Quantum dot fluorescence-based rapid detection method and system for lead in soil

Abstract

This invention discloses a rapid detection method and system for lead in soil based on quantum dot fluorescence, specifically relating to the fields of optical detection and fluorescence analysis. It addresses the problem of fluorescence signal distortion caused by non-specific heterogeneous aggregation of nanoparticles in the extract solution when existing quantum dot fluorescent probes detect lead in soil. The method acquires the fluorescence spectrum of the mixed system and separates the characteristic fluorescence peaks of the quantum dots. It performs time-resolved measurements of the fluorescence signal, divides the time window of the fluorescence lifetime decay curve according to a preset time threshold, and calculates the integral area. Simultaneously, it analyzes the quenching kinetics of the characteristic fluorescence peak intensity to obtain the fluorescence quenching rate constant, and calculates the lifetime distribution characteristic ratio based on the integral area of ​​the time window. It determines the contribution ratio of non-specific aggregation to the fluorescence quenching rate constant through cooperative deviation analysis. Based on this ratio, it compensates and corrects the intensity of the characteristic fluorescence peak, accurately calculating the lead ion concentration in the soil extract solution.

Description

Technical Field

[0001] This invention relates to the field of optical detection and fluorescence analysis technology, and more specifically, to a rapid detection method and system for lead in soil based on quantum dot fluorescence. Background Technology

[0002] Quantum dots, as an emerging nano-fluorescent material, have been widely studied and applied in the field of optical sensing of heavy metal ions due to their advantages such as tunable fluorescence properties and good photostability. Specifically, in the detection of lead pollution in soil, existing technologies have proposed a variety of detection schemes based on the fluorescence quenching or enhancement effects of quantum dots. These schemes usually use functionalized quantum dots as probes, enabling them to specifically bind to lead ions, thereby causing changes in fluorescence signals and achieving qualitative and quantitative analysis of lead ions. Due to their potential for speed and high sensitivity, they are regarded as a promising supplement or alternative to traditional laboratory detection techniques.

[0003] However, when applying quantum dot fluorescent probes to detect actual soil samples, the problem lies in the severe interference of the high complexity of the soil matrix itself on the interaction of nanoscale probes. The natural colloids and nanoparticles commonly present in soil extracts have similar size and surface properties to artificially synthesized quantum dot probes, and are prone to non-specific heterogeneous aggregation in the detection system. This heterogeneous aggregation not only increases the light scattering background, but more importantly, it directly induces non-target-dependent fluorescence quenching of quantum dot probes, resulting in severe distortion of the detection signal. Existing technologies cannot effectively distinguish and eliminate this specific interference caused by the matrix nanocomposition while ensuring detection speed, thus limiting the accuracy and reliability of such methods in actual soil samples. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, the present invention provides a rapid detection method and system for lead in soil based on quantum dot fluorescence to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Rapid detection methods for lead in soil based on quantum dot fluorescence include: S1. Obtain real-time fluorescence emission spectrum data of the mixed system of the soil extract to be tested after adding quantum dot fluorescent probe; S2. Based on real-time fluorescence emission spectral data, the characteristic fluorescence peaks of the quantum dot fluorescent probe are separated by spectral decomposition. S3. Perform time-resolved fluorescence measurement on the fluorescence signal corresponding to the characteristic fluorescence peak to obtain the fluorescence lifetime decay curve. Divide the fluorescence lifetime decay curve into multiple corresponding time windows based on at least two preset time thresholds and calculate the integral area of ​​fluorescence intensity in each time window. S4. The fluorescence quenching rate constant of the quantum dot fluorescent probe is calculated based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum peaks changing with time, and the lifetime distribution characteristic ratio is calculated based on the integral area of ​​each time window. S5. Based on the ratio of fluorescence quenching rate constant to lifetime distribution characteristics, the contribution ratio of nonspecific heterogeneous aggregation in the fluorescence quenching rate constant is determined by synergistic deviation analysis. S6. The peak intensity of the characteristic fluorescence spectrum peaks is compensated and corrected according to the contribution ratio, and the concentration of lead ions in the soil extract to be tested is calculated.

[0006] Furthermore, real-time fluorescence emission spectral data of the mixed system of the soil extract to be tested after the addition of the quantum dot fluorescent probe were obtained, including: The mixing system is continuously excited at a preset excitation wavelength, and the fluorescence signal generated by the mixing system within the preset emission wavelength range is collected simultaneously. The acquired fluorescence signal is spectrally smoothed to suppress noise, and the smoothed spectral signal is baseline calibrated to obtain real-time fluorescence emission spectrum data after removing the instrument background.

[0007] Furthermore, based on real-time fluorescence emission spectroscopy data, the characteristic fluorescence peaks of the quantum dot fluorescent probe are separated by spectral decomposition, including: Obtain the standard fluorescence spectrum of the quantum dot fluorescent probe in an interference-free solution to determine the center wavelength and spectral shape of its characteristic fluorescence peaks; Based on the center wavelength and spectral shape, nonlinear curve fitting is performed on the real-time fluorescence emission spectrum data to resolve the fluorescence components belonging to the quantum dot fluorescent probe from the mixed spectrum; The peak intensity and full width at half maximum (FWHM) of the fluorescence components are extracted from the resolved fluorescence components to complete the separation of characteristic fluorescence peaks.

[0008] Furthermore, time-resolved fluorescence measurements are performed on the fluorescence signals corresponding to the characteristic fluorescence peaks to obtain fluorescence lifetime decay curves. Based on at least two preset time thresholds, the fluorescence lifetime decay curves are divided into multiple corresponding time windows, and the integral area of ​​fluorescence intensity within each time window is calculated, including: The hybrid system was excited by a pulsed light source, and the time-series data of the arrival of fluorescent photons at the center wavelength of the characteristic fluorescence spectrum peak were collected. A fluorescence lifetime decay curve was reconstructed based on time series data to show the change in fluorescence intensity over time. Based on at least two preset time thresholds set for the fluorescence lifetime characteristics of quantum dots, the entire time axis corresponding to the fluorescence lifetime decay curve is divided into multiple continuous time windows. The intensity values ​​of the fluorescence lifetime decay curve within each time window are numerically integrated to calculate the integral area of ​​the fluorescence intensity within each time window.

[0009] Furthermore, the fluorescence quenching rate constant of the quantum dot fluorescent probe is calculated based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum over time, and the lifetime distribution characteristic ratio is calculated based on the integral area of ​​each time window, including: The kinetic curve of the peak intensity of the characteristic fluorescence spectrum peaks changing with time was fitted with a single exponential decay function, and the decay constant in the fitted function was extracted as the fluorescence quenching rate constant. Select at least two specific time windows, and calculate the lifetime distribution characteristic ratio that characterizes the change in fluorescence lifetime distribution by dividing the integral area of ​​one time window by the integral area of ​​the other time window.

[0010] Furthermore, selecting at least two specific time windows includes: selecting a first time window and a second time window, wherein the first time window corresponds to the initial stage of rapid fluorescence intensity decay in the fluorescence lifetime decay curve, and the second time window corresponds to the subsequent stage of slow fluorescence intensity decay; the lifetime distribution characteristic ratio is the ratio of the integral area of ​​the second time window to the integral area of ​​the first time window.

[0011] Furthermore, based on the ratio of the fluorescence quenching rate constant to the lifetime distribution characteristic, the contribution ratio of the fluorescence quenching rate constant derived from nonspecific heterogeneous aggregation was determined through co-variance analysis, including: A reference linear relationship between the fluorescence quenching rate constant and the lifetime distribution characteristic ratio was established using a series of standard solutions with known lead ion concentrations and no non-specific interference. In a two-dimensional coordinate system consisting of the ratio of fluorescence quenching rate constant to lifetime distribution characteristic, the coordinate points corresponding to the test sample are determined; Calculate the vertical distance or shortest geometric distance from the corresponding coordinate point to the reference linear relationship as the degree of cooperative deviation; Based on the mapping relationship between the degree of synergistic deviation and the contribution ratio of nonspecific heterogeneous aggregation, which has been experimentally calibrated in advance, the contribution ratio of nonspecific heterogeneous aggregation in the fluorescence quenching rate constant of the sample under test is determined.

[0012] Furthermore, the mapping relationship between the co-occurrence deviation and the contribution ratio of nonspecific heterogeneous aggregation pre-calibrated by experiments was obtained by preparing a series of interference standard samples containing a fixed concentration of lead ions but different known proportions of simulated soil colloidal particles, measuring their co-occurrence deviation and known interference ratios respectively, and then fitting and establishing a calibration table.

[0013] Furthermore, the peak intensity of the characteristic fluorescence spectrum peaks is compensated and corrected according to the contribution ratio, and the lead ion concentration in the soil extract is calculated, including: Based on the contribution ratio, the correction amount for the peak intensity of the characteristic fluorescence spectrum peak is determined according to the pre-determined correspondence between the contribution ratio and the fluorescence intensity correction amount. The peak intensity of the characteristic fluorescence spectrum peaks is arithmetically corrected using the correction amount to obtain the compensated and corrected peak intensity. The peak intensity after compensation and correction is compared with a pre-established calibration curve that reflects the relationship between lead ion concentration and peak intensity, and the lead ion concentration in the soil extract to be tested is determined from the calibration curve.

[0014] On the other hand, the present invention provides a rapid detection system for lead in soil based on quantum dot fluorescence, comprising: The spectral acquisition module is used to acquire real-time fluorescence emission spectral data of the mixed system of the soil extract to be tested after the addition of quantum dot fluorescent probes; The spectral decomposition module is used to separate the characteristic fluorescence peaks of quantum dot fluorescent probes based on real-time fluorescence emission spectral data through spectral decomposition. The lifetime analysis module is used to perform time-resolved fluorescence measurement on the fluorescence signal corresponding to the characteristic fluorescence peak to obtain the fluorescence lifetime decay curve. Based on at least two preset time thresholds, the fluorescence lifetime decay curve is divided into multiple corresponding time windows and the integral area of ​​fluorescence intensity in each time window is calculated. The parameter calculation module is used to calculate the fluorescence quenching rate constant of the quantum dot fluorescent probe based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum peak changing with time, and to calculate the lifetime distribution characteristic ratio based on the integral area of ​​each time window. The synergistic analysis module is used to determine the proportion of contribution from nonspecific heterogeneous aggregation in the fluorescence quenching rate constant based on the ratio of fluorescence quenching rate constant to lifetime distribution characteristics through synergistic deviation analysis. The concentration calculation module is used to compensate and correct the peak intensity of the characteristic fluorescence spectrum peaks according to the contribution ratio, and calculate the lead ion concentration in the soil extract to be tested.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Through a unique signal parsing and collaborative analysis process, the accuracy and reliability of quantitative detection of lead ions in complex soil matrices are significantly improved. Complementary information from the time and intensity domains is separated and integrated from quantum dot fluorescence signals: By performing time-resolved measurements on characteristic fluorescence and calculating the integral area based on its lifetime decay characteristics, a lifetime distribution characteristic ratio sensitive to non-specific heterogeneous aggregation states is obtained. Simultaneously, the dynamic quenching process of fluorescence intensity is kinetically analyzed to obtain the fluorescence quenching rate constant reflecting the specific binding strength of lead ions. This allows the two quenching mechanisms that were originally coupled in a single fluorescence intensity signal to be initially distinguished at the characteristic parameter level, laying a data foundation for subsequent accurate removal of interference.

[0016] 2. By quantitatively analyzing the synergistic relationship between the fluorescence quenching rate constant and the lifetime distribution characteristic ratio, the proportion of interference contribution caused by non-specific heterogeneous aggregation can be accurately calculated and quantified. Based on this proportion, the original fluorescence intensity is targeted for compensation and correction, ultimately enabling the detection results to reflect the true lead ion concentration. While maintaining the advantages of rapid quantum dot fluorescence detection, this method solves the problem of signal distortion caused by heterogeneous aggregation of soil nanoparticles, achieving a balance between speed and anti-interference, and improving the practical value of this method in the detection of actual environmental samples. Attached Figure Description

[0017] Figure 1 This is a flowchart of the rapid detection method for lead in soil based on quantum dot fluorescence according to the present invention; Figure 2 This is a schematic diagram of the structure of the rapid detection system for lead in soil based on quantum dot fluorescence of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Figure 1 This invention presents a rapid detection method for lead in soil based on quantum dot fluorescence, comprising: S1. Obtain real-time fluorescence emission spectrum data of the mixed system of the soil extract to be tested after adding quantum dot fluorescent probe; S2. Based on real-time fluorescence emission spectral data, the characteristic fluorescence peaks of the quantum dot fluorescent probe are separated by spectral decomposition. S3. Perform time-resolved fluorescence measurement on the fluorescence signal corresponding to the characteristic fluorescence peak to obtain the fluorescence lifetime decay curve. Divide the fluorescence lifetime decay curve into multiple corresponding time windows based on at least two preset time thresholds and calculate the integral area of ​​fluorescence intensity in each time window. S4. The fluorescence quenching rate constant of the quantum dot fluorescent probe is calculated based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum peaks changing with time, and the lifetime distribution characteristic ratio is calculated based on the integral area of ​​each time window. S5. Based on the ratio of fluorescence quenching rate constant to lifetime distribution characteristics, the contribution ratio of nonspecific heterogeneous aggregation in the fluorescence quenching rate constant is determined by synergistic deviation analysis. S6. The peak intensity of the characteristic fluorescence spectrum peaks is compensated and corrected according to the contribution ratio, and the concentration of lead ions in the soil extract to be tested is calculated.

[0020] In this embodiment, the specific process for obtaining real-time fluorescence emission spectrum data of the mixed system of the soil extract to be tested after the addition of the quantum dot fluorescent probe is as follows: The soil extract to be tested and the quantum dot fluorescent probe solution are mixed evenly at a predetermined volume ratio to form a mixed system for detection. This mixed system is placed in the sample chamber of a fluorescence spectrophotometer. The excitation monochromator of the fluorescence spectrophotometer is set to output a preset excitation wavelength. This preset excitation wavelength is determined based on the absorption spectral characteristics of the selected quantum dot fluorescent probe, with the aim of effectively exciting the quantum dot fluorescent probe to produce fluorescence. For example, by consulting the product specifications of the quantum dot fluorescent probe or measuring its UV-Vis absorption spectrum, the wavelength corresponding to its maximum absorption peak is found, and this wavelength, or a nearby wavelength with a strong instrument light source output, is set as the preset excitation wavelength. Common settings are 375 nm, 400 nm, or 450 nm. Simultaneously, the scanning range of the emission monochromator is set to a preset emission wavelength range. It is necessary to ensure complete coverage of the characteristic fluorescence emission peak of the quantum dot fluorescent probe and avoid strong Rayleigh scattering interference caused by the excitation light. In specific operation, the starting wavelength of the preset emission wavelength range is usually set to be more than 20 nanometers larger than the preset excitation wavelength, and the ending wavelength is set to be at least 50 nanometers larger than the peak wavelength of the characteristic fluorescence emission peak of the quantum dot fluorescent probe. For example, when the preset excitation wavelength is 400 nanometers, the preset emission wavelength range can be set to 420 nanometers to 700 nanometers. After setting the integration time, scanning speed and spectral bandwidth parameters of the fluorescence spectrophotometer, the measurement is started. The instrument continuously excites the mixed system with the preset excitation wavelength, synchronously drives the emission monochromator to perform wavelength scanning within the preset emission wavelength range, and collects the fluorescence signal intensity generated by the mixed system at each wavelength point through the detector to obtain the initial real-time fluorescence emission spectrum data.

[0021] The acquired fluorescence signal is spectrally smoothed to suppress noise. The Savitzky-Golay convolution smoothing algorithm is used for spectral smoothing. This algorithm requires pre-setting a smoothing window width and a polynomial order. The smoothing window width defines the number of original data points used to calculate each smoothed data point; its selection is based on the noise level and characteristic peak width of the initial real-time fluorescence emission spectrum data. A smoothing window width that is too small will result in insufficient smoothing, while a smoothing window width that is too large may lead to distortion of the spectral characteristic peak shape. The polynomial order defines the order of the polynomial function used for local fitting. The process is as follows: starting from the first wavelength point of the initial real-time fluorescence emission spectrum data, all data points within the window width centered on the current point are selected. Least-squares fitting is performed on these data points using the selected polynomial order. The smoothed fluorescence signal intensity value corresponding to the current center wavelength point is calculated using the fitted polynomial. The smoothing window is moved one data point backward, and the above fitting and calculation process is repeated until all data points within the preset emission wavelength range are traversed, thereby generating the smoothed spectral signal.

[0022] Baseline calibration is performed on the smoothed spectral signal. The purpose of baseline calibration is to subtract the non-specific fluorescence background signal generated by the sample cell, solvent, and instrument background. Baseline intervals are identified on the smoothed spectral signal. The baseline interval should be located in the wavelength region outside the characteristic fluorescence peak range of the quantum dot fluorescent probe, that is, the region where the fluorescence intensity is relatively stable and no significant characteristic peaks appear. For example, in the smoothed spectral signal, a continuous wavelength range before the starting wavelength of the characteristic fluorescence peak can be selected as the first baseline interval, and a continuous wavelength range after the ending wavelength of the characteristic fluorescence peak can be selected as the second baseline interval. A linear function is used to fit all fluorescence intensity data in the first and second baseline intervals to obtain a baseline line. This baseline line represents the trend of background signal change with wavelength. The fluorescence intensity value corresponding to each wavelength point on the smoothed spectral signal is subtracted from the background intensity value calculated by the baseline line at that wavelength point to complete the baseline calibration and obtain the real-time fluorescence emission spectrum data after removing the instrument background.

[0023] In this embodiment, the specific process of separating the characteristic fluorescence peaks of the quantum dot fluorescent probe through spectral decomposition based on real-time fluorescence emission spectral data is as follows: To determine the center wavelength and spectral shape of the characteristic fluorescence peaks, a standard fluorescence spectrum of the quantum dot fluorescent probe was obtained in an interference-free solution. The interference-free solution refers to a buffer solution containing no soil extract components and only the same pH and ionic strength as the soil extract to be tested. The quantum dot fluorescent probe was dissolved in this interference-free solution to prepare a standard solution. The concentration of the quantum dot fluorescent probe in this standard solution was consistent with the concentration of the quantum dot fluorescent probe added to the soil extract to be tested during subsequent actual detection. The standard solution was measured using the same instrument parameters as those used to obtain the real-time fluorescence emission spectrum data, including the same preset excitation wavelength, the same preset emission wavelength range, and the same... Using the same integration time, scan speed, and spectral bandwidth, the measured spectral data becomes the standard fluorescence spectrum of the quantum dot fluorescent probe in an interference-free solution. Analyzing this standard fluorescence spectrum, the wavelength corresponding to the data point with the highest fluorescence intensity is identified; this wavelength is determined as the center wavelength of the characteristic fluorescence peak of the quantum dot fluorescent probe. Observing the graph of the standard fluorescence spectrum, the spectral shape of the characteristic fluorescence peak is usually approximately a symmetrical bell-shaped curve. By fitting a single peak to the standard fluorescence spectral data, the specific mathematical model category of the spectral shape can be confirmed, for example, whether it is a Gaussian model or a Lorentz model. This operation provides crucial prior spectral features for subsequent spectral decomposition.

[0024] Based on the center wavelength and spectral shape, nonlinear curve fitting is performed on real-time fluorescence emission spectral data to extract the fluorescence components belonging to the quantum dot fluorescent probe from the mixed spectrum. The nonlinear curve fitting process is executed by computer software, and the input data is the real-time fluorescence emission spectral data after removing the instrument background. This data consists of a series of pairs of wavelength values ​​and fluorescence intensity values. The goal of the fitting is to construct a general mathematical function model that can most accurately describe the distribution of the real-time fluorescence emission spectral data after removing the instrument background. The general mathematical function model must include a component function representing the characteristic fluorescence peak of the quantum dot fluorescent probe. The mathematical form of this component function is determined by the spectral shape. For example, if the spectral shape is confirmed to be a Gaussian model spectral shape, then the component function adopts a Gaussian function form, which includes three adjustable variables: peak position parameter, peak height parameter, and width parameter. The initial value of the peak position parameter is set to the center wavelength value of the previously determined characteristic fluorescence peak, and the peak height parameter... The initial values ​​are set as the average fluorescence intensity values ​​of data points near the center wavelength in the real-time fluorescence emission spectrum data after removing the instrument background. The initial value of the width parameter is set as the estimated half-width at half-maximum (FWHM) of the characteristic fluorescence peaks of the standard fluorescence spectrum of the quantum dot fluorescent probe in an interference-free solution. The overall mathematical function model also includes a baseline function to describe any possible residual broadband background signal, which can be a linear function or a quadratic polynomial function. The summation function is compared with the real-time fluorescence emission spectrum data after removing the instrument background, and iterative calculation is performed using a nonlinear least squares algorithm. During the iterative calculation, the values ​​of all adjustable variables in the summation function are automatically adjusted. The goal of the iterative calculation is to minimize the sum of squares of the differences between the theoretical intensity value calculated by the summation function and the measured fluorescence intensity value. The iterative calculation continues until the change in the sum of squares of the differences obtained from two adjacent iterations is less than a pre-set convergence threshold, which can be set, for example, 1 × 10⁻⁶. -6 When the change in the sum of squared differences is less than the convergence threshold, the fitting is considered to have reached convergence. After the fitting converges, the final mathematical expression of the component function representing the characteristic fluorescence peak of the quantum dot fluorescent probe in the summation function and all its parameters are determined. This component function is the fluorescence component attributed to the quantum dot fluorescent probe that is resolved from the mixed spectrum.

[0025] The peak intensity and half-width at half-maximum (WHM) parameter are extracted from the analyzed fluorescence components to separate the characteristic fluorescence peaks. For a mathematically defined fluorescence component belonging to the quantum dot fluorescent probe, the peak intensity parameter directly corresponds to the peak height parameter in that expression. For example, for a fluorescence component in Gaussian function form, its peak intensity is equal to the peak height parameter value of that Gaussian function. The peak height parameter value is directly read from the final parameter set obtained after fitting convergence; this value is the peak intensity of the extracted characteristic fluorescence peak. The WHM parameter refers to the full width at half the height of the characteristic fluorescence peak. For a Gaussian function model with defined parameters, the WHM is calculated using the formula: WHM = Gaussian function. The width parameter of the number is multiplied by the value 2.35482; for the Lorentz function model with determined parameters, the half-width at half-maximum (WHM) is calculated using the formula: WHM equals the width parameter of the Lorentz function multiplied by 2; based on the specific function model corresponding to the fluorescence component, the corresponding calculation formula is used to calculate the specific value of WHM; thus, the two key optical parameters of the characteristic fluorescence peaks of the quantum dot fluorescent probe, peak intensity and WHM, have been separated and extracted from the real-time fluorescence emission spectrum data after removing the instrument background, completing the separation of characteristic fluorescence peaks; the separated characteristic fluorescence peaks are quantitatively characterized by their peak intensity parameter and WHM parameter, and these parameters are used in subsequent quantitative analysis steps.

[0026] In this embodiment, time-resolved fluorescence measurement is performed on the fluorescence signal corresponding to the characteristic fluorescence peak to obtain the fluorescence lifetime decay curve. The specific process of dividing the fluorescence lifetime decay curve into multiple time windows based on at least two preset time thresholds and calculating the integral area of ​​fluorescence intensity within each time window is as follows: The mixing system was excited using a pulsed light source, and time-series data of the arrival of fluorescent photons at the center wavelength of the characteristic fluorescence peak were collected. The pulsed light source was a picosecond pulsed laser or a nanosecond pulsed light-emitting diode with an emission wavelength matching the excitation requirements of the quantum dot fluorescent probe, such as a pulsed laser diode with a center wavelength of 375 nm. The mixing system was placed in the sample chamber of a time-resolved fluorescence spectrometer. The emission monochromator wavelength of the time-resolved fluorescence spectrometer was fixed at the center wavelength of the characteristic fluorescence peak, which was previously determined through spectral decomposition. A time-correlated single-photon counting system was configured. The parameters include setting the photon count rate to be 10% lower than the system's maximum count rate to avoid pulse accumulation effects, and setting the time scan range from 0 nanoseconds to the point where fluorescence completely decays, for example, setting the time scan range to 0 nanoseconds to 200 nanoseconds; starting the measurement program, the pulsed light source emits a short pulse to excite the mixed system, and the time-correlated single-photon counting system begins to record the precise arrival time of each detected fluorescent photon relative to the excitation pulse; repeating the excitation and detection cycle hundreds of thousands of times, accumulating a large number of photon arrival time events, and finally forming time series data of fluorescent photons arriving at the center wavelength of the characteristic fluorescence spectrum peak.

[0027] Based on time series data, a fluorescence lifetime decay curve showing the change in fluorescence intensity over time is reconstructed. The entire time scan range is divided into a series of continuous time channels with equal widths. The width of each time channel is set according to the time resolution capability, for example, setting the width of each time channel to 0.01 nanoseconds or 0.1 nanoseconds. The number of photons that fall into each time channel at the arrival time in the time series data is counted. This number of photons represents the relative value of the fluorescence intensity at the corresponding time point of that time channel. The graph plotted with the center time point of each time channel as the x-axis and the photon count value in the corresponding time channel as the y-axis is the fluorescence lifetime decay curve showing the change in fluorescence intensity over time.

[0028] Based on at least two preset time thresholds set for the fluorescence lifetime characteristics of quantum dots, the entire time axis corresponding to the fluorescence lifetime decay curve is divided into multiple continuous time windows. The preset time thresholds need to be set based on the average fluorescence lifetime of the quantum dot fluorescent probe in a non-quenching state. The average fluorescence lifetime is obtained by measuring and fitting the fluorescence lifetime decay curve of the quantum dot fluorescent probe in an interference-free solution. The interference-free solution refers to a buffer solution that does not contain soil extract components and whose pH value and ionic strength are consistent with the background of the soil extract to be tested. The fluorescence lifetime decay curve of the quantum dot fluorescent probe in this interference-free solution is measured, and a single exponential decay function model is used for nonlinear fitting. The function model is: I(t) = I0 × exp(-t / τ); where I(t) represents the fluorescence intensity at time t, I0 represents the initial fluorescence intensity, and τ represents the decay time constant. The fitted τ value is the average fluorescence lifetime of the quantum dot fluorescent probe in a non-quenching state. At least two preset time thresholds are calculated based on this average fluorescence lifetime τ. The first time threshold T1 is calculated as: T1 = k1 × τ; where k1 represents the first preset multiple factor; the second time threshold T... 2. The calculation is as follows: T2 = k2 × τ; where k2 represents the second preset multiplier factor; the value of the first preset multiplier factor k1 ranges from 0.1 to 1.0, and the value of the second preset multiplier factor k2 ranges from 1.5 to 5.0; the specific values ​​of k1 and k2 are determined and optimized through preliminary experiments. The optimization method is to prepare a set of experimental samples containing a fixed concentration of lead ions but different known concentrations of simulated soil colloids, calculate the time window integral area ratio obtained under different combinations of k1 and k2, and select the ratio that makes the simulated soil colloid concentration most significantly correlated. The combination of k1 and k2 values ​​is used as the final set value. The most significant correlation can be determined by the largest calculated correlation coefficient or the largest change in ratio. Based on the calculated values ​​of the first time threshold T1 and the second time threshold T2, the time axis of the fluorescence lifetime decay curve is divided into multiple consecutive time windows. For example, three time windows are divided: the first window is from time 0 to the first time threshold T1, the second window is from the first time threshold T1 to the second time threshold T2, and the third window is from the second time threshold T2 to the end time of the fluorescence lifetime decay curve.

[0029] The intensity values ​​of the fluorescence lifetime decay curve within each time window are numerically integrated to calculate the area of ​​integration of fluorescence intensity within each time window; for each defined time window, the time span is from the initial time t. start Until the end time t endWithin this time window, the fluorescence lifetime decay curve is represented by a set of discrete data points. Each data point contains a time value *ti* and a corresponding fluorescence intensity value *Ii*, where the subscript *i* represents the index of the data point, used to distinguish and traverse the data points within the window. The trapezoidal numerical integration method is used to calculate the integral area of ​​fluorescence intensity within this time window. The calculation process involves calculating and summing the trapezoidal areas of all adjacent data point pairs within the time window. For any pair of adjacent data points (t... i ,I i ) and (t {i+1} ,I {i+1} The area of ​​the trapezoid formed by these two shapes is calculated as follows: (I) i +I {i+1} )×(t {i+1} -t i ) / 2; where t i and t {i+1} I represents the time between two adjacent data points. i and I {i+1} This represents the fluorescence intensity corresponding to two adjacent data points; within this time window, from t start to t end The sum of the areas of all such trapezoids is the integral area of ​​fluorescence intensity within that time window. Following this calculation method, the calculation is performed sequentially for each time window to obtain the integral area value corresponding to each time window.

[0030] At least two preset time thresholds are set based on the average fluorescence lifetime of the quantum dot fluorescent probe in a non-quenching state. The first time threshold is set as a first preset multiple of the average fluorescence lifetime, and the second time threshold is set as a second preset multiple of the average fluorescence lifetime. The specific values ​​of the first and second preset multiples are determined according to the aforementioned pre-experiment optimization process. By setting time thresholds based on the proportion of average fluorescence lifetime, the division of the time window can be adapted to different batches or slightly different quantum dot fluorescent probes, ensuring the stability and reproducibility of the method under different experimental conditions.

[0031] In this embodiment, the fluorescence quenching rate constant of the quantum dot fluorescent probe is calculated based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum changing over time, and the lifetime distribution characteristic ratio is calculated based on the integral area of ​​each time window, as follows: The kinetic curve of the peak intensity of the characteristic fluorescence peak changing with time was fitted with a single exponential decay function, and the decay constant in the fitted function was extracted as the fluorescence quenching rate constant. The kinetic curve of the peak intensity of the characteristic fluorescence peak was obtained by immediately monitoring the peak intensity of the characteristic fluorescence peak at fixed time intervals after the quantum dot fluorescent probe was mixed with the soil extract and the reaction was initiated. Monitoring was performed using a fluorescence spectrometer. Real-time fluorescence emission spectrum data of the mixed system were rapidly acquired at each monitoring time point. The characteristic fluorescence peak of the quantum dot fluorescent probe was separated from the real-time fluorescence emission spectrum data using a spectral decomposition algorithm, and the peak intensity of the characteristic fluorescence peak was recorded. The peak intensity values ​​of the corresponding characteristic fluorescence peaks at multiple equally spaced time points after the reaction start were recorded, and these data points were plotted as the kinetic curve of the peak intensity of the characteristic fluorescence peak changing with time. This kinetic curve was then fitted with a single exponential decay function. The single exponential decay function modulus used to fit the kinetic curve was... The form is: F(t) = F∞ + (F0 - F∞) × exp(-k × t); where F(t) represents the peak intensity of the characteristic fluorescence peak measured at time t, F0 represents the peak intensity of the characteristic fluorescence peak at the initial moment of the reaction obtained by fitting, F∞ represents the peak intensity of the characteristic fluorescence peak when the reaction reaches equilibrium obtained by fitting, k represents the decay constant obtained by fitting, and exp represents the natural exponential function; the fitting process is executed in computer software using a nonlinear least squares algorithm; initial estimates of parameters F0, F∞, and k are required when fitting; the initial estimate of F0 is directly taken as the peak intensity value of the characteristic fluorescence peak at the first data point on the kinetic curve; the initial estimate of F∞ is taken as the average of the peak intensities of the characteristic fluorescence peaks at the last three to five data points on the kinetic curve; the initial estimate of k is set to a positive number, for example, 0.01 per second; the convergence condition of the fitting algorithm is set as the relative change between the sum of squares of the residuals obtained from two adjacent iterations being less than 1 × 10⁻⁶. -4 Run the fitting algorithm until the convergence condition is met, and read the value of parameter k from the final fitting result. This value is the fluorescence quenching rate constant of the quantum dot fluorescent probe.

[0032] At least two specific time windows are selected, and the integral area of ​​one time window is divided by the integral area of ​​the other time window to calculate the lifetime distribution characteristic ratio, which characterizes the change in fluorescence lifetime distribution. The time windows and the integral areas of fluorescence intensity within each time window have been obtained in advance during the step of processing the fluorescence lifetime decay curve. The selection operation follows clear rules; specifically, a first time window and a second time window are selected. The selection criterion for the first time window is that it corresponds to the initial stage of rapid fluorescence intensity decay in the fluorescence lifetime decay curve on the time axis. The selection criterion for the second time window is that it corresponds to the subsequent stage of slow fluorescence intensity decay in the fluorescence lifetime decay curve on the time axis. The initial stage of rapid fluorescence intensity decay is represented by a segment with a large slope at the beginning of the fluorescence lifetime decay curve. The subsequent stage of slow fluorescence intensity decay is represented by a smooth segment with a slope approaching zero at the end of the curve. Based on the preset time threshold used when dividing the time windows, it can be determined which time windows belong to the initial stage. For example, if the time window division scheme defines three windows, where the first window covers time 0 to the first time threshold T1, the second window covers the first time threshold T1, and the third window covers the first time threshold T1, the fourth window covers the first time threshold T1, and the fifth window covers the first time threshold T1. The time window is defined as follows: from time T1 to the second time threshold T2, and the third window covers the period from the second time threshold T2 to the end of the curve. Typically, the first window is designated as the first time window, and the third window as the second time window. During the method establishment phase, the correspondence between the first and second time windows is finalized by analyzing the fluorescence lifetime decay curve morphology of the standard quantum dot fluorescent probe under interference-free conditions, and this correspondence is recorded as part of the standard operating procedure. The lifetime distribution characteristic ratio is obtained through division; the formula is: the lifetime distribution characteristic ratio equals the integral area of ​​the fluorescence intensity within the second time window divided by... The integral area of ​​fluorescence intensity within the first time window; during calculation, the integral area of ​​fluorescence intensity within the second time window is used as the dividend, and the integral area of ​​fluorescence intensity within the first time window is used as the divisor, and a division operation is performed. The quotient obtained is the lifetime distribution characteristic ratio; the calculation premise is that the integral area of ​​fluorescence intensity within the first time window is not zero. In actual measurement, this condition is naturally satisfied due to the presence of fluorescence signal; this lifetime distribution characteristic ratio is a dimensionless number, and its value reflects the ratio of the cumulative amount of late-stage emission to the cumulative amount of early-stage emission during the fluorescence decay process.

[0033] At least two specific time windows are selected, including a first time window and a second time window. The first time window corresponds to the initial stage of rapid fluorescence intensity decay in the fluorescence lifetime decay curve, and the second time window corresponds to the subsequent stage of slow fluorescence intensity decay. The lifetime distribution characteristic ratio is the ratio of the integral area of ​​the second time window to the integral area of ​​the first time window. This specific rule, once determined during the method development stage, remains unchanged in all subsequent sample detections. After completing the previous steps of dividing the time windows and calculating the integral area of ​​fluorescence intensity within each time window, it is necessary to clearly identify which of the time windows is the first time window and which is the second time window according to this established rule. The identification information is stored together with the integral area values ​​of fluorescence intensity within each time window to ensure that the integral areas of the first and second time windows can be accurately extracted for calculation in this step.

[0034] In this embodiment, the specific process of determining the contribution ratio of nonspecific heterogeneous aggregation in the fluorescence quenching rate constant based on the ratio of the fluorescence quenching rate constant to the lifetime distribution characteristic through co-variance analysis is as follows: A reference linear relationship between the fluorescence quenching rate constant and the lifetime distribution characteristic ratio was established using a series of standard solutions with known lead ion concentrations and no non-specific interference. The standard solution series was prepared based on accurately weighed lead standard material and dilution operations, using a lead-free buffer solution as the solvent to prepare a set of solutions with known lead ion concentrations and a gradient distribution. The lead ion concentration gradient should cover the expected concentration range of lead ions in the soil extract to be tested; for example, a series of standard solutions with concentrations of 0 μg / L, 10 μg / L, 20 μg / L, 50 μg / L, and 100 μg / L could be prepared. For each concentration of standard solution, the sample processing and measurement procedures specified in steps S1 to S4 were followed. Specifically, a quantum dot fluorescent probe identical to the sample to be tested was added to each standard solution and mixed. Real-time fluorescence emission spectrum data was obtained according to step S1, the characteristic fluorescence peaks of the quantum dot fluorescent probe were separated according to step S2, and time-resolved fluorescence measurement was performed and calculated according to step S3. The integral area of ​​fluorescence intensity within each time window is used to calculate the fluorescence quenching rate constant and lifetime distribution characteristic ratio for each standard solution according to step S4. The lifetime distribution characteristic ratios measured for this series of standard solutions are used as independent variables, and the corresponding fluorescence quenching rate constants are used as dependent variables. A scatter plot is then drawn in a two-dimensional rectangular coordinate system. The scatter plot is fitted using least squares linear regression. The linear regression model is expressed as: Y = A × X + B, where Y represents the fluorescence quenching rate constant, X represents the lifetime distribution characteristic ratio, and A and B are coefficients to be fitted. Linear regression calculations are performed to obtain the slope A and intercept B of the best-fit line, as well as statistics reflecting the goodness of fit, such as the correlation coefficient. This straight line described by Y = A × X + B represents the reference linear relationship between the fluorescence quenching rate constant and the lifetime distribution characteristic ratio. This relationship characterizes the statistical correlation between two characteristic parameters in the fluorescence quenching process caused by pure lead ion binding under ideal conditions where non-specific heterogeneous aggregation is absent.

[0035] In a two-dimensional coordinate system composed of the fluorescence quenching rate constant and the lifetime distribution characteristic ratio, the coordinate point corresponding to the test sample is determined. The coordinate point of the test sample is uniquely determined by the two parameter values ​​finally calculated after the complete measurement process from step S1 to step S4. The fluorescence quenching rate constant value of the test sample calculated in step S4 is denoted as Ks, and the lifetime distribution characteristic ratio value of the test sample calculated in step S4 is denoted as Rs. With Rs as the abscissa value and Ks as the ordinate value, the coordinate point of the test sample in the two-dimensional coordinate system is formed.

[0036] The vertical distance or shortest geometric distance from the corresponding coordinate point to the reference linear relationship is calculated as the cooperative deviation. The vertical distance is calculated as follows: Let the coordinates of the sample point be Y, and the equation of the reference linear relationship be Y = A × X + B. Calculate the vertical distance D from this point to the line. The formula for the vertical distance D is: D = |A × Rs - Ks + B| / sqrt(A × A + 1); where || represents the absolute value operation, and sqrt represents the square root operation of the value within the parentheses. The calculated D value is the cooperative deviation, and its unit is the same as the unit of the fluorescence quenching rate constant. The shortest geometric distance refers to the minimum Euclidean distance from the coordinate point to all points on the reference line. It can be calculated by solving for the perpendicular coordinates from the point to the line. When the reference linear relationship is a straight line, the shortest geometric distance is equal to the vertical distance D mentioned above. In this embodiment, the vertical distance D is uniformly used as the quantitative index of the cooperative deviation.

[0037] Based on the mapping relationship between the co-occurrence deviation and the contribution ratio of nonspecific heterogeneous aggregation pre-calibrated experimentally, the contribution ratio of nonspecific heterogeneous aggregation to the fluorescence quenching rate constant of the test sample is determined. Establishing this mapping relationship is the foundation for quantitative analysis and requires systematic calibration experiments. The calibration experiments include three steps: preparing a series of interference standard samples, measuring and calculating the co-occurrence deviation, and establishing a mapping model. The preparation of the interference standard sample series requires maintaining a constant lead ion concentration while systematically varying the amount of simulated soil colloidal particles added. The lead ion concentration should be a fixed value within the concentration range used when establishing the reference linear relationship, for example, fixed at 50 micrograms per liter. The simulated soil colloidal particles should be materials that represent common interfering components in the target soil. For example, micron- to submicron-sized particles of natural clay minerals such as montmorillonite or kaolinite, after screening and characterization, can be used. Alternatively, artificially synthesized iron oxide or silica nanoparticles can be used. The known proportion refers to the percentage of the mass or number concentration of the simulated soil colloidal particles added to the solution relative to a pre-defined baseline concentration C0 that can produce significant interference. For example, if the baseline concentration C0 is set to 100 mg / L, interference standard samples with known proportions of 0%, 5%, 10%, 20%, and 30% can be prepared, corresponding to colloidal particle addition concentrations of 0 mg / L, 5 mg / L, 10 mg / L, and 20 mg / L, respectively. 30 mg / L; for each prepared interference standard sample with a known proportion, strictly follow all steps from step S1 to step S5 before calculating the degree of coordination deviation to obtain its degree of coordination deviation value; thus, a set of data pairs is obtained, each data pair containing a known proportion value Pu and its corresponding degree of coordination deviation measurement value Du, where u indicates that the symbol is used to distinguish and traverse a series of known proportion values; use this set of data pairs to establish a mapping relationship; the mapping relationship can be represented as a fitting function or a lookup table; when using a fitting function, simple models such as linear functions, quadratic polynomial functions, or exponential functions are usually chosen to fit the data to obtain a value with the degree of coordination deviation D as the self-value. The mathematical expression for the variable, with the contribution ratio P as the dependent function, is: P=f(D). When using a lookup table (i.e., calibration), the data pairs are directly organized into two columns: one column is the degree of cooperation Du, and the other column is the corresponding known ratio Pu, sorted by the degree of cooperation from smallest to largest. When analyzing unknown samples, the calculated degree of cooperation D is input into this mapping relationship. If using a function, D is directly substituted into the function P=f(D) to calculate the contribution ratio. If using calibration, the contribution ratio is obtained through table lookup and interpolation. The interpolation method is to find two adjacent calibration points (D1,P1) and (D2,P2) in the calibration table such that D1≤D≤D2. If D is exactly equal to some D1, then the contribution ratio is P1.If D lies between D1 and D2, the contribution ratio is calculated using the linear interpolation formula: P = P1 + (P2 - P1) × (D - D1) / (D2 - D1). Through this process, a value between 0% and 100% is ultimately output, which is determined as the contribution ratio of nonspecific heterogeneous aggregation to the fluorescence quenching rate constant of the sample.

[0038] The mapping relationship between the co-deviation degree and the contribution ratio of nonspecific heterogeneous aggregation, which has been experimentally calibrated beforehand, is obtained by preparing a series of interference standard samples containing a fixed concentration of lead ions but different known proportions of simulated soil colloidal particles, measuring their co-deviation degree and known interference ratios, and then fitting and establishing a calibration table. The specific construction process of the calibration table has been described in detail above. As a physical form of the mapping relationship, the calibration table needs to be established and validated in advance during the method development stage and remain unchanged in actual use to ensure the consistency of analytical results. The number of data points in the calibration table should be sufficient to reduce interpolation errors, usually no less than 5 points. The scope of use of the calibration table is limited by the maximum and minimum co-deviation degrees used when it is established. For the co-deviation degree of the test sample that exceeds this range, its contribution ratio can be estimated by extrapolation or marked as exceeding the range. The rules for handling these boundary cases should also be clearly stated in the method.

[0039] In this embodiment, the specific process of compensating and correcting the peak intensity of the characteristic fluorescence spectrum peaks according to the contribution ratio and calculating the lead ion concentration in the soil extract to be tested is as follows: Based on the contribution ratio, and according to the predetermined correspondence between the contribution ratio and the fluorescence intensity correction amount, the correction amount for the peak intensity of the characteristic fluorescence peak is determined. The contribution ratio is a quantitative value obtained by step S5, representing the degree of influence of nonspecific heterogeneous aggregation on the total fluorescence quenching rate, and is a value between 0% and 100%. The fluorescence intensity correction amount is a specific numerical value, whose physical meaning is the increase in intensity value required to counteract the suppression effect caused by nonspecific heterogeneous aggregation on the measured peak intensity of the characteristic fluorescence peak. The correspondence between the contribution ratio and the fluorescence intensity correction amount needs to be established through a pre-designed calibration experiment. The calibration experiment method is to prepare a set of interference standard samples containing a fixed lead ion concentration but different known proportions of simulated soil colloidal particles. The lead ion concentration is fixed at an intermediate value, such as 50 micrograms per liter, and the known proportions of simulated soil colloidal particles are set with a series of gradients, such as 0%, 10%, 20%, and 30%. For each of these interference standard samples, its contribution ratio is measured and calculated according to steps S1 to S5. At the same time, the fluorescence intensity of each interference standard sample is recorded in step S2. The original peak intensity measurements of characteristic fluorescence peaks obtained directly from the spectral data without any correction are used to determine the correction amount. The principle for determining the correction amount is based on the understanding that non-specific heterogeneous aggregation mainly causes fluorescence quenching. A quantitative relationship between the contribution ratio and the required intensity compensation value is established through experimental data. Specifically, the difference between the original peak intensity measurements of the characteristic fluorescence peaks of each interfering standard sample and the reference peak intensity values ​​of the characteristic fluorescence peaks of a standard solution with the same lead ion concentration but without non-specific interference is calculated. This difference characterizes the apparent intensity loss caused by non-specific interference. The apparent intensity loss value of each interfering standard sample is then subjected to linear regression analysis on its contribution ratio value. The linear regression analysis yields a straight line, and the slope of this line is the proportionality coefficient α, with units of fluorescence intensity. The established correspondence between the contribution ratio and the fluorescence intensity correction amount is a linear calculation formula, and the fluorescence intensity correction amount ΔI is calculated as: ΔI = α × P; where α represents the proportionality coefficient determined by linear regression, and P represents the contribution ratio. The value of the proportionality coefficient α is determined and recorded through the above linear regression analysis and used for subsequent correction amount calculations for all samples.

[0040] The peak intensity of the characteristic fluorescence peak is arithmetically corrected using a correction factor to obtain the compensated peak intensity. The peak intensity of the characteristic fluorescence peak is a parameter separated and extracted from the real-time fluorescence emission spectroscopy data via step S2. The fluorescence intensity correction factor for the peak intensity of the characteristic fluorescence peak is determined in the previous step. The compensated peak intensity Icorrected is calculated as: Icorrected = Imeasured + ΔI; where Imeasured represents the peak intensity of the characteristic fluorescence peak separated and extracted via step S2, and ΔI represents the fluorescence intensity correction factor for the peak intensity of the characteristic fluorescence peak. Through this arithmetic correction, the deviation in the peak intensity of the characteristic fluorescence peak that is considered to be caused by non-specific heterogeneous aggregation is compensated.

[0041] The compensated peak intensity was compared with a pre-established calibration curve reflecting the relationship between lead ion concentration and peak intensity to determine the lead ion concentration in the soil extract. The calibration curve reflecting the relationship between lead ion concentration and peak intensity was pre-established under conditions free from non-specific interference. The experimental steps for establishing the calibration curve were as follows: a series of lead ion concentration standards were prepared, covering the expected detection range, for example, 0 μg / L, 5 μg / L, 10 μg / L, 20 μg / L, 50 μg / L, and 100 μg / L. For each standard solution in this series, steps S1 and S2 were performed, and the peak intensity of the characteristic fluorescence spectrum corresponding to each concentration was recorded. A scatter plot was drawn with lead ion concentration as the x-axis and the measured peak intensity of the characteristic fluorescence spectrum as the y-axis. A linear equation model was used to fit the data points, and the expression of the fitted equation was: Istd = S × Cstd + I0, where Istd represents the standard solution. The peak intensity of the characteristic fluorescence spectrum is used, Cstd represents the lead ion concentration of the standard solution, S represents the slope of the calibration curve, and I0 represents the intercept. The fitted equation is the calibration curve reflecting the relationship between lead ion concentration and peak intensity. When analyzing the sample, the peak intensity Icorrected after compensation and correction is used as the ordinate value and substituted into the calibration curve equation Istd=S×Cstd+I0. The lead ion concentration C is obtained by solving the equation inversely, and the calculation formula is: C=(Icorrected-I0) / S; where C represents the lead ion concentration to be determined, Icorrected represents the peak intensity after compensation and correction, and I0 represents the intercept of the calibration curve. The calculated concentration value C is the corrected lead ion concentration in the soil extract to be tested. By first correcting the peak intensity of the characteristic fluorescence spectrum and then consulting the calibration curve, the interference of non-specific heterogeneous aggregation on the quantitative results is effectively eliminated, and the accuracy of lead ion detection in complex soil matrices is improved.

[0042] Example 2: Figure 2A schematic diagram of the rapid detection system for lead in soil based on quantum dot fluorescence of the present invention is provided. The rapid detection system for lead in soil based on quantum dot fluorescence includes: The spectral acquisition module is used to acquire real-time fluorescence emission spectral data of the mixed system of the soil extract to be tested after the addition of quantum dot fluorescent probes; The spectral decomposition module is used to separate the characteristic fluorescence peaks of quantum dot fluorescent probes based on real-time fluorescence emission spectral data through spectral decomposition. The lifetime analysis module is used to perform time-resolved fluorescence measurement on the fluorescence signal corresponding to the characteristic fluorescence peak to obtain the fluorescence lifetime decay curve. Based on at least two preset time thresholds, the fluorescence lifetime decay curve is divided into multiple corresponding time windows and the integral area of ​​fluorescence intensity in each time window is calculated. The parameter calculation module is used to calculate the fluorescence quenching rate constant of the quantum dot fluorescent probe based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum peak changing with time, and to calculate the lifetime distribution characteristic ratio based on the integral area of ​​each time window. The synergistic analysis module is used to determine the proportion of contribution from nonspecific heterogeneous aggregation in the fluorescence quenching rate constant based on the ratio of fluorescence quenching rate constant to lifetime distribution characteristics through synergistic deviation analysis. The concentration calculation module is used to compensate and correct the peak intensity of the characteristic fluorescence spectrum peaks according to the contribution ratio, and calculate the lead ion concentration in the soil extract to be tested.

[0043] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0044] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0045] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0046] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0048] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0049] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0050] If a function is implemented as a software 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0051] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0052] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rapid detection method for lead in soil based on quantum dot fluorescence, characterized in that, include: S1. Obtain real-time fluorescence emission spectrum data of the mixed system of the soil extract to be tested after adding quantum dot fluorescent probe; S2. Based on real-time fluorescence emission spectral data, the characteristic fluorescence peaks of the quantum dot fluorescent probe are separated by spectral decomposition. S3. Perform time-resolved fluorescence measurement on the fluorescence signal corresponding to the characteristic fluorescence peak to obtain the fluorescence lifetime decay curve. Divide the fluorescence lifetime decay curve into multiple corresponding time windows based on at least two preset time thresholds and calculate the integral area of ​​fluorescence intensity in each time window. S4. The fluorescence quenching rate constant of the quantum dot fluorescent probe is calculated based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum peaks changing with time, and the lifetime distribution characteristic ratio is calculated based on the integral area of ​​each time window. S5. Based on the ratio of fluorescence quenching rate constant to lifetime distribution characteristics, the contribution ratio of nonspecific heterogeneous aggregation in the fluorescence quenching rate constant is determined by synergistic deviation analysis. S6. The peak intensity of the characteristic fluorescence spectrum peaks is compensated and corrected according to the contribution ratio, and the concentration of lead ions in the soil extract to be tested is calculated.

2. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 1, characterized in that, Real-time fluorescence emission spectra of the mixed system of the soil extract to be tested after the addition of quantum dot fluorescent probes were obtained, including: The mixing system is continuously excited at a preset excitation wavelength, and the fluorescence signal generated by the mixing system within the preset emission wavelength range is collected simultaneously. The acquired fluorescence signal is spectrally smoothed to suppress noise, and the smoothed spectral signal is baseline calibrated to obtain real-time fluorescence emission spectrum data after removing the instrument background.

3. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 1, characterized in that, Based on real-time fluorescence emission spectroscopy data, characteristic fluorescence peaks of the quantum dot fluorescent probe were separated by spectral decomposition, including: Obtain the standard fluorescence spectrum of the quantum dot fluorescent probe in an interference-free solution to determine the center wavelength and spectral shape of its characteristic fluorescence peaks; Based on the center wavelength and spectral shape, nonlinear curve fitting is performed on the real-time fluorescence emission spectrum data to resolve the fluorescence components belonging to the quantum dot fluorescent probe from the mixed spectrum; The peak intensity and full width at half maximum (FWHM) of the fluorescence components are extracted from the resolved fluorescence components to complete the separation of characteristic fluorescence peaks.

4. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 1, characterized in that, Time-resolved fluorescence measurements are performed on the fluorescence signals corresponding to characteristic fluorescence peaks to obtain fluorescence lifetime decay curves. Based on at least two preset time thresholds, the fluorescence lifetime decay curves are divided into multiple corresponding time windows, and the integral area of ​​fluorescence intensity within each time window is calculated, including: The hybrid system was excited by a pulsed light source, and the time-series data of the arrival of fluorescent photons at the center wavelength of the characteristic fluorescence spectrum peak were collected. A fluorescence lifetime decay curve was reconstructed based on time series data to show the change in fluorescence intensity over time. Based on at least two preset time thresholds set for the fluorescence lifetime characteristics of quantum dots, the entire time axis corresponding to the fluorescence lifetime decay curve is divided into multiple continuous time windows. The intensity values ​​of the fluorescence lifetime decay curve within each time window are numerically integrated to calculate the integral area of ​​the fluorescence intensity within each time window.

5. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 1, characterized in that, The fluorescence quenching rate constant of the quantum dot fluorescent probe was calculated based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum over time, and the lifetime distribution characteristic ratio was calculated based on the integral area of ​​each time window, including: The kinetic curve of the peak intensity of the characteristic fluorescence spectrum peaks changing with time was fitted with a single exponential decay function, and the decay constant in the fitted function was extracted as the fluorescence quenching rate constant. Select at least two specific time windows, and calculate the lifetime distribution characteristic ratio that characterizes the change in fluorescence lifetime distribution by dividing the integral area of ​​one time window by the integral area of ​​the other time window.

6. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 5, characterized in that, Selecting at least two specific time windows includes: selecting a first time window and a second time window, wherein the first time window corresponds to the initial stage of rapid fluorescence intensity decay in the fluorescence lifetime decay curve, and the second time window corresponds to the subsequent stage of slow fluorescence intensity decay; the lifetime distribution characteristic ratio is the ratio of the integral area of ​​the second time window to the integral area of ​​the first time window.

7. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 1, characterized in that, Based on the ratio of the fluorescence quenching rate constant to the lifetime distribution characteristic, the contribution ratio of the fluorescence quenching rate constant derived from nonspecific heterogeneous aggregation was determined by co-deviation analysis, including: A reference linear relationship between the fluorescence quenching rate constant and the lifetime distribution characteristic ratio was established using a series of standard solutions with known lead ion concentrations and no non-specific interference. In a two-dimensional coordinate system consisting of the ratio of fluorescence quenching rate constant to lifetime distribution characteristic, the coordinate points corresponding to the test sample are determined; Calculate the vertical distance or shortest geometric distance from the corresponding coordinate point to the reference linear relationship as the degree of cooperative deviation; Based on the mapping relationship between the degree of synergistic deviation and the contribution ratio of nonspecific heterogeneous aggregation, which has been experimentally calibrated in advance, the contribution ratio of nonspecific heterogeneous aggregation in the fluorescence quenching rate constant of the sample under test is determined.

8. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 7, characterized in that, The mapping relationship between the co-deviation degree and the contribution ratio of nonspecific heterogeneous aggregation pre-calibrated by experiments was obtained by preparing a series of interference standard samples containing a fixed concentration of lead ions but different known proportions of simulated soil colloidal particles, measuring their co-deviation degree and known interference ratios respectively, and then fitting and establishing a calibration table.

9. The rapid detection method for lead in soil based on quantum dot fluorescence according to claim 1, characterized in that, The peak intensity of the characteristic fluorescence spectrum peaks was compensated and corrected based on the contribution ratio, and the lead ion concentration in the soil extract was calculated, including: Based on the contribution ratio, the correction amount for the peak intensity of the characteristic fluorescence spectrum peak is determined according to the pre-determined correspondence between the contribution ratio and the fluorescence intensity correction amount. The peak intensity of the characteristic fluorescence spectrum peaks is arithmetically corrected using the correction amount to obtain the compensated and corrected peak intensity. The peak intensity after compensation and correction is compared with a pre-established calibration curve that reflects the relationship between lead ion concentration and peak intensity, and the lead ion concentration in the soil extract to be tested is determined from the calibration curve.

10. A rapid detection system for lead in soil based on quantum dot fluorescence, used to implement the rapid detection method for lead in soil based on quantum dot fluorescence as described in any one of claims 1-9, characterized in that, include: The spectral acquisition module is used to acquire real-time fluorescence emission spectral data of the mixed system of the soil extract to be tested after the addition of quantum dot fluorescent probes; The spectral decomposition module is used to separate the characteristic fluorescence peaks of quantum dot fluorescent probes based on real-time fluorescence emission spectral data through spectral decomposition. The lifetime analysis module is used to perform time-resolved fluorescence measurement on the fluorescence signal corresponding to the characteristic fluorescence peak to obtain the fluorescence lifetime decay curve. Based on at least two preset time thresholds, the fluorescence lifetime decay curve is divided into multiple corresponding time windows and the integral area of ​​fluorescence intensity in each time window is calculated. The parameter calculation module is used to calculate the fluorescence quenching rate constant of the quantum dot fluorescent probe based on the kinetic curve of the peak intensity of the characteristic fluorescence spectrum peak changing with time, and to calculate the lifetime distribution characteristic ratio based on the integral area of ​​each time window. The synergistic analysis module is used to determine the proportion of contribution from nonspecific heterogeneous aggregation in the fluorescence quenching rate constant based on the ratio of fluorescence quenching rate constant to lifetime distribution characteristics through synergistic deviation analysis. The concentration calculation module is used to compensate and correct the peak intensity of the characteristic fluorescence spectrum peaks according to the contribution ratio, and calculate the lead ion concentration in the soil extract to be tested.

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