Method for determining SERS effect of Ag-based composite nanomaterial on dangerous chemicals

Abstract

The application provides a kind of Ag-based composite nanomaterial SERS utility determination method for dangerous chemicals, belong to the technical field of data processing of dangerous chemicals, the method includes: preparation Au nanocrystalline seed and Au@Ag core-shell nanostructure, preparation CeO2 nano cube and Ag-CeO2 nano heteropolymer, the Au@Ag core-shell nanostructure and the Ag-CeO2 nano heteropolymer are mixed to prepare surface enhanced raman detection substrate, by the detection substrate and raman spectrometer, raman spectrum standard correction matrix, substrate enhancement effect evaluation matrix, SERS enhancement coefficient correction matrix are acquired, and SERS signal intensity normalization matrix is established, finally through the SERS signal intensity normalization matrix, the substrate enhancement effect evaluation matrix and the raman spectrum standard correction matrix calculate SERS utility value, the application solves the technical problem that the accuracy of dangerous chemical SERS detection in the prior art is not high enough.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hazardous chemical data processing, and in particular relates to a method for measuring SERS effect of Ag-based composite nanomaterials on hazardous chemicals. BACKGROUND

[0002] Rapid and accurate detection of hazardous chemicals is of great significance to environmental monitoring and public safety. Existing detection methods mainly include chromatography, mass spectrometry and spectroscopy analysis methods. Although traditional gas chromatography and liquid chromatography have high detection sensitivity, they have the disadvantages of long analysis time and complex sample pretreatment, which are difficult to meet the demand of rapid detection. Mass spectrometry technology has good selectivity, but the instrument cost is high, and it is easily disturbed in complex matrix. Surface-enhanced Raman scattering (SERS) technology has great potential in the field of hazardous chemical detection due to its fingerprint characteristics and ultra-high sensitivity. However, the current SERS detection method still has the problem of insufficient quantitative analysis accuracy, mainly in the following aspects: first, the enhancement effect of traditional SERS substrate materials is not uniform, resulting in poor repeatability of detection signals; second, the existing data processing method cannot effectively eliminate the influence of instrument drift and matrix interference, affecting the reliability of quantitative analysis; third, there is a lack of systematic standardization and correction method, making it difficult to establish an accurate quantitative relationship. The commonly used SERS substrate mainly includes noble metal nanoparticles, nanorods and nanosheets, etc. These materials have strong localized surface plasmon resonance effect, but their stability is poor and they are easy to aggregate, which affects the repeatability of detection. In order to improve the accuracy of detection, researchers have tried various improvement methods, such as using internal standard method for correction, or using image processing technology to analyze the spatial distribution of SERS signal, but these methods are either complex to operate or cannot fundamentally solve the problem. In summary, the existing technology has the technical problem of insufficient accuracy of hazardous chemical SERS detection. SUMMARY

[0003] Therefore, the application provides an Ag-based composite nanomaterial SERS effect measuring method for hazardous chemicals, which can solve the technical problem of insufficient accuracy of hazardous chemical SERS detection in the prior art.

[0004] The application is implemented in the following manner: the application provides an Ag-based composite nanomaterial SERS utility determination method for hazardous chemicals, comprising the following steps: preparing Au nanoseeds and Au@Ag core-shell nanostructures, preparing CeO2 nanocubes and Ag-CeO2 nanoheterostructures, mixing the Au@Ag core-shell nanostructures and the Ag-CeO2 nanoheterostructures to prepare a surface-enhanced Raman detection substrate, acquiring a Raman spectrum standard correction matrix, a substrate enhancement effect evaluation matrix, and a SERS enhancement coefficient correction matrix through the detection substrate and a Raman spectrometer, and establishing a SERS signal intensity normalization matrix, and finally calculating a SERS utility value through the SERS signal intensity normalization matrix, the substrate enhancement effect evaluation matrix, and the Raman spectrum standard correction matrix.

[0005] The step of preparing Au nanoseeds is specifically as follows: 10 millimoles of tetrachloroauric acid are dissolved in a solution containing 100 milliliters of ethylene glycol and 2 grams of polyvinylpyrrolidone, the solution is heated to 100 degrees Celsius under magnetic stirring, 10 millimoles of sodium borohydride in 50 milliliters of ethylene glycol solution are slowly added to the solution, the solution is cooled to room temperature after constant-temperature reaction for 15 minutes, and the Au nanoseed solution is obtained.

[0006] The step of preparing Au@Ag core-shell nanostructures is specifically as follows: 50 milliliters of ethylene glycol solution containing 10 millimoles of silver nitrate is added to the Au nanoseed solution, the solution is heated to 100 degrees Celsius under magnetic stirring, the Au@Ag core-shell nanostructure reaction liquid is obtained after constant-temperature reaction for 20 minutes and cooling to room temperature, ethanol is added to the Au@Ag core-shell nanostructure reaction liquid, centrifugation is performed at 8000 revolutions per minute for 10 minutes, the precipitate is collected and redispersed with 15 milliliters of toluene, and the purified Au@Ag core-shell nanostructure is obtained after repeated cleaning for 2 to 4 times.

[0007] The step of preparing CeO2 nanocubes is specifically as follows: 15 milliliters of cerium nitrate aqueous solution with a concentration of 16.7 millimoles per liter, 15 milliliters of toluene, 1.5 milliliters of oleic acid, and 0.15 milliliters of tert-butylamine are mixed, the mixture is placed in an autoclave for constant-temperature reaction at 180 degrees Celsius for 24 hours, and the CeO2 nanocube reaction product is obtained; the CeO2 nanocube reaction product is centrifuged at 2000 revolutions per minute for 10 minutes to remove solid impurities, 10 milliliters of ethanol is added to the upper liquid, and the precipitate is collected by centrifugation at 8000 revolutions per minute for 10 minutes, and the purified CeO2 nanocube is obtained.

[0008] The step of preparing the Ag-CeO2 nanohybrid is specifically as follows: the purified CeO2 nanocube is dispersed in 15 ml of ethylene glycol solution containing 0.2 ml of oleylamine, heated to 100 DEG C under nitrogen protection, and reacted for 30 min to obtain a first reaction system; 1.0 ml of n-butyllithium normal hexane solution with a concentration of 2.2 mmol / L is injected into the first reaction system, followed by injection of 5 ml of a mixed solution of ethylene glycol and oleylamine containing 0.125 mmol of silver nitrate, to obtain a second reaction system; the second reaction system is stirred at room temperature for 10 min, heated to 80 DEG C, and reacted for 1 h, and then continuously heated to 120 DEG C and reacted for 2 h, to obtain the Ag-CeO2 nanohybrid reaction liquid; the Ag-CeO2 nanohybrid reaction liquid is collected by centrifugation at 8000 rpm for 10 min, washed with ethanol for 2-3 times, and dispersed in toluene, to obtain the Ag-CeO2 nanohybrid.

[0009] The step of preparing the surface-enhanced Raman detection substrate is specifically as follows: the purified Au@Ag core-shell nanostructure and the Ag-CeO2 nanohybrid are mixed in a mass ratio of 1:1, and ultrasonically dispersed for 15 min, to obtain the surface-enhanced Raman detection substrate; the surface-enhanced Raman detection substrate is drop-coated on the surface of a conductive glass substrate, naturally air-dried, and placed in a drying oven at 60 DEG C and dried for 2 h, to obtain the detection substrate.

[0010] The step of establishing the Raman spectrum standard correction matrix is specifically as follows: the Raman spectrometer is measured in the range of 400-3000 wave numbers, to obtain the baseline intensity matrix and the instrument response matrix in the Raman spectrum standard correction matrix.

[0011] The step of establishing the substrate enhancement effect evaluation matrix is specifically as follows: standard solutions of hazardous chemicals with concentrations of 1, 10, 50, 100 and 500 nmol / L are prepared, and the Raman signal intensity of the standard solutions of hazardous chemicals is measured, to obtain the substrate enhancement effect evaluation matrix.

[0012] The step of establishing the SERS enhancement coefficient correction matrix is specifically as follows: the SERS enhancement coefficient correction matrix is calculated according to the substrate enhancement effect evaluation matrix and the Raman spectrum standard correction matrix; the surface-enhanced Raman signal of the hazardous chemical to be tested is measured by using the Raman spectrometer, the excitation wavelength is selected as 532 nm, the laser power is set as 5 mW, and the collection time is 10 s, to obtain the Raman signal of the hazardous chemical to be tested; the SERS signal intensity normalization matrix is obtained by normalizing the Raman signal of the hazardous chemical to be tested by using the SERS enhancement coefficient correction matrix.

[0013] The step of calculating the SERS utility value is specifically to subtract the product of the SERS signal intensity normalization matrix and the substrate enhancement effect evaluation matrix from the Raman spectrum standard correction matrix.

[0014] Optionally, the Raman spectrum standard correction matrix is obtained by a series of standard sample determinations of a Raman spectrometer in a range of 400 to 3000 wave numbers, and the matrix includes two important components of a baseline intensity matrix and an instrument response matrix, wherein the baseline intensity matrix reflects the background signal intensity distribution of the instrument without the sample, and the instrument response matrix represents the sensitivity and response characteristics of the instrument in different wave number ranges. The two sub-matrices are obtained by systematically measuring standard substances with known concentrations and performing mathematical processing, and finally a standardized matrix capable of correcting instrument system errors is obtained.

[0015] Optionally, the substrate enhancement effect evaluation matrix is established by preparing a series of standard solutions of hazardous chemicals with different concentrations (1, 10, 50, 100, and 500 nanomoles per liter), performing Raman spectrum determination on each concentration gradient sample, recording the signal intensity of the characteristic peak, and establishing a corresponding relationship matrix between the concentration and the signal intensity through mathematical processing. The matrix not only reflects the enhancement effect of the surface enhanced Raman substrate, but also contains the response linear range and detection sensitivity information of the sample at different concentrations, and can be used for subsequent quantitative analysis of unknown sample concentrations.

[0016] Optionally, the SERS enhancement coefficient correction matrix is obtained based on the substrate enhancement effect evaluation matrix and the Raman spectrum standard correction matrix through mathematical operation. The matrix comprehensively considers the instrument response characteristics and the substrate enhancement effect, and can accurately reflect the actual enhancement degree of the nanomaterial substrate on the Raman signal. The calculation process involves matrix operation and mathematical transformation, and finally a standardized matrix capable of accurately correcting unknown sample signals is obtained. The establishment of the matrix is the key to realizing SERS quantitative analysis.

[0017] Optionally, the SERS signal intensity normalization matrix is obtained by mathematical operation of the original Raman signal of the sample to be measured and the SERS enhancement coefficient correction matrix. This process can eliminate the interference of factors such as instrument fluctuation and substrate non-uniformity, so that the measurement result has good repeatability and comparability. The signal intensity after normalization can more accurately reflect the actual concentration level of the measured substance, and provides a reliable data basis for subsequent quantitative analysis.

[0018] Optionally, the synthesis parameters of Au nanoseeds and Au@Ag core-shell nanostructures are obtained by a large number of experiments, including systematic control of key parameters such as reaction temperature, reaction time, and precursor concentration. The precise control of these parameters directly affects the morphology, size distribution, and surface plasmon resonance characteristics of the nano-materials, and further affects the sensitivity and reproducibility of SERS detection. Therefore, it is necessary to strictly control the quality of the synthesized products through characterization methods such as transmission electron microscopy and ultraviolet-visible spectroscopy.

[0019] Optionally, the preparation process parameters of CeO2 nanocubes and Ag-CeO2 nanoheterostructures are determined through systematic research, including reactant ratio, reaction temperature, reaction time, etc. These parameters are selected based on a deep understanding of reaction kinetics and thermodynamics. By precisely controlling the synthesis conditions, nano-materials with specific morphology and size can be obtained, and the surface properties of the nano-materials can be controlled, thereby providing high-quality substrate materials for subsequent SERS detection.

[0020] Compared with the prior art, the Ag-based composite nano-material provided by the present application is used for SERS utility determination of hazardous chemicals. The present application successfully solves the problem of insufficient quantitative analysis accuracy in SERS detection of hazardous chemicals by designing a new type of Ag-based composite nano-material and establishing a multiple matrix correction system. In terms of material design, the composite strategy of Au@Ag core-shell structure combined with CeO2 nanocubes not only provides a stable hot spot structure, but also enhances the adsorption capacity of the target molecules through the surface chemical action of CeO2, achieving significant enhancement and stability of the detection signal. In terms of data processing, a multiple matrix correction system is established, including a Raman spectrum standard correction matrix, a substrate enhancement effect evaluation matrix, and a SERS enhancement coefficient correction matrix. Through systematic mathematical processing, the influence factors such as instrument drift and matrix interference are effectively eliminated, significantly improving the accuracy of quantitative analysis. By introducing a SERS signal intensity normalization matrix, the comparability of data under different batches and different detection conditions is realized, which provides the possibility for establishing a unified detection standard. The method of the present application maintains the ultra-high sensitivity advantage of SERS technology, significantly improves the repeatability and reliability of the detection, and solves the technical problem of insufficient accuracy in SERS detection of hazardous chemicals in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The flowchart of the method of the present application.

[0022] Figure 2 The ultraviolet-visible absorption spectrum graph in Example 2.

[0023] Figure 3 The standard curve and actual sample determination result graph of 2,4-dinitrotoluene in Example 2.

[0024] Figure 4 Figure for the influence of different interfering ions on the results of the determination in Example 2.

[0025] Figure 5 Figure for the results of the stability test of the detection substrate during 30 days of storage in Example 2.

[0026] Figure 6 Figure for the preparation of Au seeds in the synthesis of Au@Ag core-shell nanoparticles in Example 3, including two sub-figures, wherein a) is a TEM image; b) is an HRTEM image.

[0027] Figure 7 Figure for Au@Ag core-shell nanoparticles in Example 3, including two sub-figures, wherein a) is a TEM image; b) is an HRTEM image.

[0028] Figure 8 Figure for the SERS spectra of pyridine on the surface of AuAg alloy nanostructures at different Au / Ag ratios in Example 3. DETAILED DESCRIPTION

[0029] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0030] The present application provides a method for determining the SERS effect of an Ag-based composite nanomaterial on hazardous chemicals, characterized by comprising the following operation steps:

[0031] S01, Au nanoseeds are prepared, 10 millimoles of tetrachloroauric acid are dissolved in a solution containing 100 milliliters of ethylene glycol and 2 grams of polyvinylpyrrolidone, heated to 100 degrees Celsius under magnetic stirring, 10 millimoles of sodium borohydride in 50 milliliters of ethylene glycol solution are slowly added, and the solution is cooled to room temperature after constant temperature reaction for 15 minutes to obtain the Au nanoseed solution;

[0032] S02, Au@Ag core-shell nanostructures are prepared, 50 milliliters of ethylene glycol solution containing 10 millimoles of silver nitrate are added to the Au nanoseed solution, heated to 100 degrees Celsius under magnetic stirring, and the solution is cooled to room temperature after constant temperature reaction for 20 minutes to obtain the Au@Ag core-shell nanostructure reaction liquid;

[0033] S03, the Au@Ag core-shell nanostructures are purified, ethanol is added to the Au@Ag core-shell nanostructure reaction liquid, centrifuged at 8000 revolutions per minute for 10 minutes, the precipitate is collected and redispersed with 15 milliliters of toluene, and the washing is repeated 2 to 4 times to obtain the purified Au@Ag core-shell nanostructures;

[0034] S04, preparing CeO2nanometer cube, mixing 15ml of cerium nitrate aqueous solution with concentration of 16.7mmol / L with 15ml of toluene, 1.5ml of oleic acid and 0.15ml of tert-butylamine, placing in autoclave for constant temperature reaction at 180℃ for 24 hours to obtain the CeO2nanometer cube reaction product;

[0035] S05, purifying the CeO2nanometer cube, centrifuging the CeO2nanometer cube reaction product at 2000rpm for 10 minutes to remove solid impurities, adding 10ml of ethanol to the upper liquid, centrifuging at 8000rpm for 10 minutes to collect the precipitate to obtain the purified CeO2nanometer cube;

[0036] S06, preparing Ag-CeO2nanometer heteropolymer, dispersing the purified CeO2nanometer cube in 15ml of ethylene glycol solution containing 0.2ml of oleylamine, heating to constant temperature at 100℃ for 30 minutes under nitrogen protection to obtain the first reaction system;

[0037] S07, injecting 1.0ml of n-butyllithium n-hexane solution with concentration of 2.2mmol / L into the first reaction system, then injecting 5ml of ethylene glycol and 1ml of oleylamine mixed solution containing 0.125mmol of silver nitrate to obtain the second reaction system;

[0038] S08, after stirring the second reaction system at room temperature for 10 minutes, heating to constant temperature at 80℃ for 1 hour, continuously heating to 120℃ for 2 hours to obtain the Ag-CeO2nanometer heteropolymer reaction liquid;

[0039] S09, collecting the Ag-CeO2nanometer heteropolymer reaction liquid by centrifugation at 8000rpm for 10 minutes, washing 2-3 times with ethanol, then dispersing in toluene to obtain the Ag-CeO2nanometer heteropolymer;

[0040] S10, establishing Raman spectrum standard correction matrix, measuring the Raman spectrometer in the range of 400-3000 wave number to obtain the baseline intensity matrix and instrument response matrix in the Raman spectrum standard correction matrix;

[0041] S11, establishing substrate enhancement effect evaluation matrix, preparing standard solutions of hazardous chemicals with concentrations of 1, 10, 50, 100 and 500nmol / L, measuring the Raman signal intensity of the standard solutions of hazardous chemicals to obtain the substrate enhancement effect evaluation matrix;

[0042] S12. Prepare the surface-enhanced Raman detection substrate by mixing the purified Au@Ag core-shell nanostructure with the Ag-CeO2 nanopolymer at a mass ratio of 1:1 and ultrasonically dispersing for 15 minutes to obtain the surface-enhanced Raman detection substrate.

[0043] S13. Establish the SERS enhancement coefficient correction matrix. The SERS enhancement coefficient correction matrix is ​​calculated based on the matrix enhancement effect evaluation matrix and the Raman spectroscopy standard correction matrix.

[0044] S14. The surface-enhanced Raman detection substrate is drop-coated onto the surface of a conductive glass substrate, allowed to air dry naturally, and then dried in a 60-degree Celsius drying oven for 2 hours to obtain the detection substrate.

[0045] S15. Raman spectroscopy determination: The surface-enhanced Raman signal of the hazardous chemical to be tested is determined using a Raman spectrometer. The excitation wavelength is selected as 532 nm, the laser power is set to 5 mW, and the acquisition time is 10 seconds to obtain the Raman signal of the hazardous chemical to be tested.

[0046] S16. Establish a SERS signal intensity normalization matrix, and use the SERS enhancement coefficient correction matrix to normalize the Raman signal of the hazardous chemical to be tested, so as to obtain the SERS signal intensity normalization matrix.

[0047] S17. Calculate the SERS utility value, which is the product of the SERS signal intensity normalization matrix and the basis enhancement effect evaluation matrix minus the Raman spectral standard correction matrix.

[0048] The specific implementation methods of the above steps are described in detail below.

[0049] The specific implementation of step S01 is to prepare Au nanocrystal seeds by dissolving tetrachloroalloyic acid in a mixed solution of ethylene glycol and polyvinylpyrrolidone, and carrying out a reduction reaction under controlled temperature and stirring speed. Polyvinylpyrrolidone is used as a protective agent in this step because it has good steric hindrance effect and surface activity, which can effectively control the growth direction and size distribution of nanocrystals. The stirring speed is controlled at 500 rpm, and the reaction temperature is controlled at 100 degrees Celsius by a constant temperature water bath. During the reaction, the pH of the solution is monitored in real time with a pH meter to maintain the pH value between 6.5 and 7.5. After the reaction, the particle size distribution of the nanocrystals is determined by dynamic light scattering method. The particle size is controlled in the range of 15 to 20 nanometers, and the dispersion coefficient is less than 0.2.

[0050] The specific implementation of step S02 is to prepare Au@Ag core-shell nanostructures. This step involves preparing the core-shell structure based on Au nanocrystals using a surface growth method. Silver nitrate is used as the silver source, and in-situ reduction is performed in an ethylene glycol solution. During the reaction, the displacement of the plasma resonance peak is monitored in real time using a UV-Vis spectrophotometer. When the resonance peak stabilizes at around 430 nm, it indicates that the shell growth is complete. The reaction temperature is controlled using a PID control system, with temperature fluctuations controlled within ±0.5 degrees Celsius. The stirring speed is maintained at 400 rpm. After the reaction, the morphology of the core-shell structure is observed using a transmission electron microscope, and the shell thickness is controlled between 5 and 8 nm.

[0051] The specific implementation of step S03 is to purify the Au@Ag core-shell nanostructure. Centrifugation is used to remove impurities and unreacted substances from the reaction system. The centrifugation process uses a temperature-controlled centrifuge set at 25 degrees Celsius. High-purity extraction of the product is achieved through repeated centrifugation and redispersion processes. After each centrifugation, the absorbance of the supernatant is measured using a UV-Vis spectrophotometer. When the absorbance is less than 0.01, it indicates that the impurities have been completely removed. The final product is dispersed in toluene, and the purity of the product is determined to be greater than 98% by thermogravimetric analysis.

[0052] The specific implementation of step S04 is to prepare CeO2 nanocubes using a solvothermal method in a high-pressure reactor. The temperature and pressure during the reaction are controlled by an intelligent control system, with the heating rate set at 2 degrees Celsius per minute. The morphology of the product is controlled by adjusting the ratio of cerium nitrate, oleic acid, and tert-butylamine. The pressure during the reaction is controlled at 2 to 3 MPa. The formation process of the crystal structure is monitored in real time using an X-ray diffractometer. The reaction is complete when the full width at half maximum (FWHM) of the diffraction peaks are stable and conform to the characteristics of a cubic crystal system.

[0053] The specific implementation of step S05 is to purify CeO2 nanocubes and use differential centrifugation technology to separate particles of different sizes. First, agglomerates and large-sized impurities are removed by low-speed centrifugation, and then the target product is collected by high-speed centrifugation. Density gradient centrifugation technology is used during the centrifugation process, and more accurate size separation is achieved by establishing an ethanol concentration gradient. The size uniformity of the final product is characterized by scanning electron microscopy, and the side length is distributed in the range of 50 to 60 nanometers.

[0054] The specific implementation of steps S06 and S07 is a preliminary step in the preparation of Ag-CeO2 nanopolymers. It is carried out under nitrogen protection and with programmed temperature rise. In this process, oleylamine is used as a reducing agent and stabilizer. The oxygen content in the reaction atmosphere is controlled to be less than 10 ppm to ensure uniform nucleation of silver nanoparticles. During the reaction, the reduction potential of the system is monitored by an electrochemical workstation. When the potential stabilizes at -0.4 volts, the next step of the reaction is carried out.

[0055] The specific implementation of step S08 is to complete the preparation of Ag-CeO2 nano-hybrids. A stepwise heating strategy is adopted to control the growth and assembly of silver nanoparticles at different temperature ranges. The changes of surface ligands are monitored in real time by infrared spectroscopy. When the intensity ratio of characteristic peaks reaches the set value, the temperature is increased. The entire process is precisely controlled by a programmable temperature control system, and the temperature error is controlled within ±1 degree Celsius.

[0056] The specific implementation of step S09 is to collect and purify Ag-CeO2 nano-polymers, separate the products using high-speed centrifugation, and repeatedly wash with ethanol to remove residual organic matter. During the washing process, the conductivity of the washing solution is monitored by a conductivity meter. The washing is stopped when the conductivity is lower than 10 μS / cm. The morphology and composition of the final product are characterized by transmission electron microscopy and energy dispersive spectroscopy.

[0057] The specific implementation of step S10 is to establish a standard calibration matrix for Raman spectroscopy. This step uses a multivariate statistical analysis method to establish a calibration model by systematically measuring standard samples. During the measurement process, the laser power fluctuation is controlled within ±1%. An automatic focusing system is used to ensure the accuracy of the focal position. The acquired data is processed by principal component analysis to reduce the dimensionality and retain the principal components with a contribution rate greater than 95%.

[0058] The specific implementation of steps S11 and S12 involves establishing a substrate enhancement effect evaluation system and preparing a test substrate. A precision pipetting system is used to prepare a standard solution, and the concentration gradient is selected based on a logarithmic distribution. An ultrasonic dispersion system is used to ensure the uniformity of the substrate material. During the dispersion process, the power is controlled at 100 watts and the time is 15 minutes. A temperature feedback control system is used during this period to prevent overheating.

[0059] The specific implementation of step S13 is to establish a SERS enhancement coefficient correction matrix, use multiple linear regression combined with partial least squares to establish a mathematical model, determine the optimal number of principal components through cross-validation, and evaluate the predictive ability of the model through root mean square error and coefficient of determination, requiring root mean square error to be less than 5% and coefficient of determination to be greater than 0.99.

[0060] The specific implementation of step S14 is to prepare a detection substrate, construct a detection area on the surface of conductive glass using micro-droplet technology, monitor the surface wettability of the substrate using a contact angle meter to ensure that the contact angle is within the range of 45 to 60 degrees, use programmed temperature rise during the drying process with a temperature rise rate of 1 degree Celsius per minute, and finally characterize the surface morphology and roughness of the substrate using atomic force microscopy.

[0061] The specific implementation of step S15 is to perform Raman spectroscopy measurement using a confocal Raman spectrometer, selecting a 532 nm laser as the excitation source, and achieving precise sample positioning through an optical microscopy system. During the measurement process, an automatic focusing system is used to maintain the stability of the focal length, and the average value is taken for 10 spectra of each sample point.

[0062] The specific implementation of steps S16 and S17 involves establishing a normalized SERS signal intensity matrix and calculating the SERS utility value. A multidimensional data analysis method is used to process the spectral data. Background noise is removed using wavelet transform. The Savitzky-Golay smoothing algorithm is used to preprocess the spectrum, with a smoothing window of 11 points and an order of 3. Finally, the final SERS utility value is calculated using the established mathematical model. The entire calculation process employs an iterative optimization algorithm, with the convergence condition set at a relative error of less than 0.1%.

[0063] Each step above was implemented using precise instrument control and data analysis methods. Multiple characterization techniques and strict quality control ensured the accuracy and repeatability of the measurement results. The entire method was established based on the fundamental principle of surface-enhanced Raman scattering and combined advanced technologies from multiple disciplines such as nanomaterial synthesis, surface modification, spectral analysis, and data processing, achieving highly sensitive detection of hazardous chemicals.

[0064] The calculation of the standard correction matrix for the Raman spectrum is expressed as follows:

[0065] ;

[0066] In the formula, This is the standard correction matrix for Raman spectroscopy; For baseline intensity matrix elements, Indicates the wavenumber point. Indicates the number of measurements; The instrument response coefficient; As a correction factor; It is the identity matrix; This represents the number of wavenumber sampling points. To measure the number of repetitions;

[0067] The calculation of the base enhancement effect evaluation matrix is ​​expressed as follows:

[0068] ;

[0069] In the formula, This is the matrix for evaluating the base enhancement effect; For the first Raman signal intensity at a given concentration; Baseline signal strength; For the first The concentration of each standard solution; For reference concentration; As an enhancing factor; The standard deviation is the Gaussian distribution. The number of concentration gradients;

[0070] The calculation of the SERS enhancement coefficient correction matrix is ​​expressed as follows:

[0071] ;

[0072] In the formula, This is the SERS enhancement coefficient correction matrix; To enhance the effect with respect to concentration; It is the inverse of the standard correction matrix; This is a correction factor; For eigenvalues; For feature vectors; The number of eigenvalues;

[0073] The calculation of the SERS signal strength normalization matrix is ​​as follows:

[0074] ;

[0075] In the formula, This is the normalized matrix of SERS signal strength; The sample signal intensity; Background signal strength; The phase angle; These are the weighting coefficients; For correction items; The number of correction items;

[0076] The calculation of the SERS utility value is expressed as follows:

[0077] ;

[0078] In the formula, This represents the SERS utility value. The compensation coefficient; This is the error compensation term; The number of compensation items;

[0079] Instructions for obtaining parameters: The values ​​were obtained by measuring standard samples, and the range was 0.8–1.2. The values ​​were obtained through experimental calibration, and the range is 0.1~0.3. The value was obtained by fitting a curve, and its range is 1.5 to 2.5. The values ​​were obtained through statistical analysis and ranged from 0.2 to 0.5. The value was obtained through an optimized algorithm, and its range is 0.4 to 0.6. Obtained through signal analysis, the range is 0~2π; The values ​​were obtained through weighted analysis and range from 0 to 1. The error was obtained through error analysis, and the range is 0.05~0.15.

[0080] Explanation of the equation construction principle: The standard correction matrix for Raman spectroscopy adopts a matrix multiplication structure, taking into account the systematic errors of baseline drift and instrument response; the evaluation matrix for the floor enhancement effect adopts a combination of exponential and Gaussian distributions, reflecting the nonlinear characteristics of signal enhancement; the SERS enhancement coefficient correction matrix introduces partial derivatives and eigenvalue decomposition, improving the accuracy and robustness of the correction; the SERS signal intensity normalization matrix adopts a complex form, taking into account the phase information of the signal and multiple correction terms; the calculation of the SERS utility value integrates multiple matrix operations and error compensation, achieving high-precision quantitative analysis.

[0081] Specifically, the standard correction matrix for Raman spectroscopy The establishment process is first based on the basic principles of Raman scattering. By performing 50 repeated measurements on pure standard samples in the wavenumber range of 400 to 3000, the baseline intensity matrix is ​​obtained. This matrix reflects the background signal distribution of the instrument at different wavenumbers, where each element represents the baseline intensity value at a specific wavenumber. Statistical reliability of the data is ensured through repeated measurements. Next, the instrument response coefficient is introduced. This coefficient was obtained by calibrating the response curves of a Rhodamine 6G standard solution of known concentration at different wavenumbers. During calibration, five different concentration gradients (1, 10, 50, 100, and 500 nanomoles per liter) were used for system measurements. The response linearity at each wavenumber point was analyzed to obtain the instrument's response characteristics across the entire spectral range. Then, considering the potential system drift due to long-term instrument use, a correction factor was introduced. With the identity matrix The product term, where The optimization adopts the least squares method, and the sum of squared residuals between the theoretical spectrum and the measured spectrum of the standard sample is minimized through iterative calculation. This process is repeated until the convergence condition is met (the residual change is less than 0.1%). The final standard calibration matrix can effectively compensate for the nonlinear characteristics of the instrument response, eliminate the influence of baseline drift, and ensure the stability and repeatability of long-term measurements.

[0082] Specifically, the evaluation matrix of the base enhancement effect The construction is based on the physical mechanism of surface-enhanced Raman scattering. First, the Raman signal intensity of standard solutions with different concentrations is measured. and the baseline signal strength The ratio establishes the signal enhancement factor, where The value was obtained by repeatedly measuring the blank substrate 20 times under the same test conditions (laser power 5 mW, acquisition time 10 seconds) and taking the average value; an enhancement factor was introduced. The power term is chosen to account for the nonlinear effect resulting from the synergistic effect of electromagnetic field enhancement and chemical enhancement. The values ​​of were obtained through optimization using a genetic algorithm. The objective function during optimization was to maximize the Pearson correlation coefficient between the fitted curve and the experimental data. The population size was set to 100, the number of generations to 1000, the crossover probability to 0.8, and the mutation probability to 0.1. The Gaussian distribution function term... The introduction of this method is based on experimental observations that the signal enhancement effect exhibits a bell-shaped distribution characteristic with concentration, where the reference concentration... The optimal operating point was selected as 100 nanomoles per liter, with a standard deviation of [missing value]. The matrix was obtained by maximum likelihood estimation from 20 sets of repeated experimental data; the final form of the matrix can not only accurately describe the substrate enhancement effect, but also effectively characterize the dynamic range of concentration response and detection sensitivity.

[0083] Specifically, the SERS enhancement coefficient correction matrix The derivation process is first based on the dynamic response characteristics of the enhancement effect, by calculating the partial derivative of the enhancement effect with respect to concentration. To obtain sensitivity information, the partial derivative reflects the instantaneous response characteristics of signal intensity changing with concentration. The calculation process uses the five-point difference method, taking the numerical differentiation of the two adjacent points to the left and right of each concentration point to improve the accuracy of the derivative calculation; then, the inverse matrix of the standard correction matrix is ​​introduced. To perform systematic error correction, the matrix inversion process employs LU decomposition to ensure the stability of the numerical computation. To further improve the reliability of the correction, eigenvalue terms are introduced. , where the eigenvalues and eigenvectors Obtained through Singular Value Decomposition (SVD). The SVD process first centers and standardizes the original data matrix, then calculates the covariance matrix, and finally performs eigenvalue decomposition on the covariance matrix, resulting in the number of eigenvalues. The choice of which set to use is determined through cross-validation. Specifically, the dataset is randomly divided into a training set (80%) and a validation set (20%), and the optimal set is determined by minimizing the prediction error of the validation set. Value; correction factor The optimization adopts the L-BFGS algorithm, with the objective function being to minimize the mean square error between the corrected signal and the theoretical value. The Wolfe criterion is used to determine the step size during the iteration process, and the convergence condition is set to the relative error being less than 0.01% or the maximum number of iterations reaching 1000.

[0084] Specifically, the SERS signal strength normalization matrix The establishment process first considers background interference in actual sample measurements, by subtracting the background signal. To eliminate the Raman scattering contribution of the substrate material itself, where The SERS enhancement coefficient correction matrix was obtained by performing 50 repeated measurements on a blank substrate under the same test conditions and smoothing it using a moving average method (window size of 5); then the SERS enhancement coefficient correction matrix was used. Preliminary normalization is performed to eliminate the effects of instrument response and floor enhancement; complex terms are introduced to account for potential phase differences in the signal. The phase angle The signal is calculated by performing a Hilbert transform on it. The FFT algorithm is used to improve computational efficiency during the transform process, and the Hanning window function is used to reduce spectral leakage. Finally, multiple correction terms are introduced. To handle nonlinear interference, where the correction term The features were obtained through wavelet transform, specifically using the Daubechies wavelet basis with a decomposition level of 3. Feature coefficients were extracted through thresholding (using a soft thresholding method, with the threshold set to 3 times the noise standard deviation); weighting coefficients... The optimization employs principal component analysis. First, a covariance matrix is ​​constructed, then eigenvalues ​​and eigenvectors are calculated, and the number of principal components with a cumulative contribution rate reaching 95% is selected as the subset of principal components. The values ​​are then used to determine the weight coefficients of each principal component through regression analysis.

[0085] Specifically, the SERS utility value calculation equation The derivation of the formula is the core of the entire method. First, the normalized signal intensity is multiplied by the base enhancement effect evaluation matrix, which converts the signal intensity to the actual concentration. Then, the standard correction matrix is ​​subtracted to eliminate residual systematic errors. Considering the random fluctuations that may exist in the actual detection process, an error compensation term is introduced. Among them, the error compensation term The error distribution characteristics are obtained through the Bootstrap method, specifically by performing 1000 samplings with replacement on the original data, calculating the deviation of each sampling result from the population mean, and then estimating the error distribution characteristics using kernel density estimation; compensation coefficients are also used. The optimization employs cross-validation, dividing the dataset into 10 parts chronologically. Nine parts are used sequentially to build the model, and the remaining part is used for validation. The optimal compensation coefficient value is determined by minimizing the root mean square error of the validation set; the number of compensation terms... The determination of the model is achieved through the Akaike Information Criterion (AIC), which ensures good model fit while avoiding overfitting.

[0086] The following is a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below. The specific implementation of step S01 is the preparation of Au nanocrystal seeds. The purpose of this step is to obtain gold nanocrystal seeds with uniform particle size and good dispersibility, which will serve as the substrate material for subsequent preparation of core-shell structures. The preparation process first involves dissolving 10 mmol of tetrachloroalloyic acid in a solution containing 100 mL of ethylene glycol, and then adding 2 g of polyvinylpyrrolidone. The use of polyvinylpyrrolidone as a protective agent in this step is based on its good steric hindrance effect and surface activity. The carbonyl functional groups in the polyvinylpyrrolidone molecule can form coordination with the surface of the gold nanoparticles, thereby effectively controlling the growth direction and size distribution of the nanocrystals. Before adding sodium borohydride, the reaction temperature needs to be stabilized at 100 degrees Celsius. Temperature control is achieved using a digital PID control system with a temperature control accuracy of ±0.1 degrees Celsius. The stirring speed was controlled at 500 rpm, and the speed was precisely adjusted by a variable frequency motor. A paddle-type stirring paddle was used during the stirring process to ensure uniform mixing of the solution. The pH of the solution was monitored in real time using a pH meter during the reaction, and the pH value was maintained between 6.5 and 7.5. The pH was adjusted using a 0.1 mol / L sodium hydroxide solution, and the addition rate was controlled by a micro-injection pump. During the reaction, the changes in the plasma resonance peak were monitored in real time using a UV-Vis spectrophotometer. When the absorption peak stabilized at 520 nm, it indicated that the growth of gold nanocrystals was complete. After the reaction, the particle size distribution of the nanocrystals was determined by a dynamic light scattering instrument. The particle size was required to be controlled within the range of 15 to 20 nm, and the polydispersity index was less than 0.2. The morphology of the particles was observed by transmission electron microscopy, and the particles were required to be spherical with clear edges.

[0087] The specific implementation of step S02 involves preparing Au@Ag core-shell nanostructures. The purpose of this step is to grow a uniform silver shell layer on the surface of gold nanocrystals, thereby forming a composite nanomaterial with a core-shell structure. The preparation process employs a surface growth method. 50 mL of an ethylene glycol solution containing 10 mmol of silver nitrate is added dropwise to the gold nanocrystal solution at a rate of 2 mL / min using a constant flow pump. During the addition, the solution temperature is maintained constant at 100°C. Temperature control is achieved using a digital PID feedback control system with a precision of ±0.1°C. The stirring speed is controlled by a program and maintained at a constant speed of 400 rpm using an electric stirrer. To achieve this, the stirring paddle was made of polytetrafluoroethylene to avoid metal ion contamination. During the reaction, the plasma resonance peak shift of the solution was monitored in real time using a UV-Vis spectrophotometer. When the resonance peak gradually moved from 520 nm and stabilized at around 430 nm, it indicated that the silver shell layer growth was complete. The test process used a flow cell for real-time monitoring with a sampling interval of 30 seconds. Parameters such as temperature, pH value, and stirring speed throughout the reaction process were recorded and controlled in real time through a data acquisition system. After the reaction, the morphology of the core-shell structure was observed using a high-resolution transmission electron microscope. The silver shell layer thickness was required to be uniform and controlled within the range of 5 to 8 nm. The elemental distribution of the core-shell structure was confirmed by energy dispersive spectroscopy analysis.

[0088] The specific implementation of step S03 is to purify the Au@Ag core-shell nanostructure. The purpose of this step is to remove unreacted substances and impurities from the reaction system to obtain a high-purity target product. The purification process is carried out using a temperature-controlled centrifuge with the temperature kept constant at 25 degrees Celsius. First, the reaction solution is centrifuged at 8000 rpm for 10 minutes. The centrifugal force is selected based on the Stokes sedimentation principle to effectively separate the target product and impurities. After centrifugation, the supernatant is discarded, and 15 ml of toluene is added to the precipitate. The mixture is then redispersed using an ultrasonic processor with an ultrasonic power set to 100 watts and a dot matrix ultrasonic probe. Each treatment lasts for 2 minutes, with a 30-second interval, and is repeated 3 times to ensure that the sample is fully dispersed. The above centrifugation and redispersion process is repeated 2 to 4 times. After each centrifugation, the absorbance of the supernatant is measured using a UV-Vis spectrophotometer. When the absorbance value is less than 0.01, it indicates that the impurities have been completely removed. Finally, the purity of the product is determined by thermogravimetric analysis, requiring a purity greater than 98% and an impurity content of less than 2%.

[0089] The specific implementation of step S04 is to prepare CeO2 nanocubes. This step adopts a solvothermal method, in which 15 mL of cerium nitrate aqueous solution with a concentration of 16.7 mmol / L is mixed with 15 mL of toluene, 1.5 mL of oleic acid and 0.15 mL of tert-butylamine. During the mixing process, mechanical stirring is used at a speed of 300 rpm. After uniform mixing, the mixture is transferred to a high-pressure reactor lined with polytetrafluoroethylene. The reactor has a volume of 50 mL and a filling degree controlled at 60%. The reaction temperature is set at 180 degrees Celsius, and the heating rate is 2 degrees Celsius per minute. A programmed temperature rise system is used for control. The reaction pressure is monitored in real time by a pressure sensor and controlled within the range of 2 to 3 MPa. During the reaction, samples are taken every 2 hours through a sampling valve. The evolution of the crystal structure is tracked by X-ray diffraction. When the characteristic peaks of the cubic crystal system appear in the diffraction pattern and the full width at half maximum (FWHM) tends to stabilize, it indicates that the reaction is complete.

[0090] The specific implementation of step S05 is to purify CeO2 nanocubes. This step uses differential centrifugation technology to separate particles of different sizes. First, the reaction product is centrifuged at 2000 rpm for 10 minutes to remove large agglomerates and impurities. An angled rotor is used during centrifugation, and the centrifuge tube is made of polypropylene. The centrifugation temperature is controlled at 25 degrees Celsius. After collecting the supernatant, 10 ml of ethanol is added, and the product is collected by centrifuging at 8000 rpm for 10 minutes. Density gradient centrifugation technology is used during centrifugation. By establishing an ethanol concentration gradient of 20% to 80%, precise separation of particle size is achieved. The final product is characterized by scanning electron microscopy and transmission electron microscopy. The nanocubes are required to have a side length distribution in the range of 50 to 60 nanometers, uniform morphology, and complete crystal faces.

[0091] Steps S06 and S07 are the initial stages in the preparation of Ag-CeO2 nanopolymers. First, purified CeO2 nanocubes are dispersed in 15 mL of ethylene glycol solution containing 0.2 mL of oleylamine. The dispersion process is performed using ultrasonic treatment at 200 W in intermittent mode, with a 2-minute operation followed by a 1-minute pause, repeated 5 times. Then, the mixture is heated to 100°C under nitrogen protection. The nitrogen purity is greater than 99.999%, and the flow rate is controlled at 100 mL per minute. A four-necked flask is used in the reaction vessel, equipped with a thermometer. The gas inlet tube, condenser reflux tube, and syringe interface are equipped with an intelligent temperature control system with an accuracy of ±0.5 degrees Celsius. After a 30-minute isothermal reaction, 1.0 mL of a 2.2 mmol / L n-butyllithium n-hexane solution is injected into the reaction system at a rate of 0.5 mL / min. Subsequently, a mixed solution of 5 mL of ethylene glycol and 1 mL of oleylamine containing 0.125 mmol of silver nitrate is injected. Throughout the process, the reduction potential of the system is monitored using an electrochemical workstation. When the potential stabilizes at -0.4 V, the next step of the reaction begins.

[0092] The specific implementation of step S08 is to complete the preparation of Ag-CeO2 nanopolymers. A programmed temperature rise strategy is adopted. First, the mixture is stirred at room temperature for 10 minutes at a stirring speed of 300 rpm. Then, the temperature is increased to 80 degrees Celsius at a rate of 2 degrees Celsius per minute and held at a constant temperature for 1 hour. During this period, the changes in surface ligands are monitored in real time using a Fourier transform infrared spectrometer. When the intensity ratio of the carboxyl characteristic peaks reaches 1.5, the temperature is further increased to 120 degrees Celsius. The temperature rise is precisely controlled by a programmed temperature control system, and the temperature error is controlled within ±1 degree Celsius. The reaction is carried out at 120 degrees Celsius for 2 hours. During the reaction, the growth of silver nanoparticles is monitored by sampling and analysis.

[0093] The specific implementation of step S09 involves collecting and purifying Ag-CeO2 nanopolymers, separating the product using high-speed centrifugation, centrifuging the reaction solution at 8000 rpm for 10 minutes using centrifuge tubes made of organic solvent-resistant polypropylene, and controlling the centrifugation temperature at 20 degrees Celsius. After collecting the precipitate, it is washed with ethanol, adding 20 ml of ethanol each time, ultrasonically dispersing for 2 minutes, and then centrifuging. This process is repeated 2 to 3 times. During the washing process, the conductivity of the washing solution is monitored using a conductivity meter. When the conductivity drops below 10 μS / cm, the washing is stopped. The final product is redispersed in 15 ml of toluene. The morphology and composition of the product are characterized by transmission electron microscopy and energy dispersive spectroscopy. It is required that the silver nanoparticles are uniformly distributed on the surface of CeO2 nanocubes with a uniform particle size distribution.

[0094] The specific implementation of step S10 involves establishing a Raman spectroscopy standard calibration matrix. This step is based on multivariate statistical analysis, establishing a calibration model through systematic standard sample measurements. During Raman spectroscopy acquisition, a laser with a wavenumber range of 400 to 3000 is used as the light source. An automatic focusing system ensures the accuracy of the focal position, with the focal plane deviation controlled within ±1 micrometer. For each standard sample, 100 data points are collected, with an integration time of 1 second for each data point. The calibration matrix is ​​then used to establish the standard calibration model. Data processing is performed, including the elements of the baseline intensity matrix. The instrument response coefficient was obtained through repeated measurements on a blank substrate. Calibration was performed using standard Rhodamine 6G solution, and the correction factor was determined. The method was determined through iterative optimization, using the least squares method. The iteration terminated when the residual change was less than 0.1%.

[0095] The specific implementation methods of steps S11 and S12 are as follows: Establishing a substrate enhancement effect evaluation matrix and preparing the detection substrate. Standard solutions of hazardous chemicals at concentrations of 1, 10, 50, 100, and 500 nanomoles per liter are prepared using a precision pipetting system. Grade A volumetric flasks and microsyringes are used during preparation to ensure an accuracy within ±0.1%. The prepared standard solutions are injected into the detection cell using an autosampler. Each concentration is measured 10 times to obtain the substrate enhancement effect evaluation matrix. ,in The Raman signal intensity at different concentrations, Baseline signal strength, enhancement factor The data was obtained through optimization using a genetic algorithm, with the optimization objective being to maximize the correlation coefficient between the fitted curve and the experimental data.

[0096] The specific implementation of step S13 is to establish the SERS enhancement coefficient correction matrix. The spectral data are processed using the eigenvalue decomposition method, where... It was calculated using the five-point difference method. The eigenvalues ​​are solved using the LU decomposition method. and eigenvectors The correction coefficients are obtained through singular value decomposition. Cross-validation was used to determine that the root mean square error of prediction should be less than 5% and the coefficient of determination should be greater than 0.99.

[0097] The specific implementation of step S14 involves preparing a detection substrate. A detection area is constructed on the surface of a conductive glass substrate using a micro-droplet technique. The droplet volume is 2 microliters, controlled by a high-precision micro-syringe. During the droplet process, the substrate temperature is maintained at 25 degrees Celsius, and the relative humidity is controlled between 40% and 50%. The surface wettability of the substrate is monitored using a contact angle meter to ensure that the contact angle is within the range of 45 to 60 degrees. The drying process uses programmed temperature rise at a rate of 1 degree Celsius per minute. Finally, the surface morphology and roughness of the substrate are characterized using an atomic force microscope, requiring the surface roughness to be within 5 nanometers.

[0098] The specific implementation of step S15 involves Raman spectroscopy measurement using a confocal Raman spectrometer. A 532 nm laser is selected as the excitation source, with a spot diameter controlled at 2 μm and a power set to 5 mW. An autofocus system is used to maintain focal length stability, and the optical path deviation is controlled within ±0.5 μm. The acquisition time is set to 10 seconds. To eliminate the influence of random noise, each sample point is measured 10 times, and the average value is taken. The result is then analyzed using a SERS signal intensity normalization matrix. Data processing is performed, among which For sample signal intensity, Background signal strength, phase angle The weighting coefficients are obtained through Fourier transform. Determined through principal component analysis.

[0099] The specific implementation of steps S16 and S17 involves calculating the final SERS utility value. The data processing employed wavelet transform for noise reduction, using the Dobsch fourth-order wavelet basis function with a decomposition level of four. The threshold selection followed the minimax principle, and a soft thresholding method was used for signal reconstruction. The spectrum was then preprocessed using the Savitzky-Gorye smoothing algorithm, with an 11-point smoothing window of order 3 and a compensation coefficient of [missing information]. The confidence interval was obtained by optimizing the bootstrap method and establishing it by repeated sampling 1000 times, requiring a confidence level of 95%. The entire calculation process adopted an iterative optimization algorithm, and the convergence condition was that the relative error was less than 0.1%.

[0100] All steps of the process employed sophisticated instrument control and data analysis methods, with strict quality control indicators at each stage. Multiple characterization techniques ensured the accuracy and repeatability of the results. The entire method is based on the fundamental principles of surface-enhanced Raman scattering (SERS), combining advanced technologies from multiple disciplines such as nanomaterial synthesis, surface modification, spectral analysis, and data processing. This approach achieves highly sensitive detection of hazardous chemicals, with a detection limit down to 1 nanomolar per liter, a linear range covering five orders of magnitude, a relative standard deviation of less than 5%, and good repeatability and stability. It provides a reliable analytical method for the rapid detection of hazardous chemicals. The innovation of this method lies in its effective elimination of interference from factors such as instrument drift and substrate inhomogeneity through multiple matrix operations and corrections, significantly improving the accuracy and reliability of the detection.

[0101] All parameters involved in the method have undergone rigorous optimization and validation, including the synthesis conditions of nanomaterials, surface modification parameters, spectral acquisition parameters, and data processing parameters. The selection of these parameters is based on extensive experimental data and theoretical calculations, ensuring the scientific rigor and reliability of the method. Furthermore, the method exhibits good versatility and scalability, allowing for adjustments to relevant parameters to suit the detection of different types of hazardous chemicals, providing crucial technical support for practical applications.

[0102] The technical principle of this invention is based on the synergistic effect of surface-enhanced Raman scattering (SERS) and multiple mathematical matrices. At the material design level, the Au@Ag core-shell structure generates a strong electromagnetic field enhancement effect through the synergistic effect of the core and shell. The gold core provides a stable plasmon resonance basis, while the silver shell provides stronger local field enhancement. The introduction of CeO2 nanocubes not only provides a regular surface structure, but its unique oxygen vacancies and surface defects also enhance the interaction with target molecules, improving the selectivity and sensitivity of detection. In the calibration system design, the Raman spectroscopy standard calibration matrix effectively corrects systematic errors by considering instrument response characteristics and baseline drift. The substrate enhancement effect evaluation matrix introduces a nonlinear response term and a Gaussian distribution function, accurately describing the dynamic characteristics of signal enhancement. The SERS enhancement coefficient calibration matrix achieves dimensionality reduction and feature extraction of complex data through eigenvalue decomposition and matrix operations. The synergistic effect of these matrices constructs a complete mathematical model that accurately reflects various physicochemical processes during detection, thus ensuring the accuracy of quantitative analysis. The technical solution of this invention, through the organic combination of material structure design and mathematical model construction, forms a logically rigorous and systematic detection method, providing a scientific and effective solution to the accuracy problem in SERS quantitative analysis.

[0103] To better understand and implement this invention, the following is a specific application scenario of the invention, Example 2: The method of the present invention is used to detect nitrobenzene hazardous chemicals in water, and the specific implementation process is as follows.

[0104] First, Au nanocrystals were prepared by dissolving 0.394 g of tetrachloroauric acid in 100 mL of ethylene glycol, adding 2 g of polyvinylpyrrolidone, stirring at 500 rpm, heating to 100°C, and then adding 0.378 g of sodium borohydride in 50 mL of ethylene glycol at a rate of 2 mL per minute. The pH was maintained at 7.2 during the reaction. After 15 minutes of reaction, the mixture was allowed to cool naturally to room temperature. Dynamic light scattering tests showed that the average particle size of the obtained Au nanocrystals was 17.5 nm and the polydispersity index was 0.156.

[0105] To further prepare Au@Ag core-shell nanostructures, 0.850 g of silver nitrate was dissolved in 50 mL of ethylene glycol. Au nanocrystal seed solution was added dropwise while stirring at 400 rpm and the temperature was controlled at 100 degrees Celsius. Ultraviolet-visible spectroscopy monitoring showed that the plasmon resonance peak gradually shifted from 520 nm to 432 nm. After reacting for 20 minutes and cooling, the product was observed by high-resolution transmission electron microscopy, which showed that the silver shell layer had a uniform thickness with an average of 6.8 nm.

[0106] like Figure 2As shown, the UV-Vis absorption spectra of Au nanocrystals and Au@Ag core-shell structures are displayed, showing the change of the plasmon resonance peak from 520 nm to 432 nm, confirming the successful preparation of the core-shell structure.

[0107] Purification and subsequent synthesis were carried out according to the steps of the present invention to prepare Ag-CeO2 nanopolymers. The final product was characterized to show that the CeO2 nanocubes had a side length of 54 nanometers, and silver nanoparticles were uniformly distributed on its surface with a particle size distribution between 15 and 20 nanometers.

[0108] The Raman spectroscopy standard correction matrix is ​​established using the method described above. The baseline intensity matrix and instrument response coefficients were obtained through 100 repeated measurements. The calibration value is 0.986, and the correction factor is... The optimized value is 0.187.

[0109] A series of 2,4-dinitrotoluene standard solutions were prepared with concentrations of 1, 10, 50, 100, and 500 nanomoles per liter, and the substrate enhancement effect evaluation matrix was measured. Enhancer factors The value is 1.876, and the standard deviation is... The value is 0.325. The experimental data are shown in Table 1 below:

[0110] Table 1 Experimental Data

[0111]

[0112] The SERS enhancement coefficient correction matrix was obtained through calculation. , main eigenvalues The distribution ranges from 0.856 to 0.998, with a correction factor. The value was set to 0.458, and the cross-validation results showed a root mean square error of 3.8% and a coefficient of determination of 0.996. The method of this invention was used to determine the content of 2,4-dinitrotoluene in wastewater from a chemical plant. Each sample was measured 10 times, and the results were analyzed using the SERS signal intensity normalization matrix. The processed data is shown in Table 2 below:

[0113] Table 2 Processed Data Table

[0114]

[0115] The final calculated SERS utility value The results show that the detection limit of this method for 2,4-dinitrotoluene is 0.8 nanomoles per liter, the linear range is 1 to 500 nanomoles per liter, the inter-batch relative standard deviation is 4.2%, the intra-batch relative standard deviation is 3.5%, and the spiked recovery rate of the method is between 96.5% and 104.5%.

[0116] like Figure 3 As shown, the standard curve and actual sample determination results of 2,4-dinitrotoluene are presented. A good linear relationship is shown in the double logarithmic coordinate system, and the actual sample determination results are in high agreement with the standard curve.

[0117] In the study of sample matrix effects, the interference of common ions (sodium ions, potassium ions, calcium ions, chloride ions, sulfate ions, etc.) was investigated. The results showed that when the concentration of interfering ions was 100 times that of the analyte, the relative error of the measurement results was less than 5%. In addition, this method also has good selectivity. For structurally similar nitrobenzene compounds, accurate quantification can be achieved by identifying characteristic Raman peaks.

[0118] like Figure 4 As shown, the effect of different interfering ions on the determination results is demonstrated. When the concentration of all ions is 100 times that of the analyte, the recovery rate of the method is still maintained above 95%.

[0119] Durability tests showed that the prepared detection substrate, stored at room temperature for 30 days, did not exhibit a significant decrease in SERS activity, and the relative standard deviation of the signal intensity was less than 6%. In practical applications, this method can be directly used for the rapid detection of nitrobenzene compounds in environmental water samples such as surface water and industrial wastewater. Furthermore, sample pretreatment is simple, and the entire detection process can be completed within 30 minutes.

[0120] like Figure 5 As shown, the stability test results of the detection substrate during 30 days of storage are presented. The change in relative signal intensity is within 6%, indicating good stability.

[0121] Compared with traditional detection methods, the method of the present invention has significant advantages, as shown in Table 3 below:

[0122] Table 3 Comparison of Detection Results

[0123]

[0124] This invention addresses the problems of insufficient detection sensitivity, long analysis time, and high sample consumption in traditional detection methods. By designing novel Ag-based composite nanomaterials and establishing a multi-matrix correction system, it significantly improves the accuracy and reliability of detection. It exhibits particularly significant advantages in suppressing matrix interference, improving repeatability, and simplifying operation, providing a practical new method for the rapid screening and quantitative analysis of hazardous chemicals in environmental water samples. The successful development of this invention not only deepens the theoretical understanding of surface-enhanced Raman scattering mechanisms but also provides crucial technical support for environmental monitoring and safety early warning in practice.

[0125] Example 3 below provides a more detailed experimental procedure for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals.

[0126] Table 4 lists the chemical reagents used in this embodiment. Unless otherwise specified, none of the organic reagents underwent purification. The experimental reaction apparatus was a reflux reaction device equipped with magnetic stirring, heating, and atmosphere protection functions. The reflux reaction device consisted of a three-necked flask, a spherical condenser with a silicone stopper, and a thermocouple sheath. The magnetic stirring and heating mantle provided heating and stirring functions. High-purity N2 (≥99.999%) was used as the protective atmosphere in the experiment.

[0127] Table 4. Specifications of the reagents and equipment used.

[0128]

[0129] The specific experimental methods are described in detail below:

[0130] Preparation of Au@Ag core-shell nanostructures

[0131] At room temperature, 10 mmol of HAuCl4·xH2O was dissolved in a solution containing 100 ml of ethylene glycol and 2 g of PVP. The solution was heated to 100.0 °C under magnetic stirring. 10 mmol of NaBH4 was dissolved in 50 ml of ethylene glycol and slowly added to the above solution, maintaining a constant temperature of 100 °C for 15 minutes. The colloidal solution was then cooled to room temperature. A portion of the Au nanoparticle colloid was removed, washed, and diluted before TEM and UV-visible testing. 50 ml of ethylene glycol containing 10 mmol of AgNO3 was added to the remaining reaction solution under magnetic stirring. The solution was heated to 100 °C while maintaining magnetic stirring, and held at this temperature for 20 minutes before cooling to room temperature. Simultaneously, transfer the product to a centrifuge tube, add an appropriate amount of ethanol, mix well, and centrifuge at 8000 rpm for 10 minutes. Nanoparticles will precipitate at the bottom of the centrifuge tube. Remove the supernatant, add 15 ml of toluene to redisperse the nanoparticle precipitate at the bottom of the centrifuge tube, and perform appropriate sonication to ensure the nanoparticles are fully dispersed in the toluene. This process removes various organic impurities from the reaction product, achieving the purpose of washing the nanoparticles. This operation can be performed 2-4 times (samples for XRD analysis are generally washed 4 times to thoroughly remove organic matter and prevent interference with the results). Use a pipette to take a small amount (generally 1-2 ml) of toluene colloid containing the nanoparticles from the centrifuge tube, dilute it, and prepare samples for TEM and UV-visible testing. Wash the remaining nanoparticle colloid with ethanol 3-4 times, then place the remaining nanoparticle precipitate after centrifugation in a drying oven at 60℃ for 2 hours. Perform powder XRD testing on the dried sample.

[0132] (2) Effect of different Au / Ag ratios on the SPR properties of Au@Ag core-shell nanostructures

[0133] In the above experiments, Au@Ag core-shell nanostructures with different compositions were synthesized by adjusting the ratio of Au / Ag to 2 / 1, 1 / 1, and 1 / 2. After cleaning and dilution, samples were prepared for UV-visible testing.

[0134] Influence of Au seed size on the formation of Au@Ag core-shell nanostructures

[0135] First, small-sized Au seed crystals were prepared: 10 mmol of HAuCl4·xH2O was dissolved at room temperature in a solution containing 5 ml of oleylamine and 20 ml of decahydronaphthalene. The solution was then injected at room temperature into a three-necked flask under N2 atmosphere protection. The flask contained 2.0 ml of n-butyllithium hexane solution (2.2 M) and 75 ml of cyclohexane. Upon injection of the Au precursor, the solution instantly turned into a red colloid. This red colloid was stirred at room temperature for 20 minutes and then heated to 120°C for 2 hours to obtain small-sized Au nanoparticles. A small amount of the product was taken out, and an appropriate amount of ethanol was added. The mixture was then centrifuged at 8000 rpm for 15 minutes. After removing the supernatant, the Au nanoparticle precipitate was obtained, redispersed in toluene, and washed with ethanol. After washing and dilution, samples were prepared for TEM and UV-visible testing.

[0136] Add an appropriate amount of ethanol to the above Au nanoparticle colloidal solution, then centrifuge at 8000 rpm for 15 minutes. After removing the supernatant, Au nanoparticle precipitate is obtained. This precipitate is redispersed in toluene and washed 2-3 times with ethanol. The washed nanoparticle precipitate is then redispersed in a solution containing 100 ml ethylene glycol and 2 g PVP. Then, 50 ml of ethylene glycol containing 10 mmol AgNO3 is added to the solution under magnetic stirring. While maintaining magnetic stirring, the solution is heated to 100°C and held at that temperature for 20 minutes, then cooled to room temperature. Samples for TEM, XRD, and UV-visible testing are prepared after washing and dilution.

[0137] (4) Synthesis of sea urchin-like Au-Ag heteropolymer nanostructures

[0138] Under a nitrogen atmosphere, 10 mmol of HAuCl4·xH2O was dissolved in a solution containing 100 ml of ethylene glycol and 10 ml of oleylamine. The solution was heated to 100.0 °C and held at this temperature for 4 hours with magnetic stirring, yielding a purple Au colloidal solution. This solution was then allowed to cool naturally to room temperature. 50 ml of ethylene glycol containing 10 mmol of AgNO3 and 10 ml of oleylamine was added to the reaction solution with magnetic stirring. Then, 10 mmol of HAuCl4·xH2O was added to the solution. Dissolve NaBH4 in 50 ml of ethylene glycol and add the above solution. Maintain a constant temperature of 100°C for 15 minutes. Then cool the colloidal solution to room temperature and transfer the product to a centrifuge tube. Add an appropriate amount of ethanol to the centrifuge tube and mix well. Centrifuge at 8000 rpm for 10 minutes. Nanoparticles will precipitate at the bottom of the centrifuge tube. Remove the supernatant and add 15 ml of toluene to redisperse the nanoparticle precipitate at the bottom of the centrifuge tube. Appropriate sonication can be performed to ensure that the nanoparticles are fully dispersed in the toluene. Add an appropriate amount of oleylamine to stabilize the dispersed nanoparticles. This process removes various organic impurities from the reaction product, achieving the purpose of washing the nanoparticles. This operation can be performed 2-4 times (samples for XRD analysis are generally washed 4 times to thoroughly wash away organic matter and prevent interference with the results). Use a pipette to take a small amount of toluene colloid containing nanoparticles (generally 1-2 ml) from the centrifuge tube, dilute it, and prepare samples for TEM and UV-visible testing. The remaining nanoparticle colloid was washed with ethanol 3-4 times. The remaining nanoparticle precipitate after centrifugation was placed in a drying oven and kept at 60℃ for 2 hours. The dried sample was then subjected to powder XRD testing.

[0139] (5) Synthesis of Ag-CeO2 metal-oxide nanopolymer structure

[0140] Synthesis of CeO2 nanocube particles: 15 mL of 16.7 mmol / L Ce(NO3)3 aqueous solution was added to a 50 mL autoclave lined with polytetrafluoroethylene. Then, 15 mL of toluene, 1.5 mL of oleic acid and 0.15 mL of tert-butylamine were added. The sealed autoclave was placed in a drying oven and kept at 180 °C for 24 hours. After naturally cooling to room temperature, the product was centrifuged at 2000 rpm for 10 minutes to remove solid impurities in the reaction. The upper brownish-yellow liquid was taken out, 10 mL of ethanol was added, and the product was centrifuged at 8000 rpm for 10 minutes. After removing the supernatant, a brown precipitate was obtained. Then, 15 mL of toluene and a small amount of oleic acid were added to redisperse the precipitate and stored for subsequent experiments and characterization.

[0141] Synthesis of Ag-CeO2 nanopolymers: 20 mL of ethanol was added to the 15 mL CeO2 toluene solution prepared above. After centrifugation at 8000 rpm for 10 minutes to remove the supernatant, CeO2 nanoparticle precipitate was obtained. 0.2 mL of oleylamine and 15 mL of ethylene glycol solvent were added, and the precipitate was redispersed and transferred to a three-necked flask. The solution in the three-necked flask was heated to 100 °C and held at that temperature for 0.5 hours under N2 atmosphere and magnetic stirring to remove moisture. At room temperature, 1.0 mL of n-butyllithium n-hexane solution (2.2 M, i.e., 2.2 mmol / L) was quickly injected into the flask using a syringe. Then, a mixed solution of 5 mL of ethylene glycol and 1 mL of oleylamine containing 0.125 mmol / L AgNO3 was injected into the three-necked flask in the same manner. After holding at room temperature for 10 minutes, the temperature was slowly increased. After holding at 80 °C for 1 hour, the temperature was increased to 120 °C and aged for 2 hours. After the reaction product is naturally cooled to room temperature, it is centrifuged at 2000 rpm for 10 minutes to remove solid impurities. The supernatant is then removed, and an appropriate amount of ethanol is added. The product is centrifuged at 8000 rpm for 10 minutes to remove the upper liquid. An appropriate amount of toluene is added to redisperse the product. The product is then washed with ethanol 2-3 times in the same manner. Finally, an appropriate amount of toluene or n-hexane is added to redissolve the precipitate and store it for subsequent experiments and characterization.

[0142] Figure 6 The images show the TEM and HRTEM images of the Au seed crystals obtained in the first step. The electron microscopy results show that the average size of the synthesized Au nanoparticles is about 17 nm. The HRTEM high-resolution transmission electron microscopy image of the sample shows that the nanoparticles are homogeneous inside and out, have a consistent color, and have a neat spherical morphology at the edges.

[0143] Figure 7 The images show TEM and HRTEM images of Au@Ag core-shell nanoparticles obtained by the seed growth method. The electron microscopy results show that the average size of the synthesized nanoparticles is approximately 20 nm. Compared to the size of the Au seeds, the bimetallic nanoparticles exhibit a certain degree of growth. Figure 7 a) The TEM image shows that each nanoparticle consists of a darker core surrounded by a lighter shell. This is because the atomic number of Au is much greater than that of Ag. Therefore, in TEM imaging, the color of Au particles is darker than that of Ag. Based on this morphological characteristic, it can be clearly determined that the prepared nanoparticles are core-shell nanoparticles with a layered structure.

[0144] Next, the AuAg alloy and Au@Ag core-shell structured nanoparticle colloid prepared earlier were cleaned and concentrated by centrifugation to ensure that the total concentration of Au and Ag remained consistent. The nanoparticle sol was then mixed with pyridine at a certain volume ratio and stirred thoroughly. After standing for a period of time, it was centrifuged and cleaned 2-3 times. Finally, an appropriate amount of sample was taken out and placed in a glass sample bottle for surface-enhanced Raman scattering spectroscopy (SERS) detection.

[0145] Figure 8 The SERS spectra of pyridine on AuAg alloy nanostructures with different Au / Ag ratios are shown, with excitation light at 632.8 nm. The figures clearly show that the relative intensities of the pyridine peaks around 1005 cm⁻¹ (fully symmetric ring breathing vibration) and around 1032 cm⁻¹ (triangular symmetry distortion) also change. Specifically, on AuAg alloy nanostructures with Au / Ag ratios of 1 / 1 and 1 / 2, the peak height of the former is basically the same as that of the latter. However, when the Au / Ag ratio is 2 / 1, the peak height of the former is slightly higher than that of the latter.

[0146] On the other hand, the AuAg alloy and Au@Ag core-shell structured nanoparticle colloid prepared earlier were cleaned and concentrated by centrifugation to ensure that the total concentration of Au and Ag remained consistent. The above nanoparticle sol was mixed with an ethylene glycol solution of cysteine ​​at a certain volume ratio and stirred thoroughly. After standing for a period of time, it was centrifuged and washed 2-3 times. Then, a certain volume of methanol solution containing hazardous chemicals such as 2,4,6-trinitrotoluene was added to the above cysteine-modified nanoparticle colloid. The mixture was mixed evenly and stirred thoroughly. After standing for a period of time, an appropriate amount of sample was taken out and placed in a glass sample bottle for surface-enhanced Raman scattering (SERS) detection.

[0147] When pure Au nanoparticles are used as the SERS substrate, the characteristic peaks of the SERS spectrum of 2,4,6-trinitrotoluene are not significant. When AuAg alloy nanoparticles are used as the SERS substrate, weak characteristic peaks can be observed in the SERS spectrum of 2,4,6-trinitrotoluene. When pure Ag nanoparticles are used as the SERS substrate, strong characteristic peaks can be observed in the SERS spectrum of 2,4,6-trinitrotoluene. When Au@Ag core-shell structured nanoparticles are used as the SERS substrate, the characteristic peaks of the SERS spectrum of 2,4,6-trinitrotoluene are the most significant. This indicates that pure Au nanoparticles have poor SERS performance in the Raman detection of 2,4,6-trinitrotoluene, while Au@Ag core-shell structured nanoparticles have the most significant SERS detection performance. The SERS enhancement effect of AuAg alloy nanoparticles is stronger than that of pure Au nanoparticles, but weaker than that of pure Ag nanoparticles. The SERS signal enhancement ability of Au@Ag core-shell structure is greater than that of Ag alone. Because the SERS enhancement effect of the AuAg alloy nanostructure itself is not significant, the SERS enhancement of the AuAg alloy nanostructure does not change significantly with the change of Au / Ag ratio.

[0148] The above research found that Au@Ag core-shell nanostructures, used as SERS substrates, provide significantly better SERS enhancement than AuAg alloys. Based on relevant theoretical and experimental studies, the possible reasons are as follows: First, when the proportion of Ag in the Au / Ag ratio is relatively high, the resulting core-shell material exhibits a shell thickness that effectively improves the SERS enhancement effect. Second, Au@Ag core-shell nanoparticles have a rougher surface morphology compared to AuAg alloy nanoparticles; according to the EM mechanism, this rough nanoscale surface is highly beneficial to the SERS enhancement effect.

[0149] It should be noted that the variables involved in this invention are explained in detail in Table 5.

[0150] Table 5. Variable Explanation Table

[0151]

[0152] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the SERS efficacy of Ag-based composite nanomaterials against hazardous chemicals, characterized in that, Includes the following steps: Au nanocrystals and Au@Ag core-shell nanostructures were prepared, as were CeO2 nanocubes and Ag-CeO2 nanopolymers. The Au@Ag core-shell nanostructures and Ag-CeO2 nanopolymers were then mixed to prepare a surface-enhanced Raman (SERS) detection substrate. Using the detection substrate and a Raman spectrometer, a standard correction matrix for Raman spectroscopy, a substrate enhancement effect evaluation matrix, and a SERS enhancement coefficient correction matrix were obtained. A normalized matrix for the SERS signal intensity was also established. Finally, the SERS utility value was calculated using the normalized matrix for the SERS signal intensity, the substrate enhancement effect evaluation matrix, and the standard correction matrix for Raman spectroscopy.

2. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 1, characterized in that, The steps for preparing Au nanocrystal seeds are as follows: 10 mmol of tetrachloroalloyic acid is dissolved in a solution containing 100 mL of ethylene glycol and 2 g of polyvinylpyrrolidone. The solution is heated to 100 degrees Celsius under magnetic stirring. 50 mL of ethylene glycol solution containing 10 mmol of sodium borohydride is slowly added to the solution. The mixture is reacted at a constant temperature for 15 minutes and then cooled to room temperature to obtain the Au nanocrystal seed solution.

3. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 2, characterized in that, The steps for preparing Au@Ag core-shell nanostructures are as follows: 50 mL of ethylene glycol solution containing 10 mmol of silver nitrate is added to the Au nanocrystal seed solution. The solution is heated to 100°C under magnetic stirring and reacted at this temperature for 20 minutes. After cooling to room temperature, the Au@Ag core-shell nanostructure reaction solution is obtained. Ethanol is added to the Au@Ag core-shell nanostructure reaction solution, and the mixture is centrifuged at 8000 rpm for 10 minutes. The precipitate is collected and redispersed with 15 mL of toluene. This washing process is repeated 2 to 4 times to obtain purified Au@Ag core-shell nanostructures.

4. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 3, characterized in that, The specific steps for preparing CeO2 nanocubes are as follows: 15 mL of a 16.7 mmol / L aqueous solution of cerium nitrate is mixed with 15 mL of toluene, 1.5 mL of oleic acid, and 0.15 mL of tert-butylamine, and the mixture is placed in an autoclave and reacted at a constant temperature of 180°C for 24 hours to obtain the CeO2 nanocube reaction product. The CeO2 nanocube reaction product is centrifuged at 2000 rpm for 10 minutes to remove solid impurities. 10 mL of ethanol is added to the upper layer of liquid, and the mixture is centrifuged at 8000 rpm for 10 minutes to collect the precipitate, thus obtaining purified CeO2 nanocubes.

5. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 4, characterized in that, The steps for preparing Ag-CeO2 nanopolymers are as follows: The purified CeO2 nanocubes are dispersed in 15 mL of ethylene glycol solution containing 0.2 mL of oleylamine. The mixture is heated to 100°C under nitrogen protection and reacted at this temperature for 30 minutes to obtain a first reaction system. 1.0 mL of a 2.2 mmol / L n-butyllithium hexane solution is injected into the first reaction system, followed by the injection of a mixed solution containing 5 mL of ethylene glycol and 1 mL of oleylamine containing 0.125 mmol of silver nitrate, to obtain a second reaction system. The second reaction system is stirred at room temperature for 10 minutes, then heated to 80°C and reacted at this temperature for 1 hour. The temperature is then further increased to 120°C and reacted for 2 hours to obtain the Ag-CeO2 nanopolymer reaction solution. The Ag-CeO2 nanopolymer reaction solution is collected by centrifugation at 8000 rpm for 10 minutes, washed 2 to 3 times with ethanol, and then dispersed in toluene to obtain the Ag-CeO2 nanopolymers.

6. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 5, characterized in that, The steps for preparing the surface-enhanced Raman detection substrate are as follows: the purified Au@Ag core-shell nanostructure and the Ag-CeO2 nanopolymer are mixed at a mass ratio of 1:1 and ultrasonically dispersed for 15 minutes to obtain the surface-enhanced Raman detection substrate; the surface-enhanced Raman detection substrate is drop-coated onto the surface of a conductive glass substrate, allowed to air dry naturally, and then dried in a 60-degree Celsius drying oven for 2 hours to obtain the detection substrate.

7. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 1, characterized in that, The steps for establishing the standard calibration matrix for Raman spectroscopy are as follows: the Raman spectrometer is used to measure within the wavenumber range of 400 to 3000, and the baseline intensity matrix and instrument response matrix in the standard calibration matrix for Raman spectroscopy are obtained.

8. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 1, characterized in that, The steps for establishing the matrix enhancement effect evaluation matrix are as follows: prepare standard solutions of hazardous chemicals with concentrations of 1, 10, 50, 100, and 500 nanomoles per liter, measure the Raman signal intensity of the standard solutions of the hazardous chemicals, and obtain the matrix enhancement effect evaluation matrix.

9. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 1, characterized in that, The specific steps for establishing the SERS enhancement coefficient correction matrix are as follows: the SERS enhancement coefficient correction matrix is ​​calculated based on the substrate enhancement effect evaluation matrix and the Raman spectroscopy standard correction matrix. The surface-enhanced Raman signal of the hazardous chemical to be tested was measured using a Raman spectrometer. The excitation wavelength was selected as 532 nm, the laser power was set to 5 mW, and the acquisition time was 10 seconds to obtain the Raman signal of the hazardous chemical to be tested. The Raman signal of the hazardous chemical to be tested was normalized using the SERS enhancement coefficient correction matrix to obtain the SERS signal intensity normalization matrix.

10. The method for determining the SERS efficacy of Ag-based composite nanomaterials for hazardous chemicals according to claim 1, characterized in that, The specific steps for calculating the SERS utility value are as follows: subtract the Raman spectral standard correction matrix from the product of the SERS signal intensity normalization matrix and the basis enhancement effect evaluation matrix.

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