A method, terminal device and storage medium for identifying microplastics or nanoplastics

Abstract

This invention relates to a method, terminal device, and storage medium for identifying microplastics or nanoplastics. The method includes: S1: acquiring the Raman spectrum of a sample to be identified, where the sample is a microplastic or nanoplastic; S2: analyzing the Raman spectrum of the sample to be identified and the Raman spectra of known types of plastics using a multi-source analysis method, matching the analytical spectrum of the sample to be identified with the analytical spectrum of the known types of plastics, and taking the type of the analytical spectrum with the highest matching degree as the type of the sample to be identified; S3: obtaining a Raman image based on the Raman spectrum of the sample to be identified, and reconstructing the Raman image using Gaussian fitting. The position of the sample to be identified is obtained based on the position of the peak apex in the reconstructed image, and the size of the sample to be identified is obtained based on the peak width of the Gaussian peak in the reconstructed image. This invention realizes the imaging and detection of microplastics and nanoplastics, solving the problem that nanoplastics cannot be imaged and resolved.

Description

Technical Field

[0001] This invention relates to the field of microplastic and nanoplastic detection, and more particularly to a method, terminal device and storage medium for identifying microplastics or nanoplastics. Background Technology

[0002] Microplastics refer to plastic materials with a particle size of less than 5 mm in the environment, including fragments, fibers, particles, foams, and films. They are a new type of environmental pollutant. Due to their large specific surface area, microplastics can adsorb organic and inorganic pollutants and provide a stable living environment for harmful microorganisms. If microplastics are ingested by organisms, they can cause significant biotoxic effects and pose potential risks to ecosystems and humans along the food chain. Therefore, microplastic pollution is receiving increasing global attention.

[0003] Unlike traditional environmental pollutants, microplastics are a class of complex organic polymers characterized by diverse morphologies and tiny particle sizes. Therefore, accurate detection and characterization of microplastics are crucial for studying their sources, distribution, migration patterns, and the risk of cumulative toxicity. Currently, microscopic techniques alone are insufficient to distinguish microplastics from similarly sized natural particulate matter. Molecular information detection techniques, such as infrared spectroscopy, Raman spectroscopy, and mass spectrometry, are typically required for identification.

[0004] Raman spectroscopy has seen rapid development in the identification of microplastics. Due to its laser excitation, it offers higher imaging resolution compared to infrared spectroscopy and can identify microplastics through unique fingerprint spectra. Compared to mass spectrometry, Raman spectroscopy offers the advantages of non-destructive and label-free detection. Furthermore, when combined with confocal laser scanning, it provides not only visualized molecular information about microplastics but also information on their size, morphology, and abundance. However, when microplastics become even smaller, such as nanoplastics (<1000 nm), the signal becomes extremely weak. Simultaneously, when nanoplastics are smaller than the laser spot size, the imaging becomes distorted, reflecting more information about the laser spot size than the size of the nanoplastic itself. Overcoming the laser scattering limit will determine whether effective imaging analysis of nanoplastics is possible. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method, terminal device, and storage medium for identifying microplastics or nanoplastics.

[0006] The specific plan is as follows:

[0007] A method for identifying microplastics or nanoplastics includes the following steps:

[0008] S1: Collect the Raman spectrum of the sample to be identified, which is a microplastic or nanoplastic;

[0009] S2: After analyzing the Raman spectra of the sample to be identified and the Raman spectra of known types of plastics using a multi-source analysis method, the analytical results spectrum of the sample to be identified is matched with the analytical results spectrum of known types of plastics, and the type of the analytical result spectrum with the highest matching degree is taken as the type of the sample to be identified.

[0010] S3: Obtain a Raman image based on the Raman spectrum of the sample to be identified, and reconstruct the Raman image using Gaussian fitting. Obtain the position of the sample to be identified based on the position of the peak in the reconstructed image, and obtain the size of the sample to be identified based on the peak width of the Gaussian peak in the reconstructed image.

[0011] Furthermore, step S1 also includes preprocessing the Raman spectrum of the sample to be identified to remove interference from the fluorescence background signal.

[0012] Furthermore, in step S2, the matching process uses the correlation matrix method to calculate the correlation value between the spectra of the two analysis results, with the largest correlation value indicating the highest matching degree.

[0013] Furthermore, step S2 includes preprocessing of all analytical result spectra before matching. Preprocessing includes one or more of smoothing, baseline correction, interpolation, and normalization.

[0014] Furthermore, in step S3, obtaining a Raman image based on the Raman spectrum of the sample to be identified includes: imaging based on the intensity information obtained by analyzing the Raman spectrum of the sample to be identified using a multi-source analysis method, and the imaging result is the Raman image of the sample to be identified.

[0015] Furthermore, in step S3, obtaining a Raman image based on the Raman spectrum of the sample to be identified includes: using the intensities of multiple different characteristic peaks, plotting the Raman spectrum of the sample to be identified into multiple Raman images, performing calculations on the multiple Raman images using logical algorithms and / or algebraic algorithms, and using the calculated Raman image as the Raman image of the sample to be identified.

[0016] Furthermore, logical algorithms include logical OR, logical AND, and logical subtraction operations.

[0017] Furthermore, the algebraic algorithm includes: after adjusting all Raman images to the same fixed interval, assigning corresponding weights according to the magnitude of the characteristic peaks of different Raman images, and then performing weighted algebraic operations on all Raman images.

[0018] A microplastic or nanoplastic identification terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described above in the embodiments of the present invention.

[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above in the embodiments of the present invention.

[0020] This invention employs the above-described technical solution to successfully extract and image the weak signals of microplastics and nanoplastics, which are susceptible to laser scattering and cannot be precisely focused. Super-resolution imaging reconstruction is then performed to achieve a resolution of <50nm. Compared with existing testing techniques, this invention enables the imaging and detection of microplastics and nanoplastics, solving the problem of the inability to image and resolve nanoplastics. Attached Figure Description

[0021] Figure 1 The diagram shown is a flowchart of Embodiment 1 of the present invention.

[0022] Figure 2 The diagram shown illustrates the imaging and analysis process of particles captured by the spider web in this embodiment.

[0023] Figure 3 The diagram shows the process of identifying suspicious microplastics or nanoplastics using the PCA algorithm.

[0024] Figure 4 The diagram shows a super-resolution visualization analysis process for a mixture of PS nanoplastics with diameters of 300 nm and 100 nm.

[0025] Figure 5 The diagram shows a PCA analysis and super-resolution imaging process of a mixed PS nanoplastic with diameters of 300 nm and 100 nm.

[0026] Figure 6 The diagram shows a super-resolution imaging process of PS nanoplastic with a diameter of less than 100 nm.

[0027] Figure 7 The diagram shows the logic algorithm analysis and super-resolution imaging process of PS nanoplastics with a diameter of less than 100 nm. Detailed Implementation

[0028] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments, and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention.

[0029] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0030] Example 1:

[0031] This invention provides a method for identifying microplastics or nanoplastics, such as... Figure 1 The diagram shown is a flowchart of a microplastic or nanoplastic identification method according to an embodiment of the present invention. The method includes the following steps:

[0032] S1: Collect the Raman spectrum of the microplastic or nanoplastic sample to be identified.

[0033] In this embodiment, the sample to be identified is a nanoplastic sample. The method for collecting the nanoplastic sample is as follows: dilute polystyrene (PS) nanoplastics 10 times (v / v) in ultrapure water, sonicate for 5 minutes, and take a small amount (about 2 μL of droplet) of the sample dispersed on the clean mesh-like silicon wafer or glass slide as the nanoplastic sample.

[0034] It should be noted that since the Raman spectral signals emitted by microplastics or nanoplastics are relatively weak, the Raman spectral scanning of the microplastic or nanoplastic sample to be identified should be performed with the aim of obtaining as much signal as possible. In practice, this should be achieved through high-resolution scanning.

[0035] In this embodiment, Raman spectra are acquired using a confocal Raman microscope with a 532nm (or other wavelength) laser diode. Stokes Raman signals are acquired using a CCD detector with a 100x objective lens (or other lens) at room temperature (approximately 24°C), forming a scanning matrix. The pixel size for each Raman spectrum can include, but is not limited to, 1μm × 1μm, 500nm × 500nm, 100nm × 100nm, and 40nm × 40nm. The integration time for each pixel remains constant at 0.5s, 1s, or other durations. When time permits, the scanning should be as slow as possible, and the number of scanned pixels should be as high as possible (or the pixel size should be as small as possible). The laser spot size is not considered at this stage.

[0036] After Raman spectroscopy acquisition is completed, since the acquired Raman spectrum may include fluorescence background signals, in order to reduce the influence of fluorescence background signals on subsequent detection, this embodiment also includes Raman spectrum preprocessing, that is, subtracting the acquired Raman spectrum baseline from the Raman spectrum.

[0037] The preprocessing process can be implemented using software such as WITec Project, Excel, and Origin, and is not limited to these specific software programs.

[0038] S2: After analyzing the Raman spectra of the sample to be identified and the Raman spectra of known types of plastics using a multi-source analysis method, the analytical results spectrum of the sample to be identified is matched with the analytical results spectrum of known types of plastics, and the type of the analytical result spectrum with the highest matching degree is taken as the type of the sample to be identified.

[0039] Multi-source analysis methods can employ algorithms such as principal component analysis (PCA). This embodiment uses PCA, which orthogonally decodes the Raman spectrum into two new datasets: one containing the fundamental element spectrum (principal component characteristic variable scores) and the other containing the signal intensity (principal component loading coefficients). The advantage of multi-source analysis is that signal intensity imaging is based on the entire fundamental element spectrum, rather than just selected characteristic peaks, thus resulting in more accurate imaging.

[0040] The type of a sample can be identified using basic meta-spectroscopy. The matching process uses the correlation matrix method to calculate the correlation value between the spectra of two analysis results (i.e., basic meta-spectroscopy), with the highest correlation value indicating the highest matching degree.

[0041] In this embodiment, the matching process includes preprocessing of all analytical result spectra before matching. Preprocessing includes one or more of smoothing, baseline correction, interpolation, and normalization.

[0042] S3: Obtain a Raman image based on the Raman spectrum of the sample to be identified, and reconstruct the Raman image using Gaussian fitting. Obtain the position of the sample to be identified based on the position of the peak in the reconstructed image, and obtain the size of the sample to be identified based on the peak width of the Gaussian peak in the reconstructed image.

[0043] In this embodiment, Gaussian fitting reconstruction is performed using two-dimensional or three-dimensional Gaussian fitting reconstruction.

[0044] Super-resolution imaging won the Nobel Prize in 2014, but these achievements were all obtained over a wide field and are mostly applied to fluorescence; Raman spectroscopy has not yet realized it. Confocal imaging is a far-field technique, limited by laser scattering. That is, when the size of the nanoplastic is smaller than the laser spot size, the image of the nanoplastic will be a spot much larger than the nanoplastic, and its exact location is unclear, possibly anywhere within the laser spot. We found that not only is the intensity within the laser spot Gaussian distributed, but the intensity of the spot in the Raman image of this nanoplastic also follows a Gaussian distribution. After Gaussian fitting reconstruction, the location of the peak apex corresponds to the location of the microplastic, and the size of the microplastic can be deduced from the peak width of the Gaussian peak. Gaussian fitting reconstruction can also make the peak sharper, even if the corresponding signal is enhanced. At this point, the Raman image can break through the scattering limit of the laser (λ / 2NA, for a 532nm laser, the scattering limit is approximately 295nm), thus achieving super-resolution imaging to detect microplastics or nanoplastics <50nm.

[0045] This embodiment describes two methods for obtaining Raman images based on the Raman spectrum of the sample to be identified. The first method involves imaging the sample by analyzing the intensity information (a series of intensity values) obtained from the Raman spectrum using a multi-source analysis method. The resulting image is the Raman image of the sample. Specifically, the intensity values ​​are converted into colors and mapped to pixel locations, giving each pixel a color. The colors of all pixels are combined to form the image. The second method uses the intensities of multiple different characteristic peaks to plot the Raman spectrum of the sample into multiple Raman images. These images are then processed using logical and / or algebraic algorithms, and the resulting Raman image is used as the Raman image of the sample to be identified. Those skilled in the art can choose the appropriate method in practical applications based on their needs (primarily considering the size of the microplastics or nanoplastics; the second method is chosen for larger sizes, the first method for smaller sizes, or both).

[0046] In this embodiment, the intensity of the characteristic peak of polystyrene sodium plastic is at 1000 cm⁻¹. -1 1230cm -1 1600cm -1 2900cm -1 and 3055cm -1 Draw five Raman images.

[0047] Logical algorithms include logical OR, logical AND, and logical subtraction operations, performed on two or more Raman images. In a logical OR operation, the mapping signal of any pixel at a specific location in the previous generation image is picked up and merged into a new image (second-generation image). In a logical AND operation, only signals appearing simultaneously in two or more first-generation images can be extracted in the second-generation image; although some signal loss may occur, noise is reduced. In a logical subtraction operation, noise or interference can be directly removed when "noisy or interfering" images can be effectively identified and selected. In practical applications, those skilled in the art can choose one or more of these logical algorithms and mix them to obtain third- or fourth-generation images, enabling the final image to contain more of the required information.

[0048] Since the logical algorithm is based on a threshold value, meaning the pixel value in the image must reach a calculated switching value, and different feature peaks have different intensities, resulting in different image pixel values, the logical algorithm does not consider these differences during processing. To address this technical problem, this embodiment proposes an algebraic algorithm: after adjusting all Raman images to the same fixed interval (pixel value interval 0-255, 0-1, others, etc.), corresponding weights are assigned based on the magnitude of the feature peak values ​​corresponding to different Raman images. Then, weighted algebraic operations are performed on all Raman images to achieve Raman image superposition and obtain a more optimized analysis result. The weighted algebraic operations include addition, subtraction, multiplication, and division operations after weighting.

[0049] The following is a specific experiment to verify this.

[0050] Experiment 1:

[0051] Figure 2 The diagram illustrates the imaging analysis process of particles captured by a spider web. The images show a photograph (a) and Raman intensity images (be, hi) of the particles captured by the spider web. In (a), the scanned area, 10 μm × 10 μm in size, is marked by a red box. Raman spectra were collected on a 100x objective lens with a scan pixel size of 0.33 μm × 0.33 μm (generating a 30 × 30 spectral scan database) and an integration time of 1 s. The spectral intensity at the characteristic peak positions can be used for imaging (be), with peak positions and widths marked below the image, along with background correction and a 10% intensity color shift correction. (f) shows the preprocessing of the collected spectra. (g) shows the preprocessed spectra, with the relative heights of the four characteristic peaks marked, while others are ignored. (h) shows the logical OR superposition of the four characteristic peak imaging images (be). (i) shows the "algebraic" superposition of the four characteristic peak imaging images (be).

[0052] exist Figure 2In the diagram, (a) is white light imaging. (be) is imaging of four characteristic peaks, some strong and some weak, due to their varying intensities. (f) is the preprocessing of the Raman spectrum. After preprocessing, especially intensity normalization, it is magnified to (g). The relative peak heights of different characteristic peaks are marked, and these four peak heights are used for imaging (be). Other peaks are not included in the calculation and are ignored. (h) is a logical algorithm used to superimpose the images (be). Because the logical algorithm is based on a threshold value and does not distinguish between the different peak heights of different characteristic peaks, the superimposed image (h) does not accurately reflect the true image. (i) is an algebraic algorithm used to superimpose the images (be). We first unify the images (be) to a threshold value range, such as 0-255, 0-1, or others. Then, using their relative peak heights marked in (g), a weighted superposition is performed, i.e., (0.28b+0.66c+1d+0.83e) / 2.77. This results in a more realistic superimposed image (i).

[0053] Experiment 2:

[0054] Figure 3 The diagram shows the process of identifying suspicious microplastics or nanoplastics using the PCA algorithm. Figure 3 (a) lists the suspected spectra (samples) and compares them with the spectra of 10 common plastics, including PS, PET, PE, PVC, PP, PA, PMMA, PC, PUR, and Teflon. (b) Preprocessing is performed on the suspected spectra, including smoothing, baseline correction, interpolation, and normalization. Once preprocessed, all spectra in (a) are converted to (c). The spectral matrix in (c) is then subjected to PCA analysis to generate (df). (d) is a plotted graph using PC1, PC3, and PC5 as the x, y, and z axes. (e) The correlation matrix is ​​mapped. (f) Correlation values ​​are extracted from the samples.

[0055] Figure 3In (a), comparing the spectrum of the suspected particle (sample) with the Raman spectra of 10 common plastics, we cannot effectively identify the particle. To facilitate identification, we preprocess the spectra, as shown in (b). After all the spectra in (a) have been preprocessed, we obtain (c), but identification is still quite difficult. At this point, we use the PCA algorithm to extract correlation values. In (d), it is clear that the sample is very likely PET. (e) shows the correlation matrix, where the sample is more likely to be correlated with PET, as indicated by the dashed box. After extracting the correlation values, we can display (f), where the suspected particle sample is most strongly correlated with PET, or in other words, we can identify the particle as PET plastic.

[0056] Experiment 3:

[0057] Figure 4 The diagram shows a super-resolution visualization analysis process of mixed PS nanoplastics with diameters of 300 nm and 100 nm. Figure 4 (a) is a photograph of the microplastic, (b) is the Raman spectrum of the microplastic, (c, d) are Raman images of the microplastic, and (eh) is the fitted and reconstructed image of the microplastic. Nanoplastics with diameters of 300 nm and 100 nm were diluted 10-fold and mixed (1:1, v:v) and coated onto a silicon-gold wafer. All Raman spectra were acquired under a 100x objective lens with a pixel size of 200 nm × 200 nm. (c) 1003 cm⁻¹ -1 The characteristic peak intensity is 20 cm. -1 (d) is another representation of (c), displayed in 3D with a white background and normalized color values ​​of 0-255. (e) is the Gaussian-fitted reconstruction of (d), and (f) is a combination of (d) and (e). The enlarged bounded area is shown as (g). The arrows in (g) indicate the positions of the fitted peaks. (h) is a top view of the bounded area in (g), where the equations are the fitting equations and parameters. When image (c) is 1024×1024 pixels, the x / y axis values ​​are used, and the z-axis values ​​are normalized to an intensity of 0-255.

[0058] Figure 4 The sample contained a mixture of PS nanoplastics with diameters of 300 nm and 100 nm. Figure 4 In (a), the sample surface can be observed, and aggregates of approximately 300 nm may exist within the PS nanoplastics. A single Raman spectrum was acquired at the location indicated in (a), and after intensity and color shift correction, it is displayed in (b). The standard spectrum of PS is listed as a reference. Significant PS signals are visible in the spectra at positions 1-2. (A 1003 cm⁻¹) -1The characteristic peaks at the point were imaged, and the resulting image is shown in Figure (c). Essentially, the generated pattern matches (a) very well, and the PS nanoplastic can be visually observed. (d) provides another version of the image (c), converted to an RGB color image with an intensity range of 0-255, and represented in 3D with a white background. The 100nm PS is likely negligible here, mainly due to shielding effects and the signal being too weak; its signal is significantly weaker than that emitted by a 300nm PS compared to its size.

[0059] Will Figure 4 Image (d) is obtained by Gaussian fitting. Compared to (d), each patch is better focused, and the intensity of some patches is significantly enhanced, exceeding the intensity range of 0-255. Combining images (d, e) into (f) shows that almost every peak has a light-colored bulge, which represents the central apex of the fitted reconstructed peak. Upon local magnification, (g) shows this more clearly, including two peaks with bulges (indicated by arrows) and one peak with a central pole. Further magnification of the dashed box area reveals the well-focused signal of the light-colored reconstructed peak within the dashed box of (g) in the top view (h). The contour lines represent the peaks and centers of the fitted reconstruction and can be used to identify the physical location of the nanoplastic.

[0060] Experiment 4:

[0061] Figure 5 As shown Figure 4 PCA results for PS microplastics: (a) PCA spectrum, (b) PCA image of PC2, (c) fitted reconstruction and merged plot of (b), and (d) magnified view of the dashed box area in (c).

[0062] We directly from Figure 4 Starting with the Raman spectrum, the focus is no longer limited to individual characteristic peaks. There are many Raman spectroscopy decoding algorithms, and we have found that principal component analysis (PCA) can effectively orthogonally decode the original Raman spectrum into two new datasets: one containing the elementary information of the entire spectrum, and the other containing pure intensity information. In this way, the elementary spectrum can be used to identify the detection object, and the intensity information can be used for imaging. The advantage is that the entire elementary spectrum or the entire spectrum can be used in imaging, rather than just a single characteristic peak. Alternatively, previously ignored spectral information is effectively utilized here.

[0063] Figure 5 Principal component analysis (PCA) results for PS microplastics with diameters of 300 nm and 100 nm are shown. From the PCA spectra shown in (a) (intensity shift correction for better visualization), it can be seen that the standard spectrum of PS matches well with PC2. Therefore, PC2 is effectively loaded and mapped as shown in the image in (b), which is consistent with... Figure 4(a,d) corresponds very well, but the determinism of the image is significantly improved because the entire spectrum is involved in the imaging process, not just... Figure 4 One of them is 1003cm -3 The peaks at the point. We re-fit (b) with a two-dimensional Gaussian peak value to reconstruct (c). Here, the fitted reconstructed image (light color) is placed alongside the original PCA image (dark color) for easy comparison. We can see that almost all the peaks have been reconstructed and refocused. (d) is a magnified view of the area; even very weak signals can be fitted and reconstructed as light colors. These small peaks, possibly 100 nm PS (they were ignored earlier due to shielding effects and weak signals). In contrast, PCA analysis can visualize them more effectively.

[0064] Experiment 5:

[0065] Figure 6 The diagram illustrates the super-resolution imaging process of PS nanoplastics with a diameter less than 100 nm. It includes SEM images (a), Raman spectra (b), Raman images (ce, hj), and fitted reconstructed images (f, g, j). Nanoplastics with diameters of 30-80 nm are distributed on a silicon surface. Raman spectra were collected under a 100x objective lens in a 50×50 array to scan a 2 μm × 2 μm region (pixel size 40 nm × 40 nm). (c) At 3500 cm⁻¹ -1 Draw a 100cm wide area. -1 The blank wavenumber window serves as an internal reference image for the background. (d) At 1230cm -1 The characteristic peak of PS is drawn at the location, with a peak width of 20cm. -1 As shown in (b). (e) is another version of (d), using a white background and 3D rendering, with the intensity normalized to 0-255. (f) is the fitted reconstructed image. The dashed box area in (d) is shown in (g) (top view), where image (e) serves as the background at the same location. The interpolated values ​​are the fitting parameters. Also at the same location, the collected Raman signal (net peak intensity) is shown in (h, top), with the boundaries marked by dashed lines. Once color interpolation is performed, the image is converted to (h, bottom) and (i) (3D rendering). The fitted surface is described in detail in (j).

[0066] Figure 6 In (a), the diameters of the PS nanoplastics range from 30 to 80 nm. We reduced the pixel size of the scan to 40 nm × 40 nm and collected Raman spectra. The characteristic peak of silicon is at 520 cm⁻¹. -1 and 900-1100cm -1 It can interfere with PS. Therefore, we chose the characteristic peak at 1230 cm⁻¹. -1Image (d) is obtained. Simultaneously, as an internal reference, (c) uses blank areas of silicon and PS for imaging, which are almost entirely random noise. In comparison, (d) can visualize PS nanoplastics better, even comparable to (a). (d) can be displayed in 3D as (e), and then reconstructed as (f). The local magnification of the dashed box in (d) is (g), which simultaneously displays the original image (dark) and the reconstructed image (light). It can be seen that a resolution of <50nm is achievable, and even lower is possible, as long as the pixel size during scanning is small enough, the collected signal position is accurate enough, and the density is high enough, it is possible to break through the laser scattering limit (λ / 2NA; for a 532nm laser, the scattering limit is approximately ~295nm). (h) shows a more original image of this selected area; the upper part is the collected characteristic peak imaging, and the lower part is the image after interpolation. (i) shows its 3D imaging. (j) is the reconstructed image. Clearly, the reconstructed image is clearer.

[0067] Experiment 6:

[0068] Figure 7 The diagram shows the logic algorithm analysis and super-resolution imaging process for PS nanoplastics with a diameter less than 100 nm. (a) shows the process using a hybrid logic algorithm ((1230 cm)). -1 OR 1600cm -1 AND (2900cm) -1 OR 3055cm -1 (a) is a Raman image of four separately mapped PS feature peaks. (b) is a superposition with the initial image and the fitted reconstructed image. (c) is a magnified area of ​​the dashed box in (b). (d) is another view of (c).

[0069] Figure 7 The image in (a) has been processed by a hybrid logic algorithm, resulting in a higher signal-to-noise ratio. The image, initially dark, can be reconstructed into a lighter color, and the images are then superimposed to obtain (b). A magnified view is (c), where almost all the original dark peaks have been reconstructed and refocused into lighter colors. The contour lines in (d) clearly show the reconstruction and refocusing process.

[0070] This invention addresses the challenges of weak signals and inaccurate focusing due to laser scattering in microplastics and nanoplastics. It successfully extracts and images these weak signals, followed by super-resolution imaging reconstruction, achieving a resolution of <50nm. Compared to existing testing techniques, this invention enables the imaging and detection of microplastics and nanoplastics, solving the problem of the inability to image and resolve nanoplastics.

[0071] Example 2:

[0072] The present invention also provides a microplastic or nanoplastic identification terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the method embodiment described above in Embodiment 1 of the present invention.

[0073] Furthermore, as an executable solution, the microplastic or nanoplastic identification terminal device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The microplastic or nanoplastic identification terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above-described composition of the microplastic or nanoplastic identification terminal device is merely an example and does not constitute a limitation on the microplastic or nanoplastic identification terminal device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the microplastic or nanoplastic identification terminal device may also include input / output devices, network access devices, buses, etc., and the embodiments of the present invention do not limit this.

[0074] Furthermore, as an executable solution, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the microplastic or nanoplastic identification terminal device, connecting various parts of the entire microplastic or nanoplastic identification terminal device through various interfaces and lines.

[0075] The memory can be used to store the computer programs and / or modules. The processor, by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the microplastic or nanoplastic identification terminal device. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0076] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the embodiments of the present invention.

[0077] If the modules / units integrated in the microplastic or nanoplastic identification terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.

[0078] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for identifying microplastics or nanoplastics, characterized in that, Includes the following steps: S1: Collect the Raman spectrum of the sample to be identified, which is a microplastic or nanoplastic; S2: After analyzing the Raman spectra of the sample to be identified and the Raman spectra of known types of plastics using a multi-source analysis method, the analytical results spectrum of the sample to be identified is matched with the analytical results spectrum of known types of plastics, and the type of the analytical result spectrum with the highest matching degree is taken as the type of the sample to be identified. S3: Obtain a Raman image based on the Raman spectrum of the sample to be identified, and reconstruct the Raman image using Gaussian fitting. The position of the sample to be identified is determined based on the position of the peak vertices in the reconstructed image, and the size of the sample to be identified is determined based on the peak width of the Gaussian peaks in the reconstructed image. There are two methods for obtaining a Raman image based on the Raman spectrum of the sample to be identified: The first method involves imaging the sample based on the intensity information obtained from the Raman spectrum of the sample through multi-source analysis. The imaging result is the Raman image of the sample to be identified. Specifically, a series of intensity values ​​are converted into colors, mapped to pixel positions, and the colors of all pixels are combined to form an image. The second method involves using the intensities of multiple different characteristic peaks to plot the Raman spectrum of the sample to be identified into multiple Raman images. Logical algorithms and / or algebraic algorithms are used to calculate the values ​​of these multiple Raman images, and the calculated Raman image is used as the Raman image of the sample to be identified. The algebraic algorithm includes: adjusting all Raman images to the same fixed interval, assigning corresponding weights based on the magnitude of the characteristic peak values ​​of different Raman images, and then performing weighted algebraic operations on all Raman images.

2. The method for identifying microplastics or nanoplastics according to claim 1, characterized in that: Step S1 also includes preprocessing the Raman spectrum of the sample to be identified to remove interference from the fluorescence background signal.

3. The method for identifying microplastics or nanoplastics according to claim 1, characterized in that: In step S2, the matching process uses the correlation matrix method to calculate the correlation value between the spectra of the two analysis results. The maximum correlation value indicates the highest matching degree.

4. The method for identifying microplastics or nanoplastics according to claim 1, characterized in that: Before matching in step S2, all analytical results spectra are preprocessed, including one or more of smoothing, baseline correction, interpolation, and normalization.

5. The method for identifying microplastics or nanoplastics according to claim 1, characterized in that: Step S3, obtaining a Raman image based on the Raman spectrum of the sample to be identified, includes: imaging based on the intensity information obtained by multi-source analysis of the Raman spectrum of the sample to be identified, and the imaging result is the Raman image of the sample to be identified.

6. The method for identifying microplastics or nanoplastics according to claim 1, characterized in that: Logical algorithms include logical OR, logical AND, and logical subtraction.

7. A microplastic or nanoplastic identification terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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