Method and device for detecting heavy metals by synergistically enhancing double light beams

Abstract

The invention provides a double-beam synergistic enhancement heavy metal detection method and device, and relates to the field of heavy metal element detection. The heavy metal element detection method comprises the following steps: focusing femtosecond laser on a liquid sample to generate bubbles in the liquid sample; nanosecond laser is focused on the bubbles, the gasified liquid sample in the bubbles is excited, and plasma is generated; and predicting the content of heavy metal elements in the liquid sample according to the spectral signal of the plasma. According to the heavy metal element detection method provided by the embodiment of the invention, the treated liquid sample is directly subjected to laser induction to generate bubbles, the bubbles are subjected to laser-induced breakdown detection, and the obtained spectrum is subjected to independent component analysis; not only is the defect of amplification error in the sample preparation process overcome, but also the processing of spectral data is optimized, and the accuracy and the stability of heavy metal element detection are integrally improved.

Description

Technical Field

[0001] The present application relates to the field of heavy metal element detection, and more specifically, to a method and device for dual-beam collaboratively enhanced heavy metal detection. Background Art

[0002] Heavy metals such as lead, mercury, cadmium, and arsenic pose a serious threat to the environment and human health, potentially leading to a range of health problems, including neurological damage, kidney disease, and cancer; therefore, heavy metal detection is necessary. Common heavy metal element detection methods include atomic absorption spectrometry (AAS), inductively coupled plasma mass spectrometry (ICP-MS), X-ray fluorescence spectrometry (XRF), and laser-induced breakdown spectroscopy (LIBS). These methods are capable of detecting different sample types and have varying levels of sensitivity, and are widely used in environmental monitoring, food safety, and industrial emissions.

[0003] Laser-induced breakdown spectroscopy (LIBS) is a laser-based analytical method that generates a localized plasma on a sample surface by focusing high-energy laser pulses. This excites the elements in the sample, emitting a characteristic spectrum. Analysis of these spectral signals can reveal the identity and concentration of each element in the sample. LIBS is particularly well-suited for real-time analysis of complex samples and is widely used in fields such as environmental monitoring, materials science, and chemical analysis.

[0004] However, current methods for detecting heavy metal elements mainly rely on direct detection of liquids using laser-induced breakdown spectroscopy. This direct detection of liquids suffers from low plasma generation efficiency and poor stability and accuracy of the generated spectral signals, which in turn affects the accuracy and stability of heavy metal element detection. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a dual-beam synergistically enhanced heavy metal detection method and device, which generates microbubbles through femtosecond laser to enhance the spectral signal intensity, thereby improving detection sensitivity and accuracy.

[0006] In a first aspect, an embodiment of the present application provides a method for detecting heavy metal elements, which includes: focusing a femtosecond laser on a liquid sample to generate bubbles in the liquid sample; focusing a nanosecond laser on the bubbles to excite the vaporized liquid sample in the bubbles to generate plasma; and predicting the heavy metal element content in the liquid sample based on the spectral signal of the plasma.

[0007] In the above-mentioned implementation process, the heavy metal element detection method provided in the embodiment of the present application utilizes a femtosecond laser to induce the generation of microbubbles in a liquid sample, and then utilizes a nanosecond laser to excite the vaporized liquid sample in the tiny bubbles. The generation of microbubbles helps to increase the interaction area between the laser and the sample. The optical properties inside the microbubbles (such as refractive index changes) can concentrate the laser energy and enhance the emission intensity of the plasma, while avoiding the quenching of the plasma by the liquid due to direct breakdown of the liquid. In this way, the nanosecond laser is focused on the position of the air bubble for breakdown, and the plasma can expand more freely at the bubble position, producing a brighter and larger plasma. Through this mechanism, the use of nanosecond lasers to generate plasma spectral signals has been significantly improved, especially in trace element detection, the characteristic spectrum of the target element can be better distinguished, and the reliability and accuracy of heavy metal element detection can be improved.

[0008] Optionally, in an embodiment of the present application, the concentration value of the target heavy metal element in the liquid sample is predicted based on the spectral signal of the plasma, including: converting the spectral signal of the plasma into spectral data; inputting the spectral data into a concentration prediction model, and obtaining the concentration value of the target heavy metal element in the liquid sample.

[0009] In the above implementation process, the heavy metal element detection method in the embodiments of this application captures plasma spectral signals and converts them into spectral data. Using a concentration prediction model, the spectral characteristics at specific wavelengths are precisely analyzed to accurately predict the concentration of the target heavy metal element in the liquid sample. This not only improves the accuracy and stability of detection, but also uses the trained machine learning model to rapidly analyze unknown samples, effectively reducing the impact of background interference and the coexistence of multiple elements on concentration analysis, making heavy metal concentration detection more efficient and reliable.

[0010] Optionally, in an embodiment of the present application, the concentration prediction model is specifically used to: decompose the spectral data of the plasma into multiple independent spectral data; extract characteristic spectral data of heavy metal elements from the multiple independent spectral data; and determine the concentration value of the target heavy metal element in the liquid sample based on the relationship between the intensity of the characteristic spectral data of the known heavy metal element and the known heavy metal concentration value.

[0011] In the above implementation process, the heavy metal element detection method provided by the embodiments of this application effectively reduces interference from other components and background noise in the sample by decomposing spectral data into independent components and extracting the characteristic spectrum of the target heavy metal, making the detection results more accurate and reliable. Utilizing the quantitative relationship between characteristic spectral intensity and concentration, it achieves rapid and accurate prediction of the concentration of the target heavy metal element in the sample, improving detection sensitivity and data reliability.

[0012] Optionally, in the embodiment of the present application, after converting the spectral signal of the plasma into spectral data, the method further includes: performing baseline correction and normalization processing on the spectral data.

[0013] In the above implementation process, the heavy metal element detection method provided in the embodiment of the present application converts the spectral signal of the plasma into spectral data, which contains high-quality spectral signals with noise and background interference removed. It retains the characteristic spectrum related to the target element and provides a high-quality data basis for subsequent heavy metal content analysis.

[0014] Optionally, in an embodiment of the present application, before focusing the femtosecond laser on the liquid sample, the heavy metal element detection method further includes: collecting the liquid sample and filtering the liquid sample; and centrifuging the filtered liquid sample to obtain a liquid sample.

[0015] In this implementation process, the liquid sample is filtered and centrifuged to remove most impurities, suspended particles, and sediment. This provides a pure liquid sample for subsequent laser-induced breakdown spectroscopy analysis, ensuring the accuracy of the test results and the signal-to-noise ratio.

[0016] In a second aspect, an embodiment of the present application provides a heavy metal element detection device, which includes: a spectral data acquisition module and a data processing module; the spectral data acquisition module is used to focus a femtosecond laser on a liquid sample to generate bubbles in the liquid sample; and to focus a nanosecond laser on the bubbles to excite the vaporized liquid sample in the bubbles to generate plasma; the data processing module is used to predict the heavy metal element content in the liquid sample based on the spectral signal of the plasma.

[0017] Optionally, in an embodiment of the present application, the spectral data acquisition module includes: a dual laser emission assembly having a nanosecond laser and a femtosecond laser and an optical path combining mirror group; the nanosecond laser is used to emit nanosecond laser to the optical path combining mirror group; the femtosecond laser is used to emit femtosecond laser to the optical path combining mirror group; and the optical path combining mirror group is used to focus the femtosecond laser and the nanosecond laser on the liquid sample.

[0018] Optionally, in an embodiment of the present application, the data processing module includes a spectral signal acquisition component, a spectral signal conversion component and a concentration prediction unit; the spectral signal acquisition component is used to acquire the spectral signal of the plasma; the spectral signal conversion component is used to convert the spectral signal into spectral data; the concentration prediction unit is configured to determine the concentration value of the target heavy metal element in the liquid sample based on the spectral data.

[0019] In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein program instructions are stored in the memory, and when the processor reads and runs the program instructions, it executes the steps in any implementation of the above-mentioned heavy metal element detection method.

[0020] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer program instructions are stored in the computer-readable storage medium. When the computer program instructions are read and executed by a processor, the steps in any implementation of the above-mentioned heavy metal element detection method are executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. 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 relevant drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 Flowchart of heavy metal element detection provided in the embodiment of this application;

[0023] Figure 2 A workflow diagram of the concentration prediction model provided in the embodiments of the present application;

[0024] Figure 3 A flow chart of liquid sample pretreatment provided in an embodiment of the present application;

[0025] Figure 4 A schematic diagram of the structure of a heavy metal element detection device provided in an embodiment of the present application;

[0026] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0027] Reference numerals: heavy metal element detection device 1000; spectral data acquisition module 100; dual laser emission assembly 110; nanosecond laser 111; femtosecond laser 112; optical path beam combiner assembly 120; reflecting mirror 121; dichroic mirror 122; focusing lens 123; data processing module 200; spectral signal acquisition assembly 210; acquisition lens 211; optical fiber probe 212; optical fiber 213; spectral signal conversion assembly 220; concentration prediction unit 230; concentration prediction model 231; timing pulse generator 300; electronic device 400; processor 401; memory 402; DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the drawings. For example, two consecutive boxes may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, may be implemented using a dedicated hardware-based system that performs the specified functions or actions, or may be implemented using a combination of dedicated hardware and computer instructions. In addition, the functional modules in the various embodiments of the present invention may be integrated together to form a separate part, or each module may exist separately, or two or more modules may be integrated to form a separate part.

[0029] Laser-induced breakdown spectroscopy (LIBS) is a highly efficient heavy metal analysis technique widely used in materials science, environmental monitoring, and chemical analysis. This method focuses high-energy laser pulses onto the sample surface, instantaneously generating a localized plasma that excites the elements in the sample and emits a characteristic spectrum. By analyzing these spectral signals, the identity and content of each element in the sample can be rapidly determined, making it particularly suitable for the immediate analysis of complex samples.

[0030] Currently, the detection of heavy metals based on light-absorbing microstructures uses a femtosecond laser to create periodic micro-nanostructures on aluminum foil, forming a light-absorbing substrate. This substrate's micro-nanostructure enhances light absorption, thereby improving the interaction between the laser and the sample and enhancing the spectral signal. Samples are extracted from agricultural ditch water or contaminated water samples. After centrifugation, the supernatant is added to the surface of the light-absorbing substrate and dried to obtain the test sample. A pulsed laser is used to ablate the sample surface, exciting the sample to generate a plasma, and the plasma spectral signal is collected. The pulsed laser is processed by a photodetector and a high-resolution spectrometer to obtain the plasma characteristic spectrum. The collected spectral data is processed by first removing outliers and performing spectral baseline correction. Quantitative analysis is then performed using the partial least squares (PLS) method combined with a simulated annealing algorithm. The simulated annealing algorithm optimizes the characteristic wavelengths in the spectral data, finding the optimal characteristic wavelength combination through multiple iterations, thereby improving the accuracy of heavy metal content prediction.

[0031] In this implementation, the high breakdown threshold of the liquid medium results in low plasma generation efficiency, while the high density and fluidity of the liquid cause splashing and disturbances, which affect the stability and accuracy of the spectral signal. Furthermore, the sample preparation process is cumbersome, often requiring heating and drying, which is time-consuming and prone to errors. Furthermore, existing spectral data processing methods fail to optimize the details of complex spectral signals, affecting the accuracy of the analysis results.

[0032] Based on this, the present application proposes a dual-beam synergistically enhanced heavy metal detection method and device. This heavy metal element detection method utilizes a nanosecond laser and a femtosecond laser, with dual beams working together. The femtosecond laser is focused on a liquid sample, generating tiny bubbles in the liquid sample. The nanosecond laser is then focused on the vaporized sample within the bubbles, thereby generating a plasma. The spectral signal from the plasma is then analyzed to determine the heavy metal content in the liquid sample. The heavy metal element detection method proposed in the present application embodiment uses femtosecond laser-generated microbubbles to enhance the spectral signal intensity, thereby improving detection sensitivity and accuracy.

[0033] Please see Figure 1 , Figure 1 This is a flowchart of heavy metal element detection provided in the embodiment of the present application; This application provides a heavy metal element detection method, which can be Figure 5 The heavy metal element detection method includes the following steps:

[0034] Step S100 : focusing a femtosecond laser on a liquid sample to generate bubbles in the liquid sample.

[0035] In step S100, a femtosecond laser is focused on the liquid sample, causing it to absorb high energy locally and instantaneously, rapidly heating and vaporizing, generating tiny bubbles. The ultrashort pulse characteristics of the femtosecond laser ensure concentrated energy transfer, avoiding disturbance of the surrounding liquid and excessive heat conduction, thereby forming a stable bubble structure within the sample.

[0036] Step S200: focusing a nanosecond laser on a bubble to excite a vaporized liquid sample in the bubble to generate plasma.

[0037] In step S200, a nanosecond laser is focused onto the bubble generated in step S100, applying energy to the vaporized liquid sample. Since the bubble's interior is a gaseous environment with a lower breakdown threshold than liquid, the nanosecond laser can more efficiently induce plasma generation from the material within the bubble. The bubble structure not only reduces the difficulty of plasma generation but also minimizes the absorption and scattering of light energy by the liquid medium, helping to enhance the intensity and stability of the spectral signal emitted by the plasma.

[0038] Notably, the inventors discovered during their research that in laser-induced breakdown spectroscopy (LIBS), a technique used to directly detect liquids, the laser energy is more easily absorbed and scattered by liquid molecules because liquids have a higher breakdown threshold than solids and gases. This reduces the efficiency of plasma generation in this method. Furthermore, the high density and viscosity of liquids restrict the free expansion of the plasma, limiting its spatial distribution and thus affecting the intensity and stability of its emission spectrum.

[0039] Furthermore, the surface of liquid samples is susceptible to fluctuations caused by interference from the external environment and laser pulses. These fluctuations can lead to instability in the laser focus, which in turn affects plasma generation and the stability of the spectral signal. Furthermore, in traditional direct liquid detection techniques, such as those using jets, the fluidity of the liquid can lead to uneven distribution of heavy metal elements in the sample, resulting in poor spectral signal repeatability and increased detection uncertainty.

[0040] Furthermore, when directly detecting liquids, factors such as the solute, pH, and ionic strength in the liquid sample can affect plasma generation and spectral signals. Different matrices can alter the excitation efficiency and emission intensity of elements in the sample, increasing the complexity of the spectral signal and reducing the accuracy of quantitative analysis.

[0041] Therefore, the present invention provides a method for inducing the generation of microbubbles in a liquid sample using a femtosecond laser, which can significantly lower the threshold for nanosecond laser-induced plasma excitation. The low-density gas phase within the microbubbles requires a lower breakdown energy than the liquid medium, allowing stable plasma generation at lower energies. This can improve the performance of subsequent plasma generation from the gas phase within the microbubbles.

[0042] It should be noted that the reason for using a femtosecond laser to excite the liquid sample to generate bubbles in step S100 of the embodiment of the present application is that the advantage of a femtosecond laser is that the interaction between the laser and the substance has ended before the microbubbles are generated. Compared with nanosecond lasers, femtosecond laser pulse peak power density is larger, the probability of multi-photon generation is large, and the ablation and evaporation of the sample can be completed under the laser diffraction limit (that is, the full ablation of the sample is completed in the smallest space, avoiding the impact oscillation of the liquid caused by the long-term effect of the nanosecond laser). Therefore, under the action of the femtosecond laser, the microbubbles will be stable for a certain period of time after generation, and the focal point of the nanosecond laser in the subsequent step S200 in the microbubbles is stable, reducing the influence of surface fluctuations and liquid fluidity on the laser focal point. At the same time, nanosecond lasers can be used to excite plasma at the optimal time of microbubble generation through precise timing control, making maximum use of the presence of microbubbles and improving the stability and repeatability of the signal.

[0043] Step S300: predicting and obtaining the heavy metal element content in the liquid sample based on the spectral signal of the plasma.

[0044] In step S300, after exciting the sample, the generated plasma emits a specific spectral signal. This spectral signal contains characteristic spectral lines of different elements in the sample. By collecting and analyzing the plasma spectral signal, the presence and concentration of heavy metal elements in the sample can be identified.

[0045] pass Figure 1 It can be seen that the heavy metal element detection method provided in the embodiment of the present application uses a femtosecond laser to induce the generation of microbubbles in a liquid sample, and then uses a nanosecond laser to excite the vaporized liquid sample in the tiny bubbles. The generation of microbubbles helps to increase the interaction area between the laser and the sample. The optical properties inside the microbubbles (such as refractive index changes) can concentrate the laser energy and enhance the emission intensity of the plasma. At the same time, it avoids directly penetrating the liquid and causing the liquid to quench the plasma. In this way, the nanosecond laser is focused on the position of the air bubble for breakdown, and the plasma can expand more freely at the bubble position, producing a brighter and larger plasma. Through this mechanism, the use of nanosecond lasers to generate plasma spectral signals has been significantly improved, especially in trace element detection, it can better distinguish the characteristic spectrum of the target element and improve the reliability and accuracy of heavy metal element detection.

[0046] In an optional embodiment, the above-mentioned step S300 predicts and obtains the concentration value of the target heavy metal element in the liquid sample based on the spectral signal of the plasma, which can be achieved by: converting the spectral signal of the plasma into spectral data; inputting the spectral data into the concentration prediction model, and obtaining the concentration value of the target heavy metal element in the liquid sample.

[0047] In the above implementation process, the spectral signal of the plasma is captured and converted into spectral data; the spectral data includes the characteristic wavelength of each element in the liquid sample and its corresponding intensity.

[0048] Furthermore, the spectral data is input into a concentration prediction model (such as a quantitative model based on independent component analysis or machine learning). The model accurately predicts the concentration value of the target heavy metal element in the sample by analyzing the spectral characteristics of specific wavelengths and combining it with a known element concentration database.

[0049] The concentration prediction model in the embodiments of the present application, for example, a machine learning regression model, establishes a prediction model by using a large amount of sample spectral data of known concentrations in a training phase, and then predicts the concentration based on the spectral characteristics of new samples in a testing phase.

[0050] As can be seen, the heavy metal element detection method in the embodiments of this application captures plasma spectral signals and converts them into spectral data. Using a concentration prediction model, the spectral characteristics of specific wavelengths are precisely analyzed, thereby accurately predicting the concentration of target heavy metal elements in liquid samples. This not only improves the accuracy and stability of detection, but also uses a trained machine learning model to rapidly analyze unknown samples, effectively reducing the impact of background interference and the coexistence of multiple elements on concentration analysis, making heavy metal concentration detection more efficient and reliable.

[0051] Please see Figure 2 , Figure 2 This is a workflow diagram of the concentration prediction model provided in the embodiment of the present application; in the embodiment of the present application, the concentration prediction model is specifically used to:

[0052] Step S1: Decompose the spectrum data of the plasma into multiple independent spectrum data.

[0053] In step S1, the plasma spectral data is decomposed into multiple independent spectral data sets. For example, independent component analysis (ICA) or other unmixing methods are used to separate the different components in the spectral data, so that each independent spectral data set represents the characteristic spectrum of a different substance. This helps distinguish the target element from other components in the sample and reduces the influence of background interference and complex matrix effects.

[0054] For example, using the Independent Component Analysis (ICA) algorithm, spectral data X can be represented as a linear combination of several independent components S: X = AS, where X is an n×m matrix representing the spectral data of n samples, each with spectral intensities at m wavelengths. A is an n×n mixing matrix representing the contribution coefficients of the independent components in each sample. S is an n×m matrix representing the spectral intensities of the m independent components at each wavelength.

[0055] The goal of ICA is to find an inverse matrix W such that S = WX, by maximizing non-Gaussianity or minimizing information redundancy. W is the n×n unmixing matrix. S is the extracted independent component matrix, where each row represents the spectral signal of an independent component, associated with the target heavy metal element in the liquid sample.

[0056] It should be noted that in drinking water, the spectral signals of target heavy metal elements are often obscured by complex background noise and spectral overlap with other elements. The ICA algorithm can decompose the mixed spectrum into independent signal components, separating the characteristic spectrum of the target element from the background noise and other elements, significantly improving the signal purity and recognition of the target element.

[0057] Step S2: extracting characteristic spectrum data of heavy metal elements from a plurality of independent spectrum data.

[0058] In the above step S2, characteristic spectral data of heavy metal elements are extracted from multiple independent spectral data, i.e., independent component calibration. The decomposed independent components are compared with the standard spectral library of the National Institute of Standards and Technology (NIST) to identify components related to the target heavy metal elements.

[0059] The independent component signals associated with the target heavy metal elements obtained through ICA decomposition are purer and more representative, and their linear relationship with the target element concentration is more significant. Using the separated independent components for regression analysis can effectively improve the accuracy of quantitative predictions of target heavy metal elements, especially in complex samples with the coexistence of multiple elements, showing higher reliability.

[0060] Notably, ICA decomposition can separate overlapping spectral signals into independent components, thereby clearly separating the spectral characteristic peaks of different elements. This capability is particularly important when multiple heavy metals coexist, as it allows for accurate identification of the spectral characteristic peaks of each element, avoiding misjudgment and quantitative errors caused by spectral overlap.

[0061] Extract the components corresponding to the target elements (such as Pb, Cd, etc.) from the decomposed independent components S and record them as S i (i-th independent component). Determine the correspondence between each independent component and the specific element in the sample to provide a basis for subsequent concentration prediction.

[0062] Specifically, standard spectral information related to the target heavy metal element is obtained from the NIST spectral database. The database contains a large number of elements and their emission spectra under different conditions. For each target element (such as Pb, Cd, As, etc.), its standard spectral line position (i.e. wavelength) and its corresponding emission intensity are obtained. By comparing the spectral peak wavelength extracted from the independent component spectrum with the characteristic wavelength in the NIST standard spectrum, the spectral line positions of the two are aligned. Based on the results obtained by the comparison, the characteristic spectral signal related to the target heavy metal element in the independent component is identified. For each independent component, record which target element's standard spectrum matches it, and determine whether the independent component can be attributed to a specific element. Finally, the calibrated independent component is used for subsequent concentration prediction and quantitative analysis.

[0063] Step S3: Determine the concentration value of the target heavy metal element in the liquid sample based on the relationship between the intensity of the characteristic spectrum data of the known heavy metal element and the known heavy metal concentration value.

[0064] In the above step S3, the concentration value of the target heavy metal element in the liquid sample is determined based on the relationship between the characteristic spectrum intensity of the known heavy metal element and its known concentration value.

[0065] For example, the concentration C of the target element i is i , assuming that it is the signal strength S of the corresponding independent component i There is a linear relationship: C i =k·S i +b; where C i Indicates the concentration of target element i (such as the concentration of Pb and Cd), S i is the independent component signal intensity related to the target element i, k and b are regression coefficients, and are calibrated using known sample data.

[0066] pass Figure 2 As can be seen, the heavy metal detection method provided in the embodiments of this application, by decomposing spectral data into independent components and extracting the characteristic spectrum of the target heavy metal, can effectively reduce interference from other components and background noise in the sample, making the detection results more accurate and reliable. By utilizing the quantitative relationship between characteristic spectral intensity and concentration, it achieves rapid and accurate prediction of the concentration of the target heavy metal element in the sample, improving detection sensitivity and data reliability.

[0067] In an optional embodiment, after converting the spectral signal of the plasma into spectral data, the heavy metal element detection method further includes: performing baseline correction and normalization processing on the spectral data.

[0068] After the spectral signal of the plasma is converted into spectral data, the spectral data includes the mixed spectral information of all components in the sample, including the characteristic spectrum of the target heavy metal elements in drinking water and background noise.

[0069] The spectral data obtained by processing with algorithms such as baseline correction and normalization can remove background interference and noise signals; it can optimize the quality of spectral data and improve the signal-to-noise ratio of the data.

[0070] It can be seen from this that the heavy metal element detection method provided in the embodiment of the present application converts the spectral signal of the plasma into spectral data, which contains high-quality spectral signals with noise and background interference removed. It retains the characteristic spectrum related to the target element and provides a high-quality data basis for subsequent heavy metal content analysis.

[0071] Please refer to Figure 3 , Figure 3 This is a flow chart of liquid sample pretreatment provided in an embodiment of the present application; in an optional embodiment, before focusing the femtosecond laser on the liquid sample, the heavy metal element detection method further includes the following steps:

[0072] S11: Collect a liquid sample and filter the liquid sample.

[0073] In the above step S11 , an untreated liquid sample is collected. The untreated liquid sample may contain suspended particles, dissolved substances and impurities, and the liquid sample is filtered using a filtering device.

[0074] For example, using drinking water source samples as an example, clean sample containers are used to collect drinking water source samples. Ensure that the containers are free of contamination and minimize the entry of external factors (such as dust or other impurities). A 0.45μm microporous filter membrane is used to remove large insoluble particles and suspended solids from drinking water source samples. During operation, the filter membrane must be prevented from clogging. If clogging occurs, the filter membrane can be replaced.

[0075] S12: Centrifuging the filtered liquid sample to obtain a liquid sample.

[0076] In step S12, the filtered liquid sample is centrifuged to obtain a liquid sample. Still using the drinking water source sample as an example, setting the centrifuge speed to 5000 rpm can separate fine particles and sediment in the water sample, thereby obtaining a clear supernatant as a high-purity sample for testing.

[0077] It is worth noting that in the examples of this application, the femtosecond laser is used to directly induce microbubble generation in the liquid, thereby achieving the excitation of heavy metal elements in the sample, eliminating the need for complex sample pretreatment (such as quantitative addition, heating and evaporation, etc.). This simplifies the sample preparation process, reduces the number of steps and time, reduces the complexity and labor cost of the experiment, and avoids the potential loss and contamination of elements in the sample during pretreatment.

[0078] Furthermore, while traditional substrate-enhanced techniques (such as surface-enhanced Raman scattering (SERS)) typically require the addition of a substrate material (such as metal nanoparticles) to the liquid sample to improve detection sensitivity, the heavy metal element detection method provided in the present application achieves signal enhancement through laser-induced microbubbles, eliminating the need for additional substrate material. This not only simplifies experimental procedures but also avoids potential chemical reactions or physical interference between the substrate material and sample components, further improving the reliability of the detection results.

[0079] pass Figure 3 It can be seen that after filtering and centrifugation, most impurities, suspended particles, and sediments are removed from the liquid sample, providing a pure liquid sample for subsequent laser-induced breakdown spectroscopy analysis, ensuring the accuracy of the test results and the signal-to-noise ratio.

[0080] Please see Figure 4 , Figure 4 This is a structural diagram of a heavy metal element detection device 1000 provided in an embodiment of the present application; This application provides a heavy metal element detection device 1000, Figure 4 In the embodiment, the sample to be tested is placed in a cuvette. The heavy metal element detection device 1000 includes a spectrum data acquisition module 100 and a data processing module 200 .

[0081] The spectral data acquisition module 100 is used to focus a femtosecond laser on a liquid sample to generate bubbles in the liquid sample; and to focus a nanosecond laser on the bubbles to excite the vaporized liquid sample in the bubbles to generate plasma.

[0082] The data processing module 200 is used to predict the content of heavy metal elements in the liquid sample based on the spectral signal of the plasma.

[0083] Please continue to see Figure 4 , the spectral data acquisition module 100 includes: a dual laser emission assembly having a nanosecond laser 111 and a femtosecond laser 112 and an optical path combining lens assembly 120;

[0084] Nanosecond laser 111 is used to emit nanosecond laser light toward optical path combiner 120, and femtosecond laser 112 is used to emit femtosecond laser light toward optical path combiner 120. For example, the femtosecond laser light emitted by femtosecond laser 112 has a wavelength of 800 nm, a pulse duration of 100 fs, a pulse frequency of 1 Hz, and an energy of 1 mJ. By focusing the laser light in the liquid sample, microbubbles are generated, thereby enhancing the nanosecond laser-induced plasma. Nanosecond laser 111 emits nanosecond laser light with a wavelength of 532 nm, a pulse frequency of 1 Hz, an energy of 30 mJ, and a spot size of 400 μm. It is used to excite the vaporized sample within the microbubbles, generating plasma.

[0085] The optical beam combining lens assembly 120 is used to focus the femtosecond laser and the nanosecond laser on the liquid sample.

[0086] Please continue to see Figure 4 The optical path combining lens assembly 120 includes a reflecting mirror 121 , a dichroic mirror 122 and a focusing lens 123 .

[0087] The 532 nm laser beam output by the nanosecond laser 111 first passes through the reflector 121 . The reflector 121 is located in the optical path of the nanosecond laser 111 and is arranged at an angle of 45° to the nanosecond laser 111 , for reflecting the laser beam to the dichroic mirror 122 .

[0088] The 800nm ​​laser light emitted by the femtosecond laser 112 passes directly through the dichroic mirror 122, while the nanosecond laser light is reflected by the reflector 121 and then also reflected by the dichroic mirror 122 onto the same optical path. The dichroic mirror 122 is used to combine the femtosecond laser and nanosecond laser beams into a coaxial optical path.

[0089] The focusing lens 123 is placed on the combined laser light path to ensure that the femtosecond laser and the nanosecond laser are simultaneously focused on the surface of the liquid sample. The femtosecond laser is used to generate microbubbles, and the nanosecond laser is used to excite plasma.

[0090] Please continue to see Figure 4 The data processing module 200 includes a spectral signal acquisition component 210 , a spectral signal conversion component 220 and a concentration prediction unit 230 .

[0091] The spectrum signal acquisition component 210 is used to acquire the spectrum signal of the plasma.

[0092] Exemplarily, the spectrum signal acquisition component 210 includes a collection lens 211 and a fiber optic probe 212. Figure 4 As shown, the collection lens 211 is placed on the side of the liquid sample, perpendicular to the laser light path, and is used to collect the light signal emitted by the plasma generated by the sample. The collection lens 211 should ensure that it can cover the spectral range of the plasma emission.

[0093] The spectral signal conversion component 220 is used to convert the spectral signal into spectral data. In the embodiment of the present application, the spectral signal conversion component 220 can be a high-resolution spectrometer.

[0094] like Figure 4 As shown, the fiber optic probe 212 is aligned with the collection lens 211, and the fiber optic probe 212 is connected to the optical fiber 213. The fiber optic probe 212 transmits the optical signal collected by the collection lens 211 to the spectral signal conversion component 220 (high-resolution spectrometer). The optical fiber 213 is used to transmit the optical signal to the spectral signal conversion component 220 (high-resolution spectrometer) and convert the optical signal into an electrical signal.

[0095] The high-resolution spectrometer receives the spectral signal emitted by the plasma through the optical fiber 213 and converts the optical signal into an electrical signal. The spectrometer has a high spectral resolution (0.1nm) and covers a wavelength range from ultraviolet to near infrared (200nm-900nm) to ensure that the characteristic spectra of multiple heavy metal elements can be detected.

[0096] The concentration prediction unit 230 is configured to determine the concentration value of the target heavy metal element in the liquid sample according to the spectral data.

[0097] In the embodiment of the present application, the concentration prediction unit 230 is a computer including a concentration prediction model 231 .

[0098] A computer is electrically connected to the high-resolution spectrometer, receiving and processing the electrical signal data transmitted by the spectrometer. The computer analyzes the electrical signal in real time, converting it into a spectral dataset used to quantitatively predict and calculate the concentration of the target heavy metal element. Finally, the computer records and outputs the spectral dataset for subsequent analysis and processing. The concentration prediction model further processes the spectral data to provide information on the heavy metal content in the target sample.

[0099] In one embodiment, the heavy metal element detection device 1000 further includes a sequential pulse generator 300 , which can precisely control the triggering timing of the nanosecond and femtosecond lasers, as well as the spectral acquisition time, with picosecond accuracy. This sequential pulse generator 300 coordinates the pulse synchronization of the femtosecond and nanosecond lasers, ensuring that the nanosecond laser excites the plasma within the appropriate time after the femtosecond action generates microbubbles, and subsequently precisely controls the high-resolution spectrometer to acquire the spectrum.

[0100] exist Figure 4 In the embodiment, the timing pulse generator 300 is electrically connected to the nanosecond laser 111 and the femtosecond laser 112 to control the triggering timing of the two laser beams, and controls the emission time of the nanosecond laser emitted by the nanosecond laser 111 to be 1μs later than the emission time of the femtosecond laser emitted by the femtosecond laser 112, ensuring that after the femtosecond laser generates microbubbles, the nanosecond laser excites the plasma within the set delay time.

[0101] In summary, the dual-beam collaborative enhanced heavy metal detection method and device provided by the embodiment of the present application has the following advantages: First, the femtosecond laser induced microbubble enhancement technology produces a focusing effect when the nanosecond laser excites the plasma by regulating the size and formation timing of the microbubbles, thereby reducing the generation threshold of the plasma in the liquid, enhancing the intensity and stability of the spectral signal, and significantly improving the detection sensitivity of trace heavy metal elements. Secondly, in response to the problem of spectral signal overlap of multiple heavy metal elements in liquid samples, the embodiment of the present application introduces an independent component analysis (ICA) algorithm to decompose the mixed spectral data into independent components, thereby effectively eliminating background noise and interference signals, improving the resolution of the spectral signal, and solving the quantitative error caused by the overlap of element spectra in traditional analysis methods. Finally, the linear regression prediction model based on independent components overcomes the lack of accuracy of traditional regression models in multi-component samples by fitting the relationship between independent component signals and heavy metal element concentrations, and has strong sample matrix adaptability and superior practical application effects. This solution can achieve high-precision and high-sensitivity multi-component detection of heavy metal elements and has broad application prospects.

[0102] See Figure 5 , Figure 5This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. An electronic device 400 provided in an embodiment of the present application includes: a processor 401 and a memory 402, wherein the memory 402 stores machine-readable instructions executable by the processor 401, and when the machine-readable instructions are executed by the processor 401, the above method is performed.

[0103] Based on the same inventive concept, an embodiment of the present application also provides a computer-readable storage medium, which stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps in any implementation method of the above-mentioned heavy metal element detection method are executed.

[0104] The computer-readable storage medium can be 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 programmable read-only memory (EEPROM), or other media that can store program code.

[0105] In this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the elements.

[0106] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for detecting heavy metal elements, characterized in that: The heavy metal element detection method comprises: focusing a femtosecond laser on a liquid sample to generate bubbles in the liquid sample; focusing a nanosecond laser on the bubble to excite the vaporized liquid sample in the bubble to generate plasma; The heavy metal element content in the liquid sample is predicted and obtained according to the spectral signal of the plasma.

2. The method according to claim 1, characterized in that Predicting and obtaining the concentration value of the target heavy metal element in the liquid sample according to the spectral signal of the plasma includes: converting the spectral signal of the plasma into spectral data; The spectral data is input into a concentration prediction model to obtain the concentration value of the target heavy metal element in the liquid sample.

3. The method according to claim 2, characterized in that The concentration prediction model is specifically used for: Decomposing the spectrum data of the plasma into a plurality of independent spectrum data; Extracting characteristic spectrum data of heavy metal elements from the plurality of independent spectrum data; The concentration value of the target heavy metal element in the liquid sample is determined based on the relationship between the intensity of the characteristic spectrum data of the known heavy metal element and the known heavy metal concentration value.

4. The method according to claim 2, characterized in that After converting the spectrum signal of the plasma into spectrum data, the method further includes: The spectral data were baseline corrected and normalized.

5. The method according to claim 1, characterized in that Before focusing the femtosecond laser on the liquid sample, the heavy metal element detection method further includes: collecting a liquid sample and filtering the liquid sample; The filtered liquid sample is centrifuged to obtain the liquid sample.

6. A heavy metal element detection device, characterized in that: The heavy metal element detection device includes: a spectrum data acquisition module and a data processing module; The spectral data acquisition module is configured to focus a femtosecond laser on a liquid sample to generate bubbles in the liquid sample; and to focus a nanosecond laser on the bubbles to excite the vaporized liquid sample in the bubbles to generate plasma. The data processing module is used to predict and obtain the heavy metal element content in the liquid sample based on the spectral signal of the plasma.

7. The heavy metal element detection device according to claim 6, characterized in that: The spectrum data acquisition module includes: a dual laser emission component with a nanosecond laser and a femtosecond laser and an optical path beam combining mirror group; The nanosecond laser is used to emit nanosecond laser light toward the optical path beam combining mirror group; The femtosecond laser is used to emit femtosecond laser toward the optical path beam combining mirror group; The optical path beam combining lens assembly is used to focus the femtosecond laser and the nanosecond laser on the liquid sample.

8. The heavy metal element detection device according to claim 6, characterized in that: The data processing module includes a spectral signal acquisition component, a spectral signal conversion component and a concentration prediction unit; The spectrum signal acquisition component is used to acquire the spectrum signal of the plasma; The spectral signal conversion component is used to convert the spectral signal into spectral data; The concentration prediction unit is configured to determine a concentration value of a target heavy metal element in the liquid sample based on the spectral data.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein program instructions are stored in the memory, and when the processor runs the program instructions, the steps of the method according to any one of claims 1 to 5 are executed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are executed.

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