CN109470683B - Method for carrying out 2,4-D rapid detection by combining SERS substrate with multivariate linear regression model - Google Patents
Method for carrying out 2,4-D rapid detection by combining SERS substrate with multivariate linear regression model Download PDFInfo
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Abstract
The invention belongs to the technical field of agricultural product rapid detection, and particularly relates to a method for performing 2,4-D rapid detection by combining an SERS substrate with a multivariate linear regression model; the method comprises the following specific steps: firstly, preparing a sample liquid, collecting SERS spectra of the sample liquid with different 2,4-D concentrations, preprocessing, then introducing the SERS spectra into matlab, setting the evolution times, the lowest sampling times and the variation coefficient in the CARS algorithm, performing variable screening on all preprocessed SERS spectra by using the CARS algorithm, selecting a variable combination with the lowest cross-validation root-mean-square error for establishing a prediction model, and substituting the variable combination into the SERS spectra of the sample liquid to be detected to realize the detection of the 2,4-D concentrations in the sample liquid; the invention has the advantages of high detection speed, wide detection range, high stability and sensitivity, and good application prospect in the technical fields of food safety, environmental monitoring and the like.
Description
Technical Field
The invention belongs to the technical field of agricultural product rapid detection, and particularly relates to a method for performing 2,4-D rapid detection by combining an SERS substrate with a multivariate linear regression model.
Background
In the process of cultivating and producing agricultural products, pesticides are inevitably used, but in the pesticide application process, the phenomenon of pesticide residue excess is often caused by wrong guidance and illegal operation. In particular, water-soluble pesticides such as 2,4-D, chlordimeform, trichlorfon and the like not only easily remain on the surface of agricultural products and in the environment, but also permeate into the agricultural products, and finally enter human bodies along with eating of people, and the risks of tumors, chromosome lesions, neuron injuries and the like are increased in the human bodies by the pesticides.
The tea is a traditional beverage in China and also one of main economic crops, and is extremely praised worldwide due to the unique sensory quality and the efficacies of promoting the production of body fluid to quench thirst, reducing blood fat and the like. However, 2,4-D is very easy to remain due to the use of pesticides in the growth process of the tea, and can enter tea water along with brewing, so that the tea can enter a human body. Therefore, the rapid and sensitive detection of the pesticide residue in the tea leaves and the tea soup is of great significance for protecting the health of consumers.
The existing means for detecting pesticide residues comprise conventional physicochemical detection, High Performance Liquid Chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), enzyme-linked immunosorbent assay (ELISA) and other means, and the detection means can meet the requirements of detection limit and detection precision, but have the defects of long detection period, high cost and complex pretreatment; spectroscopic detection methods such as raman spectroscopy and infrared spectroscopy are increasingly used for detecting organic residues because they can reflect structural information such as functional groups of components of the analyte. However, unlike conventional data collection, each set of data collected by Surface Enhanced Raman Scattering (SERS) technique is a complete scattering spectrum, and usually contains thousands of variables, which causes great hindrance to data processing and analysis. Moreover, the spectrum often contains many variables that are not related to the actual sample, which can affect the accuracy of the quantitative calculation.
Disclosure of Invention
Aiming at the defects of long detection period and complicated pretreatment existing in the traditional detection means at present, the invention aims to solve one of the problems; the method is based on SERS technology and combines a multiple linear regression model for rapidly detecting 2,4-D in the tea soup, wherein the multiple linear regression model is used for performing wave number variable screening by using a Partial Least Square method (PLS) and combining a Competitive Adaptive weighted Sampling (CARS).
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for carrying out 2,4-D rapid detection by taking silver-zinc oxide as an SERS substrate comprises the following steps:
(1) preparation of sample liquid: mixing the sample with water, heating, and vacuum filtering to obtain clear solution as sample solution;
(2) adding zinc nitrate hexahydrate into water to obtain a zinc nitrate hexahydrate aqueous solution, stirring, adding ammonia water, adjusting the temperature, continuing stirring, reacting to obtain milky turbid liquid, sequentially performing centrifugal separation, ethanol cleaning and deionized water cleaning, and vacuum drying to obtain white zinc oxide powder;
(3) adding the zinc oxide powder prepared in the step (2), polyvinylpyrrolidone and glucose into deionized water, heating to a certain temperature, stirring, adding a silver nitrate solution, continuing stirring for the second time, sequentially performing centrifugal separation, ethanol cleaning and deionized water cleaning on the obtained grey brown turbid liquid, and performing vacuum drying to obtain black silver-zinc oxide composite powder;
(4) establishing a 2,4-D concentration prediction model in the sample liquid: respectively adding 2,4-D pesticide standards into the sample liquid prepared in the step (1) to prepare different 2,4-D concentrations, introducing the pretreated spectrum into matlab, setting the evolution times, the lowest sampling times and the variation coefficient in the CARS algorithm, performing variable screening on all pretreated SERS spectra by using the CARS algorithm, and selecting a variable combination with the lowest cross validation root mean square error for establishing a prediction model;
(5) collecting an SERS spectrum in a sample liquid: dissolving the silver-zinc oxide compound powder prepared in the step (2) in ethanol, performing ultrasonic dispersion, uniformly dripping the silver-zinc oxide compound powder on the surface of a silicon wafer, drying, uniformly dripping the sample liquid prepared in the step (1) on the surface of the silicon wafer, setting the wavelength of a laser, the spectrum integration time, the spectrum repeated grabbing times, the light source power and the scanning wave number range, and performing SERS spectrum collection;
(6) and (4) substituting the SERS spectrum of the sample liquid collected in the step (5) into the prediction model established in the step (4), so that the detection of the concentration of 2,4-D in the sample liquid can be realized.
Preferably, in the step (1), the mass ratio of the sample to water is 1:10, and the heating temperature is 40-60 ℃.
Preferably, in the step (2), the concentration of the zinc nitrate hexahydrate aqueous solution is 0.004-0.01 g/mL, and the concentration of the ammonia water is 28 wt%; the volume ratio of the zinc nitrate hexahydrate aqueous solution to the ammonia water is 125: 1.
Preferably, in the step (2), the stirring temperature is 40 ℃, the rotation speed is 200rpm, and the time is 10 min; the adjusting temperature is 30-60 ℃; the reaction time was 18 h.
Preferably, the ratio of the zinc oxide powder, the polyvinylpyrrolidone, the glucose, the deionized water and the silver nitrate solution in the step (3) is 0.5 g: 2.5 g: 2 g: 50mL of: 5 mL; the concentration of the silver nitrate solution is 0.5 mol/L.
Preferably, in the step (3), the certain temperature is 95-115 ℃, the stirring speed is 200rpm, and the time is 5-15 min; and the second stirring is carried out under the conditions that the temperature is 95-115 ℃, the rotating speed is 200rpm, and the time is 30-120 min.
Preferably, in steps (2) and (3), the temperature of the vacuum drying is 60 ℃ and the time is 12 h.
Preferably, in the step (4), the preprocessing is one or more of first derivative processing (D1), second derivative processing (D2) or Standard Normal Variable Transformation (SNVT).
Preferably, in the step (4), the number of evolutions in the CARS algorithm is 20-120, the minimum sampling number is 25, and the coefficient of variation is 2-8.
Preferably, in the step (4), the concentration of the pesticide is 10-2～106μg/L。
Preferably, in the step (5), the ratio of the silver-zinc oxide composite powder to ethanol is 0.2 g: 5 mL.
Preferably, in the step (5), the laser wavelength is 785 nm; the spectrum integration time is 2s, the spectrum is repeatedly grabbed for 3 times, the power of a light source is 350mW, and the scanning wave number range is 400-1800 cm-1。
Compared with the prior art, the invention has the advantages that:
(1) according to the surface-enhanced Raman substrate prepared by the invention, the selected silver-zinc oxide composite nanoflower has a multi-branch 3D (three-dimensional) structure, and the silver nanoparticles are combined on the surface of the silver-zinc oxide composite nanoflower, so that a large number of Raman hot spots can be provided, and the Raman enhancement effect of the surface-enhanced Raman substrate is higher than that of the conventional silver nanoparticles. After the obtained SERS spectrum is screened by the CARS algorithm, a large number of irrelevant variables are removed, the stability of the model can be improved, the modeling efficiency can be improved, and the model prediction capability can be positively influenced.
(3) According to the invention, the SERS spectrum is preprocessed by adopting a first-order derivative, a second-order derivative and a standard normal variable transformation method, so that the modeling efficiency and stability of the CARS-PLS prediction model can be effectively improved.
(4) The detection method prepared by the invention can be used for in-situ detection of 2,4-D in tea soup, has the advantages of high detection speed, wide detection range, high stability and sensitivity, good application prospect in the technical fields of food safety, environmental monitoring and the like, and has the potential of detecting pesticide residues in other agricultural products.
Description of the drawings:
in fig. 1, a is an SERS spectrum of a tea soup sample solution after pesticides with different concentrations are added; b is a spectrogram of the tea soup obtained in example 1 after pretreatment by D1.
A, B and C in FIG. 2 are graphs showing the relationship between the number of times the CARS-PLS model was run and the cross-validation root mean square error, regression coefficients and variables in example 1, respectively.
FIG. 3 is a graph of the training and prediction effect of the CARS-PLS model obtained in example 1.
The specific implementation mode is as follows:
example 1:
(1) mixing 10g of tea leaves with water, heating to 60 ℃, and then carrying out vacuum filtration to obtain clear liquid serving as a sample liquid;
(2) 200mL of zinc nitrate hexahydrate solution with the concentration of 0.006g/mL is prepared, preheated to 40 ℃ in a magnetic stirring oil bath pot, and stirred for 10min at the rotating speed of 200 rpm; then adding 2mL of ammonia water with the concentration of 28wt% into the solution, setting the temperature to be 30 ℃, and keeping the rotation speed of 200rpm for reaction for 18 h; after the reaction is finished, cooling the obtained white turbid liquid to room temperature, then centrifuging for 15min at the rotation speed of 8000rpm, discarding the supernatant, and washing the collected white precipitate by using deionized water; and continuing centrifuging under the same condition, removing the supernatant, washing the white precipitate by using absolute ethyl alcohol, centrifuging for 15min at the rotating speed of 8000rpm again, cleaning by using deionized water, centrifuging, collecting the obtained white precipitate, namely pure zinc oxide particles, and drying the precipitate for 12h at the temperature of 60 ℃ to finally obtain white zinc oxide powder.
(3) Dissolving 0.5g of the nano zinc oxide particles, 2.5g of PVP K30 and 2g of glucose into 50mL of deionized water, heating to 80 ℃ in a magnetic stirring oil bath, stirring for 10min at the rotating speed of 200rpm, adding 5mL of silver nitrate solution with the concentration of 0.5mol/L into the solution, keeping the temperature and the rotating speed for reaction for 1h, centrifuging the final grey brown turbid liquid at the rotating speed of 8000rpm for 15min, thoroughly cleaning the obtained grey brown precipitate according to the cleaning process of the zinc oxide particles, and drying the cleaned precipitate at the temperature of 50 ℃ for 8h to obtain black powder, namely the silver-zinc oxide composite powder.
(4) Adding 2,4-D pesticide standard into the tea soup sample liquid to make the pesticide concentration in the tea soup sample liquid 10-2μg/L、10-1μg/L、0μg/L、101μg/L、102μg/L、103μg/L、104μg/L、105μ g/L and 106μ g/L, 10 replicates per concentration setting; then SERS spectrum collection is carried out on the tea soup with 9 concentration gradients, and D1 pretreatment is carried out; introducing the preprocessed spectrum into matlab, and then setting the evolution times, the lowest sampling times and the variation coefficient in the CARS algorithm, wherein the evolution times are 20, the lowest sampling times are 25 and the variation times are 2; and (3) carrying out variable screening on all the preprocessed SERS spectra by using a CARS algorithm, and selecting a variable combination with the lowest cross validation root mean square error for establishing a prediction model.
(5) Dispersing 0.2g of prepared silver-zinc oxide composite powder in 5mL of ethanol, performing ultrasonic dispersion for 10min, uniformly dripping the silver-zinc oxide composite powder on a silicon wafer with the thickness of 1 multiplied by 1cm, completely drying, taking 5 mu L of the sample liquid prepared in the step (1) to drip on a region attached with the silver-zinc oxide composite nano material, and then performing SERS spectrum collection, wherein the wavelength of a laser is 785 nm; the spectrum integration time is 2s, the spectrum is repeatedly grabbed for 3 times, the power of a light source is 350mW, and the scanning wave number range is 400-1800 cm-1。
(6) And (4) substituting the SERS spectrum of the sample liquid collected in the step (5) into the prediction model established in the step (4), so that the detection of the concentration of 2,4-D in the sample liquid can be realized.
Fig. 1A is a graph of 90 SERS spectra of tea soup containing different concentrations of 2,4-D pesticides, in which it can be clearly seen that the spectra have baseline drift, which is not favorable for stability and reliability of later modeling. FIG. 1B is a SERS spectrum of the sample liquid obtained after D1 pretreatment, and it can be seen that the problem of baseline drift is well solved.
FIG. 2 is a graph of the relationship between the number of runs and the cross-validation root mean square error, regression coefficients and the number of selected variables during the CARS algorithm run under the parameters of example 1.
Fig. 3 is a fitting relationship between the true value and the predicted value of the sample liquid in the established prediction model, and it can be seen that the fitting degree is good and can be used for predicting pesticides in the true sample liquid.
Example 2:
(1) mixing 10g of tea leaves with water, heating to 40 ℃, and then carrying out vacuum filtration to obtain clear liquid serving as a sample liquid;
(2) 200mL of zinc nitrate hexahydrate solution with the concentration of 0.004g/mL is prepared, preheated to 40 ℃ in a magnetic stirring oil bath pot, and stirred for 10min at the rotating speed of 200 rpm; then adding 2mL of ammonia water with the concentration of 28wt% into the solution, setting the temperature to be 50 ℃, and keeping the rotation speed of 200rpm for reaction for 18 h; after the reaction is finished, cooling the obtained white turbid liquid to room temperature, then centrifuging for 15min at the rotation speed of 8000rpm, discarding the supernatant, and washing the collected white precipitate by using deionized water; and continuing centrifuging under the same condition, removing the supernatant, washing the white precipitate by using absolute ethyl alcohol, centrifuging for 15min at the rotating speed of 8000rpm again, cleaning by using deionized water, centrifuging, collecting the obtained white precipitate, namely pure zinc oxide particles, and drying the precipitate for 12h at the temperature of 60 ℃ to finally obtain the nano zinc oxide particles.
(3) Dissolving 0.5g of the nano zinc oxide particles, 2.5g of PVP K30 and 2g of glucose into 50mL of deionized water, heating to 80 ℃ in a magnetic stirring oil bath, stirring for 10min at the rotating speed of 200rpm, adding 5mL of silver nitrate solution with the concentration of 0.5mol/L into the solution, keeping the temperature and the rotating speed for reaction for 1h, centrifuging the final grey brown turbid liquid at the rotating speed of 8000rpm for 15min, thoroughly cleaning the obtained grey brown precipitate according to the cleaning process of the zinc oxide particles, and drying the cleaned precipitate at the temperature of 50 ℃ for 8h to obtain black powder, namely the silver-zinc oxide composite nano particles.
(4) Adding 2,4-D pesticide standard into tea soup sample liquidThe concentrations of the pesticide in the tea soup sample liquid are respectively 10-2μg/L、10-1μg/L、0μg/L、101μg/L、102μg/L、103μg/L、104μg/L、105μ g/L and 106μ g/L, 10 replicates per concentration setting; then SERS spectrum collection is carried out on the tea soup with 9 concentration gradients, and D2 pretreatment is carried out; introducing the preprocessed spectrum into matlab, and then setting the evolution times, the lowest sampling times and the variation coefficient in the CARS algorithm, wherein the evolution times are 50, the lowest sampling times are 25 and the variation times are 5; and (3) carrying out variable screening on all the preprocessed SERS spectra by using a CARS algorithm, and selecting a variable combination with the lowest cross validation root mean square error for establishing a prediction model.
(5) Dispersing 0.2g of prepared silver-zinc oxide composite powder in 5mL of ethanol, performing ultrasonic dispersion for 10min, uniformly dripping the silver-zinc oxide composite powder on a silicon wafer with the thickness of 1 multiplied by 1cm, completely drying, taking 5 mu L of the sample liquid prepared in the step (1) to drip on a region attached with the silver-zinc oxide composite nano material, and then performing SERS spectrum collection, wherein the wavelength of a laser is 785 nm; the spectrum integration time is 2s, the spectrum is repeatedly grabbed for 3 times, the power of a light source is 350mW, and the scanning wave number range is 400-1800 cm-1。
(6) And (4) substituting the SERS spectrum of the sample liquid collected in the step (5) into the prediction model established in the step (4), so that the detection of the concentration of 2,4-D in the sample liquid can be realized.
Example 3:
(1) mixing 10g of tea leaves with water, heating to 50 ℃, and then carrying out vacuum filtration to obtain clear liquid serving as a sample liquid;
(2) 200mL of zinc nitrate hexahydrate solution with the concentration of 0.01g/mL is prepared, preheated to 40 ℃ in a magnetic stirring oil bath pot, and stirred for 10min at the rotating speed of 200 rpm; then adding 2mL of ammonia water with the concentration of 28wt% into the solution, setting the temperature to be 60 ℃, and keeping the rotation speed of 200rpm for reaction for 18 h; after the reaction is finished, cooling the obtained white turbid liquid to room temperature, then centrifuging for 15min at the rotation speed of 8000rpm, discarding the supernatant, and washing the collected white precipitate by using deionized water; and continuing centrifuging under the same condition, removing the supernatant, washing the white precipitate by using absolute ethyl alcohol, centrifuging for 15min at the rotating speed of 8000rpm again, cleaning by using deionized water, centrifuging, collecting the obtained white precipitate, namely pure zinc oxide particles, and drying the precipitate for 12h at the temperature of 60 ℃ to finally obtain the nano zinc oxide particles.
(3) Dissolving 0.5g of the nano zinc oxide particles, 2.5g of PVP K30 and 2g of glucose into 50mL of deionized water, heating to 80 ℃ in a magnetic stirring oil bath, stirring for 10min at the rotating speed of 200rpm, adding 5mL of silver nitrate solution with the concentration of 0.5mol/L into the solution, keeping the temperature and the rotating speed for reaction for 1h, centrifuging the final grey brown turbid liquid at the rotating speed of 8000rpm for 15min, thoroughly cleaning the obtained grey brown precipitate according to the cleaning process of the zinc oxide particles, and drying the cleaned precipitate at the temperature of 50 ℃ for 8h to obtain black powder, namely the silver-zinc oxide composite nano particles.
(4) Adding 2,4-D pesticide standard into the tea soup sample liquid to make the pesticide concentration in the tea soup sample liquid 10-2μg/L、10-1μg/L、0μg/L、101μg/L、102μg/L、103μg/L、104μg/L、105μ g/L and 106μ g/L, 10 replicates per concentration setting; then SERS spectrum collection is carried out on the tea soup with 9 concentration gradients, and combined treatment of D1 and SNVT is carried out; introducing the preprocessed spectrum into matlab, and then setting the evolution times, the lowest sampling times and the variation coefficient in the CARS algorithm, wherein the evolution times are 120, the lowest sampling times are 25 and the variation times are 8; and (3) carrying out variable screening on all the preprocessed SERS spectra by using a CARS algorithm, and selecting a variable combination with the lowest cross validation root mean square error for establishing a prediction model.
(5) Dispersing 0.2g of prepared silver-zinc oxide composite powder in 5mL of ethanol, performing ultrasonic dispersion for 10min, uniformly dripping the silver-zinc oxide composite powder on a silicon wafer with the thickness of 1 multiplied by 1cm, completely drying, taking 5 mu L of the sample liquid prepared in the step (1) to drip on a region attached with the silver-zinc oxide composite nano material, and then performing SERS spectrum collection, wherein the wavelength of a laser is 785 nm; the spectrum integration time is 2s, the spectrum is repeatedly grabbed for 3 times, the power of a light source is 350mW, and the scanning wave number range is 400-1800 cm-1。
(6) And (4) substituting the SERS spectrum of the sample liquid collected in the step (5) into the prediction model established in the step (4), so that the detection of the concentration of 2,4-D in the sample liquid can be realized.
Description of the drawings: the above embodiments are only used to illustrate the present invention and do not limit the technical solutions described in the present invention; thus, while the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted; all such modifications and variations are intended to be included herein within the scope of this disclosure and the present invention and protected by the following claims.
Claims (3)
1. A method for carrying out 2,4-D rapid detection by combining an SERS substrate with a multiple linear regression model is characterized by comprising the following steps:
(1) preparation of sample liquid: mixing the sample with water, heating, and vacuum filtering to obtain clear solution as sample solution;
(2) adding zinc nitrate hexahydrate into water to obtain a zinc nitrate hexahydrate aqueous solution, stirring, adding ammonia water, adjusting the temperature, continuing stirring, reacting to obtain milky turbid liquid, sequentially performing centrifugal separation, ethanol cleaning and deionized water cleaning, and vacuum drying to obtain white zinc oxide powder;
(3) adding the zinc oxide powder prepared in the step (2), polyvinylpyrrolidone and glucose into deionized water, heating to a certain temperature, stirring, adding a silver nitrate solution, continuing stirring for the second time, sequentially performing centrifugal separation, ethanol cleaning and deionized water cleaning on the obtained grey brown turbid liquid, and performing vacuum drying to obtain black silver-zinc oxide composite powder; the using amount ratio of the zinc oxide powder, the polyvinylpyrrolidone, the glucose, the deionized water and the silver nitrate solution is 0.5 g: 2.5 g: 2 g: 50mL of: 5 mL; the concentration of the silver nitrate solution is 0.5 mol/L; in the step (3), the certain temperature is 95-115 ℃, the stirring speed is 200rpm, and the stirring time is 5-15 min; the second stirring condition is that the temperature is 95-115 ℃, the rotating speed is 200rpm, and the time is 30-120 min;
(4) establishing a 2,4-D concentration prediction model in the sample liquid: respectively adding 2,4-D pesticide standards into the sample liquid prepared in the step (1) to prepare different pesticide concentrations, and collecting an SERS spectrum and carrying out pretreatment; introducing the preprocessed spectrum into matlab, then setting the evolution times, the minimum sampling times and the variation coefficient in the CARS algorithm, carrying out variable screening on the SERS spectrum, and selecting a variable combination with the lowest cross validation root mean square error for establishing a prediction model; the concentration of the pesticide in the sample liquid in the step (4) is 10-2~106Mu g/L; the CARS algorithm has the advantages that the number of evolutionary times is 20-120, the minimum sampling time is 25, and the coefficient of variation is 2-8;
(5) and (3) collecting the SERS spectrum of the sample liquid: dissolving the silver-zinc oxide compound powder prepared in the step (2) in ethanol, wherein the dosage ratio of the silver-zinc oxide compound powder to the ethanol is 0.2 g: 5 mL; dispersing by ultrasonic waves, uniformly dripping the sample liquid on the surface of a silicon wafer, drying, uniformly dripping the sample liquid prepared in the step (1) on the surface of the silicon wafer, setting the wavelength of a laser, the spectrum integration time, the spectrum repeated grabbing times, the light source power and the scanning wave number range, and collecting the SERS spectrum; the wavelength of the laser is 785 nm; the spectrum integration time is 2s, the spectrum is repeatedly grabbed for 3 times, the power of a light source is 350mW, and the scanning wave number range is 400-1800 cm-1；
(6) And (4) substituting the SERS spectrum of the sample liquid collected in the step (5) into the prediction model established in the step (4), so that the detection of the concentration of 2,4-D in the sample liquid can be realized.
2. The method for 2,4-D rapid detection by using a SERS substrate combined with a multivariate linear regression model as claimed in claim 1, wherein the mass ratio of the sample to water in the step (1) is 1:10, and the heating temperature is 40-60 ℃; in the step (2), the concentration of the zinc nitrate hexahydrate aqueous solution is 0.004-0.01 g/mL, and the concentration of the ammonia water is 28 wt%; the volume ratio of the zinc nitrate hexahydrate aqueous solution to the ammonia water is 125: 1; the stirring temperature is 40 ℃, the rotating speed is 200rpm, and the time is 10 min; the adjusting temperature is 30-60 ℃; the reaction time was 18 h.
3. The method for 2,4-D fast detection by using a SERS substrate combined with a multiple linear regression model according to claim 1, wherein the pretreatment in step (4) is one or more of a first derivative treatment, a second derivative treatment or a standard normal variable transformation.
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| CN104162681B (en) * | 2014-07-30 | 2016-01-27 | 青岛科技大学 | A kind of preparation method of silver-ZnO nano composite structure |
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| CN108344726A (en) * | 2018-04-25 | 2018-07-31 | 江苏大学 | A kind of 2,4 dichlorophenoxyacetic acid based on gold nanometer film SERS technologies（2,4-D）The detection method of pesticide |
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