A microplastic number concentration rapid detection device and method based on on-chip imaging

Abstract

The application discloses a kind of rapid detection device and detection method of microplastic number concentration based on on-chip imaging, the present application utilizes the digital image sensor chip of commercial small pixel size and high pixel quantity, constructs microplastic detection device, can directly load microplastic sample to image sensor chip photosensitive surface, quickly obtains the shadow image of microplastic projection on chip;On this basis, the microplastic in image is counted by artificial analysis method or machine learning method, and then the number concentration of microplastic is calculated according to the volume of microplastic sample, which overcomes the bottleneck problem that the existing analysis method of microplastic is difficult to directly and quickly collect sample wide field of view, high-resolution image.The microplastic detection device of the present application is portable, and the method is simple and easy to popularize, which provides a new technical support for environmental microplastic pollution site monitoring, and can also be applied to the rapid determination of the number concentration of microparticle other than microplastic.

Description

TECHNICAL FIELD

[0001] The application relates to a microplastic number concentration rapid detection device and method based on on-chip imaging, and belongs to the technical field of new pollutant treatment. BACKGROUND

[0002] Microplastics are new pollutants that are internationally focused on, and an important prerequisite for realizing accurate prevention and control of microplastic pollution is to obtain reliable data about the pollution situation in a timely manner. However, after microplastics are separated from environmental media, rapid analysis of the number concentration of the microplastics is currently a key methodological challenge.

[0003] Generally, in order to visually check whether microplastics exist in a processed sample and analyze the number and size of the microplastics, the sample needs to be filtered on a filter membrane surface, and then observed and analyzed under a conventional optical microscope. Since a conventional (lens) optical microscope cannot have both a wide field of view and high-resolution imaging, it is difficult to manually obtain an image of the entire filter membrane at a high magnification and count all the particulate matters retained by the filter membrane. Although an automatic optical microscope can capture an image of the entire filter membrane by means of an electric displacement table, automatic focusing and image stitching technology, it is time-consuming (several tens of minutes), and the pixel size and number of the image sensor configured by the automatic optical microscope are limited, so that an observer can only identify and determine >30 mu m particulate matters from the image of the entire filter membrane, and small-size microplastics are missed. Therefore, the lens optical imaging technology is time-consuming and low in accuracy for microplastic detection, and it is urgent to establish a new method to improve the imaging, counting efficiency and reliability of microplastic samples.

[0004] Lens-free holographic imaging is considered to have application prospects in microplastic detection, but the sensor of the technology obtains a coherent diffraction pattern of a sample, and needs to use a reconstruction algorithm to recover the phase information of the sample, so that an optical image of the microplastic sample cannot be directly and quickly obtained. Therefore, it is urgent to establish a method for applying lens-free optical imaging technology to microplastic number concentration detection. SUMMARY

[0005] The first object of the application is to provide a microplastic number concentration rapid detection device based on on-chip imaging, and the second object of the application is to provide a method for rapidly detecting the number concentration of microplastics by using the detection device.

[0006] Technical solution: The microplastic number concentration rapid detection device based on on-chip imaging provided by the application comprises a light source, an image sensor chip, a control system and an image processing assembly, the control system is connected with the light source, the image sensor chip and the image processing assembly respectively, wherein the light source is vertically arranged above the image sensor chip, the control system is connected with the image processing assembly through a data transmission line, and the upper surface of the image sensor chip is provided with a sample loading area, and the microplastic sample to be measured is placed in the sample loading area.

[0007] Further, the light source is monochromatic or multicolor light.

[0008] Further, the wavelength of the light source is 300-700 nm.

[0009] Further, the vertical distance from the light source to the image sensor chip is 5-10 cm.

[0010] Further, the photosensitive area of the image sensor chip is 10-20 mm long and 10-20 mm wide, the pixel size is 0.5-1 μm, and the pixel number is 1-1.6 billion.

[0011] Further, the vertical distance from the microplastic sample to be measured to the image sensor chip is 5-10 μm.

[0012] Further, the sample loading area is a square area surrounded by a cured glue, the sample loading area is 10-20 mm long and 10-20 mm wide, the volume of the sample loading area is 100-400 μL, and the height of the sample loading area is 0.5-1.5 mm.

[0013] Further, the projection of the microplastic sample to be measured in the sample loading area of the image sensor chip under the vertical irradiation of the light source is directly recorded by the image sensor chip and converted into an electrical signal, the control system drives and controls the light source and the image sensor chip to work, receives the output signal from the image sensor chip, and then sends it to the image processing assembly through the data transmission line to display the shadow image of the microplastic.

[0014] The method for rapidly detecting the number concentration of microplastics by the detection device provided by the application comprises the following steps:

[0015] (1) Under clean environmental conditions, place the microplastic sample to be measured in the sample loading area on the surface of the image sensor chip and stand still;

[0016] (2) Turn on the light source, and collect the shadow image of the microplastic deposited in the sample loading area of the image sensor chip;

[0017] (3) Analyze the microplastic sample image obtained by the artificial analysis method or the machine learning method, count and calculate the number concentration of the microplastic.

[0018] (4) adding anhydrous ethanol to the sample area, wiping off the microplastics deposited on the surface of the sample area of the image sensor chip, checking the cleanliness by collecting a shadow image, and using the same for the detection of the next batch of microplastic samples.

[0019] Further, in step (1), the microplastic sample is a suspension.

[0020] Further, in step (1), the standing time is 90 s or more.

[0021] Further, in step (1), the clean environment condition is a clean room or a super-clean bench, and the cleanliness is 100 level (ISO 5) (the number of particles ≥1 μm is 832 / m 3 or more.

[0022] Further, the mass concentration of the microplastic suspension is less than 100 mg / L, and the volume of the microplastic suspension added to the sample area is 100-400 μL.

[0023] Further, in step (2), the light source uses a light-emitting diode for illumination.

[0024] Further, in step (2), the time for collecting the image is 90 s or more.

[0025] Further, in step (3), the manual analysis method is to import the image into image processing software, manually mark and count the microplastics.

[0026] Further, in step (3), the machine learning method is to automatically identify and count the microplastics in the image by constructing and training a mathematical model, wherein the mathematical model is a target key point detection algorithm based on an existing model, which has been trained and tested, and the size of the microplastics that can be marked or identified is 3-6 μm.

[0027] Further, in step (3), the number concentration of the microplastic sample is calculated according to the following formula:

[0028] Number concentration of microplastics = number of microplastics in the microplastic sample (pieces) / volume of the microplastic sample (L).

[0029] Further, in step (4), the microplastics deposited on the surface of the sample area of the image sensor chip are wiped off by absorbing the waste liquid with a disposable dust-free purification cotton swab, and then gently wiping the image sensor chip with a dry disposable dust-free purification cotton swab.

[0030] Further, in step (4), the cleanliness is that the number of particles is <10 / cm 2 .

[0031] The present application adopts lensless shadow imaging, which is a "what you see is what you get" imaging method, and the shadow generated by the microplastic sample on the sample area of the image sensor chip is directly formed into an image after being recorded by the sensor, and the imaging field of view and resolution depend on the pixel number and size of the image sensor chip and the distance of the sample to the image sensor chip. The present application adopts an image sensor chip with small pixel size and high pixel number, and realizes rapid detection of the number concentration of microplastics based on the lensless shadow imaging technology.

[0032] Advantages: Compared with the prior art, the present application has the following significant advantages:

[0033] (1) The method of the present application is suitable for microplastic pure samples or samples extracted from environmental media, and can rapidly determine the number concentration thereof, and the operation process is simple and easy to popularize, and faces the major needs of national new pollutant control.

[0034] (2) The method of the present application greatly shortens the imaging time cost of microplastic samples, has the characteristics of low carbon and energy saving, and responds to the national policy of energy saving and low carbon.

[0035] (3) The method of the present application is also suitable for imaging and counting of microparticle substances other than microplastics. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 It is a structure diagram of the microplastic number concentration rapid detection device based on on-chip imaging of the present application.

[0037] In the figure: 1. Light source; 2. Image sensor chip; 3. Control system; 4. Data transmission line; 5. Image processing component.

[0038] Figure 2 It is a step flow chart of the microplastic number concentration rapid detection method of the present application.

[0039] Figure 3 It is a lensless shadow microscopic full field of view image of the polyethylene microplastic obtained in Example 2.

[0040] Figure 4 It is Figure 3 A local enlarged view of S1 area in the figure.

[0041] Figure 5 It is Figure 4 A local enlarged view of S2 area in the figure.

[0042] Figure 6 It is an optical microscopic image of the polyethylene microplastic obtained in Example 2. Figure 7 It is a comparison diagram of diameter statistics of lensless shadow microscopic imaging and optical microscopic imaging of the polyethylene microplastic in Example 2.

[0043] Figure 8 Graph of the results of the effect of standing time on the number concentration of melamine microfibers in Example 3.

[0044] Figure 9 Graph of the results of the effect of standing time on the length distribution of melamine microfibers in Example 3. DETAILED DESCRIPTION

[0045] The technical solutions of the present application are further described below in conjunction with the accompanying drawings.

[0046] Example 1

[0047] As shown in Figure 1 , the microplastic number concentration rapid detection device based on on-chip imaging of the present application comprises a control system 3, which is connected with a light source 1, an image sensor chip 2 and an image processing assembly 5 respectively, wherein the light source 1 is vertically arranged above the image sensor chip 2, the control system 3 is connected with the image processing assembly 5 through a data transmission line 4, and the surface of the image sensor chip 2 is provided with a sample loading area, and the microplastic sample to be detected is placed in the sample loading area. The projection of the microplastic sample to be detected in the sample loading area of the image sensor chip 2 under the vertical irradiation of the light source 1 is directly recorded by the image sensor chip 2 and converted into an electrical signal. The control system 3 drives and controls the light source 1 and the image sensor chip 2 to work, receives the output signal from the image sensor chip 2, and then sends it to the image processing assembly 5 through the gigabit data transmission line 4 to display the shadow image of the microplastic.

[0048] Example 2

[0049] The detection device of Example 1 is used to detect the number concentration of microplastics, and the rapid detection method step flow is as shown in Figure 2 . Polyethylene microspheres (diameter about 10-20 μm) are selected as microplastic representatives, 1 mg of polyethylene microspheres is weighed, transferred to a glass bottle, 10 mL of 80% ethanol solution is added, mixed to prepare a 100 mg / L dispersion, 100 μL of the dispersion is taken out using a micro glass pipette, quickly loaded into the sample loading area on the surface of the image sensor chip 2, the light source 1 is turned on, and the shadow image is collected after standing for 90 s; the results are as shown in Figures 3-5 .

[0050] At the same time, 100 μL of the dispersion is filtered onto an aluminum oxide filter membrane, and a reflection light microscope with a lens in the prior art is used to collect the reflection light image on the surface of the filter membrane at 40 times; the results are as shown in Figure 6 .

[0051] By comparison Figures 3-6 , it can be seen that, compared with the lens optical microscopic imaging, the lensless shadow microscopic imaging can obtain a high-resolution image of a single microplastic in a larger field of view.

[0052] The number and diameter of microspheres in the collected shadow images and reflected light images were determined manually by image processing software (Adobe Photoshop and ImageJ), and the results are shown in Table 1. Figure 7 Figure 7 A comparison chart of the diameter statistics of the lensless shadow microscopic imaging and the lens optical microscopic imaging of the polyethylene microplastics in Example 2 is shown in Figure 2. Figure 7 It can be seen that there is no significant difference in the diameter of microspheres obtained by lensless shadow microscopic imaging and lens optical microscopic imaging, indicating that the accuracy of the lensless shadow microscopic imaging of the present application can accurately detect the number concentration of polyethylene microspheres.

[0053] The polyethylene microsphere sample was repeatedly measured three times, and the results of the three counts were 605, 586 and 600.

[0054] The number concentration of polyethylene microspheres was calculated according to the following formula:

[0055] The number concentration of polyethylene microspheres = the count of polyethylene microspheres in the dispersion (pieces) / the volume of the dispersion (L)

[0056] According to the above formula, the number concentrations of polyethylene microspheres were 6.05 x 10 6 pieces / L, 5.86 x 10 6 pieces / L and 6.00 x 10 6 pieces / L, respectively. The relative standard deviation between the three parallel samples was 1.6%, indicating that the method of the present application has high detection accuracy for the number concentration of spherical microplastics.

[0057] Example 3

[0058] The experimental process was the same as that of Example 2, and melamine microfibers were selected as the representative of microplastics (the length of melamine microfibers was about 1-200 μm). 1 mg of melamine microfibers was weighed and transferred to a glass bottle, 10 mL of 80% ethanol solution was added, and the mixture was mixed to prepare a dispersion of 100 mg / L, which was then diluted to 24 mg / L. 100 μL of the dispersion was removed using a micro glass pipette and quickly loaded onto the sample area on the surface of the image sensor chip 2. Shadow images were collected after 1.5 min, 3.0 min and 10 min of standing, respectively. The number of microfibers in the images was counted manually by image processing software, and the number concentration of microfibers was obtained by dividing the number of microfibers by the sample volume (100 μL). The results are shown in Table 3. Figure 8 The length of the microfibers was measured, and the results are shown in Table 4. Figure 9

[0059] Figure 8 A comparison chart of the diameter statistics of the lensless shadow microscopic imaging and the lens optical microscopic imaging of the polyethylene microplastics in Example 2 is shown in Figure 2. Figure 8 It can be seen that the standing time has no significant effect on the number concentration of melamine microfibers. Figure 9 ​​The result graph of the standing time on the length distribution of melamine microfibers in Example 3. From Figure 9 It can be seen that the standing time has no significant effect on the length distribution of melamine microfibers. Therefore, the method of the present application can ensure the accuracy of sample image acquisition within 90s, and thus the detection time of the present application is greatly reduced compared with the tens of minutes of the existing automatic optical microscope, and rapid detection of microplastic number concentration can be achieved.

Claims

1. A method for rapid detection of the number concentration of microplastics by an on-chip imaging-based microplastics number concentration rapid detection device, characterized in that, The microplastic number concentration rapid detection device based on on-chip imaging comprises a light source (1), an image sensor chip (2), a control system (3) and an image processing component (5), the control system (3) is connected with the light source (1), the image sensor chip (2) and the image processing component (5) respectively, wherein the light source (1) is vertically arranged above the image sensor chip (2), the control system (3) is connected with the image processing component (5) through a data transmission line (4), the upper surface of the image sensor chip (2) is provided with a sample loading area, the microplastic sample to be measured is placed in the sample loading area, the sample loading area is a square area surrounded by a solidified glue, the vertical distance between the microplastic sample to be measured and the image sensor chip (2) is 5-10 μm, the vertical distance between the light source (1) and the image sensor chip (2) is 5-10 cm, the light source (1) is single-color or multi-color light, the wavelength of the light source (1) is 300-700 nm, the photosensitive area of the image sensor chip (2) is 10-20 mm long and 10-20 mm wide, the pixel size is 0.5-1 μm, the pixel number is 1-1.6 billion, the sample loading area is 10-20 mm long and 10-20 mm wide, the volume of the sample loading area is 100-400 μL, and the height of the sample loading area is 0.5-1.5 mm. The rapid detection method comprises the following steps: (1) Under clean environmental conditions, the microplastic sample to be measured is placed on the sample loading area on the surface of the image sensor chip (2), and the sample is allowed to stand for more than 90 s; (2) Turn on the light source (1), and collect the shadow image of the microplastics deposited in the sample loading area of the image sensor chip (2); (3) Analyze the obtained microplastic sample image by manual analysis method or machine learning method, count and calculate the number concentration of microplastics; (4) Add anhydrous ethanol to the sample loading area, wipe off the microplastics deposited on the surface of the sample loading area of the image sensor chip (2), and test the cleanliness by collecting the shadow image, which is used for the detection of the next batch of microplastic samples; The mass concentration of the microplastic suspension is less than 100 mg / L, and the volume of the microplastic suspension added to the sample loading area is 100-400 μL.

2. The method of claim 1, wherein, In step (1), the microplastic sample is a suspension, and the clean environmental conditions are a clean room or an ultra-clean workbench with a cleanliness of more than 100 levels.

3. The method of claim 1, wherein, In step (2), the light source (1) uses a light-emitting diode for illumination, and the image collection time is more than 90 s.

4. The method of claim 1, wherein, In step (3), the manual analysis method is to import the image into an image processing software, manually mark and count the microplastics; the machine learning method is to automatically identify and count the microplastics in the image by constructing and training a mathematical model, wherein the mathematical model is a target key point detection algorithm based on an existing model, which is trained and tested, and the size of the microplastics that can be marked or identified is 3-6 μm.

5. The method of claim 1, wherein, In step (4), the microplastics deposited on the surface of the sample area of the image sensor chip (2) are removed by using a disposable dust-free purification cotton swab to absorb the waste liquid, and then using a dry disposable dust-free purification cotton swab head to gently wipe the image sensor chip (2). The cleanliness is that the particle number is <10 / cm 2 .

Images