Method for rapidly detecting content of micro-plastics in water body

Abstract

The invention provides a method for rapidly detecting the content of micro-plastics in a water body, which comprises the following steps: mixing the water body to be detected with a fluorescent dye, standing, filtering, and irradiating with light with specific wavelength to obtain a fluorescent image; identifying to obtain the content of the microparticles; according to the method disclosed by the invention, rapid coloring of the fluorescent dye on the micro-plastic can be realized, and rapid dyeing success can be realized without heating.

Description

Technical Field

[0001] The present invention relates to the technical field of microplastic detection, and in particular to a method for rapid detection of microplastic content in water. Background Art

[0002] Microplastics refer to plastic particles with a diameter of less than 5 mm, which are a major carrier of pollution. Compared with "white pollution" plastics, the harm of microplastics is reflected in the tiny diameter of their particles, which is why they are more harmful to the environment than general non-degradable plastics. There are four major categories of new pollutants that are widely concerned internationally: persistent organic pollutants, endocrine disruptors, antibiotics, and microplastics, which are defined as new pollutants after being discharged into the environment. In order to provide accurate and scientific reference for the management of microplastics in water, it is first necessary to detect and evaluate the content of microplastics in water bodies to measure potential risks and facilitate the subsequent management process.

[0003] At present, among the methods for detecting microplastics, physical characterization and analysis methods mainly include microscopic counting and scanning electron microscopy; chemical characterization and analysis methods mainly include Raman spectroscopy, pyrolysis-gas chromatography / mass spectrometry (Py-GC / MS) and high performance liquid chromatography; among these methods, the physical characterization and analysis methods are relatively inefficient, time-consuming, and greatly affected by subjective factors, with limited accuracy and the staining selectivity needs to be improved; the chemical characterization and analysis methods have high requirements on sample purity, complex pretreatment steps, limited analytical capabilities for complex mixture samples, high equipment costs and maintenance costs, etc. Therefore, a fast and accurate detection method is needed to detect microplastics in water bodies. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method for rapid detection of microplastic content in water.

[0005] A rapid detection method for microplastic content in water, comprising:

[0006] The water to be tested is mixed with the fluorescent dye, left to stand for 1 to 20 minutes, and then filtered through a micron-grade filter membrane to obtain first-level microplastics and filtrate; the mixing treatment is ultrasonic oscillation; the frequency of the ultrasonic wave is 40 to 45 kHz, and the oscillation time is 5 to 8 minutes;

[0007] The first-level microplastics are irradiated with light of a specific wavelength to obtain a first fluorescent image;

[0008] The first fluorescent image is identified to obtain the content of first-level microplastics.

[0009] Description: Through the above method, the fluorescent dye can be quickly colored on microplastics. Through the setting of oscillation and mixing, the contact between microplastics and fluorescent dyes can be promoted, thereby achieving coloring. Through illumination and identification after dyeing, the content of microplastics in water can be clearly determined. In the existing technical research, heating is required and the dyeing time is long. For example, most of the current research is 70℃, more than 1h. The above method can successfully dye quickly at room temperature.

[0010] Furthermore, the fluorescent dye is a coumarin dye.

[0011] Furthermore, the fluorescent dye is a coumarin fluorescent dye aqueous solution with a concentration of 1-50 μmol / L; the volume ratio of the water body to be detected and the coumarin fluorescent dye aqueous solution is 1-5:10-100.

[0012] Description: The study found that the coumarin fluorescent dye in the above ratio has a good dyeing effect and can be colored relatively quickly, with the coloring time maintained at 5 to 10 minutes. This may be because the dye is easier to oscillate than other dyes. Coumarin dyes have a chemical structure and polarity that are compatible with the surface of microplastics, allowing them to be quickly and effectively adsorbed on the surface of microplastics. The chemical structure of rhodamine B dye and 1-benzoylsulfonic acid dye may make their interaction with the surface of microplastics weaker, resulting in a slower adsorption process; in addition, under the action of oscillation, coumarin dyes may have better solubility in water and a faster diffusion rate, making the dyeing speed very fast and coumarin dyes have better selectivity for microplastics than other fuels.

[0013] Furthermore, the light of the specific wavelength is blue light of 450-500nm.

[0014] Note: The setting of the above parameters can ensure that all microplastic particles labeled with fluorescent dyes can be fully excited.

[0015] Furthermore, the micron-grade filter membrane has a pore size of 45 microns.

[0016] Note: Filter membranes with the above pore sizes are more commonly used and can filter microplastics at the micron level or larger, thereby identifying and calculating the content of these microplastics.

[0017] Further, the filtrate contains second-level microplastics;

[0018] The method further comprises: screening the second-level microplastics in the filtrate, wherein the screening method comprises:

[0019] Separating the dye in the filtrate that is not combined with the second-level microplastics from the second-level microplastics to obtain a mixed liquid of the second-level microplastics and water or the second-level microplastics;

[0020] Then, a mixture of the second-level microplastics and water or the second-level microplastics is irradiated with light of a specific wavelength to obtain a second fluorescent image;

[0021] The second fluorescent image is identified to obtain the content of second-level microplastics.

[0022] Note: The above method can be used to detect smaller microplastics. In actual applications, microplastics of micron size and larger are more abundant, while microplastics below micron size are less abundant. However, these smaller microplastics still have certain hazards. Therefore, the above secondary treatment method can accurately reflect the content of microplastics in water bodies, which is convenient for subsequent research and treatment of microplastics in water bodies, and avoids the problem of low filtration efficiency and inaccurate identification effect caused by directly filtering primary and secondary microplastics.

[0023] Furthermore, the method for separating the dye in the filtrate that is not combined with the second-level microplastics from the second-level microplastics includes: one or more of extraction, centrifugation, membrane separation, and density separation.

[0024] Note: The above method can separate microplastics from dyes. Specifically, the coumarin dye can be dissolved by an extractant and precipitated in water to separate the dye from the microplastics, or the microplastics can be screened out from the filtrate by separation methods such as centrifugation and membrane separation.

[0025] Furthermore, the method for identifying the first fluorescent image includes:

[0026] The first fluorescent image is collected using a fluorescent microscope or a fluorescent imaging system, wherein the fluorescent microscope selects a green filter with a central wavelength of 530 nm and a half bandwidth of 30 nm;

[0027] Then, identifying the first fluorescent image acquired by a first image analysis method;

[0028] The first image analysis method is: establishing and training a deep learning model, and then based on the deep learning model, identifying the first fluorescent image to obtain the distribution and content of the first-level microplastics in the first fluorescent image.

[0029] Note: The above method can realize the acquisition and recognition of fluorescence images.

[0030] Further, the filtrate is subjected to irradiation identification treatment in batches to obtain the content of the second-level microplastics in the filtrate;

[0031] The irradiation identification process includes:

[0032] First, multiple wavelengths of excitation light are used for irradiation in sequence;

[0033] Then, a fluorescence microscope is used to collect the second fluorescence image; wherein the fluorescence microscope selects a green filter, the central wavelength of the green filter is 525 nm, and the half bandwidth is 30 nm;

[0034] Then, an image analysis method is used to identify the second fluorescent image.

[0035] Note: The above provides a method that does not separate the second microplastic from the filtrate, directly irradiates, collects images and identifies. This method can more conveniently obtain the content of microplastics in the water body; and because the first-level microplastics and the second-level microplastics are different in size, direct photography may cause inaccurate fluorescent image recognition, and then cause inaccurate calculation of the microplastic content. By first identifying the first-level microplastics and then processing and identifying the second-level microplastics again, the accuracy of the calculation of the microplastic content can be guaranteed to be higher.

[0036] Furthermore, the image analysis method is:

[0037] Preprocessing the second fluorescence image, wherein the preprocessing includes fluorescence correction;

[0038] Build and train deep learning models;

[0039] Based on the deep learning model, the second fluorescent image after preprocessing is identified to obtain the distribution and content of the second-level microplastics

[0040] Note: The above image recognition is performed through a deep learning model. The trained model can more accurately identify the content of fluorescent microplastics (i.e., the content of microplastics is measured according to area / volume).

[0041] Furthermore, the method of establishing and training a deep learning model includes:

[0042] Acquire multiple groups of fluorescence image data to form a training set;

[0043] The first step is model training;

[0044] Define a YOLO model based on deep learning, input the training samples in the training set into the YOLO model, calculate the predicted value, calculate the loss function value between the predicted value and the true value, perform gradient backpropagation, and update the model parameters according to the set optimization algorithm.

[0045] It should be understood that model training is a process of multiple rounds of iterations. Each round of iteration traverses the training set once, and each time a small batch of samples is obtained from it and sent to the model to perform forward calculations to obtain predicted values. The effect of model training is judged by observing the decreasing trend of the loss value in each round of iteration. For example, when the decreasing trend of the loss value remains unchanged, it means that the model tends to converge.

[0046] Step 2: Model evaluation;

[0047] The test set is input into the trained model for evaluation to obtain the predicted value, calculate the loss function value between the predicted value and the true value, and calculate the evaluation index value to facilitate the evaluation of the model effect.

[0048] Step 3: Model reasoning;

[0049] The preparation data of the biochar to be verified is input into the trained model to perform reasoning, and the reasoning results are observed and verified to see whether they meet expectations. If they meet expectations, the model weights are obtained.

[0050] Step 4: Model identification;

[0051] Then load the pre-trained model weights into the model; read the image data to be detected, and perform necessary data preprocessing (such as adjusting image size, normalization, etc.);

[0052] The model is then used to identify the image and obtain the distribution of microplastic particles; the test results are visualized and the content of microplastics is calculated.

[0053] Furthermore, the present invention also provides a mixing auxiliary agent, which is added to the water to be tested during the mixing process, and the addition amount is 1-2wt%; the mixing auxiliary agent can further enhance the selectivity of the coumarin dyeing process, that is, enhance the binding ability of coumarin with microplastics and reduce the binding force between coumarin and other impurities, so as to improve the pertinence of the present invention;

[0054] The mixing auxiliary agent is an active agent; the active agent is obtained by mixing fatty alcohol polyoxyethylene ether and sodium lauryl sulfate in a mass ratio of 1:2.

[0055] Furthermore, the present invention also provides a coumarin with a "DA-π-A" structure; the preparation method of the coumarin with a "DA-π-A" structure is as follows: 10 mL of anhydrous toluene is added as a solvent to 0.5 mmol of a coumarin derivative (D), 0.55 mmol of a boronic acid derivative of dicyanobenzothiophene, 40.5 mol of Pd(PPh3), 0.1 mol of PPh3 and 0.1 mol of K3PO4 to obtain a mixture; under nitrogen protection, the mixture is heated to reflux for 12 to 15 hours at a heating temperature of 100°C; an intermediate is obtained,

[0056] The intermediate was mixed with 0.55mmol of a halogenated derivative of thiophene, 40.05mol of Pd(PPh3), 0.1mol of PPh3 and 0.1mol of K3PO4, and 10mL of anhydrous toluene, and heated to reflux for 12 to 15h at a heating temperature of 100°C; a coumarin with a "DA-π-A" structure was obtained.

[0057] Note: The above-mentioned "DA-π-A" structured coumarin fluorescent dye has better photostability and can further improve the dyeing effect on microplastics.

[0058] The beneficial effects of the present invention are:

[0059] The method of the present invention can achieve rapid coloring of fluorescent dyes on microplastics. Through the setting of oscillation mixing, the contact between microplastics and fluorescent dyes can be promoted to achieve coloring. Through illumination and identification after dyeing, the content of microplastics in water can be clearly determined. In the existing technical research, heating is required and the dyeing time is long. At present, most of the research is 70°C for more than 1 hour. The above method can successfully dye quickly at room temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of a method for detecting microplastics according to an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the results of dyeing microplastics (PMMA) with four different dyes in an embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram of the results of dyeing microplastics (PE) with four different dyes in an embodiment of the present invention;

[0063] Figure 4 This is a schematic diagram of the results of dyeing microplastics (PS) with four different dyes in an embodiment of the present invention;

[0064] Figure 5 This is a schematic diagram of the dyeing results of microplastics (TPU) using four different dyes in an embodiment of the present invention;

[0065] Figure 6 is the effect of different temperatures on the staining of microplastics in the embodiments of the present invention;

[0066] Figure 7 is the effect of different incubation times on microplastic staining in the embodiments of the present invention;

[0067] Figure 8 It is a visual display of the model counting picture results of the embodiment of the present invention;

[0068] Fig. 9 It is the correlation analysis of the manual counting and model counting results of the embodiment of the present invention;

[0069] Fig.10 This is a data graph of the number and distribution of microplastics in water samples according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The terms "first" and "second" etc. in the description and claims of the present invention are used to distinguish different objects rather than to describe a specific order of the objects.

[0071] “And / or” in the embodiment of the present invention represents the relationship between objects. For example, A and / or B can represent the following three situations: A exists alone, B exists alone, and A and B exist at the same time.

[0072] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0073] The method provided in an embodiment of the present invention relates to microplastic detection and can be used to identify and detect microplastics in water bodies. Specifically, fluorescent dyes are used to quickly dye microplastics, and then image recognition of the stained microplastics is performed to obtain the content of microplastics in the water body.

[0074] In the prior art, both physical characterization and chemical characterization analysis methods have limitations. In the existing fluorescent staining methods, for example, the staining process using rhodamine B dye and 1-benzoylsulfonic acid dye requires heating treatment, and the staining time is relatively slow. This may be due to the limitations of dye selection, oscillation and other mixing conditions, resulting in a long staining time and the selectivity of staining needs to be improved.

[0075] Example 1

[0076] The embodiment of the present invention provides a method for rapid detection of microplastic content in water, through which microplastic particles can be efficiently and accurately detected and identified, providing technical support for microplastic pollution control, having the advantages of being fast, accurate, and easy to operate, and suitable for environmental monitoring and scientific research. Specifically including the following S1-S6.

[0077] S1. Mix the water to be tested with the fluorescent dye, let it stand for 5 minutes, and then filter it through a micron-grade filter membrane to obtain first-level microplastics and filtrate; the mixing treatment is ultrasonic oscillation; the frequency of the ultrasonic wave is 42kHz, and the oscillation time is 6 minutes;

[0078] The fluorescent dye is a coumarin dye; the coumarin dye is a coumarin fluorescent aqueous solution with a concentration of 20 μmol / L; the volume ratio of the water body to be detected and the coumarin fluorescent aqueous solution is 2:60; the light of the specific wavelength is 480nm blue light; the micron-grade filter membrane uses a filter membrane with a pore size of 45 microns.

[0079] After dyeing, microplastic particles labeled with fluorescent dye are suspended in the solution, which is filtered through a filter membrane with a pore size of 45 microns to obtain microplastic particles labeled with fluorescent dye.

[0080] S2, irradiating the first-level microplastics with light of a specific wavelength to obtain a first fluorescent image;

[0081] In order to excite the fluorescent dye on the microplastic particles marked with fluorescent dye, an excitation light source with a specific wavelength is selected for irradiation. Fluorescent dyes usually have a high absorption capacity for light of a specific wavelength and emit fluorescence of another wavelength after absorbing light energy. Therefore, choosing the appropriate light source wavelength is one of the key steps to excite fluorescent dyes.

[0082] In the present invention, the light source used can be a blue or ultraviolet light source, and the specific wavelength depends on the selected fluorescent dye. For example, using coumarin as a fluorescent dye, a suitable excitation wavelength is a blue light of about 470-500nm. The light source can be an LED lamp, a laser or other device that can emit light of the required wavelength. Position the light source above the sample so that the light evenly illuminates the entire sample, thereby ensuring that all microplastic particles marked with fluorescent dyes can be fully excited.

[0083] S3. Identify the first fluorescent image to obtain the content of first-level microplastics. The method for identifying the first fluorescent image includes:

[0084] The first fluorescent image is collected using a fluorescent microscope or a fluorescent imaging system, wherein the fluorescent microscope selects a green filter with a central wavelength of 530 nm and a half bandwidth of 30 nm;

[0085] Then, identifying the first fluorescent image acquired by a first image analysis method;

[0086] The first image analysis method is: establishing and training a deep learning model, and then based on the deep learning model, identifying the first fluorescent image to obtain the distribution and content of the first-level microplastics in the first fluorescent image.

[0087] After the excitation light source irradiates the sample microplastics, the fluorescent dye absorbs the light energy and emits fluorescence, which has a specific wavelength and color. In order to capture these emitted fluorescence, a fluorescence microscope or fluorescence imaging system is used for observation and recording.

[0088] The working principle of fluorescence microscopy is to separate the fluorescence emitted by the sample from the excitation light through a set of filters and lens systems, filter out the excitation light, and retain only the fluorescence emitted by the fluorescent dye. Usually, the microscope is equipped with multiple filters, and the appropriate filter combination can be selected to ensure that only the fluorescent signal is captured. The appropriate filter combination is selected according to the characteristics of the dye to capture fluorescence of a specific wavelength. For example, when using coumarin dye, a green filter is selected to capture the yellow-green fluorescence it emits (wavelength is about 520-550nm).

[0089] The fluorescence imaging system includes a highly sensitive camera that can record the fluorescence images emitted by the sample. The system is usually connected to a computer and image acquisition and processing is performed through dedicated software. The position and focal length of the camera need to be precisely adjusted to ensure that the image is clear and distortion-free. The fluorescence image can be viewed in real time through the imaging software, and necessary image enhancement and analysis can be performed.

[0090] When capturing fluorescent images, keep steady and avoid any vibration or movement to obtain high-quality images. After image acquisition, further processing such as denoising, contrast adjustment, and feature extraction can be performed to identify and count microplastic particles.

[0091] According to the results of the fluorescence microscope image shooting, coumarin dye can dye microplastics well; then the first fluorescence image is identified by an image analysis method;

[0092] The image analysis method is:

[0093] Build and train deep learning models;

[0094] Based on the deep learning model, the first fluorescent image / the second fluorescent image is identified to obtain the distribution and content of the first / the second fluorescent image;

[0095] Methods for building and training deep learning models include:

[0096] Acquire multiple groups of fluorescence image data to form a training set;

[0097] The first step is model training;

[0098] Define a YOLO model based on deep learning, input the training samples in the training set into the YOLO model, calculate the predicted value, calculate the loss function value between the predicted value and the true value, perform gradient backpropagation, and update the model parameters according to the set optimization algorithm.

[0099] It should be understood that model training is a process of multiple rounds of iterations. Each round of iteration traverses the training set once, and each time a small batch of samples is obtained from it and sent to the model to perform forward calculations to obtain predicted values. The effect of model training is judged by observing the decreasing trend of the loss value in each round of iteration. For example, when the decreasing trend of the loss value remains unchanged, it means that the model tends to converge.

[0100] Step 2: Model evaluation;

[0101] The test set is input into the trained model for evaluation to obtain the predicted value, calculate the loss function value between the predicted value and the true value, and calculate the evaluation index value to facilitate the evaluation of the model effect.

[0102] Step 3: Model reasoning;

[0103] Input the data to be verified into the trained model to perform reasoning, observe and verify whether the reasoning results meet expectations, and obtain the model weights if they meet expectations.

[0104] Step 4: Model identification;

[0105] Then load the pre-trained model weights into the model; read the image data to be detected, and perform necessary data preprocessing (such as adjusting image size, normalization, etc.);

[0106] The model is then used to identify the image and obtain the distribution of microplastic particles; the test results are visualized and the content of microplastics is calculated.

[0107] S4, the filtrate contains second-level microplastics; the dye in the filtrate that is not combined with the second-level microplastics is separated from the second-level microplastics to obtain a mixed liquid of second-level microplastics and water or second-level microplastics;

[0108] The method for separating the dye in the filtrate that is not combined with the second-level microplastics from the second-level microplastics includes: one or more of extraction, centrifugation, membrane separation, and density separation.

[0109] For example, the coumarin dye can be dissolved by an extractant, such as dichloromethane, and precipitated in water to separate the dye from the microplastics.

[0110] S5. Then, light of a specific wavelength is used to irradiate the mixture of the second-level microplastics and water or the second-level microplastics to obtain a second fluorescent image; the light of the specific wavelength is 490nm blue light;

[0111] S6. Collect and identify the second fluorescent image to obtain the content of the second-level microplastics. The method of this step is consistent with the above-mentioned step S3 and will not be repeated here.

[0112] Example 2

[0113] Another implementation method of the embodiment of the present invention is: without performing S4, S5, and S6 of the above embodiment 1, after completing S1-S3 in embodiment 1, the following steps ①-③ are performed in sequence:

[0114] The filtrate is subjected to irradiation identification treatment in batches to obtain the content of second-level microplastics in the filtrate;

[0115] The irradiation identification process includes:

[0116] ① First, use multiple wavelengths of excitation light for irradiation in sequence;

[0117] The multiple wavelengths are: first irradiate with 490nm laser for 1min, then irradiate with 500nm laser for 1min, and finally irradiate with 510nm fluorescent lamp for 30s, and the treatment is complete;

[0118] ② Then, a fluorescence microscope is used to collect the second fluorescence image; wherein, the fluorescence microscope selects a green filter, the central wavelength of the green filter is 525 nm, and the half bandwidth is 30 nm;

[0119] ③ Then use image analysis method to identify the second fluorescent image.

[0120] The image analysis method is:

[0121] Firstly, preprocessing the second fluorescence image, wherein the preprocessing includes fluorescence correction;

[0122] Fluorescence calibration methods include:

[0123] The local contrast is enhanced without introducing excessive noise by using Unsharp Masking and applying a local adaptive algorithm, such as CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm.

[0124] Then establish a deep learning model and train it; this process is the same as the method in the above embodiment 1;

[0125] Finally, based on the deep learning model, the second fluorescent image after preprocessing is identified to obtain the distribution and content of the second-level microplastics. This process is the same as the method in the above-mentioned Example 1.

[0126] Example 3

[0127] The difference from Example 1 is that the water to be detected is mixed with the fluorescent dye, left to stand for 1 minute, and then filtered through a micron-grade filter membrane to obtain first-level microplastics and filtrate; the mixing treatment is ultrasonic oscillation; the frequency of the ultrasonic wave is 40 kHz, and the oscillation time is 5 minutes;

[0128] The fluorescent dye is a coumarin fluorescent dye aqueous solution with a concentration of 50 μmol / L; the volume ratio of the water to be detected and the coumarin fluorescent dye aqueous solution is 5:100;

[0129] The light of the specific wavelength is 500nm blue light;

[0130] The embodiment of the present invention further provides a mixing auxiliary agent, which is added to the water to be tested during the mixing process, and the addition amount is 1.5wt%; the mixing auxiliary agent can further enhance the selectivity of the coumarin dyeing process, that is, enhance the binding ability of coumarin with microplastics and reduce the binding force between coumarin and other impurities, so as to improve the pertinence of the present invention;

[0131] The mixing auxiliary agent is an active agent; the active agent is obtained by mixing fatty alcohol polyoxyethylene ether and sodium lauryl sulfate in a mass ratio of 1:2.

[0132] Example 4

[0133] The difference from Example 1 is that the water to be detected is mixed with the fluorescent dye, left to stand for 20 minutes, and then filtered through a micron-grade filter membrane to obtain first-level microplastics and filtrate; the mixing treatment is ultrasonic oscillation; the frequency of the ultrasonic wave is 45kHz, and the oscillation time is 8 minutes;

[0134] The fluorescent dye is a coumarin fluorescent dye aqueous solution with a concentration of 1-50 μmol / L; the volume ratio of the water body to be detected and the coumarin fluorescent dye aqueous solution is 1:10.

[0135] The light of the specific wavelength is 450nm blue light.

[0136] The embodiment of the present invention also provides a coumarin with a "DA-π-A" structure; the preparation method of the coumarin with a "DA-π-A" structure is as follows: 10 mL of anhydrous toluene is added as a solvent to 0.5 mmol of a coumarin derivative (D), 0.55 mmol of a boronic acid derivative of dicyanobenzothiophene, 40.5 mol of Pd(PPh3), 0.1 mol of PPh3 and 0.1 mol of K3PO4 to obtain a mixture; under nitrogen protection, the mixture is heated to reflux for 14 h at a heating temperature of 100° C.; an intermediate is obtained,

[0137] The intermediate was mixed with 0.55mmol of a halogenated derivative of thiophene, 40.05mol of Pd(PPh3), 0.1mol of PPh3 and 0.1mol of K3PO4, and 10mL of anhydrous toluene and heated to reflux for 13h at a heating temperature of 100°C; a coumarin with a "DA-π-A" structure was obtained.

[0138] Example 5

[0139] The difference from Example 4 is that in the preparation method of the coumarin with the "DA-π-A" structure, the mixture is heated to reflux for 12 hours under nitrogen protection; and mixed and heated to reflux for 15 hours.

[0140] Example 5

[0141] The difference from Example 4 is that in the preparation method of the coumarin with the "DA-π-A" structure, the mixture is heated to reflux for 15 hours under nitrogen protection; and mixed and heated to reflux for 12 hours.

[0142] Experimental example

[0143] Staining experiments were conducted using configured microplastics, including (1)-(3);

[0144] (1) Microplastic sample preparation

[0145] Choose from various types of microplastics, such as polymethyl methacrylate (PMMA), polyethylene (PE), polypropylene (PP), polystyrene (PS), thermoplastic polyurethane (TPU), polyethylene terephthalate (PET), and more.

[0146] Use an organic solvent (such as methanol, ethanol or acetone) to prepare a microplastic suspension with a concentration of 1-10 mg / ml. The specific steps are as follows: weigh a certain mass of microplastic particles (for example, 10 mg). Add the microplastic particles to 1 ml of organic solvent and mix well to obtain a microplastic suspension of the desired concentration.

[0147] (2) Selection and preparation of fluorescent dyes:

[0148] Select fluorescent dyes suitable for the type of microplastics, such as Nile red, rhodamine B, 1-benzoylsulfonic acid, and coumarin dyes. After using different dyes to dye microplastics, it was found that coumarin dyes had a good dyeing effect on all microplastics (such as Figure 2-5 ), prepare the coumarin fluorescent dye solution and ensure the concentration is appropriate (e.g., 1-50 μM)

[0149] (3) Mixing and incubation of microplastics with fluorescent dyes:

[0150] Mix the prepared microplastic suspension with the fluorescent dye solution, ensuring that the volume of the fluorescent dye is sufficient to completely cover the microplastic particles. The specific steps are as follows:

[0151] Take 1-5 ml of microplastic suspension and add it to 10-100 ml of fluorescent dye solution. Mix the solution by ultrasonic oscillation to ensure that the microplastic particles are evenly distributed in the fluorescent dye solution.

[0152] Finally, the treated microplastic particles are resuspended in an appropriate amount of organic solvent to form a microplastic suspension to be tested.

[0153] Through the above experimental examples, the following conclusions can be obtained:

[0154] 1. Verify the relationship between dyeing and temperature;

[0155] In order to explore the effect of reaction temperature on dyeing effect: four reaction temperatures in the range of 20-100℃ were selected to explore the dyeing of microplastics by coumarin dyes. Figure 6 As shown, according to the results, the dyeing effect is not inhibited or promoted with the increase of temperature, which shows that the reaction temperature does not affect the dyeing effect of coumarin dye on microplastics.

[0156] 2. Verify the relationship between staining and time;

[0157] To explore the effect of incubation time on the dyeing effect: four incubation time periods from 1 to 120 minutes were selected to explore the dyeing of coumarin dyes on microplastics. Figure 7 As shown, the results show that coumarin dyes can quickly dye microplastics and can be successfully dyed within 1-20 minutes; in addition, the dyeing time is 5 minutes when dyeing using the method of Example 3, and the dyeing time is 3-4 minutes when using the coumarin of Example 4, and when dyeing using Examples 3 and 4, dyeing can be achieved within 5 minutes without oscillation treatment.

[0158] like Figure 3 , Figure 4 , Figure 5 As shown, compared with other fluorescent dyes, coumarin dyes can achieve fast and efficient dyeing of microplastics. Coumarin dyes generally have a high affinity for microplastic materials. This is because the coumarin dye molecules contain polar groups that can form strong interactions with chemical groups on the surface of microplastics. This high affinity allows the dye to be quickly adsorbed on the surface of microplastics, thereby achieving rapid dyeing.

[0159] Fluorescent visual identification:

[0160] Illuminate the treated sample with a light source of a specific wavelength to excite the fluorescent dye:

[0161] like Figure 8 , Fig. 9 As shown, this is image processing and analysis:

[0162] Process the captured fluorescence images to identify and count fluorescently labeled microplastic particles:

[0163] Use image analysis software and the YOLO model based on deep learning to analyze and identify the preprocessed images.

[0164] YOLO Model:

[0165] (1) Define a deep learning-based YOLO model to detect and count microplastic particles in images.

[0166] (2) Load the pre-trained model weights to improve the accuracy and efficiency of detection.

[0167] (3) Read the image data to be detected and perform necessary data preprocessing (such as adjusting image size, normalization, etc.).

[0168] (4) Use the model to predict the image and obtain the distribution and quantity of microplastic particles.

[0169] (5) Visualize the detection results and save them in the specified folder.

[0170] Fluorescently labeled microplastic particles were identified using the YOLO model, and image analysis algorithms were used to measure microplastic particles.

[0171] Output analysis results:

[0172] like Fig.10 As shown in the figure, according to the results of image analysis, the number and distribution of microplastics in the water sample are calculated; the output analysis results of the total number of microplastic particles detected in the image can be presented in the form of pictures and data tables and saved for further analysis and reporting. The comparison and analysis of the manual counting results of microplastics with the model counting results showed that the determination coefficient R 2 =0.83, indicating that this research scheme can accurately identify and detect microplastic particles.

[0173] In summary, through the above-mentioned marking of microplastics in each stained image, model training and calculation, each image can accurately reflect the content of microplastics.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rapid detection method for microplastic content in water, characterized in that: include: The water to be tested is mixed with the fluorescent dye, left to stand for 1 to 20 minutes, and then filtered through a micron-grade filter membrane to obtain first-level microplastics and filtrate; the mixing treatment is ultrasonic oscillation; the frequency of the ultrasonic wave is 40 to 45 kHz, and the oscillation time is 5 to 8 minutes; The first-level microplastics are irradiated with light of a specific wavelength to obtain a first fluorescent image; The first fluorescent image is identified to obtain the content of first-level microplastics.

2. A method for rapid detection of microplastic content in water according to claim 1, characterized in that: The fluorescent dye is a coumarin dye.

3. A rapid detection method for microplastic content in water as claimed in claim 2, characterized in that: The coumarin dye is a coumarin fluorescent aqueous solution with a concentration of 1-50 μmol / L; the volume ratio of the water body to be detected to the coumarin fluorescent aqueous solution is 1-5:10-100.

4. A rapid detection method for microplastic content in water as claimed in claim 3, characterized in that: The light of the specific wavelength is blue light of 450-500nm.

5. A method for rapid detection of microplastic content in water as claimed in claim 3, characterized in that: The micron-grade filter membrane has a pore size of 45 microns.

6. A method for rapid detection of microplastic content in water as claimed in claim 3, characterized in that: The filtrate contains second-level microplastics; The method further comprises: screening the second-level microplastics in the filtrate, wherein the screening method comprises: Separating the dye in the filtrate that is not combined with the second-level microplastics from the second-level microplastics to obtain a mixed liquid of the second-level microplastics and water or the second-level microplastics; Then, a mixture of the second-level microplastics and water or the second-level microplastics is irradiated with light of a specific wavelength to obtain a second fluorescent image; The second fluorescent image is identified to obtain the content of second-level microplastics.

7. A rapid detection method for microplastic content in water according to claim 6, characterized in that: The method for separating the dye in the filtrate that is not combined with the second-level microplastics from the second-level microplastics includes: one or more of extraction, centrifugation, membrane separation, and density separation.

8. A method for rapid detection of microplastic content in water as claimed in claim 3, characterized in that: The method for identifying the first fluorescent image comprises: The first fluorescent image is collected using a fluorescent microscope or a fluorescent imaging system, wherein the fluorescent microscope selects a green filter with a central wavelength of 530 nm and a half bandwidth of 30 nm; Then, identifying the first fluorescent image acquired by a first image analysis method; The first image analysis method is: establishing and training a deep learning model, and then based on the deep learning model, identifying the first fluorescent image to obtain the distribution and content of the first-level microplastics in the first fluorescent image.

9. A method for rapid detection of microplastic content in water as claimed in claim 6, characterized in that: The filtrate is subjected to irradiation identification treatment in batches to obtain the content of second-level microplastics in the filtrate; The irradiation identification process includes: First, multiple wavelengths of excitation light are used for irradiation in sequence; Then, a fluorescence microscope is used to collect the second fluorescence image; wherein the fluorescence microscope selects a green filter, the central wavelength of the green filter is 525 nm, and the half bandwidth is 30 nm; Then, a second image analysis method is used to identify the second fluorescent image.

10. A method for rapid detection of microplastic content in water according to claim 9, characterized in that: The second image analysis method is: Preprocessing the second fluorescence image, wherein the preprocessing includes fluorescence correction; Build and train deep learning models; Based on the deep learning model, the second fluorescent image after preprocessing is identified to obtain the distribution and content of the second-level microplastics.

Images