Cyanogen-activated copper-based nanosensor array and application thereof

Abstract

The application is suitable for the technical field of pesticide detection, and provides a cyanogen-activated copper-based nanoenzyme array sensor and application thereof.The sensor comprises four independent sensing units, each of which is based on a copper-based nanoenzyme, and is arabinosyl cytosine-Cu, isophthalic acid-Cu, 5'-guanylate-Cu and thiazole-5-carboxylic acid-Cu respectively.The application selects four copper-based nanoenzymes as sensing pathways to construct a colorimetric pathway, which not only has extremely high selectivity, realizes rapid and convenient detection of second-generation pyrethroid pesticides, and does not need complex material design, and can realize on-site convenient detection of second-generation pyrethroid pesticides.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pesticide detection, and particularly relates to a copper-based nano-enzyme array sensor based on cyanide activation and application thereof. BACKGROUND

[0002] Pyrethroid pesticides are a kind of bionic insecticides synthesized by imitating the components of natural pyrethrins. Due to their liposolubility, pyrethroid pesticides have unknown hazards to the human body, certain harm to hormone secretion and the nervous system of the human body, and adverse reactions in the male reproductive system after long-term exposure to pyrethroid pesticides. Children exposed to pyrethroid pesticides will increase the risk of tumors, leukemia and other diseases.

[0003] Second-generation pyrethroid pesticides are synthesized by introducing alpha-cyanogen on the basis of first-generation pyrethroid pesticides. Compared with first-generation pyrethroid pesticides, the introduction of cyanogen enhances the light stability of pyrethroid pesticides, but also increases the toxicity of pyrethroid pesticides to fish and other aquatic organisms, and the potential risk to the environment is increasing. Second-generation pyrethroid pesticides have high cytotoxicity. For example, deltamethrin has long-term toxicity in soil and can cause damage to the central nervous system of fetuses, affecting children's intelligence; fenvalerate can cause metabolic disorders in lettuce, affecting lettuce yield. Therefore, it is very important to develop a rapid detection method for second-generation pyrethroid pesticides.

[0004] Enzyme inhibition method is a common method for rapid detection of pesticides. This method realizes the recognition of pesticides by inhibiting the activity of acetylcholine esterase. However, this detection method can only detect organophosphorus pesticides and carbamate pesticides, and has no response to pyrethroid pesticides. At present, the detection methods for second-generation pyrethroid pesticides include mass spectrometry, chromatography and fluorescence method. Mass spectrometry and chromatography can realize accurate detection of second-generation pyrethroid pesticides, but they need to rely on large instruments and professional operation, have certain technical requirements, and the operation is complex. The limitation of large instruments makes it difficult to realize convenient on-site detection of second-generation pyrethroid pesticides. Fluorescence method can realize rapid detection of second-generation pyrethroid pesticides, but the current detection relies on the design of complex fluorescent materials, and the synthesis cost is high. The fluorescence signal will be affected and disturbed by environmental light, quenching agent or background fluorescence. SUMMARY

[0005] The purpose of the embodiment of the present application is to provide a copper-based nano-enzyme array sensor based on cyanide activation, which aims to solve the problems raised in the background.

[0006] The embodiment of the present application is implemented in such a way that a cyan group activated copper-based nanosensor array is provided, which comprises four independent sensing units, each of which is based on a copper-based nanosensor, namely cytarabine-Cu, isophthalic acid-Cu, 5'-guanylate-Cu and thiazole-5-carboxylic acid-Cu.

[0007] Another object of the embodiment of the present application is to provide a preparation method of the cyan group activated copper-based nanosensor array, which comprises the following steps:

[0008] (1) Synthesis of copper-based nanosensor:

[0009] Synthesis of cytarabine-Cu: mix an aqueous solution containing cytarabine and NaOH with an aqueous solution containing copper chloride, and react at room temperature, after the reaction is completed, centrifugal water washing is performed for several times, the obtained precipitate is uniformly dispersed in ultrapure water again to obtain a cytarabine-Cu nanosensor solution;

[0010] Synthesis of isophthalic acid-Cu: dissolve isophthalic acid in an aqueous solution containing NaOH, and then mix with an aqueous solution of copper nitrate trihydrate, and react at room temperature, after the reaction is completed, centrifugal water washing is performed for several times, the obtained precipitate is uniformly dispersed in ultrapure water again to obtain an isophthalic acid-Cu nanosensor solution;

[0011] Synthesis of 5'-guanylate-Cu: mix an aqueous solution of 5'-guanylate disodium, a copper chloride solution, a Tris-HCl buffer and ultrapure water, and react at room temperature, after the reaction is completed, centrifugal water washing is performed for several times, and then the obtained precipitate is dispersed in ultrapure water to obtain a 5'-guanylate-Cu nanosensor solution;

[0012] Synthesis of thiazole-5-carboxylic acid-Cu: mix an aqueous solution of thiazole-5-carboxylic acid with an aqueous solution containing copper nitrate trihydrate, and react under stirring at 50℃, after the reaction is completed, centrifugal water washing is performed for several times, and then the obtained precipitate is dispersed in ultrapure water to obtain a thiazole-5-carboxylic acid-Cu nanosensor solution;

[0013] (2) Construction of detection system: mix acetic acid-sodium acetate buffer, nanosensor solution, ultrapure water and TMB solution, and add into a hole of a 96-hole plate to complete the construction of one sensing unit, four sensing units are created according to the above method, and the four sensing units are arranged in parallel to form a complete four-channel sensor array, thereby obtaining the cyan group activated copper-based nanosensor array.

[0014] Another object of the embodiment of the present application is to provide an application of the cyan group activated copper-based nanosensor array in pesticide detection, and the pesticide is a second generation pyrethroid pesticide.

[0015] The sensor provided by the embodiment of the present application is composed of four specific copper-based nanoenzyme sensing units, specifically, Arac-Cu, 13-Cu, GMP-Cu and T5CA-Cu. The cyanide ions released by the second-generation pyrethroid pesticide under alkaline conditions can specifically enhance the oxidase activity of the copper-based nanoenzyme, and then catalyze the color developing substrate to generate a detectable optical signal, so as to realize the identification of the pesticide.

[0016] Compared with the existing second-generation pyrethroid pesticide detection technology, the embodiment of the present application has the following advantages and effects:

[0017] High specificity and strong anti-interference ability: the specific activation of cyanide on copper-based nanoenzyme can accurately identify the second-generation pyrethroid pesticide from a complex system after hydrolysis, effectively excluding the influence of other types of pesticides, common metal ions, amino acids and sugars and other interference substances.

[0018] Excellent classification and identification performance: the sensor array constructed based on four different copper-based nanoenzymes can generate differentiated response signal patterns, and combined with chemometrics analysis, can accurately distinguish and identify five second-generation pyrethroid pesticides (high-efficiency chlorocylal, cythioate, bromocylal, phenoxycylal and high-efficiency fluorocylal).

[0019] Fast detection and simple operation: the entire detection process including hydrolysis, centrifugation, array reaction and reading can be completed in a short time without complex pretreatment and expensive large instruments, and is suitable for on-site or laboratory rapid screening.

[0020] Simple material synthesis and low cost: the synthesis method of the copper-based nanoenzyme is simple, mild and easy to obtain raw materials, and the construction and detection system of the sensor array is low in cost, which is convenient for large-scale preparation and application promotion.

[0021] Strong adaptability in practical application: by introducing the centrifugation step, the turbidity interference caused by incomplete hydrolysis of pesticides in actual samples can be overcome, and the sensor array can still maintain reliable identification ability in complex matrices such as fruits and vegetables, and has good practical application potential and expandability. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 Comparison of hydrolysis effects of different concentrations of NaOH solutions provided by the embodiment of the present application;

[0023] Figure 2 Differentiation map of five second-generation pyrethroid pesticides provided by the embodiment of the present application;

[0024] Figure 3 A fitting curve diagram of a linear discriminant analysis factor 1 provided by an embodiment of the present application varying with a cypermethrin residue concentration;

[0025] Figure 4 A distinguishing map of second-generation pyrethroid pesticides in actual samples provided by an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present application clearer and more apparent, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0027] Nanoplasma is a kind of nano material with enzyme-like activity. Compared with natural enzymes, nanoplasma has a simple synthesis process, low price, high stability and strong adaptability to the environment, and can have activity under more extensive pH and temperature conditions. In recent years, nanoplasma has been widely used in the field of environmental pollutant detection, and has been used for the detection of pesticides, antibiotics, phenolic pollutants, etc.

[0028] Array sensor is an intelligent detection system based on multi-channel signal acquisition and pattern recognition analysis. The inspiration comes from the simulation of mammalian olfactory or gustatory system, so it is also defined as "electronic nose" or "electronic tongue". Because the response degree of each channel in the array to the detection object is different, through the cross response of multiple non-specific sensing units and combined with linear discriminant analysis (LDA), hierarchical cluster analysis (PCA) and other data processing methods, the characteristic point cluster (fingerprint) corresponding to different detection objects is generated, so that multiple detection objects can be detected at the same time. Traditional one-to-one detection cannot be used to distinguish and identify multiple detection objects, which affects the detection efficiency and detection sensitivity. The one-to-many detection method can realize the identification of multiple detection objects, and generate specific identification of multiple analytes using fewer sensor channels.

[0029] The construction of colorimetric array using nanoplasma can realize the simultaneous detection of multiple substances, and has high anti-interference performance. The embodiment of the present application constructs a sensing array based on the oxidase activity of copper-based nanoplasma, which can not only realize high sensitivity detection and high specificity detection of pyrethroid pesticides, but also realize accurate identification of second-generation pyrethroid pesticide molecules. Specifically,

[0030] The second-generation pyrethroid pesticide is hydrolyzed to release cyanide ions (CN - ) under alkaline conditions. -Strong coordination with copper ions in specific copper-based nanozymes significantly enhances the oxidase activity of the nanozymes, thereby catalyzing the oxidation of the chromogenic substrate 3,3',5,5'-tetramethylbenzidine (TMB), resulting in a color change (the solution turns blue with a characteristic absorption peak at 652 nm). By constructing a sensor array containing four different copper-based nanozymes (Arac-Cu, 13-Cu, GMP-Cu, T5CA-Cu), the differential absorbance signals after their reaction with different target substances are collected. Combined with pattern recognition algorithms (such as linear discriminant analysis and hierarchical clustering analysis) to process multidimensional data, highly selective identification, accurate classification, and quantitative analysis of various second-generation pyrethroid pesticides (cypermethrin, deltamethrin, fenvalerate, deltamethrin, and lambda-cyhalothrin) can be achieved. It can effectively eliminate the influence of other types of pesticides, metal ions, amino acids, sugars, and other common interfering substances, and has good detection specificity.

[0031] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0032] Example 1: A copper-based nanozyme array sensor based on cyano activation, consisting of four independent sensing units, each based on a specific copper-based nanozyme. The sensing carrier is typically a 96-well plate (fully transparent microplate). Its preparation method includes the following steps:

[0033] (1) Synthesis of copper-based nanozymes:

[0034] Cytarabine-Cu (Arac-Cu): 5.2 mL of aqueous solution containing 0.126 g cytarabine and 0.02 g NaOH was mixed with 5.2 mL of aqueous solution containing 0.08 g copper chloride (CuCl2). The mixture was reacted at room temperature for 30 minutes. After the reaction was completed, the mixture was centrifuged and washed three times with water to remove unreacted ligands and metals. Finally, the precipitate was redispersed uniformly in ultrapure water to obtain the Arac-Cu nanozyme stock solution.

[0035] Isophthalic acid-Cu (13-Cu): 1 mmol of isophthalic acid was dissolved in an aqueous solution containing 0.08 g of NaOH, and then mixed with an aqueous solution of 1 mmol of copper nitrate trihydrate [Cu(NO3)2·3H2O]. The mixture was reacted at room temperature for 2 hours. After the reaction was completed, the mixture was centrifuged and washed with water 3 times to remove unreacted ligands and metals. Finally, the precipitate was redispersed uniformly in ultrapure water to obtain 13-Cu nanozyme stock solution.

[0036] 5'-GMP-Cu: 5'-GMP disodium aqueous solution (10 mM), copper chloride solution (50 mM), Tris-HCl buffer (100 mM, pH 8.5) and ultrapure water were mixed at a volume ratio of 2:1:1:6, and the reaction was carried out at room temperature. After the reaction was completed, the precipitate was dispersed in ultrapure water after centrifugation and water washing 3 times to remove unreacted ligands and metals, and a GMP-Cu nanoscale enzyme stock solution was obtained;

[0037] T5CA-Cu: 10 mL aqueous solution containing 0.4 mmol thiazole-5-carboxylic acid was mixed with 10 mL aqueous solution containing 0.2 mmol copper nitrate trihydrate [Cu(NO3)2·3H2O], and the reaction was stirred at 50°C for 1 hour. After the reaction was completed, the precipitate was dispersed after centrifugation and water washing 3 times to obtain a T5CA-Cu nanoscale enzyme stock solution;

[0038] The concentration of all synthesized nanoscale enzyme stock solutions was adjusted to 1 mg / mL;

[0039] (2) Construction of detection system: The reaction system of each sensing unit was constructed in the same way, that is, acetic acid-sodium acetate buffer (60 mM, pH 5.0), nanoscale enzyme solution (1 mg / mL), ultrapure water and TMB solution (20 mM dissolved in acetone) were mixed at a volume ratio of 5:2:8:1, and then added to the corresponding wells of a 96-well plate in turn, that is, the construction of a sensing unit (one detection channel) was completed, and four sensing units were arranged in parallel to form a complete four-channel sensor array.

[0040] Example 2, optimization analysis of basic hydrolysis conditions of second-generation pyrethroid pesticides (aimed at determining the optimal basic hydrolysis conditions for the complete release of cyanide ions (CN - ) from pesticide molecules), comprising the following steps:

[0041] (1) Sample hydrolysis and treatment: A certain second-generation pyrethroid pesticide (for example, lambda-cyhalothrin) standard solution with a concentration of 100 μg / mL was prepared, and an equal volume of a series of different concentrations of sodium hydroxide (NaOH) solution (for example, 0.01 M, 0.05 M, 0.1 M, 0.2 M, 0.5 M) was mixed, and the reaction was carried out at room temperature for 10 minutes. After the reaction was completed, each reaction system was immediately neutralized to neutral with an equal concentration of hydrochloric acid solution. Since the unhydrolyzed pesticide may cause the solution to be turbid, all the neutralized reaction liquids were centrifuged at 10000 rpm for 1 minute, and the clear supernatant was taken as the test liquid;

[0042] (2) Detection and signal collection: Take the corresponding test solution of each concentration of NaOH hydrolysis, add it to the corresponding hole of the four-channel sensor array constructed in Example 1, react at room temperature for 5 minutes, and use an enzyme marker to read the absorbance value of each hole at 652 nm wavelength. Each NaOH concentration condition is repeated 5 times in parallel;

[0043] (3) Data analysis and results: Take the average of the absorbance data of the five parallel experiments, import it into Origin software, take the concentration of sodium hydroxide as the horizontal coordinate, and take the average or total of the absorbance of the four sensor units as the vertical coordinate. Draw a line graph to get the results as shown in Figure 1 . By analyzing the platform interval of the curve, the corresponding NaOH concentration that reaches the maximum and stable signal response is determined, that is, the optimal hydrolysis concentration. The results show that 0.1 M NaOH can effectively hydrolyze the pesticide and release CN - in the system, producing a stable color signal, so it is selected as the actual hydrolysis concentration.

[0044] Example 3, Classification and identification of five second-generation pyrethroid pesticides (used to verify the ability of the array sensor prepared in Example 1 to distinguish different target pesticides with similar structures), including the following steps:

[0045] (1) Standard sample processing and detection: Prepare five standard sample solutions of second-generation pyrethroid pesticides with a concentration of 100 μg / mL: high-efficiency cypermethrin, cyhalothrin, deltamethrin, phenothiocarb, and high-efficiency fluoro-cypermethrin. Each standard sample solution is mixed with 0.1 M NaOH solution at room temperature and reacted for 10 minutes (to hydrolyze and break the cyanide group in the pesticide molecule and release cyanide ions). After the reaction, neutralize with an equal concentration of HC1 solution. To eliminate the turbidity interference on subsequent optical detection, centrifuge the reaction solution (at 10,000 rpm for 1 minute), and take the clear supernatant as the pretreated test solution;

[0046] (2) Array reaction and data collection: Add the pretreated test solution of the five pesticides to independent sensor arrays, react at room temperature for 5 minutes, and then use an enzyme marker to read the absorbance value of each hole at 652 nm. Each pesticide is repeated 5 times (n=5);

[0047] (3) Pattern recognition and classification: Import the obtained raw data (4-channel absorbance values for each sample, forming a 4-dimensional response vector) into SPSS statistical software, perform linear discriminant analysis (LDA) on all data (5 pesticides x 5 repetitions = 25 samples), and extract the score values of the first two linear discriminant functions (factor 1 and factor 2);

[0048] Results: Take the factor 1 score as the horizontal coordinate and the factor 2 score as the vertical coordinate to draw a two-dimensional discriminant score graph, as shown inFigure 2 As shown, the data points of the five pesticides each form independent and tightly clustered clusters, and the clusters of different pesticides are clearly separated. This indicates that the array sensor and detection application prepared based on the embodiments of the present invention can clearly and accurately classify and identify five structurally similar second-generation pyrethroid pesticides, and has extremely high distinguishing ability.

[0049] Example 4: Concentration response and quantitative analysis of second-generation pyrethroid pesticides (used to verify the concentration response characteristics of the method to the target pesticide and to establish a standard curve for semi-quantitative analysis), including the following steps:

[0050] (1) Preparation and treatment of concentration gradient samples: Taking high-efficiency cypermethrin as an example, a series of standard solutions with concentrations of 1, 2.5, 5, 10, 25, 50, 100, 125, 150 and 200 μg / mL were accurately prepared. Each standard solution was mixed with 0.1 M NaOH solution, hydrolyzed at room temperature for 10 minutes, neutralized with hydrochloric acid of equal concentration, and centrifuged to obtain the pretreatment solution to be tested at that concentration point;

[0051] (2) Detection and data acquisition: The clarified pretreatment solution corresponding to each concentration gradient was added to the sensor array prepared in Example 1. After reacting at room temperature for 5 minutes, the absorbance value of each well at 652 nm was measured using an enzyme-linked immunosorbent assay (ELISA) reader. Five parallel experiments were performed for each concentration point.

[0052] (3) Data analysis and standard curve establishment: The absorbance values ​​obtained by each sample (each concentration point) on the four sensing units are combined into a 4-dimensional response vector. All concentration gradient data (10 concentrations × 5 repetitions = 50 samples) are imported into SPSS software for linear discriminant analysis (LDA). After analysis, the score of the first linear discriminant function (LD1) is extracted. The standard concentration of high efficiency cypermethrin is used as the x-axis and the average score of the corresponding factor 1 is used as the y-axis for linear fitting.

[0053] The results are as follows Figure 3 As shown, within the concentration range of 1 to 200 μg / mL, there is a good linear relationship between the concentration of high-efficiency cypermethrin and the factor 1 score obtained from LDA analysis. The establishment of this linear standard curve indicates that the method of the present invention can not only be used for qualitative identification, but also for semi-quantitative detection of target pesticides, thus expanding its application scope.

[0054] Example 5: Spiked recovery and identification verification of second-generation pyrethroid pesticides in actual environmental water samples (used to verify the ability of the method to directly distinguish and identify target pesticides in actual complex environmental matrices), including the following steps:

[0055] (1) Actual water sample plus standard sample preparation: taking the pretreated lake water as an actual sample matrix, five different second-generation pyrethroid pesticide standards were added to multiple equal lake water samples respectively, to prepare a series of spiked simulated positive samples, wherein the concentrations of the added pesticides (high-efficiency cypermethrin, cypermethrin, deltamethrin, fenpropathrin and high-efficiency fluoro-cypermethrin) were all 100 μg / mL;

[0056] (2) Spiked sample treatment and detection: the spiked lake water samples prepared above were subjected to alkaline hydrolysis (0.1 M NaOH, room temperature, 10 min), neutralization and centrifugation according to the procedure determined in Example 2, to obtain the detection solutions, which were added to independent sensor arrays respectively, and after reaction at room temperature for 5 minutes, the absorbance values of each well at 652 nm were read using an enzyme-labeled instrument, and each spiked sample was subjected to 5 parallel experiments;

[0057] (3) Data processing and analysis: the absorbance values obtained on the four sensing units for each spiked sample were combined to form a 4-dimensional response vector, and the response data of all spiked samples (5 pesticides x 5 repetitions = 25 samples) were imported into SPSS software for linear discriminant analysis (LDA), and the score values of the first two linear discriminant functions (factor 1 and factor 2) were extracted;

[0058] The factor 1 score was used as the horizontal coordinate and the factor 2 score was used as the vertical coordinate to draw a two-dimensional discriminant score graph, and the results are shown in Figure 4 The results show that the response data points of the spiked lake water samples of the five different pesticides form independent and well-separated clusters, which directly proves that the array sensor and detection application constructed in the embodiment of the application can effectively overcome the interference of the actual water sample matrix, and realize accurate differentiation and identification of different types of second-generation pyrethroid pesticide residues.

[0059] The above only describes the preferred embodiments of the present application and should not be used to limit the present application, and any modifications, equivalent replacements and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A cyanogen group-activated copper-based nanosensor array sensor, characterized in that, The sensor comprises four independent sensing units, each of which is based on a copper-based nanoscale enzyme, namely cytarabine-Cu, isophthalic acid-Cu, 5'-guanylate-Cu and thiazole-5-carboxylic acid-Cu.

2. A method for preparing a cyanogen group-activated copper-based nanosensor array sensor according to claim 1, characterized by, The method comprises the following steps: (1) synthesis of copper-based nanoscale enzyme: Synthesis of cytarabine-Cu: mix an aqueous solution containing cytarabine and NaOH with an aqueous solution containing copper chloride, and react at room temperature; after the reaction is completed, centrifugal washing is performed for several times, and the obtained precipitate is uniformly dispersed in ultrapure water again to obtain a cytarabine-Cu nanoscale enzyme solution; Synthesis of isophthalic acid-Cu: dissolve isophthalic acid in an aqueous solution containing NaOH, and then mix with an aqueous solution of copper nitrate trihydrate, and react at room temperature; after the reaction is completed, centrifugal washing is performed for several times, and the obtained precipitate is uniformly dispersed in ultrapure water again to obtain an isophthalic acid-Cu nanoscale enzyme solution; Synthesis of 5'-guanylate-Cu: mix an aqueous solution of 5'-guanylate disodium, a copper chloride solution, a Tris-HCl buffer solution and ultrapure water, and react at room temperature; after the reaction is completed, centrifugal washing is performed for several times, and the obtained precipitate is dispersed in ultrapure water to obtain a 5'-guanylate-Cu nanoscale enzyme solution; Synthesis of thiazole-5-carboxylic acid-Cu: mix an aqueous solution of thiazole-5-carboxylic acid with an aqueous solution containing copper nitrate trihydrate, and stir and react at 50 DEG C; after the reaction is completed, centrifugal washing is performed for several times, and the obtained precipitate is dispersed in ultrapure water to obtain a thiazole-5-carboxylic acid-Cu nanoscale enzyme solution; (2) construction of detection system: mix acetic acid-sodium acetate buffer solution, nanoscale enzyme solution, ultrapure water and TMB solution, and add into a hole of a 96-hole plate to complete the construction of one sensing unit; four sensing units are created according to the above method, and the four sensing units are arranged in parallel to form a complete four-channel sensor array, that is, a cyan-activated copper-based nanoscale enzyme array sensor is obtained.

3. The method for preparing a cyanogen group-activated copper-based nanosensor array according to claim 2, wherein, In the step of synthesizing cytarabine-Cu, the mass ratio of cytarabine, NaOH and copper chloride is 6.3:1:

4.

4. The method for preparing a cyanogen group-activated copper-based nanosensor array according to claim 2, wherein, In the step of synthesizing isophthalic acid-Cu, the molar ratio of isophthalic acid and copper nitrate trihydrate is 1:

1.

5. The method for preparing a cyanogen group-activated copper-based nanosensor array according to claim 2, wherein, In the step of synthesizing 5'-guanylate-Cu, the concentration of the aqueous solution of 5'-guanylate disodium is 10 mM, the concentration of the copper chloride solution is 50 mM, the concentration of the Tris-HCl buffer solution is 100 mM, and the pH is 8; the volume ratio of the aqueous solution of 5'-guanylate disodium, the copper chloride solution, the Tris-HCl buffer solution and ultrapure water is 2:1:1:

6.

6. The method for preparing a cyanogen group-activated copper-based nanosensor array according to claim 2, wherein, In the step of synthesizing thiazole-5-carboxylic acid-Cu, the molar ratio of thiazole-5-carboxylic acid and copper nitrate trihydrate is 2:

1.

7. The method for preparing a cyanogen group-activated copper-based nanosensor array according to claim 2, wherein, In the step of constructing the detection system, the concentration of the acetic acid-sodium acetate buffer solution is 60 mM, the pH is 5.0, the concentration of the nanoscale enzyme solution is 1 mg / mL, and the concentration of the TMB solution is 20 mM; the volume ratio of the acetic acid-sodium acetate buffer solution, the nanoscale enzyme solution, ultrapure water and the TMB solution is 5:2:8:

1.

8. Use of the cyano-activated copper-based nanosensor array of claim 1 in pesticide detection. The pesticide is a second-generation pyrethroid pesticide.

9. Use according to claim 8, characterized in that, The method comprises the following steps: (1) Alkaline hydrolysis and pretreatment of pesticide samples: the second-generation pyrethroid pesticide samples to be tested are mixed with NaOH solution at room temperature, reacted, and after the reaction is completed, the same concentration of HCl solution is added to neutralize to neutral, and then centrifugal treatment is performed, and the supernatant is taken as a clear pretreated liquid to be tested; (2) Array reaction and signal generation: the pretreated liquid to be tested is added to each reaction well of a cyanogen-activated copper-based nanosensor array sensor, reacted at room temperature, and the absorbance value of each well at a wavelength of 652 nm is read using an enzyme-labeled instrument. Each pesticide sample obtains absorbance values in four channels, respectively, and is introduced into statistical analysis software for linear discriminant analysis.

10. Use according to claim 9, characterized in that, In step (1), the concentration of the NaOH solution is 0.1 M.

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