A detection method of small-sized microplastics based on micro-Raman mapping

50µm±5nm, uniform particle size, good dispersion performance. PP, PE, PET, PA are powdery solid forms with irregular sizes (40~1000 mesh). Use quartz sand (FCP, 100~200 mesh) , Shanghai Yuanye Biotechnology Co., Ltd.) to simulate impurities in actual samples.

Six commonly used filter membranes (mesh) were selected for comparison, and the information is shown in Table 1.

1.2 Experimental methods

The surface morphology of the filter membrane (mesh) was identified by high-resolution field emission scanning electron microscopy (Zeiss Gemini SEM300, Germany). Before scanning, an ion sputtering instrument (COWAQ S150L-1, UK) was used for gold spraying treatment. Electron beam evaporation was used The polycarbonate film was coated with a device (Canon Anelva L-400EK, Japan) under the conditions of vacuum degree 8×10-4 Pa and evaporation rate 1Å/s. The coating material was aluminum and the coating thickness was 100nm. Microlaser Raman was used Spectrometer (HORIBA LabRAM HR Evolution, France) performed single-point and area scan measurements of Raman spectra of microplastics and other materials. Microplastics with a particle size of 1µm were filtered using an alumina membrane with a pore size of 0.2µm. In order to increase the filtration speed, microplastics of other sizes were filtered. Plastics are filtered using an aluminized polycarbonate membrane with a pore size of 1.2µm. The basic parameters are: reflection mode, excitation light source 532nm, grating 600gr/mm, Synapse CCD detector, laser power 50mW. When quantitatively analyzing microplastics, use ultrasonic Pure water is used to prepare microplastic standard solutions of different concentrations. According to the particle size of microplastics, three different concentration gradients (from small to large are A, B, and C) are designed. The specific concentrations are shown in Table 2. Take each 100mL of each standard solution

Filter with membrane. The filtration device uses stainless steel suction filtration components. The actual filter area is circular in shape, with a diameter of 2.5mm and an area of 4.9mm2.

Table 2   Standard solution concentrations of microplastics of different sizes

1.3 Calculation method for quantitative analysis of microplastics

The five-point sampling method was used to quantitatively calculate microplastics. Five quadrats in the upper, left, middle, right, and lower parts of the filtering area were taken for scanning (Figure 1). The side lengths and step lengths of the quadrats are shown in Table 3. By After counting the number of Ni microplastics in each quadrat from the Raman surface scanning image, the total number of microplastics N is calculated according to Equation (1), and then the recovery rate P is calculated according to Equation (2).

In the formula: N is measured as the total number of microplastics on the entire filter area, pieces; Ni is the number of microplastics on each quadrat, pieces; S total is the entire filter area, S total = 4.9mm2; S quadrat is each The area of the quadrat is calculated according to Table 3,

In the formula: P is the recovery rate of microplastics, %; V is the filtration volume of the standard solution, V=100mL; C is the concentration of microplastic particles, see Table 2, pieces/mL.

Fig.1 Schematic diagram of the five - point sampling method

Table 3 Design parameters of  Raman map ping for quantitative experiments

1.4 Pollution control measures

Research shows that a large amount of synthetic fibers floating in the air and fibers falling off clothes may become sources of microplastics pollution, affecting experimental results . Therefore , this study took strict measures to avoid interference : the operators wore pure cotton lab coats and nitrile gloves for experimental operations ; various experimental devices and containers were made of glass or stainless steel , avoiding plastic materials and using Wash thoroughly with ultrapure water at least twice before and after use , and cover with aluminum foil promptly after use ; all water used during the experiment is ultrapure water ; filtered samples are placed in a covered glass petri dish for airtight storage .

1.5 Data analysis

Origin 2018 software was used to draw line graphs and histograms; CrystalSleuth software was used to remove cosmic rays in Raman spectral lines; Labs pec 6 software was used to draw pseudo-color graphs of Raman data.

2 Results and discussion

2.1 Single-point characterization of microplastics

Use standard plastic particles to adjust the Raman parameters and test the performance of the Raman instrument. Place the plastic particles on a glass slide for Raman spectrum analysis. The experimental results are shown in Figure 2.

Fig.2   Raman spectra of different types of microplastic particles

The signal strength of the Raman spectrum is related to many factors. After testing, the greater the laser power, the stronger the spectrum signal. However, when the laser power reaches 100%, the sample will be burned, so a 50% power attenuator is selected. , that is, the laser power is 50%. Regarding the selection of the scanning wave number range, according to the research of Kappler et al., the Raman vibration peaks of most plastics are in the range of 500~3500cm-1, so this range is used as the microstructure in this study. Scanning range for Raman determination of plastics.

2.2 Lower limit of particle size detection for microplastics

The spatial resolution of Raman is determined by the laser spot size, which is mainly determined by the laser wavelength and the numerical aperture of the microscope objective used. The laser spot of a standard micro-Raman spectrometer is generally in the range of 0.5~1.0µm, and the spatial resolution is Around 1µm. Therefore, in theory, the lower limit of the particle size of microplastics that can be detected by micro-Raman spectroscopy is 1µm. Before scanning PS microplastics of each particle size, switch the white light image mode to the laser image mode (Laser On). Adjust the focus to minimize the laser spot, which completes the focusing process.

The laser images of PS microplastics of different sizes are shown in Figure 3. The size of the laser spot is related to the size and shape of the particles, and can reflect the accuracy of focusing. Since the shape of the microplastics selected in this experiment is spherical, as the size increases Large, 5, 20, and 50µm PS microplastic spots are also gradually increasing, and for 1µm microplastics, their size is close to the theoretical minimum spot size of the micro-Raman spectrometer, and in reflection mode, the spherical The reflected light from microplastics is more dispersed , Therefore, precise focusing cannot be achieved , and the spot size becomes larger ( Fig. 3a), making it impossible for the detector to collect enough scattered light to form an obvious spectrum , Figure 3e validates this hypothesis . Compared to other sizes of microplastics , 1µm The intensity of the Raman spectrum of microplastic particles is weak and the peaks are unclear. is obvious , but its characteristic peaks still exist and can be identified , Therefore will 1µm is defined as the lower limit of the size of microplastics detected by Raman spectroscopy . This is also consistent with the smallest size of microplastics actually detected so far .

Fig.3 Raman laser spot image and Raman spectrum of P.S. microplastics with different particles sizes

2.3 Selection of filter membrane ( mesh )

Fig.4 Raman spectra of different filter membranes (mesh) surfaces

When using Raman spectroscopy to detect thin or transparent particles , the signal from the substrate beneath the particle can be detected . Therefore , in order to obtain higher quality To obtain a large amount of Raman spectrum , the filter material should be avoided during the spectrum collection process. Background and signal interference occur . As can be seen from Figure 4 , the background spectral lines of glass fiber membrane , stainless steel screen, mixed cellulose membrane, and alumina membrane are all relatively small. It is flat and has no obvious peaks , which has no impact on the detection of microplastics . The polyvinylidene fluoride membrane has an obvious group peak , located at about 2900cm - 1 may interfere with the Raman spectral lines of the plastic being detected . Polycarbonate film has multiple group peaks , which are related to the peaks of PS and other types of plastics. There are many overlaps . In addition , polycarbonate ( PC ) itself is a plastic material , so it cannot be used as a matrix for identifying microplastics by Raman surface scanning . Metal materials have no infrared reflection and Raman scattering, so they are a good background. Materials, Ossmann et al. [proved that metal coating can show ideal effects as a detection substrate for micro-Raman spectroscopy. Therefore, this study selected aluminum as the coating, plated on the surface of the polycarbonate film with a diameter of 3cm (Figure 5), as a new filter membrane material.

Fig.5    Schematic diagram of aluminum-coated polycarbonate membrane filter

The scanning electron microscope images of each filter membrane (mesh) are shown in Figure 6 (except for the stainless steel mesh, all were gold-plated before scanning). The surfaces of the glass fiber membrane, polyvinylidene fluoride membrane and mixed cellulose membrane showed chaotically ordered fibers. The internal pore structure of the matrix is an intertwined three-dimensional network structure, which has a large capacity to filter particles. The trapped particles are located on the surface of the filter membrane and inside the entire fiber matrix, which is a depth filter membrane. Stainless steel screens also have similar structure, retaining particles both on the surface and in deep layers. This type of filter membrane is not suitable as a substrate for filtration and characterization of microplastics for two reasons: (1) The trapped smaller particles are most likely not on the surface, but hidden in the filter The inside of the membrane cannot be identified, resulting in a reduced detection rate; (2) Even if only the particles trapped on the surface of the filter membrane are detected, when the area scanning mode is used, especially when scanning a large area, the unevenness of the filter membrane causes the particles to be different from each other. On the horizontal plane, everything cannot be in clear focus, and the resulting image is not good. The surface of polycarbonate film, aluminized polycarbonate film and aluminum oxide film is very smooth, with a uniform and continuous pore structure, uniform pore size, and is intercepted The particles are completely located on the surface of the filter membrane, so there are no above-mentioned two defects, and it can be used as a matrix for filtering and carrying microplastics. In addition, as shown in Figure 6f, the holes of the aluminized polycarbonate membrane are not coated with metal. The layer is sealed and does not affect its filtration performance.

Fig.6 Scanning electron microscope images of different filter membrane anes (mesh)

Table 4 Characteristics and applicability of the filter membranes (mesh) used in the experiment

The characteristics of various filter membranes ( mesh ) ( table 4), to meet the two characteristics of background-free interference and smooth surface , screen out 2 A filter material suitable for detecting microplastics using Raman surface scanning : aluminized polycarbonate Ester film and aluminum oxide film .

To verify this again 2 The seed film is a suitable base material and collected here 2 PS on seed film Raman spectrum of microplastics and compared with standard PS Compare the spectra , as shown in the figure 7 Shown.2 _ The Raman spectrum of the seed film itself is almost No peaks and filtered on the membrane Raman spectroscopy and standard light of PS microplastics The characteristic peaks of the spectra are the same , indicating that this 2 Seed film can indeed be used as microplastics Substrate for Raman surface scanning detection . The advantages and disadvantages of these two membranes are shown in Table 5 .

Fig.7 Comparison of the Raman spectrum of the surface of aluminum - coated polycarbonate filter, the anodisc filter and the PS above them with the standard PS Raman spectrum

Table 5 Advantages and disadvantages of aluminum - coated polycarbonate filter and anodisc filter

2.4 Surface scanning of microplastics

2.4.1 Single-component standard surface scanning used Raman surface scanning (mapping) mode to scan PS microplastics of different sizes on the film. Labspec 6 software was used for drawing. As can be seen from Figure 4, the drawing of PS microplastics The Mann characteristic peak is at 1000cm-1, so the peak clipping method is used to create a pseudo-color image. The scale numbers represent the peak heights, as shown in Figure 8. The white light image and the pseudo-color image in the overlay image can accurately overlap, indicating that pseudo-color images can be obtained through Raman imaging. Use the bright spots in the color image to determine the position, shape and size of the microplastics on the film, and count the number of microplastics in this scanning area. The substances in the red area in Figure 8a are easily judged as plastic particles with the naked eye, but the results are not Without Raman signal, the above hypothesis can be ruled out, so Raman surface scanning can ensure the accuracy of microplastic identification. From Figure 8b, it can be seen that 5, 20, and 50µm spherical microplastics can all show strong Raman signals. The 1µm microplastic Raman signal is weak, but it can be clearly distinguished from the background, which verifies the conclusion in 2.2 about the lower limit of detection size.

2.4.2 Scan the multi-component standard surface to collect different types of microplastics Conducted face scanning experiment . Scanned 95µm×95µm square area , step size 5µm, the sphere on the left is P.S. Particles , irregular crystals on the right are PET Particles , as shown in the figure 9a. Use Labspec 6 The software's two imaging analysis methods draw pseudo-color maps .

(1) Peak clipping method : Using the qualitative spectra of various plastics in Figure 4 as a reference , 1000 and 1616cm - were selected respectively . 1 as P.S. and PET characteristic peaks . After subtracting the background , use the red and blue imaging cursors to clip these two peaks respectively. , the wavelength range is 980~1020, 1580~ respectively 1640cm - 1 ( Fig . 9b), the peak spectrum intensity imaging image of the clamped range can be displayed to further obtain these 2 The distribution of substances , as shown in the figure 9c and 9d, The types and shapes of the two plastics can be clearly distinguished . The Raman intensity of each microplastic particle in the two-dimensional image is presented at a high level in the three-dimensional image , which can represent the surface morphology of the real sample .

(2) Classical least squares (CLS) fitting method : CLS Fitting can Imaging based on the entire spectrum , The standard spectrum and scan area will soon be known Each spectrum is fitted using the least squares method . If there is no standard spectrum Figure , you can select the spectrum directly from the window , that is, in P.S. and PET particles Select a point on the image , and collect its spectrum as the standard spectrum . Select any point on the image , and you can get its fit to each standard. The fitting score of the spectrum ( score ), expressed in terms of X , Y coordinates and fitting score (score) generates a pseudo-color image , as shown in Figure 10a and 10b As shown , it is important to note that the brightness of the 2D plot or the height of the 3D plot do not represent the spectrum The intensity only indicates the degree of fitting . This method is more suitable for spectra containing a large number of overlapping bands , and can analyze the distribution of any multiple components. ( The peak-clipping method can only analyze up to 3 components ).

Fig.8    Raman mapping images of PS microplastics

Fig.9   Multi-component standard microplastics mapping(characteristic peaks method)

2.4.3 Select quartz sand (100~200 Purpose ) to simulate impurities in environmental samples , using aluminum-plated aluminum with a pore size of 1.2µm Polycarbonate membrane filtration quartz sand with 50µm PS Microplastic Mix _ object , and perform a Raman surface scan . The surface scan area is 207µm × 2 31µm Rectangular area , step length 10µm, as shown in the figure 11a As shown . Use peak clipping method ( quartz sand : 470~485cm - 1 ;PS:980~1020cm - 1 ) and the CLS fitting method to draw pseudo-color maps of the scanned Raman spectra, as shown in Figures 11b and 11c. Both mapping methods can identify microplastics from the quartz sand background, but due to the peaks of the quartz sand The height is low, only 50~100counts, while the peak height of PS can reach 00~500counts. Therefore, under the same intensity axis, the background color of quartz sand cannot be displayed in the imaging image using the peak clipping method. When using the CLS fitting method When , since the depth of the image color corresponds to the degree of fitting and has nothing to do with the spectral intensity, the color of the quartz sand background can be seen in Figure 11c.

Study shows microplastics in hand-picked sediment samples when , Due to the large amount of inorganic impurities such as sand , some black or white microplastics will be ignored , while many colored non-plastic particles will be misjudged are microplastics. The results of surface scanning experiments on simulated environmental samples show that Raman surface scanning can be used to remove other inorganic impurities in environmental samples. Interference in detection results , accurately identify microplastics from impurity background Come out . Therefore, this method is not only suitable for tap water, bottled water and other dry Clean samples are suitable for natural water bodies, sewage, sediments and other impurities. The same applies to environmental samples .

Fig.10   Multi - component standard microplastics mapping (CLS fitting method)

Fig.11   Mapping of simulated environmental samples

2.5 Microplastic surface scanning quantification and recovery rate calculation

Quantitative experiments on small-sized microplastics detected by Raman surface scanning and recovery rate calculation , as shown in Figure 12 shown . Since the particle size is 1µm micro plastic The material particles need a shorter step size . In order to control the surface scanning time , therefore narrow The area of the surface scan area (25µm×25µm) was determined using the five-point sampling method. The total area of the actual scanned area only accounts for 1% of the filtered area. 0.064%, Poor representativeness may result in larger errors . Therefore, in this study, no Calculate the recovery rate of 1µm particle size microplastics . 5, 20, 50µm particle size microplastics The recovery rates of materials range from 55% to 65% respectively. , 30%~50% and 75%~80%, when the particle size is 5µm , the recovery rate under different concentration gradients is not much different ; the particle size is At 20µm , the recovery rate increases with concentration Reduced somewhat . Due to the low configuration concentration , 50µm particle size microplastics are A Not detected under gradient concentration (7.6×10 - 3 / mL) .

The recovery rates of microplastics with different particle sizes and concentrations vary greatly . There may be several reasons : (1) Edge effect : after detection During the process , it was found that microplastics accumulated at the edge of the filter area , with a higher concentration than in the central filter area , and a higher filter flow rate . This situation The situation becomes more obvious .But during the five-point sampling process , Didn't get to the edge points at , resulting in overall low detection results . (2)Uneven distribution : When performing quantitative experiments and recovery calculations , it is believed that the microplastics in the solution are evenly mixed and completely dispersed , filtering microplastics on the membrane evenly distributed .However , during the actual inspection , it was found that the local network in the filtering area The phenomenon of microplastic aggregation appears in the body , and the smaller the particle size and the higher the concentration , The more obvious the aggregation phenomenon is . (3) The concentration is low. : When the concentration of microplastics is When it is low , the amount of microplastics distributed in the filter area is very small . due to taking Accidental errors caused by this may increase , even appears in sampling No microplastics detected in the area , causing the test results to be low or high.

Fig.12  Recovery rate of PS microplastics of different sizes under three concentration gradients

For the above reasons , it is recommended to reduce the filtration flow rate and Methods such as increasing sampling points and increasing sample volume can improve recovery rate and accuracy. accuracy .

3 In conclusion

3.1 Both aluminized polycarbonate film and aluminum oxide film can be used as Raman Filter membrane for detecting microplastics , and the cost is similar . Comprehensive advantages and disadvantages of the two membranes point , aluminized polycarbonate film can meet more requirements , if there are coated strips It is recommended to apply the former .

3.2 Single-point detection of microplastic particle size using micro-Raman spectroscopy limited to 1µm, the particle size can also be identified using the surface scan mode. 1µm of Microplastics .

3.3 Raman surface scanning can detect single-component and multi-component microplastics , and can identify and display them from the quartz sand background . Utilize Characteristic peaks and characteristic spectral lines are drawn as pseudo-color diagrams to represent plastic composition and distribution , making the detection results more intuitive and accurate .

3.4 Raman Face scan to different Particle size micro plastic The material recovery rate is Between 33.3% and 79.0% , reduce the filtration flow rate and increase the sampling points. Methods such as increasing sample volume can improve recovery and accuracy.