Sensor based on graphdiyne nano-enzyme as well as preparation method and application of sensor

Abstract

The invention relates to a sensor based on graphdiyne nano-enzyme as well as a preparation method and application thereof, and belongs to the technical field of sensors. The preparation method comprises the following steps: firstly, preparing graphdiyne loaded with Co < 2 + >, graphdiyne loaded with Ni < 2 + > and graphdiyne loaded with Co < 2 + > and + Ni < 2 + >; taking the obtained graphdiyne loaded with Co < 2 + >, graphdiyne loaded with Ni < 2 + > and graphdiyne loaded with Co < 2 + > and Ni < 2 + > as sensing units to construct the sensor based on the graphdiyne nano-enzyme. The sensor disclosed by the invention has the advantages of high distinguishing capability, high specificity and convenience in operation, and shows huge potential of antibiotic analysis outside a laboratory. Besides, the constructed machine learning auxiliary array sensor can be expanded to design other intelligent nano platforms and is used for identifying various environmental pollutants with similar chemical structures, and wide, accurate and rapid classification is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sensors, and particularly to a sensor based on graphdiyne nanosensor and a preparation method and application thereof. BACKGROUND

[0002] Antibiotics have been widely used in the fields of medicine, animal husbandry and aquaculture, not only because they can prevent and treat diseases, but also because they can promote animal growth. However, the abuse of antibiotics has attracted global attention, mainly due to its harmful effects on human health. In particular, the problem of antibiotic residues may further lead to the generation of drug-resistant genes and drug failure phenomena. These antibiotic residues in the form of animal excreta enter the environment, which may interfere with the normal metabolism of the human body, change the human body's microbial community, and thus trigger a series of diseases. On the other hand, antibiotic-resistant genes may lead to the generation of drug-resistant bacteria and cause long-term harm to ecological health. Therefore, in order to meet the needs of food safety, environmental protection and clinical trials, it is particularly important to accurately and rapidly detect antibiotic residues in the environment.

[0003] In the past few decades, researchers have developed a variety of methods for detecting antibiotics, including chromatography, capillary electrophoresis, immunoassay and electrochemical analysis. Although these traditional instrumental techniques perform well in terms of analytical performance, they also have some inherent limitations, such as high equipment costs, complex operation and long detection periods, which make them unsuitable for on-site analysis. In addition, most of the sensors currently reported are usually designed based on the "lock and key" model, which can only provide ideal analytical performance for a single specific antibiotic. However, in the actual environment, it is often necessary to detect multiple antibiotics at the same time, and it is difficult to accurately identify and distinguish a variety of antibiotics using a single sensor, which is not suitable for multidimensional, nonlinear and multimodal antibiotic analysis. Therefore, efforts need to be made to develop rapid, low-cost and portable antibiotic identification methods.

[0004] The integration of biochemical sensors with artificial intelligence exhibits great potential in improving analytical performance. These sensors can generate complex multi-dimensional data sets that require advanced artificial intelligence algorithms for effective data analysis and pattern recognition. As an important branch of artificial intelligence, machine learning has been widely applied in the biomedical field. Machine learning models can reveal complex patterns and relationships in raw sensor data, enabling more accurate predictions and detection functions than traditional analysis. The introduction of machine learning algorithms can enhance the ability to identify biomarkers and complex biomolecular interactions, which is difficult to achieve using traditional methods. In particular, through data fusion, machine learning greatly improves the detection performance of biochemical sensors, achieving high levels of selectivity, precision, and calibration-free quantification. The deep integration of biochemical sensors and machine learning is expected to completely change the face of multiple fields, such as mobile medical diagnosis, environmental monitoring, drug discovery, and biomarker identification.

[0005] Graphdiyne (GDY) is a two-dimensional carbon material containing double acetylene bonds, which was first prepared by cross-coupling reactions on the surface of copper foil. The structural feature of this material is composed of benzene rings and carbon-carbon triple bonds, with each benzene ring connected to six adjacent benzene rings through two carbon-carbon triple bonds, forming a flat porous structure. The unique acetylene-rich structure and high carrier mobility (10 4 ~10 5 cm 2 V -1 s -1 ) of GDY make it an excellent candidate for catalyzing small molecules such as H2O2, O2, and H2O. This is mainly due to the uneven distribution of electric charge, which facilitates the polarization of active sites, while also having high chemical stability and intrinsic nanoscale enzyme activity, such as oxidase and peroxidase activity. It has been reported that the doping of non-metallic and metallic elements can enhance the GDY nanoscale enzyme activity by enriching π electrons and introducing additional active centers. For example, Qi et al. reported a boron-doped and ketone carbonyl-enriched GDY double-site carbon nanoscale enzyme that showed strong peroxidase activity in the detection of glucose. The inventors' research group developed a unique oxidase-like nanoscale enzyme composed of self-assembled hemoglobin molecules on GDY, which can efficiently generate O 2·- in a wide pH range and achieve accurate detection of glutathione molecules at the cellular level. However, there is still a lack of research on developing peroxidase-like graphdiyne-based sensors to accurately identify antibiotics in complex matrices with the aid of machine algorithms. SUMMARY

[0006] To solve the above technical problems, the application provides a sensor based on graphdiyne nanoscale enzyme and a preparation method and application thereof. The application synthesizes a peroxidase-like nanoscale enzyme based on GDY by using a wet chemical method, takes GDY as a carrier, and loads Co 2+ and Ni 2+ (GDY / Co / Ni). The nanoscale enzyme taking GDY as a carrier and loading Co 2+ and Ni 2+ has the characteristics of structural stability, uniformly dispersed active center and high catalytic efficiency, and has potential application value in the fields of biosensing and environmental pollutant degradation by optimizing the catalytic performance through regulating the loading conditions (such as metal ratio).

[0007] The application establishes a high-efficiency three-channel antibiotic recognition array sensor based on GDY / Co / Ni nanoscale enzymes with different peroxidase-like activities and two other materials (GDY / Co and GDY / Ni).

[0008] The material prepared by the application has peroxidase (POD) activity, can generate OH from H2O2 under acidic conditions, and thus can oxidize TMB to generate blue oxTMB. In the presence of antibiotics, the active sites of the nanoscale enzyme are adsorbed and masked, resulting in a decrease in the POD-like activity of the nanoscale enzyme, a lighter blue color, and a decrease in the ultraviolet absorbance at 652 nm. Based on the different responses of the three materials, a three-dimensional channel array sensor is constructed for detecting and distinguishing seven kinds of antibiotics. The array sensor has good selectivity, stability and short response time, and can accurately and quickly identify the seven kinds of antibiotics present in fruits with the help of linear discriminant analysis. In addition, the antibiotics are selected as analytes, and a mathematical diagnostic model for evaluating the quality of fruits is constructed by using a support vector machine algorithm. Finally, a high detection accuracy of 97.5% is achieved in 32 fruit samples, indicating that it has great potential for antibiotic sample detection and diagnosis in practical applications.

[0009] The application is implemented by the following technical solutions:

[0010] The first object of the application is to provide a preparation method of a sensor based on graphdiyne nanoscale enzyme, comprising the following steps:

[0011] (1) mixing and stirring a suspension of graphdiyne and a cobalt salt under light-proof conditions, and obtaining graphdiyne loaded with Co 2+ after washing and drying;

[0012] (2) mixing and stirring a suspension of graphdiyne and a nickel salt under light-proof conditions, and obtaining graphdiyne loaded with Ni 2+ after washing and drying;

[0013] (3) Under light-protected conditions, a suspension of graphdiene was mixed and stirred with cobalt and nickel salts, washed, and dried to obtain a Co-loaded... 2 and + Ni 2+ Graphdiyne;

[0014] (4) The resulting load Co 2+ Graphdiyne, Ni-supported 2+ Graphdiyne and Co-supported 2 and Ni 2+ A sensor based on graphyne nanozymes was constructed using graphyne as the sensing unit.

[0015] In one embodiment of the present invention, the concentration of the graphyne suspension is 0.5 mg / mL to 10 mg / mL.

[0016] In one embodiment of the present invention, in step (1), the cobalt salt is selected from one or more of cobalt sulfate, cobalt nitrate and cobalt chloride;

[0017] And / or, the concentration of the cobalt salt is 0.5 mg / mL to 10 mg / mL.

[0018] In one embodiment of the present invention, in step (2), the nickel salt is selected from one or more of nickel chloride, nickel sulfate and nickel nitrate;

[0019] And / or, the concentration of the nickel salt is 0.5 mg / mL to 10 mg / mL.

[0020] In one embodiment of the present invention, in step (3), the concentration ratio of the cobalt salt to the nickel salt is 1:10-10:1.

[0021] A second objective of this invention is to provide a sensor based on graphynyne nanozymes obtained by the aforementioned preparation method.

[0022] A third objective of this invention is to provide the application of the aforementioned graphynyne nanozyme-based sensor in the detection of antibiotics.

[0023] In one embodiment of the present invention, the antibiotic is isoniazid, catechol, norfloxacin, ampicillin sodium, kanamycin, moxifloxacin, and levofloxacin.

[0024] In one embodiment of the present invention, the method of application is as follows:

[0025] S1. The sensor based on graphynyne nanozyme is mixed with antibiotic standard solution, buffer, chromogenic substrate and hydrogen peroxide and incubated to acquire images of the colorimetric array and form a training data matrix.

[0026] S2. The sensor based on graphdiyne nanozyme is mixed with the test sample, buffer solution, chromogenic substrate and hydrogen peroxide and incubated. The image of the colorimetric array is acquired, and after numerical processing, it is input into the obtained training data matrix. The type and content of antibiotics in the test sample are analyzed by support vector machine classification algorithm and linear discriminant analysis method.

[0027] In one embodiment of the present invention, the concentration of antibiotic in the sample to be tested is 10 μM-1000 μM;

[0028] And / or, the chromogenic substrate is one or more of 3,3',5,5'-tetramethylbenzidine, 2,2'-azido-bis-3-ethylbenzothiazoline-6-sulfonic acid, and o-phenylenediamine dihydrochloride;

[0029] And / or, the buffer solution is a NaOAc-HOAc buffer solution.

[0030] The technical solution of the present invention has the following advantages compared with the prior art:

[0031] (1) This invention provides a sensor based on graphynyne nanozymes, its preparation method, and its application. The Co used in this invention... 2+ and Ni 2+ As a transition metal ion, Co exhibits variable oxidation states. In POD-like catalysis, Co... 2+ and Ni 2+ It can accept and donate electrons, promoting the decomposition of hydrogen peroxide to form the highly oxidizing hydroxyl radical ·OH. Among them, Co... 2+ It can be oxidized to Co 3+ These ions participate in the redox cycle, thereby oxidizing the substrate. Co and Ni ions of different valence states can form different coordination intermediates with hydrogen peroxide. These intermediates further decompose to generate highly reactive free radicals, which attack substrate molecules and achieve catalytic oxidation.

[0032] This invention increases the number of active sites by adjusting the loading of Co and Ni ions, thereby improving POD-like activity. However, when the loading is too high, metal ions will aggregate, leading to a decrease in the effective utilization rate of active sites and thus reducing catalytic activity.

[0033] (2) The GDY used in this invention has a unique two-dimensional planar structure and a large π-conjugated system, which endows it with excellent electron transport capabilities. After loading Co and Ni ions, GDY can bind to substrate molecules through π-π stacking, electrostatic interactions, etc., enriching the substrate molecules around the metal ions and increasing the probability of contact between the substrate and the active site. Furthermore, the porous structure of GDY provides a large specific surface area, which is beneficial for the dispersion of metal ions, exposing more active sites and improving catalytic efficiency. At the same time, the porous structure also provides channels for the diffusion of substrate and hydrogen peroxide, facilitating the catalytic reaction.

[0034] (3) This invention utilizes an array sensor system based on machine learning algorithms to achieve intelligent and rapid identification of antibiotics. Co and Ni, as active sites, endow the composite material with excellent POD-like active sites. Under the action of antibiotics, the active sites of nanozymes are adsorbed and masked, resulting in a decrease in their POD-like activity. By regulating the changes in peroxidase activity based on the three materials, unique colorimetric signal fingerprints of different antibiotics can be obtained. Through optimization of various reaction conditions, the optimal conditions were screened, and using the array sensor and LDA algorithm, seven common antibiotics can be detected and distinguished in a short time.

[0035] (4) Compared with current antibiotic detection methods such as liquid chromatography and mass spectrometry, the sensor of this invention has the advantages of high discrimination ability, high specificity and convenient operation, showing its great potential for antibiotic analysis outside the laboratory. In addition, the constructed machine learning-assisted array sensor can be extended to design other smart nanoplatforms for identifying various environmental pollutants with similar chemical structures, achieving broad, accurate and rapid classification. Attached Figure Description

[0036] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0037] Figure 1 The machine learning-assisted colorimetric array sensor based on metal ions anchored on GDY in this invention is used for the identification of antibiotics and apples.

[0038] Figure 2 This is an ACTEM image of GDY / Co / Ni in this invention;

[0039] Figure 3 These are the ESR spectra of GDY / Co / Ni before and after the reaction with and without H2O2 in this invention;

[0040] Figure 4 This is a standard score chart of the array sensor detecting 1000μM antibiotics in this invention;

[0041] Figure 5 This is a schematic diagram of the POCT device in this invention. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0043] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, and the materials and reagents used are commercially available.

[0044] Example 1: Preparation of Materials

[0045] 1 mL of 1 mg / mL graphylene suspension was mixed with 1 mL of 2 mg / mL CoCl2 solution in a light-proof glass bottle and magnetically stirred at 350 rpm for 48 h. After 48 h, the mixture was centrifuged at 10000 rpm for 5 min, washed 2-3 times with isopropanol, and then vacuum-dried overnight at room temperature to obtain GDY / Co. A 1 mg / mL GDY / Co dispersion was prepared using isopropanol as the dispersant.

[0046] 1 mL of 1 mg / mL graphdiyne turbidity was mixed with 1 mL of 2 mg / mL NiCl2 in a light-proof glass bottle and magnetically stirred at 350 rpm for 48 h. After 48 h, the mixture was centrifuged at 10000 rpm for 5 min, washed 2-3 times with isopropanol, and then vacuum-dried overnight at room temperature to obtain GDY / Ni. A 1 mg / mL GDY / Ni dispersion was prepared using isopropanol as the dispersant.

[0047] Mix 1 mL of 1 mg / mL graphylene suspension, 1 mL of 2 mg / mL CoCl2 solution, and 1 mg / mL NiCl2 solution in a light-proof glass bottle and magnetically stir at 350 rpm for 48 h. After 48 h, centrifuge the mixture at 10000 rpm for 5 min, wash 2-3 times with isopropanol, and vacuum dry the washed GDY / Co / Ni overnight at room temperature. Prepare a 1 mg / mL GDY / Co / Ni dispersion using isopropanol as the dispersant. All three materials obtained should be stored in the dark at 4°C before testing.

[0048] GDY / Co / Ni composite materials are prepared through self-assembly methods, such as...Figure 1 As shown. The morphology of the GDY / Co / Ni nanocomposite was characterized using ACTEM. The ACTEM images ( Figure 2 A high density of white bright spots was observed uniformly dispersed on the carbon skeleton. Since Co and Ni atoms have a larger atomic mass than N and C, the bright spots in the image are Co and Ni elements, and the diameter of these bright spots is approximately [missing information]. Its size is comparable to that of a single Co and Ni atom, and Co and Ni are uniformly distributed in GDY, proving that Co and Ni have been successfully composited on GDY.

[0049] Example 2: Determination of enzyme activity

[0050] In four 0.6 mL centrifuge tubes, 2 μL of ultrapure water and 10 μL of solutions of the three synthetic materials were added to each tube. Then, 178 μL of NaOAc-HOAc buffer (100 mM, pH 4.8), 10 μL of H₂O₂ (50 mM), and 10 μL of TMB (10 mM) were added sequentially to each tube, and the mixture was incubated at 37 °C for 25 min. The color change of the solution was then observed, and 200 μL of the solution was added to a quartz cuvette with a 1 mm slit width. The absorbance in the wavelength range of 400-800 nm was measured using a UV-Vis spectrophotometer. The experiment showed that the three synthetic materials, GDY / Co / Ni, GDY / Co, and GDY / Ni, could catalyze the formation of ·OH from H₂O₂ in the presence of H₂O₂, thus proving that all three synthetic materials exhibit POD activity characteristics.

[0051] To comprehensively investigate the mechanism by which GDY / Co / Ni promotes TMB color development, ESR spectroscopy was used to study the ROS generated in the catalytic system. Figure 3 ESR spectra show that GDY / Co / Ni generates stronger hydroxyl radicals in the presence of H2O2. Without H2O2, no typical hydroxyl radical signal is observed.

[0052] Example 3: Effect of antibiotics on peroxidase-like activity

[0053] Seven antibiotics—isoniazid, catechol, norfloxacin, ampicillin sodium, kanamycin, moxifloxacin, and levofloxacin—were selected and dissolved in ultrapure water. In a 96-well plate, a 3×8 region was selected, with the blank and the seven antibiotics each occupying 8 wells. 2 μL of GDY / Co / Ni solution and 10 μL of the antibiotic were added to each well sequentially. Then, 178 μL of NaOAc-HOAc buffer (100 mM, pH 4.8), 10 μL of H2O2 (50 mM), and 10 μL of TMB (10 mM) were added to each well sequentially, and the plate was incubated at 37°C for 25 min. After incubation, the 96-well plate was placed in a microplate reader for analysis, and the absorbance of each well at 652 nm was measured. The results showed that the addition of antibiotics significantly reduced the absorbance and color of the system.

[0054] Example 4: Antibiotic Identification Using Array Sensors

[0055] This embodiment constructs a colorimetric array sensor using three nanozymes (GDY / Co / Ni, GDY / Co, GDY / Ni) as sensing units. This array sensor is used to distinguish seven antibiotics (1000 μM). A colorimetric response mode is constructed to reduce potential errors caused by fluctuations in the initial UV absorption intensity of the nanozymes. Then, linear discriminant analysis (LDA) is performed, and the top two canonical correlation factors are used to plot a two-dimensional standard score graph. Figure 4 The results accurately show seven independent clusters, where each point represents the UV absorption response of a three-dimensional array sensor for each antibiotic, and six points of the same color represent six parallel replicate experiments.

[0056] Example 5: Analysis of Training Data Matrix and Actual Samples

[0057] To further enhance the intelligence level and field application capabilities of the testing platform, artificial intelligence algorithms are introduced for image data processing and result judgment, constructing a three-in-one intelligent analysis system integrating "image recognition—data interpretation—model judgment." A schematic diagram of the POCT device is shown below. Figure 5 As shown.

[0058] In a 96-well plate, a 4×6 region was selected, and 10 μL of a standard mixture of seven antibiotics at different concentrations (10 μM, 100 μM, 300 μM, 500 μM, 800 μM, 1000 μM) was added to each well. Then, 2 μL of material (three nanozymes) was added to each well, followed by 178 μL of NaOAc-HOAc buffer (100 mM, pH 4.8), 10 μL of H2O2 (50 mM), and 10 μL of TMB (10 mM) to each well. The plate was then incubated at 37°C for 25 min. Images of the colorimetric array after the reaction were acquired. The images were first preprocessed, including image cropping, noise removal, and background normalization. Then, the OpenCV open-source image processing library was used to automatically identify the position of each channel in the colorimetric array and extract the color feature parameters of the target region (such as RGB mean, HSV hue value, grayscale level, etc.). By numerically processing these parameters, the system standardizes them into vector features for model analysis, forming a training data matrix.

[0059] Considering the practical application of antibiotics, apples were chosen as the real sample for analysis. The apples were purchased from a local supermarket. Antibiotics were extracted from the surface of the apples using 10 mL of NaOAc-HOAc buffer to obtain the apple test samples. The colorimetric array sensor described in Example 5 was used to detect the apple test samples. Images of the colorimetric array were acquired, numerically processed, and input into the resulting training data matrix. Support Vector Machine (SVM) classification algorithm and Linear Discriminant Analysis (LDA) method were used to identify and classify the samples. LDA was used for dimensionality reduction of high-dimensional features and sample clustering, while SVM determined the decision boundaries for whether the sample contained a specific antibiotic and whether it was qualified or unqualified.

[0060] This embodiment uses 32 apple samples to construct a dataset. The sensor exhibits good response sensitivity and 97.5% accuracy at different antibiotic concentrations, demonstrating good generalization ability and analytical performance.

[0061] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for preparing a sensor based on graphynyne nanozymes, characterized in that, Includes the following steps: (1) Under light-protected conditions, a suspension of graphdiene was mixed and stirred with cobalt salt, washed, and dried to obtain a Co-loaded solution. 2+ Graphdiyne; (2) Under light-protected conditions, a suspension of graphdiene was mixed and stirred with nickel salt, washed, and dried to obtain a Ni-loaded solution. 2+ Graphdiyne; (3) Under light-protected conditions, a suspension of graphdiene was mixed and stirred with cobalt and nickel salts, washed, and dried to obtain a Co-loaded... 2 and + Ni 2+ Graphdiyne; (4) The resulting load Co 2+ Graphdiyne, Ni-supported 2+ Graphdiyne and Co-supported 2 and Ni 2+ A sensor based on graphyne nanozymes was constructed using graphyne as the sensing unit.

2. The preparation method according to claim 1, characterized in that, The concentration of the graphyne suspension is 0.5 mg / mL to 10 mg / mL.

3. The preparation method according to claim 1, characterized in that, In step (1), the cobalt salt is selected from one or more of cobalt sulfate, cobalt nitrate, and cobalt chloride; And / or, the concentration of the cobalt salt is 0.5 mg / mL to 10 mg / mL.

4. The preparation method according to claim 1, characterized in that, In step (2), the nickel salt is selected from one or more of nickel chloride, nickel sulfate, and nickel nitrate; And / or, the concentration of the nickel salt is 0.5 mg / mL to 10 mg / mL.

5. The preparation method according to claim 1, characterized in that, In step (3), the concentration ratio of the cobalt salt to the nickel salt is 1:10-10:

1.

6. A sensor based on graphynyne nanozymes obtained by the preparation method according to any one of claims 1-5.

7. The application of the graphynyne nanozyme-based sensor according to claim 6 in the detection of antibiotics.

8. The application according to claim 7, characterized in that, The antibiotics mentioned are isoniazid, catechol, norfloxacin, ampicillin sodium, kanamycin, moxifloxacin, and levofloxacin.

9. The application according to claim 7, characterized in that, The method of application is as follows: S1. The sensor based on graphynyne nanozyme is mixed with antibiotic standard solution, buffer, chromogenic substrate and hydrogen peroxide and incubated to acquire images of the colorimetric array and form a training data matrix. S2. The sensor based on graphynyne nanozyme is mixed with the sample to be tested, buffer solution, chromogenic substrate and hydrogen peroxide and incubated. The image of the colorimetric array is acquired, and after numerical processing, it is input into the obtained training data matrix. The types and contents of antibiotics in the test samples were analyzed using the support vector machine classification algorithm and the linear discriminant analysis method.

10. The application according to claim 9, characterized in that, The concentration of antibiotics in the test samples was 10 μM-1000 μM; And / or, the chromogenic substrate is one or more of 3,3',5,5'-tetramethylbenzidine, 2,2'-azido-bis-3-ethylbenzothiazoline-6-sulfonic acid, and o-phenylenediamine dihydrochloride; And / or, the buffer solution is a NaOAc-HOAc buffer solution.

Images