Heavy metal detection method and device based on hydrophilic microstructure

Abstract

The invention provides a heavy metal detection method and device based on a hydrophilic microstructure, and relates to the technical field of spectrum analys.The method comprises the steps that a collected sample is added to the surface of a hydrophilic microstructure substrate to be dried, and a to-be-detected sample is obtained; wherein the hydrophilic microstructure substrate is prepared by femtosecond laser processing on the surface of a metal substrate; ablating the surface of the to-be-detected sample by adopting pulse laser, and detecting a plasma characteristic spectral signal generated after ablating; and quantitatively predicting the plasma characteristic spectrum signal to obtain the content of the heavy metal elements in the sample. The solute in the to-be-detected sample can be rapidly enriched by preparing the substrate with the hydrophilic structure, the defect that in the prior art, the trace metal in the irrigation water is difficult to detect is overcome, and detection development and application of the technology in the trace metal in the irrigation water and liquid are facilitated.

Description

Technical Field

[0001] The present application relates to the field of spectral analysis technology, and in particular to a heavy metal detection method and device based on a hydrophilic microstructure. Background Art

[0002] The quality of agricultural irrigation water directly affects crop growth and food safety. The monitoring of heavy metal content in irrigation water, especially chromium (Cr), becomes particularly important. Traditional heavy metal detection methods, such as atomic absorption spectroscopy, are accurate but complex to operate, costly, and cannot achieve rapid on-site detection. Laser induced breakdown spectroscopy (LIBS) technology has gradually become a powerful alternative technology with its advantages of non-destructive, rapid, and direct analysis. However, the existing LIBS technology is often limited by the intensity and stability of the signal when detecting low-concentration heavy metals, and cannot achieve rapid and accurate detection of low-concentration heavy metal chromium in agricultural irrigation water. Summary of the invention

[0003] In view of this, the purpose of the embodiment of the present application is to provide a heavy metal detection method and device based on a hydrophilic microstructure, wherein a hydrophilic microstructure substrate (for example, an aluminum plate) is prepared by an ultrashort pulse laser, and irrigation water is filtered and treated, a fixed volume of a sample to be tested (for example, agricultural irrigation water) is dripped onto the substrate by a pipette, and heated and dried to obtain a sample to be tested enriched with elements of the sample to be tested; a laser beam with a wavelength of 532nm is used to ablate the sample to be tested to generate plasma, and the plasma signal is transmitted to a high-resolution spectrometer through a photodetector, and a pulse signal is generated by the laser to control the high-resolution spectrometer to collect plasma characteristic spectral signals; a quantitative prediction model of a preset nonlinear partial least squares algorithm model is configured to process and analyze the collected spectrum, so as to predict the heavy metal content, meet the detection sensitivity of the detection system, and solve the problems of the existing detection system being complex, cumbersome to operate, poor stability and inability to work continuously, and can realize rapid, accurate and green detection of low-concentration heavy metal Cr content in agricultural irrigation water.

[0004] In a first aspect, an embodiment of the present application provides a heavy metal detection method based on a hydrophilic microstructure, the method comprising: Adding the collected sample to the surface of a hydrophilic microstructure substrate and drying it to obtain a sample to be tested; wherein the hydrophilic microstructure substrate is prepared by femtosecond laser processing of the surface of a metal substrate; ablating the surface of the sample to be tested by using a pulsed laser, and detecting a characteristic spectrum signal of the plasma generated after ablation; The plasma characteristic spectrum signal is quantitatively predicted to obtain the content of heavy metal elements in the sample.

[0005] In the above implementation process, a substrate with a hydrophilic structure is prepared to quickly enrich the sample to be tested, thereby alleviating the difficulty of detecting trace metals in irrigation water in the existing technology, which is conducive to the development and application of this technology in the detection of trace metals in irrigation water and liquids.

[0006] Optionally, the metal substrate comprises: an aluminum plate; The preparation method of the hydrophilic microstructure substrate comprises: An ultrashort pulse laser is used to micro-process the surface of an aluminum plate to form multiple periodic micro-nano structures to obtain a hydrophilic microstructure substrate; The roughness of the aluminum plate surface is modified due to the presence of multiple periodic micro-nano structures.

[0007] In the above implementation process, the aluminum plate can significantly enhance the light absorption effect when the laser induced breakdown spectroscopy technology is applied, thereby enhancing the coupling effect between the laser and the sample and enhancing the detection capability of the LIBS technology; the hydrophilic microstructure substrate helps to improve the uniformity of sample analysis and is beneficial to improving the sensitivity and accuracy of LIBS detection.

[0008] Optionally, the plurality of periodic micro-nano structures include a plurality of rectangular processing areas arranged according to a preset period; the rectangular processing area is composed of a plurality of scanning lines with equal intervals and equal sizes.

[0009] In the above implementation process, compared with random non-periodic micro-nano structures, periodic micro-nano structures can help the light-absorbing substrate to form a fixed structure with stable and reliable performance. The periodic micro-nano structure only contains scanning lines of equal size, which makes it easy to control the scanning parameters during femtosecond laser processing, reduces the technical threshold, and has high repeatability.

[0010] Optionally, the sample includes: an irrigation water contamination sample; The step of adding the collected sample to the surface of the hydrophilic microstructure substrate for drying to obtain a sample to be tested comprises: The collected irrigation water pollution sample is filtered, and the supernatant after the filtration is added to the surface of the hydrophilic microstructure substrate for drying to obtain a sample to be tested.

[0011] In the above implementation process, the dissolved solutes in the irrigation water contaminated sample can be better deposited on the substrate surface by filtering and drying, reducing the interference of undissolved impurities in the sample and improving the stability and accuracy of subsequent quantitative analysis.

[0012] Optionally, the step of ablating the surface of the sample to be tested by using a pulsed laser and detecting a characteristic spectrum signal of the plasma generated after ablation includes: Emitting a pulsed laser and focusing the pulsed laser onto the surface of the sample to be tested for ablation; Detect the plasma signal generated during the ablation process; The plasma signal is dispersed into spectra with different characteristic wavelengths to obtain a plasma characteristic spectrum signal.

[0013] In the above implementation process, the plasma signal is generated by ablating the sample to be tested, and the spectral information is detected to predict the heavy metal element content of the sample. This not only has the general advantages of non-contact and rapid LIBS analysis, but also further improves the accuracy and efficiency of heavy metal content prediction on this basis.

[0014] Optionally, the quantitative prediction of the plasma characteristic spectrum signal to obtain the heavy metal element content in the sample includes: Based on the preset principal component analysis combined with the Mahalanobis distance method, outliers are removed from the characteristic spectrum signal of the plasma; Performing spectral baseline correction on the plasma characteristic spectral signal after outliers are removed; The corrected plasma characteristic spectrum signal is quantitatively predicted based on a preset nonlinear partial least squares algorithm model to obtain the heavy metal element content in the sample.

[0015] In the above implementation process, nonlinear modeling was used to overcome the complex interactions between elements and other substances in complex composition samples, and the heavy metal content in the complex environmental matrix (irrigation water spectral data) was effectively analyzed and predicted.

[0016] Optionally, the method for constructing the preset nonlinear partial least squares algorithm model includes: The sample set established by the characteristic spectrum signal of plasma is subjected to partial least squares regression to obtain the principal components and dependent variables corresponding to the independent variables; The principal component corresponding to the independent variable is used as the input of the back-propagation neural network model, and the dependent variable is used as the output of the back-propagation neural network model to perform network training to obtain the preset nonlinear partial least squares algorithm model.

[0017] In the above implementation process, the complex relationship between elements is analyzed more accurately by presetting the nonlinear partial least squares algorithm model, which not only improves the accuracy of content analysis, but also enhances the understanding of environmental pollution, thereby providing strong support for the formulation of effective environmental protection strategies.

[0018] In a second aspect, an embodiment of the present application provides a heavy metal detection device based on a hydrophilic microstructure, the device comprising: a preprocessing module, an ablation module and a data processing module; The pre-processing module is used to: add the collected sample to the surface of the hydrophilic microstructure substrate for drying to obtain a sample to be tested; wherein the hydrophilic microstructure substrate is prepared by femtosecond laser processing on the surface of a metal substrate; The ablation module is used to: ablate the surface of the sample to be tested using a pulsed laser, and detect a characteristic spectrum signal of the plasma generated after ablation; The data processing module is used to: quantitatively predict the plasma characteristic spectrum signal to obtain the content of heavy metal elements in the sample.

[0019] Optionally, the ablation module includes: a pulsed laser, a loading platform and a high-resolution spectrometer; The light outlet of the pulse laser is arranged directly opposite to the object-carrying platform; the high-resolution spectrometer is electrically connected to the pulse laser; The loading platform is used to: place the sample to be tested; The pulse laser is used to: emit pulse laser and generate pulse laser electrical signal at the same time, and focus the pulse laser onto the surface of the sample to be tested for ablation; The high-resolution spectrometer is used to receive the pulse laser electrical signal through electrical connection, collect the plasma signal generated during the ablation process, disperse the plasma signal into spectra with different characteristic wavelengths, and obtain plasma characteristic spectrum signals.

[0020] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the machine-readable instructions are executed by the processor to perform the steps of the above method. In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, and the computer program executes the steps of the above method when executed by a processor.

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and understandable, embodiments are given below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1A flowchart of a heavy metal detection method provided in an embodiment of the present application; Figure 2 A schematic diagram of a hydrophilic microstructure substrate preparation device provided in an embodiment of the present application; Figure 3 A schematic diagram of a hydrophilic microstructure substrate provided in an embodiment of the present application; Figure 4 A schematic diagram of a device for preparing a sample to be tested provided in an embodiment of the present application; Figure 5 A schematic diagram of a spectrum detection device provided in an embodiment of the present application; Figure 6 A schematic diagram of the functional modules of a heavy metal detection device provided in an embodiment of the present application; Figure 7 A schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Icon: 210 - pre-processing module; 220 - ablation module; 230 - data processing module; 300 - electronic device; 311 - memory; 312 - storage controller; 313 - processor; 314 - peripheral interface; 315 - input and output unit; 316 - display unit. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0026] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. The terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the statement "comprising one..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements. The terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] Before introducing the embodiments of the present application, a brief introduction to the technical concepts involved in the present application is first given.

[0028] Laser-induced breakdown spectroscopy (LIBS) is a spectral analysis technique that uses high-energy pulsed lasers to ablate the surface of the sample to be tested, causing trace amounts of the sample to evaporate and ionize instantly to form a high-density laser-induced plasma. By collecting the characteristic spectral line signals of the elements released during the cooling process of the plasma, this technique can determine the material composition and content of the sample. In practice, the detection limit of LIBS technology is affected by many factors, including the plasma excitation temperature, the light collection window, and the intensity of the observed transition spectral lines. In addition, changes in laser energy can also have a significant effect on the intensity of the elemental spectral lines. Therefore, when using LIBS technology for analysis, these factors need to be considered comprehensively to obtain accurate results.

[0029] Partial least squares (PLS) is a new multivariate statistical data analysis method proposed by S. Wold and C. Albano in 1983. This method can simultaneously achieve regression modeling (multivariate linear regression), data structure simplification (principal component analysis), and correlation analysis between two groups of variables (canonical correlation analysis) under one algorithm. When quantitatively predicting spectral data, the spectral data usually contains a large number of wavelengths or characteristic variables, and there may be multicollinearity between these variables, which makes the traditional multivariate linear regression model unstable. The partial least squares method can effectively deal with these problems by combining the advantages of principal component analysis and multivariate linear regression.

[0030] The inventor of this application has noticed that, on the one hand, for the traditional laser induced breakdown spectroscopy detection system, the commonly used pulse delay generator is expensive, and the time sequence operation of controlling the laser, spectrometer and other equipment is complicated. At the same time, the time sequence has a great influence on the detection accuracy and stability. Therefore, it is necessary to improve and optimize the timing control of the detection system so that the detection system meets the time sequence control accuracy, and at the same time simplify the timing control equipment and lower the threshold for timing control setting. On the other hand, LIBS technology is limited by the intensity and stability of the signal when detecting low-concentration heavy metals, which hinders the development and application of this technology. However, the emergence of ultrashort pulse laser technology provides a new way or idea for the improvement of LIBS technology.

[0031] In view of this, the LIBS technology based on femtosecond laser-induced hydrophilic microstructure enhancement in the embodiments of the present application can not only realize the rapid and accurate detection of low-concentration heavy metal chromium in agricultural irrigation water, but also make on-site rapid detection possible by simplifying the operating procedures and reducing equipment costs, which is of great significance for ensuring the safe growth of crops and maintaining the ecological environment.

[0032] Specifically, the embodiments of the present application provide a heavy metal detection method and device based on a hydrophilic microstructure as described below, which can be applied to scenarios where heavy metal detection is required in water quality monitoring, such as drinking water, groundwater, domestic wastewater, industrial sewage treatment plants, as well as heavy metal content detection in soil and agricultural products, food and medicine, and other scenarios.

[0033] See also Figure 1 , Figure 1 A flowchart of a heavy metal detection method based on a hydrophilic microstructure is provided in an embodiment of the present application. The embodiment of the present application is explained in detail below. The method includes: step 100, step 120 and step 140.

[0034] Step 100: adding the collected sample to the surface of a hydrophilic microstructure substrate and drying it to obtain a sample to be tested; wherein the hydrophilic microstructure substrate is prepared by femtosecond laser processing on the surface of a metal substrate; Step 120: ablating the surface of the sample to be tested by using a pulsed laser, and detecting a characteristic spectrum signal of the plasma generated after ablation; Step 140: Quantitatively predict the plasma characteristic spectrum signal to obtain the heavy metal element content in the sample.

[0035] Exemplarily, a hydrophilic microstructure substrate can be: a fixed structure with stable and reliable performance is formed by laser processing a regular, orderly, and periodic microstructure on the surface of a metal substrate through femtosecond laser processing technology, so that the surface of the metal substrate is modified due to the roughness and obtains better hydrophilicity, thereby promoting the sample liquid to spread along the hydrophilic microstructure, with better uniformity, and the stability / robustness of the detection will be better. Specifically, wetting characteristics: due to the presence of the microstructure on the surface of the substrate, the surface tension between water and the substrate surface can be effectively reduced, making it easy for water to spread on its surface, which causes water droplets to form a thinner liquid film on the substrate instead of maintaining a round drop shape, thereby facilitating the uniform distribution of the solution. It helps the liquid / sample to dry evenly: during the liquid drying process, the water molecules on the hydrophilic substrate evaporate evenly, which helps the solute to be deposited on the substrate in a more uniform manner. On a non-hydrophilic substrate, it is difficult for water to evaporate evenly, resulting in excessive enrichment of solutes in some areas and less in other areas. Reduce the coffee ring effect: On ordinary substrates, the "coffee ring effect" often occurs during drying, that is, the solute accumulates at the edge to form a dark ring, while the central area is relatively clean. Hydrophilic substrates can effectively reduce the coffee ring effect due to their high wettability and uniform liquid film formation ability, thereby achieving a more uniform solute distribution on the entire substrate surface. Among them, the metal substrate can be: gold, silver, aluminum and other metal substrates; when the liquid is dried and added on the surface of the substrate, the solute dissolved in the liquid will be quickly enriched on the surface of the substrate due to the evaporation of the solution and the hydrophilicity of the substrate, which will lead to the enrichment of elements, which is beneficial to the subsequent spectral analysis of the sample.

[0036] The sample to be tested can be: Since a hydrophilic microstructure is used as a substrate, which has many laser-processed microstructures, the sample solution (e.g., irrigation water sample) can be directly dripped onto the substrate for drying. Due to the enrichment effect and hydrophilicity, a stable sample structure to be tested can be quickly formed without complex chemical treatment and without pollution. The technical requirements for operators are low, which can simplify the sample preparation process and make LIBS analysis faster and more cost-effective.

[0037] Quantitative prediction can be: since complex component samples such as irrigation water are affected by factors such as atmospheric precipitation, pesticide application, and fertilizer application, the chemical composition is complex, and the collected spectral data presents a nonlinear relationship. If the partial least squares algorithm model is still used for quantitative analysis, the analysis results may have a large deviation. Therefore, a nonlinear partial least squares algorithm (NL-PLS) that combines a nonlinear internal model with a partial least squares algorithm model can be used to improve the prediction accuracy. Therefore, the embodiment of the present application can first perform nonlinear processing to accurately analyze these complex relationships, and then combine the advantages of partial least squares algorithm principal component analysis and multivariate linear regression for quantitative prediction. It can not only effectively parse and predict the heavy metal content in a complex environmental matrix (for example: irrigation water), even at a low concentration level, but also effectively improve the model performance by accurately analyzing these complex relationships, thereby improving the accuracy of the final heavy metal element content prediction.

[0038] Optionally, the substrate is an aluminum plate, and the sample is agricultural irrigation water. First, a surface microstructure with hydrophilic properties can be prepared on an aluminum plate with a purity of 99.99% by ultrashort pulse laser to form a microstructure aluminum plate with hydrophilic properties. The agricultural irrigation water sample to be tested can be applied to the prepared microstructure aluminum plate, and heated and dried to obtain the sample to be tested. The method is simple and pollution-free, avoiding the defects of the existing pretreatment method, which is complicated, highly polluting, and cannot meet the needs of rapid and accurate detection of trace heavy metal Cr content in irrigation water. The Nd:YAG laser can generate a laser beam with a wavelength of 532nm to ablate the sample solute enriched on the microstructured aluminum plate to generate plasma. The light signal generated by the plasma is transmitted to the high-resolution spectrometer. The Nd:YAG laser generates a pulse signal after emitting a pulse laser, and transmits the pulse signal to the high-resolution spectrometer through an electrical connection to control the high-resolution spectrometer to collect spectral data. Finally, the collected spectral data are analyzed and processed, and a quantitative prediction model of a preset nonlinear partial least squares algorithm model can be configured to quantitatively predict the heavy metal content of the sample according to the processed spectrum, and the heavy metal Cr content in agricultural irrigation water can be obtained, thereby realizing rapid, accurate and green detection of trace heavy metal Cr content information in agricultural irrigation water.

[0039] Since it is difficult to detect trace heavy metal Cr in irrigation water in the existing technology, the present application uses ultrashort pulse laser to prepare a substrate with a hydrophilic structure to quickly enrich the irrigation water to be tested, thereby alleviating the difficulty of detecting trace metals in irrigation water in the existing technology, which is conducive to the development and application of this technology in the detection of trace metals in irrigation water and liquids.

[0040] In one embodiment, the metal substrate includes: an aluminum plate; and the preparation method of the hydrophilic microstructure substrate includes: An ultrashort pulse laser is used to micro-process a plurality of periodic micro-nano structures on the surface of an aluminum plate to obtain a hydrophilic microstructure substrate; wherein the roughness of the surface of the aluminum plate is modified due to the presence of the plurality of periodic micro-nano structures.

[0041] For example, aluminum plates can significantly enhance the light absorption effect when using laser induced breakdown spectroscopy technology, thereby enhancing the coupling effect between the ablation laser and the sample and enhancing the detection capability of LIBS technology. At the same time, aluminum plates can be ideally coupled with existing laser induced breakdown spectroscopy systems. This innovative method can provide convenience for on-site rapid element detection and is of great significance for applications in many fields, especially in application scenarios with resource constraints and efficient analysis requirements. Therefore, a microstructure can be prepared on the surface of an aluminum plate by a femtosecond laser to form a roughness-modified hydrophilic microstructure aluminum plate. Optionally, such as Figure 2 As shown, the substrate preparation device may include an ultrashort pulse laser, a galvanometer, and a substrate for preparing irrigation water sample detection. Among them, the ultrashort pulse laser can be a femtosecond laser with a central wavelength of 1036nm, a pulse width of 450fs, and a repetition frequency of 300kHz; the galvanometer, also known as a scanning mirror or a swinging mirror, is used to achieve fast and accurate scanning and positioning of the laser beam. The specific steps for preparing the hydrophilic substrate can be: the first step is to ultrasonically clean an aluminum plate with a purity of 99.99%, a size of 5cm×5cm, and a thickness of 1mm to remove surface impurities; the second step is to prepare a hydrophilic surface microstructure on the aluminum plate by setting the ultrashort pulse laser parameters to 2W, a scanning speed of 110mm / s, and a scanning spacing of 15μm; the third step is to ultrasonically clean the processed hydrophilic substrate again, the cleaning time is 20min, and the cleaning solution is anhydrous ethanol.

[0042] The prepared hydrophilic microstructure substrate has the following advantages: 1) It helps to improve the uniformity of sample analysis. Due to the modification of the roughness of the metal surface, better hydrophilicity is obtained, which promotes the spreading of liquid along the hydrophilic microstructure. In LIBS analysis, the uniformly spread sample provides a more consistent analysis area, which helps to improve the accuracy and repeatability of the analysis. 2) It helps to improve the sensitivity and accuracy of LIBS detection. Since the liquid is dried on the surface of the hydrophilic microstructure substrate, the solute dissolved in the liquid will be quickly enriched on the surface of the substrate due to the evaporation of the solution and the hydrophilicity, which leads to the enrichment of elements, which is beneficial to the analysis of the sample by LIBS. The enrichment effect allows the detection of lower concentrations of elements for LIBS analysis, especially for the detection of trace metals in irrigation water. 3) Simplify the sample preparation process. Since the hydrophilic microstructure is used as the substrate, the irrigation water sample can be directly dripped onto the substrate and heated to dry, without complex chemical treatment, without pollution, and with low technical requirements for operators. By simplifying the sample preparation process, LIBS analysis is faster and more cost-effective. 4) It is suitable for a variety of liquid samples. By modifying the surface of the aluminum substrate to make it a hydrophilic microstructure substrate, its hydrophilic properties make it suitable for a variety of liquid samples, not just irrigation water solutions. This is expected to expand the application range of hydrophilic microstructure substrate-enhanced LIBS analysis, enabling it to analyze more types of liquid samples.

[0043] In one embodiment, the plurality of periodic micro-nano structures include a plurality of rectangular processing areas arranged according to a preset period; the rectangular processing areas are composed of a plurality of scanning lines with equal intervals and equal sizes.

[0044] For example, Figure 3 As shown, a schematic diagram of a hydrophilic microstructure substrate prepared is shown. The substrate can be an aluminum plate with a purity of 99.99% and a width of 5 cm. The difference is that three micro-nano periodic rectangular area structures with a size of 1 cm×1 cm in each column can be prepared on the surface by ultrashort pulse laser. The structures are spaced 1 cm apart from each other, and each periodic structure includes multiple scanning lines of equal length and width. The gaps between the scanning lines are easy to form a deposition effect on the elements of the sample to be tested, thereby increasing the roughness of the aluminum plate surface and achieving modification. Among them, the period and size of the periodic micro-nano structure are not limited to the size of this embodiment, and can be adjusted according to actual needs, which will not be elaborated here. Compared with random non-periodic micro-nano structures, periodic micro-nano structures can help form fixed structures with stable and reliable performance. The periodic micro-nano structure only contains scanning lines of equal size, which makes it easy to control the scanning parameters during femtosecond laser processing, reduces the technical threshold, and has high repeatability.

[0045] In one embodiment, the sample comprises: an irrigation water contamination sample; step 100 may comprise: Step 101: filtering the collected irrigation water pollution sample, adding the supernatant after filtering to the surface of the hydrophilic microstructure substrate for drying, and obtaining a sample to be tested.

[0046] For example, Figure 4 As shown, the pretreatment device for pretreatment of agricultural irrigation water samples may include a filter for filtering agricultural irrigation water samples to form clarified agricultural irrigation water and a pipette for transferring clarified agricultural irrigation water to a hydrophilic substrate, which are connected in sequence. The specific steps of pretreatment to obtain the sample to be tested may be: the first step is to filter the collected agricultural irrigation water through a filter to filter insoluble solid impurities, which can improve the stability of subsequent quantitative analysis; the second step is to draw 400 μL of clarified irrigation water onto the hydrophilic substrate through a pipette; the third step is to heat and dry the hydrophilic substrate dripped with the clarified irrigation water sample through a constant temperature heating plate. Among them, the drying method is not limited to heating drying, but can also be a variety of drying methods for evaporating liquids such as natural drying, vacuum drying, microwave drying, and freeze drying.

[0047] By filtering and drying, the dissolved solutes in the irrigation water contaminated samples can be better deposited on the substrate surface, reducing the interference of undissolved impurities in the samples and improving the stability and accuracy of subsequent quantitative analysis.

[0048] In one embodiment, step 120 may include: Step 121: emitting a pulse laser and focusing the pulse laser onto the surface of the sample to be tested for ablation; Step 122: Detecting a plasma signal generated during the ablation process; Step 123: Disperse the plasma signal into spectra with different characteristic wavelengths to obtain a plasma characteristic spectrum signal.

[0049] For example, Figure 5As shown, the device for ablation detection spectrum may include Nd:YAG laser a, high-resolution spectrometer b, reflector c, focusing lens d, collection lens e, fiber optic probe f, XY displacement platform g, and processing terminal h. The laser beam output by the Nd:YAG laser is in the same plane as the reflector, and the reflector is located at 45° in the horizontal direction of the Nd:YAG laser. The focusing lens is set directly below the reflector. The XY displacement platform is used to place the dried irrigation water pretreatment sample. The Nd:YAG laser is electrically connected to the high-resolution spectrometer, the collection lens is placed on the left side of the XY displacement rotation platform, the fiber optic probe is placed on the left side of the collection lens, and the fiber optic probe is connected to the high-resolution spectrometer through an optical fiber. The high-resolution spectrometer is electrically connected to the processing terminal. Among them, the Nd:YAG laser can be a Q-switched Nd-YAG laser, which uses a crystal electro-optical Q-switching method to generate pulses, the wavelength of the emitted laser beam is 532nm, and the stability of the laser energy is better than 2%; the high-resolution spectrometer can integrate an enhanced CCD detector with a pixel of 1024x256; the XY displacement includes a controller, a driver, and a stage, which can accurately control the linear velocity and acceleration of the specified path during the spectrum acquisition process, so as to ensure that the laser evenly hits the surface of the hydrophilic substrate enriched with the irrigation water to be tested, and ensure the consistency of the defocus amount. The optical signal generated by the plasma contains the chemical and physical information of the sample, and the received optical signal is dispersed into light of different wavelengths to obtain the spectral information of the sample. By analyzing these spectral information, the chemical composition and physical state of the sample can be determined.

[0050] Optionally, a Nd:YAG laser generates a laser beam with a wavelength of 532 nm, which is changed in direction by a reflector and then focused by a focusing lens to ablate a pretreated sample on an XY displacement platform to generate plasma. The light signal generated by the plasma is collected by a fiber optic probe through a collection lens, and then the light signal is transmitted to a high-resolution spectrometer through an optical fiber. The Nd:YAG laser generates a pulsed electrical signal after emitting a pulsed laser (optical signal), and transmits the pulsed electrical signal to the high-resolution spectrometer through an electrical connection between the laser and the spectrometer, controls the high-resolution spectrometer to collect a spectrum, and the high-resolution spectrometer converts the received optical signal into an electrical signal and transmits it to a processing terminal through an electrical connection. Finally, a quantitative prediction model with a preset nonlinear partial least squares algorithm model is configured on the processing terminal to analyze and process the collected spectrum.

[0051] The timing control of the detection process is as follows: the laser generates a light signal for ablation while also emitting a pulse signal (electrical signal), which is directly used to control the spectrometer to collect spectral data generated by ablation through the electrical connection between the laser and the spectrometer. Since the digital pulse delay generator is omitted, the detection device is simpler (fewer devices and the detection system itself is simpler) and more cost-effective.

[0052] The heavy metal content of the sample is predicted by ablating the sample to be tested to generate plasma signals and detecting the spectral information. This method not only has the general advantages of LIBS analysis, which is non-contact and fast, but also further improves the accuracy and efficiency of heavy metal content prediction.

[0053] In one embodiment, step 140 may include: Step 141: removing outliers from plasma characteristic spectrum signals based on a preset principal component analysis combined with a Mahalanobis distance method; Step 142: performing spectral baseline correction on the plasma characteristic spectral signal after the outliers are removed; Step 143: quantitatively predict the corrected plasma characteristic spectrum signal based on a preset nonlinear partial least squares algorithm model to obtain the heavy metal element content in the sample.

[0054] Exemplarily, the process of performing data analysis on the spectral data set generated by steps 121-123 and calculating a quantitative prediction model for the predicted value of Cr content in agricultural irrigation water may include: a pre-processing algorithm of a preset principal component analysis combined with a Mahalanobis distance method may be configured to perform outlier removal on the generated spectral data set to generate a first data set; a pre-processing algorithm of a preset spectral baseline correction may be configured to further pre-process the first data set to generate a second data set; a preset simulated annealing algorithm may be established for the second data set to optimize the partial least squares algorithm model for quantitative prediction, and the final agricultural irrigation water heavy metal Cr value may be output.

[0055] The single partial least squares algorithm model has the following advantages: it can project both the independent and dependent variables at the same time, so it can distinguish the spectral feature information from the redundant information well, and it can also distinguish the non-random noise of the spectrum, and it has strong interpretability; it can still be used when there are multiple linear relationships between the independent variables or when there are few observations and a large number of variables, etc., and it plays a huge role in multivariate statistical analysis. However, because the PLS model and the linear regression model cannot describe the nonlinear relationship in the data, it cannot capture the nonlinear characteristic relationship between the independent and dependent variables, which reduces the applicability of the model to a certain extent.

[0056] For the application scenario of femtosecond laser induced hydrophilic microstructure enhanced detection of heavy metal Cr content in irrigation water, since irrigation water samples are affected by atmospheric precipitation, pesticide application, fertilizer application and other factors, the collected spectral data presents a nonlinear relationship. If the PLS model is still used for quantitative analysis, the analysis results may have a large deviation. Therefore, a nonlinear PLS combining a nonlinear internal model with a PLS model can be used to improve the prediction accuracy. Optionally, 20 spectra are collected for each sample, each of which is the result of 10 laser accumulations, and the average is taken. Finally, the heavy metal Cr content in irrigation water is quantitatively predicted in combination with the model database.

[0057] In the quantitative analysis of heavy metals in irrigation water, due to the complex chemical composition of irrigation water, the core advantage of NL-PLS compared with the PLS quantitative prediction model lies in its powerful nonlinear modeling capability. Since these elements in irrigation water often have complex interactions with other substances, even at low concentration levels, this internal model combined with the partial least squares algorithm can effectively parse and predict the heavy metal content in a complex environmental matrix (irrigation water spectral data).

[0058] In one embodiment, step 143 may include: Step 1431: performing partial least squares regression on the sample set established by the plasma characteristic spectrum signal to obtain the principal components and dependent variables corresponding to the independent variables; Step 1432: Use the principal component corresponding to the independent variable as the input of the back-propagation neural network model, and the dependent variable as the output of the back-propagation neural network model to perform network training to obtain a preset nonlinear partial least squares algorithm model.

[0059] Exemplarily, partial least squares regression and back propagation neural network (BPNN) are combined to construct a nonlinear internal model for processing spectral data sets with complex nonlinear relationships of irrigation water. After PLS regression of the sample set using the nonlinear PLS of the neural network internal model, the principal component t of the independent variable X and the output u corresponding to the dependent variable Y are obtained respectively, and then t and u are used as the input and output of the neural network for training to obtain a nonlinear internal model, in which the independent variable is the intensity value of the characteristic spectral wavelength point. Since the wavelength point is often multiple wavelength points, the independent variable often corresponds to multiple, so it is necessary to extract the principal component of the independent variable. The dependent variable is the content value or concentration value of the target heavy metal element. When the target heavy metal quantity is 1, there is often only one dependent variable.

[0060] Quantitative prediction uses BPNN to establish a nonlinear internal model of t and u, where BPNN adopts a typical three-layer structure, namely, an input layer, a hidden layer, and an output layer. The learning process of BPNN includes forward propagation and back propagation. Forward propagation refers to the transfer of spectral data from the input layer to the hidden layer and then to the output layer. The state of each layer will only affect the next layer. This layer-by-layer transfer allows the network to learn complex relationships in spectral data. However, initially, the output may not meet the required accuracy and precision. In order to improve the performance of the network, the back propagation method is used to correct the connection weights between neurons one by one to reduce the error. When there is a difference between the output and the actual spectral data, the error information is returned along the original forward propagation path and propagated from the output layer to the hidden layer and input layer. In this way, the neural network can gradually adjust the weights to reduce the error and make the output of the network closer to the actual spectral data. This process requires multiple iterations, and each iteration will improve the performance of the network and eventually reduce the error to within the allowable range.

[0061] By presetting the nonlinear partial least squares algorithm model, the complex relationship between elements can be analyzed more accurately, which not only improves the accuracy of content analysis, but also enhances the understanding of environmental pollution, thus providing strong support for the formulation of effective environmental protection strategies.

[0062] See also Figure 6 , Figure 6 A functional module diagram of a heavy metal detection device based on a hydrophilic microstructure provided in an embodiment of the present application. The embodiment of the present application is explained in detail below. The device includes: a pre-processing module 210, an ablation module 220 and a data processing module 230; The pre-processing module 210 is used to: add the collected sample to the surface of the hydrophilic microstructure substrate for drying to obtain a sample to be tested; wherein the hydrophilic microstructure substrate is prepared by femtosecond laser processing on the surface of a metal substrate; The ablation module 220 is used to: ablate the surface of the sample to be tested using a pulsed laser, and detect the characteristic spectrum signal of the plasma generated after ablation; The data processing module 230 is used to quantitatively predict the plasma characteristic spectrum signal to obtain the content of heavy metal elements in the sample.

[0063] Exemplarily, since the principle and specific implementation of the heavy metal detection device in the embodiment of the present application to solve the problem are similar to any one of the heavy metal detection methods in the above embodiments, the implementation of the heavy metal detection device in this embodiment can refer to the above description, and the repeated parts will not be repeated.

[0064] In one embodiment, the ablation module includes: a pulsed laser, a loading platform, and a high-resolution spectrometer; The light outlet of the pulse laser is arranged directly opposite to the loading platform; the high-resolution spectrometer is electrically connected to the pulse laser; The loading platform is used to: place the sample to be tested; The pulse laser is used to: emit pulse laser and generate pulse laser electrical signal at the same time, and focus the pulse laser onto the surface of the sample to be tested for ablation; The high-resolution spectrometer is used to receive the pulse laser electrical signal through electrical connection, collect the plasma signal generated during the ablation process, disperse the plasma signal into spectra with different characteristic wavelengths, and obtain plasma characteristic spectrum signals. For example, the pulse laser can be a Q-switched Nd-YAG laser, which uses a crystal electro-optical Q-switching method to generate pulses, with a wavelength of 532nm and a laser energy stability of better than 2%; the high-resolution spectrometer can be integrated with an enhanced CCD detector with 1024x256 pixels; the XY displacement platform includes a controller, a driver, and a stage, which can accurately control the linear velocity and acceleration of the specified path during the spectrum acquisition process, so that the laser hits the pre-treated sample surface evenly and ensures the consistency of the defocus amount. Please continue to view Figure 5 The specific structural components of the ablation module may include Nd:YAG laser a, high-resolution spectrometer b, reflector c, focusing lens d, collection lens e, fiber optic probe f, XY displacement platform g, and processing terminal h. The laser beam output by the Nd:YAG laser is in the same plane as the reflector, and the reflector is located at 45° in the horizontal direction of the Nd:YAG laser. The focusing lens is set directly below the reflector. The XY displacement platform is used to place the dried irrigation water pretreatment sample. The Nd:YAG laser is electrically connected to the high-resolution spectrometer, the collection lens is placed on the left side of the XY displacement rotation platform, the fiber optic probe is placed on the left side of the collection lens, and the fiber optic probe is connected to the high-resolution spectrometer through an optical fiber. The high-resolution spectrometer is also electrically connected to the processing terminal. The high-resolution spectrometer can be integrated with an enhanced CCD detector with a pixel of 1024x256. The specific implementation process and beneficial effects are the same as the implementation method of the above steps 121-step 123, which will not be repeated here.

[0065] See also Figure 7 , Figure 7 31 is a block diagram of an electronic device. The electronic device 300 may include a memory 311, a storage controller 312, a processor 313, a peripheral interface 314, an input and output unit 315, and a display unit 316. A person skilled in the art may understand that Figure 7 The structure shown is only for illustration and does not limit the structure of the electronic device 300. For example, the electronic device 300 may also include Figure 7 More or fewer components as shown, or with Figure 7 Different configurations shown.

[0066] The above-mentioned memory 311, storage controller 312, processor 313, peripheral interface 314, input / output unit 315, and display unit 316 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The above-mentioned processor 313 is used to execute the executable module stored in the memory.

[0067] The memory 311 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory 311 is used to store programs, and the processor 313 executes the program after receiving the execution instruction. The method executed by the electronic device 300 defined by the process disclosed in any embodiment of the present application can be applied to the processor 313, or implemented by the processor 313.

[0068] The processor 313 may be an integrated circuit chip with signal processing capability. The processor 313 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0069] The above-mentioned peripheral interface 314 couples various input / output devices to the processor 313 and the memory 311. In some embodiments, the peripheral interface 314, the processor 313 and the memory controller 312 can be implemented in a single chip. In some other examples, they can be implemented by separate chips.

[0070] The input and output unit 315 is used to provide input data to the user. The input and output unit 315 can be, but is not limited to, a mouse and a keyboard.

[0071] The display unit 316 provides an interactive interface (e.g., a user operation interface) between the electronic device 300 and the user for the user to refer to. In this embodiment, the display unit 316 may be a liquid crystal display or a touch display. The liquid crystal display or the touch display may display the process of the processor executing the program.

[0072] The electronic device 300 in this embodiment can be used to execute each step in each method provided in the embodiments of the present application.

[0073] In addition, an embodiment of the present application further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are executed.

[0074] The computer program product of the above method provided in the embodiment of the present application includes a storage medium storing program code, and the instructions included in the program code can be used to execute the steps in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0075] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some communication interfaces, devices or units, which can be electrical, mechanical or other forms. The functional modules in the embodiments of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0076] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0077] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0078] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A heavy metal detection method based on hydrophilic microstructure, characterized in that: The method comprises: Adding the collected sample to the surface of a hydrophilic microstructure substrate and drying it to obtain a sample to be tested; wherein the hydrophilic microstructure substrate is prepared by femtosecond laser processing of the surface of a metal substrate; ablating the surface of the sample to be tested by using a pulsed laser, and detecting a characteristic spectrum signal of the plasma generated after ablation; The plasma characteristic spectrum signal is quantitatively predicted to obtain the content of heavy metal elements in the sample.

2. The method according to claim 1, characterized in that in, The metal substrate comprises: an aluminum plate; The preparation method of the hydrophilic microstructure substrate comprises: An ultrashort pulse laser is used to micro-process the surface of an aluminum plate to form multiple periodic micro-nano structures to obtain a hydrophilic microstructure substrate; The roughness of the aluminum plate surface is modified due to the presence of multiple periodic micro-nano structures.

3. The method according to claim 2, characterized in that in, The multiple periodic micro-nano structures include multiple rectangular processing areas arranged according to a preset period; the rectangular processing areas are composed of multiple scanning lines with equal intervals and equal sizes.

4. The method according to any one of claims 1 to 3, characterized in that: in, The samples include: irrigation water contamination samples; The step of adding the collected sample to the surface of the hydrophilic microstructure substrate for drying to obtain a sample to be tested comprises: The collected irrigation water pollution sample is filtered, and the supernatant after the filtration is added to the surface of the hydrophilic microstructure substrate for drying to obtain a sample to be tested.

5. The method according to claim 1, characterized in that The method of ablating the surface of the sample to be tested by using a pulsed laser and detecting a characteristic spectrum signal of the plasma generated after ablation includes: Emitting a pulsed laser and focusing the pulsed laser onto the surface of the sample to be tested for ablation; Detect the plasma signal generated during the ablation process; The plasma signal is dispersed into spectra with different characteristic wavelengths to obtain a plasma characteristic spectrum signal.

6. The method according to claim 1, characterized in that The quantitative prediction of the plasma characteristic spectrum signal to obtain the heavy metal element content in the sample includes: Based on the preset principal component analysis combined with the Mahalanobis distance method, outliers are removed from the characteristic spectrum signal of the plasma; Performing spectral baseline correction on the plasma characteristic spectral signal after outliers are removed; The corrected plasma characteristic spectrum signal is quantitatively predicted based on a preset nonlinear partial least squares algorithm model to obtain the heavy metal element content in the sample.

7. The method according to claim 6, characterized in that The method for constructing the preset nonlinear partial least squares algorithm model includes: The sample set established by the characteristic spectrum signal of plasma is subjected to partial least squares regression to obtain the principal components and dependent variables corresponding to the independent variables; The principal component corresponding to the independent variable is used as the input of the back-propagation neural network model, and the dependent variable is used as the output of the back-propagation neural network model to perform network training to obtain the preset nonlinear partial least squares algorithm model.

8. A heavy metal detection device based on a hydrophilic microstructure, characterized in that: The device comprises: a pre-processing module, an ablation module and a data processing module; The pre-processing module is used to: add the collected sample to the surface of the hydrophilic microstructure substrate for drying to obtain a sample to be tested; wherein the hydrophilic microstructure substrate is prepared by femtosecond laser processing on the surface of a metal substrate; The ablation module is used to: ablate the surface of the sample to be tested using a pulsed laser, and detect a characteristic spectrum signal of the plasma generated after ablation; The data processing module is used to: quantitatively predict the plasma characteristic spectrum signal to obtain the content of heavy metal elements in the sample.

9. The device according to claim 8, characterized in that The ablation module includes: a pulse laser, a loading platform and a high-resolution spectrometer; The light outlet of the pulse laser is arranged directly opposite to the object-carrying platform; the high-resolution spectrometer is electrically connected to the pulse laser; The loading platform is used to: place the sample to be tested; The pulse laser is used to: emit pulse laser and generate pulse laser electrical signal at the same time, and focus the pulse laser onto the surface of the sample to be tested for ablation; The high-resolution spectrometer is used to receive the pulse laser electrical signal through electrical connection, collect the plasma signal generated during the ablation process, disperse the plasma signal into spectra with different characteristic wavelengths, and obtain plasma characteristic spectrum signals.

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