</td><td>9</td><td>31</td><td>7</td><td>8</td><td>12</td><td>16</td><td>20</td><td>16</td><td>23</td><td>22</td><td>15</td></tr><tr><td>Mean</td><td>N/A</td><td>N/A</td><td>10</td><td>13</td><td>11</td><td>13</td><td>12</td><td>15</td><td>17</td><td>15</td><td>14</td><td>18</td><td>19</td></tr></tbody></table><table><thead><tr><th rowspan="2">Sample</th><th rowspan="2">Polymer type</th><th rowspan="2">Concentration (n/mL)</th><th rowspan="2">Total RSD% (count)</th><th rowspan="2">Total RSD% (mass)</th><th colspan="8">EasyMP | Fibers</th></tr><tr><th>50-100</th><th>100-150</th><th>150-200</th><th>200-250</th><th>250-300</th><th>300-350</th><th>350-400</th><th>400-450</th><th>450-500</th></tr></thead><tbody><tr><td>#1</td><td>Cellulose</td><td>384</td><td>9</td><td>8</td><td>29</td><td>12</td><td>12</td><td>12</td><td>7</td><td>34</td><td>25</td><td>27</td><td>49</td></tr><tr><td>#2</td><td>PET</td><td>490</td><td>8</td><td>9</td><td>17</td><td>12</td><td>35</td><td>18</td><td>4</td><td>18</td><td>31</td><td>12</td><td>28</td></tr><tr><td>#3</td><td>PAN</td><td>200</td><td>7</td><td>7</td><td>41</td><td>38</td><td>19</td><td>36</td><td>24</td><td>39</td><td>20</td><td>47</td><td>26</td></tr><tr><td>#4</td><td>PET</td><td>142</td><td>9</td><td>12</td><td>104</td><td>12</td><td>14</td><td>12</td><td>35</td><td>31</td><td>41</td><td>36</td><td>34</td></tr><tr><td>#5</td><td>PA6,6</td><td>161</td><td>12</td><td>13</td><td>65</td><td>55</td><td>27</td><td>20</td><td>15</td><td>59</td><td>29</td><td>54</td><td>66</td></tr><tr><td>Mean</td><td>N/A</td><td>N/A</td><td>9</td><td>10</td><td>51</td><td>26</td><td>21</td><td>20</td><td>17</td><td>36</td><td>29</td><td>35</td><td>41</td></tr></tbody></table>

Note: The mean RSD of particle count, regardless of size, was estimated at 10% and 9% (n = 5) for fragments and fibers, respectively. For fragments within size groups on the order of 10 µm, mean RSD remained below 20% (n = 5), while mean RSD of fibers within size groups on the order of 50 µm, was between 17% and 51% (n = 5).
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

□ Mean relative standard deviation (RSD)

■ Mean RSD irrespective of size

○ Mean particle size distribution (PSD)

**FIGURE 4** | Mean RSD of particle count within size groups for both fragments (top) and fibers (bottom), based on RSD values of five individual samples. The green line represents mean RSD irrespective of particle size while the histogram bars represents mean RSD within specific size groups. The line graph represents mean PSD within specific size groups. For both fragments and fibers, an increase in RSD with decreasing PSD was observed.

<figure><img src="image_7.png" /></figure>

**FIGURE 5** | Photomicrograph captured under darkfield illumination showing red polyethylene (PE) fragments in the 10–100 µm size range mixed with generic indoor dust particles (left). A mask of the same image is displayed on the right, where red particles have been visually isolated using ImageJ. In certain applications, colored microplastics can eliminate the need for chemical identification during recovery experiments.
enables a broader selection of environmentally relevant MP
RMs tailored to specific experimental setups, thereby helping
to advance our understanding of environmental MP pollution.
EasyMP MP RMs are commercially available on a global scale on
www.microplastic.store, presenting the world's first customizable
microplastic RMs.

Acknowledgments

The authors thank Jeremy M. Lerner and LightForm Inc., for their
ongoing technical support.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

All data will be made available upon request.

References

1. R. C. Thompson, Y. Olsen, R. P. Mitchell, et al., “Lost at Sea: Where Is all the Plastic?,” *Science* 304 (2004): 838–838, https://doi.org/10.1126/science.1094559.

2. R. Thompson, W. Courtene-Jones, J. Boucher, S. Pahl, K. Raubenheimer, and A. A. Koelmans, “Twenty Years of Microplastics Pollution Research—What Have We Learned?,” *Science* 386, no. 6720 (2024): ead12746, https://doi.org/10.1126/science.adl2746.

3. B. R. Kiran, H. Kopperi, and S. Venkata Mohan, “Micro/Nano-Plastics Occurrence, Identification, Risk Analysis and Mitigation: Challenges and Perspectives,” *Reviews in Environmental Science and Bio/Technology* 21 (2022): 169–203, https://doi.org/10.1007/s11157-021-09609-6.

4. E. Martínez-Francés, B. van Bavel, R. Hurley, et al., “Innovative Reference Materials for Method Validation in Microplastic Analysis Including Interlaboratory Comparison Exercises,” *Analytical and Bioanalytical Chemistry* 415 (2023): 2907–2919, https://doi.org/10.1007/s00216-023-04636-4.

5. J. Seghers, E. A. Stefaniak, R. La Spina, et al., “Preparation of a Reference Material for Microplastics in Water—Evaluation of Homogeneity,” *Analytical and Bioanalytical Chemistry* 414 (2022): 385–397, https://doi.org/10.1007/s00216-021-03198-7.

6. E. von der Esch, M. Lanzinger, A. J. Kohles, et al., “Simple Generation of Suspensible Secondary Microplastic Reference Particles via Ultrasound Treatment,” *Frontiers in Chemistry* 8 (2020): 169, https://doi.org/10.3389/fchem.2020.00169.

7. D. Mitrano, M. L. Diamond, J. H. Kim, K. C. Tam, M. Yang, and Z. Wang, “Balancing New Approaches and Harmonized Techniques in Nano- and Microplastics Research,” *ACS Sustainable Chemistry & Engineering* 11 (2023): 8702–8705, https://doi.org/10.1021/acssuschemeng.3c03221.

8. J. M. Molin, W. E. Groth-Andersen, P. J. Hansen, M. Kühl, and K. E. Brodersen, “Microplastic Pollution Associated With Reduced Respiration in Seagrass (*Zostera marina* L.) and Associated Epiphytes,” *Frontiers in Marine Science* 10 (2023): 3, https://doi.org/10.3389/fmars.2023.1216299.

9. C. L. Mills, J. Savanagouder, M. de Almeida Monteiro Melo Ferraz, and M. J. Noonan, “The Need for Environmentally Realistic Studies on the Health Effects of Terrestrial Microplastics,” *Microplastics and Nanoplastics* 3 (2023): 11, https://doi.org/10.1186/s43591-023-00059-1.

10. C. Way, M. D. Hudson, I. D. Williams, and G. J. Langley, “Evidence of Underestimation in Microplastic Research: A Meta-Analysis of Recovery Rate Studies,” *Science of The Total Environment* 805 (2022): 150227, https://doi.org/10.1016/j.scitotenv.2021.150227.

11. D. Allen, S. Allen, G. Le Roux, A. Simonneau, D. Galop, and V. R. Phoenix, “Temporal Archive of Atmospheric Microplastic Deposition Presented in Ombrotrophic Peat,” *Environmental Science & Technology Letters* 8, no. 11 (2021): 954–960, https://doi.org/10.1021/acs.estlett.1c00697.

12. H. Boettcher, T. Kukulka, and J. H. Cohen, “Methods for Controlled Preparation and Dosing of Microplastic Fragments in Bioassays,” *Scientific Reports* 13 (2023): 5195, https://doi.org/10.1038/s41598-023-32250-y.

13. R. Dris, J. Gasperi, V. Rocher, M. Saad, N. Renault, and B. Tassin, “Microplastic Contamination in an Urban Area: A Case Study in Greater Paris,” *Environmental Chemistry* (2015): 592, https://doi.org/10.1071/EN14167.

14. A.dJ. Negrete Velasco, L. Rard, W. Blois, et al., “Microplastic and Fibre Contamination in a Remote Mountain Lake in Switzerland,” *Water* 12, no. 9 (2020): 2410, https://doi.org/10.3390/w12092410.

15. H. Stefánsson, M. Peternell, M. Konrad-Schmolke, H. Hannesdóttir, E. J. Ásbjörnsson, and E. Sturkell, “Microplastics in Glaciers: First Results From the Vatnajökull Ice Cap,” *Sustainability* 13 (2021): 4183, https://doi.org/10.3390/su13084183.

16. O. Hagelskjær, G. Le Roux, R. Liu, B. Dubreuil, P. Behra, and J. Sonke, “The Recovery of Aerosol-Sized Microplastics in Highly Refractory Vege- tal Matrices for Identification by Automated Raman Microspectroscopy,” *Chemosphere* 328 (2023): 138487, https://doi.org/10.1016/j.chemosphere.2023.138487.

17. A. Vianello, R. L. Jensen, L. Liu, and J. Vollertsen, “Simulating human Exposure to Indoor Airborne Microplastics Using a Breathing Thermal Manikin,” *Scientific Reports* 9 (2019): 8670, https://doi.org/10.1038/s41598-019-45054-w.

18. S. Fuller and A. Gautam, “A Procedure for Measuring Microplastics Using Pressurized Fluid Extraction,” *Environmental Science & Technology* 50 (2016): 5774–5780, https://doi.org/10.1021/acs.est.6b00816.

19. Z. Gerdes, M. Hermann, M. Ogonowski, and E. Gorokhova, “A Novel Method for Assessing Microplastic Effect in Suspension Through Mixing Test and Reference Materials,” *Scientific Reports* 9 (2019): 10695, https://doi.org/10.1038/s41598-019-47160-1.

20. F. Weber and J. Kerpen, “Underestimating Microplastics? Quantification of the Recovery Rate of Microplastic Particles Including Sampling, Sample Preparation, Subsampling, and Detection Using µ-Ramanspectroscopy,” *Analytical and Bioanalytical Chemistry* 415 (2023): 2963–2973, https://doi.org/10.1007/s00216-022-04447-z.

21. S. Primpke, S. H. Christiansen, W. Cowger, et al., “Critical Assessment of Analytical Methods for the Harmonized and Cost-Efficient Analysis of Microplastics,” *Applied Spectroscopy* 74 (2020): 1012–1047, https://doi.org/10.1177/0003702820921465.

22. O. Hagelskjær, A. Crézé, G. Le Roux, and J. E. Sonke, “Investigating the Correlation Between Morphological Features of Microplastics (5–500 µm) and Their Analytical Recovery,” *Microplastics and Nanoplastics* 3 (2023): 22, https://doi.org/10.1186/s43591-023-00071-5.

23. C. J. McColley, J. A. Nason, B. J. Harper, and S. L. Harper, “An Assessment of Methods Used for the Generation and Characterization of Cryomilled Polystyrene Micro- and Nanoplastic Particles,” *Microplastics and Nanoplastics* 3 (2023): 20, https://doi.org/10.1186/s43591-023-00069-z.

24. O. Hagelskjær, F. Hagelskjær, H. Margenat, N. Yakovenko, J. E. Sonke, and G Le Roux. (2024), “Majority of Potable Water Microplastics are Smaller Than the 20 µM EU Methodology Limit for Consumable Water Quality,” *Earth ArXiv* June 8, 2024, https://doi.org/10.31223/X5098J.

25. A. Dehaut, C. Himber, M. Colin, and G. Duflos, “Think Positive: Proposal of a Simple Method to Create Reference Materials in the Frame of Microplastics Research,” *MethodsX* 10 (2023): 102030, https://doi.org/10.1016/j.mex.2023.102030.

26. T. L. de Oliveira, F. J. C. Teran, R. M. F. Cuba, and F. F. Freitas, “Effect of Tween 20 and Linear Alkylbenzene Sulfonate on Microplastic Coagulation,” *Desalination and Water Treatment* 290 (2023): 12–25, https://doi.org/10.5004/dwt.2023.29444.
27. F. van Ruissen, M. Le, J. M. Carroll, P. G. van der Valk, and J. Schalkwijk, "Differential Effects of Detergents on Keratinocyte Gene Expression," *Journal of Investigative Dermatology* 110, no. 4 (1998): P358-363, https://doi.org/10.1046/j.1523-1747.1998.00155.x.

**Supporting Information**

Additional supporting information can be found online in the Supporting Information section.