A high-activity nanoscale enzyme for simultaneous detection of multiple chlorophenols and a preparation method and application thereof

Abstract

This invention belongs to the field of analytical detection technology, specifically relating to a highly active nanozyme for the simultaneous detection of multiple chlorophenols, its preparation method, and its application. The nanozyme is a composite material formed by in-situ chelation of copper nitrate, adenine, and a hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce). Copper-adenine, coordinated by copper nitrate and adenine, is uniformly loaded onto the surface or pore interface of the hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce) and forms a stable binding structure. The highly active nanozyme exhibits excellent laccase activity. Based on the differences in catalytic reaction kinetics of different chlorophenols, this invention constructs a colorimetric sensing array for the detection of multiple chlorophenols, enabling the identification and analysis of multiple target analytes. This method does not rely on complex electrode systems or multiple buffer conditions, and features structural stability, simple operation, and suitability for rapid detection.

Description

Technical Field

[0001] This invention belongs to the field of analytical detection technology, and particularly relates to a highly active nanozyme for the simultaneous detection of multiple chlorophenols, its preparation method, and its application. Background Technology

[0002] Chlorophenols (CPs) are common industrial chemicals widely used as wood preservatives, pesticides, and disinfectants. CPs are highly toxic, persistent, and bioaccumulative. Long-term exposure to the environment can severely impact the central nervous, endocrine, and immune systems of humans and animals, making them a class of persistent organic pollutants of great concern. Currently, the World Health Organization and the U.S. Environmental Protection Agency list CPs as priority pollutants, and my country's Ministry of Ecology and Environment has also included pentachlorophenol and its derivatives in the list of key new pollutants under control.

[0003] The main detection methods for polychlorinated pollutants (CPs) include gas chromatography (GC), high-performance liquid chromatography (HPLC), chromatography-mass spectrometry (GC-MS), and fluorescence spectroscopy. For example, invention patent CN105842376B discloses a method for detecting polychlorinated phenols (PCPs) in dyes. This method solves the problems of large extraction solvent volume, large waste liquid volume, insufficient accuracy in detecting monochlorophenol, dichlorophenol, and trichlorophenol, and the difficulty of removing impurities from dyes using existing methods. It employs steps such as acetylation of acetic anhydride, ultrasonic mixing, mixed solvent extraction, carbon powder purification, column chromatography, and centrifugation, combined with gas chromatography-mass spectrometry detection. Selected ions and internal standards are used for numerical verification, reducing the volume of extraction solution and waste liquid, and improving detection accuracy. Invention patent CN108802243B discloses a method for the simultaneous detection of metribuzin, 2,4-dichlorophenoxyacetic acid 2,4-D, 2,4-dichlorophenol, 2,4,6-trichlorophenol, and pentachlorophenol in water using liquid chromatography. The method includes multiple steps such as preparing a mixed standard solution of the five substances, activating the solid-phase extraction column, adsorption of the water sample, drying, elution, treatment of the eluent, and injection after mixing to obtain the chromatogram of the sample. This method can simultaneously extract and detect the content of the five compounds in water, significantly reducing pretreatment and detection time. Although these methods are highly sensitive and accurate, they still suffer from problems such as complex sample pretreatment processes, long detection cycles, the need for specialized personnel, and high instrument costs and maintenance. Furthermore, samples collected on-site need to be transported back to the laboratory for analysis, making it difficult to achieve real-time, rapid, and on-site detection of pollutants.

[0004] In recent years, nanozyme-based colorimetric sensing methods have attracted widespread attention in the field of rapid detection of organic pollutants due to their simplicity, speed, economy, and visualization. However, the similar structures and physicochemical properties of various CP isomers result in small differences in corresponding signal changes during colorimetric sensing, making effective differentiation and identification difficult. Furthermore, existing nanozyme systems often rely on single materials or simple combinations, limiting catalytic response and making it difficult to construct stable and discriminative detection systems.

[0005] This invention provides an HM UIO-66(Ce) / Cu-adenine composite nanozyme, which optimizes catalytic performance by constructing a composite structure, and establishes a colorimetric sensing system for the detection of various chlorophenols based on this material, thereby realizing the identification and analysis of multiple target substances. Summary of the Invention

[0006] To address the problems and shortcomings of the existing technologies, this invention aims to provide a highly active nanozyme for the simultaneous detection of multiple chlorophenols, its preparation method, and its application.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The primary objective of this invention is to provide a highly active nanozyme for the simultaneous detection of multiple chlorophenols. This nanozyme is a highly active nanozyme HM UIO-66(Ce) / Cu-adenine. The highly active nanozyme HM UIO-66(Ce) / Cu-adenine is a composite material formed by in-situ chelation of copper nitrate, adenine, and a hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce). The copper-adenine formed by the coordination of copper nitrate and adenine is uniformly loaded on the surface or pore interface of the hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce) and forms a stable binding structure.

[0008] A second objective of this invention is to provide a method for preparing the highly active nanozyme for simultaneous detection of multiple chlorophenols, comprising the following steps: (1) Preparation of hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce) by template method; (2) Using the hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce) prepared in step (1) as a porous carrier, add an ethanol solution of copper nitrate trihydrate, and centrifuge after reaction; (3) Add an aqueous solution of adenine and react under heating conditions to allow copper ions to coordinate and chelate with adenine, forming a Cu-adenine complex structure on the surface or pore interface of HM UIO-66(Ce). (4) Cooling, washing, and freeze-drying yielded the highly active nanozyme HM UIO-66(Ce) / Cu-adenine.

[0009] Preferably, the concentration of the copper nitrate trihydrate solution in step (2) is 10-40 mmol / L, the concentration of the adenine aqueous solution in step (3) is 10-80 mmol / L, and the reaction time in step (3) is 20-60 min.

[0010] A third objective of this invention is to provide a method for detecting multiple chlorophenols based on the dual-modal characteristic fusion of the highly active nanozyme for simultaneous detection of multiple chlorophenols or the highly active nanozyme prepared by the aforementioned method, comprising: (1) The single nanozyme HM UIO-66(Ce) / Cu-adenine was mixed with the chromogenic substrate and the sample to be tested to form a reaction system; (2) Collect the ultraviolet absorption signal of the reaction system described in step (1) at multiple time points during the reaction process, and construct a sensor array based on the absorbance signal; (3) Use a smartphone to simultaneously acquire image data of the reaction system described in step (1) at multiple time points, and perform color processing to obtain color feature values, and construct a sensor array based on the image R / G value signal; (4) Perform feature fusion processing on the sensor array based on absorbance signal described in step (2) and the sensor array based on image R / G value signal described in step (3) and establish a machine learning recognition model; (5) Based on the two data acquisition and analysis results of the obtained absorbance value and R / G value, the category and / or concentration of the chlorophenol to be tested are visualized and output.

[0011] Preferably, the reaction system in step (1) includes a reaction system of different types of chlorophenol at the same concentration and a reaction system of different concentrations of the same type of chlorophenol.

[0012] Preferably, the color feature value obtained in step (3) is obtained by using a color picker APP to read the RGB values ​​of the image captured by the mobile phone, calculate the R / G value, and establish the corresponding R / G information of the sensor array based on absorbance signal in step (2) and the sensor array based on image R / G value signal in step (3).

[0013] Preferably, the chlorophenol mentioned in step (5) is one or more of 2-chlorophenol, 3-chlorophenol, 4-chlorophenol, 2,4-dichlorophenol, and 3,4-dichlorophenol.

[0014] Preferably, the visualization output in step (5) is a combination of two types of data provided by a UV-Vis spectrophotometer and a smartphone, which are obtained through principal component analysis, hierarchical cluster analysis and machine learning, to mutually corroborate and distinguish the types and concentrations of chlorophenol in the sample to be tested.

[0015] The fourth objective of this invention is to provide the application of the highly active nanozyme for the simultaneous detection of multiple chlorophenols, or the highly active nanozyme prepared by the aforementioned preparation method, in the simultaneous detection of multiple chlorophenols.

[0016] The beneficial effects of the present invention are: (1) The present invention provides a highly active nanozyme for the simultaneous detection of multiple chlorophenols, its preparation method and application. The present invention forms a stable interfacial bond between Cu-adenine and HM UIO-66(Ce) through in-situ chelation, thereby constructing a stable HM UIO-66(Ce) / Cu-adenine nanozyme composite material. Experimental results show that, under the same component conditions, the laccase-like catalytic activity of the highly active nanozyme prepared by the present invention is significantly higher than that of HM UIO-66(Ce), Cu-adenine and a simple mixture of the two.

[0017] (2) Based on the catalytic response of the aforementioned highly active nanozymes, this invention constructs a colorimetric sensor array system for the detection of various chlorophenols. By utilizing the differences in the catalytic response of different chlorophenols in this system, the identification and analysis of multiple target analytes can be achieved. Compared with traditional single detection methods, this invention does not rely on complex electrode systems or multiple buffer systems, and can achieve simultaneous detection of multiple components. It features simple operation, high detection throughput, and suitability for rapid screening.

[0018] (3) This invention combines ultraviolet absorption signals and image color signals for data acquisition, which can realize the combination of laboratory analysis and on-site detection and has good application prospects. Attached Figure Description

[0019] Figure 1 A is the XRD pattern of HM UIO-66(Ce) / Cu-adenine, HM UIO-66(Ce), and UIO-66(Ce)NP; Figure 1 B is a 200nm transmission electron microscope image; Figure 1 C is a 20nm transmission electron microscope image; Figure 1 D is the elemental surface distribution diagram of energy-dispersive X-ray spectroscopy.

[0020] Figure 2 A is a 200 nm transmission electron microscope (TEM) image of HM UIO-66(Ce) / Cu-adenine prepared in Example 4; Figure 2 B is a 20 nm transmission electron microscope (TEM) image of HM UIO-66(Ce) / Cu-adenine prepared in Example 4.

[0021] Figure 3This is a comparison of laccase-like activities between HM UIO-66(Ce) / Cu-adenine and HM UIO-66(Ce), HM UIO-66(Ce)+Cu-adenine and Cu-adenine.

[0022] Figure 4 A is a principal component analysis plot based on absorbance values ​​for chlorophenols at different concentrations of 25 μM; Figure 4 B is a hierarchical clustering analysis diagram based on absorbance values ​​for different concentrations of chlorophenol at 25 μM. Figure 4 C is the principal component analysis plot based on R / G values ​​for different concentrations of chlorophenol at 25 μM; Figure 4 D is a hierarchical clustering analysis diagram based on R / G values ​​for different concentrations of chlorophenol at 25 μM.

[0023] Figure 5 A is the principal component analysis plot based on absorbance values ​​for different concentrations of 3-chlorophenol; Figure 5 B represents the linear correlation between the discriminant factor 1 (Factor 1) and the concentration of 3-CP at different concentrations of 3-chlorophenol; Figure 5 C is a hierarchical clustering analysis diagram based on absorbance values ​​for different concentrations of 3-chlorophenol; Figure 5 D is the principal component analysis plot based on R / G values ​​for different concentrations of 3-chlorophenol.

[0024] Figure 6 A is a principal component analysis plot based on absorbance values ​​for a 25 μM dichlorophenol mixture; Figure 6 B is a hierarchical clustering analysis diagram based on absorbance values ​​for a 25 μM dichlorophenol mixture; Figure 6 C is the principal component analysis plot based on R / G values ​​for a 25 μM dichlorophenol mixture; Figure 6 D is a hierarchical clustering analysis diagram based on R / G values ​​for a 25 μM dichlorophenol mixture.

[0025] Figure 7 A is a confusion matrix diagram for the qualitative identification of multiple chlorophenols using a machine learning-assisted sensor array; Figure 7 B is a concentration prediction graph for quantitative detection of 3-CP using a machine learning-assisted sensor array. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to embodiments, so that those skilled in the art can better understand the invention, but it is not limited to the following embodiments.

[0027] It should be noted that, unless otherwise specified, the methods described in the following embodiments are all conventional methods, and the reagents described are all commercially available.

[0028] In this specification, the term "2-CP" refers to 2-chlorophenol, the term "3-CP" refers to 3-chlorophenol, the term "4-CP" refers to 4-chlorophenol, the term "2,4-DCP" refers to 2,4-chlorophenol, the term "3,4-DCP" refers to 3,4-chlorophenol, and the term "4-AAP" refers to "4-aminoantipyrine," and the two terms are used interchangeably.

[0029] The trade name for F127 is Pluronic® F-127 or Pluronic 407.

[0030] Principal Component Analysis (PCA) is a commonly used multivariate statistical method, primarily used for data dimensionality reduction and feature extraction. It transforms the original data into a set of linearly independent representations across all dimensions, called principal components, reducing data complexity while preserving as much variation information as possible. This method is implemented using Origin software, and the specific steps include: importing relevant data → selecting data → statistics → multivariate analysis → principal component analysis → opening the dialog box (modifying parameters): Input – select variables and observation labels; Settings – correlation matrix, number of components to extract (e.g., determined based on the eigenvalue greater than 1 criterion or cumulative variance contribution rate); quantities to calculate – check eigenvalues, eigenvectors; plotting – check scree plot, loading plot, score plot, bipolar plot, components. Figure 2 D → Output result.

[0031] Hierarchical cluster analysis (HCA) is used to group data objects according to their characteristics, revealing the underlying structure and patterns of the data. Euclidean distance is commonly used as a distance measure between data points. The smaller the Euclidean distance, the greater the similarity of the data. This method is implemented using Origin software, including: importing relevant data → selecting data → statistics → multivariate analysis → hierarchical cluster analysis → opening the dialog box (modifying parameters): Input – variable and observation labels; Settings – clustering method: Ward, distance type: Euclidean, number of clusters: number of sample categories; Output – check clustering stage, observation and cluster center distance, centroid information → output results.

[0032] Example 1: Preparation method of highly active HM UIO-66(Ce) / Cu-adenine nanozyme (1) Preparation of HM UIO-66(Ce) 100 mg of F127 was dissolved in 6 mL of deionized water, and 0.3 mL of glacial acetic acid (HAc) and 500 mg of sodium perchlorate (NaClO4) were added. The mixture was stirred until the solution became homogeneous. Subsequently, 548 mg of cerium ammonium nitrate (NH4)2Ce(NO3)6 and 166 mg of terephthalic acid (H2BDC) (molar ratio of (NH4)2Ce(NO3)6 to H2BDC was added, and the mixture was reacted at 60 °C for 20 min. The collected solid was washed three times with water and N,N-dimethylformamide, and then transferred to ethanol and soaked at 60 °C for two days, with the ethanol solution being replaced once a day. Finally, the solution was filtered and dried at 60 °C to obtain HM UIO-66(Ce) powder.

[0033] (2) Preparation of copper-adenine-loaded hierarchical porous Ce-MOF (HM UIO-66(Ce) / Cu-adenine) 12 mg of copper nitrate trihydrate Cu(NO3)2·3H2O was dissolved in 5 mL of ethanol solution (10 mmol / L). 25 mg of HM UIO-66(Ce) prepared in step (1) was added. After stirring at 60℃ for 2 h, the mixture was centrifuged and filtered to obtain a solid powder. Then, 14 mg of adenine (20 mmol / L) previously dissolved in 5 mL of hot water was added, and the mixture was reacted at 70℃ for 20 min. After the reaction was complete, the mixture was cooled to room temperature in an ice bath, centrifuged, washed three times with deionized water, and finally freeze-dried for 12 h to obtain HM UIO-66(Ce) / Cu-adenine powder. This powder was dispersed in ultrapure water to prepare a solution with a concentration of 1 mg / mL for subsequent use. XRD pattern analysis showed that the diffraction peaks of HM UIO-66(Ce) and UIO-66(Ce) NP were well matched, proving the crystal structure and phase purity of HM UIO-66(Ce). Figure 1 A), and after being combined with Cu-adenine, its crystal structure and phase purity did not change significantly; as shown by TEM images, HM UIO-66(Ce) / Cu-adenine exhibits a polyhedral structure, and a regularly arranged porous structure can be clearly observed ( Figure 1 BC), and carbon, nitrogen, cerium, and copper are uniformly distributed in the material ( Figure 1 (D) indicates that HM UIO-66(Ce) / Cu-adenine with a porous structure was successfully synthesized.

[0034] Examples 2-5: Preparation methods of highly active nanozymes The preparation steps of the highly active nanozyme for simultaneous detection of multiple chlorophenols involved in this embodiment are the same as those in Example 1. The specific amounts of raw materials and reaction conditions are shown in Table 1.

[0035] Table 1 shows the specific reaction conditions for Examples 2-5.

[0036] Examples 2-5 show that HM UIO-66(Ce) / Cu-adenine composite nanozymes with different loadings were prepared by adjusting the concentration and ratio of Cu(NO3)2·3H2O and adenine solution, as well as the reaction time. A TEM image of the HM UIO-66(Ce) / Cu-adenine composite nanozyme prepared in Example 4 is shown below. Figure 2 As shown. Compared to Example 1, the nanozyme prepared in Example 4 has a regularly arranged porous channel structure (as shown). Figure 2 A), but the gaps between the channels are significantly reduced, and the edges of the channels are no longer clear ( Figure 2 (B) Under conditions of lower Cu(NO3)2·3H2O and adenine solution concentrations and shorter reaction times, the Cu-adenine loading in the obtained nanozyme was lower than that in Example 1. Further increasing the concentrations of both would lead to a reduction in pore size, potentially affecting mass transfer. Therefore, considering both nanozyme activity and mass transfer effects in subsequent assays, the nanozyme prepared in Example 1 was chosen for subsequent experiments.

[0037] Example 6: Comparison of laccase-like activities of HM UIO-66(Ce) / Cu-adenine nanozymes Different control groups were set up: Cu-adenine, HM UIO-66(Ce), and HM UIO-66(Ce) + Cu-adenine mixture, and the experimental group: HM UIO-66(Ce) / Cu-adenine. The laccase activity of each group was compared. The specific experimental steps are as follows: 300 μL of PBS buffer (10 mM) at pH=7, 100 μL of nanozyme solution (1 mg / mL), 50 μL of 4-aminoantipyrine (4-AAP, 4 mM) and 50 μL of 2,4-dichlorophenol (2,4-DCP, 500 μM) were added to a 2 mL centrifuge tube, mixed and shaken for 30 min, and the spectrum at 510 nm was recorded using a UV-Vis spectrophotometer.

[0038] The preparation method of Cu-adenine is as follows: 0.24 g of copper nitrate trihydrate Cu(NO3)2·3H2O and 0.14 g of adenine were dissolved in 10 mL of hot water and reacted at 70℃ for 20 min. After the reaction was completed, the mixture was cooled to room temperature in an ice bath, filtered, and finally transferred to a dialysis bag for further purification. The final solution was frozen overnight and dried in a freeze dryer for 12 hours to obtain a solid powder.

[0039] Figure 3The UV spectra of the various nanozyme materials show that the laccase activity ranking is: HM UIO-66(Ce) / Cu-adenine > HM UIO-66(Ce) > HM UIO-66(Ce)+Cu-adenine > Cu-adenine. These results indicate that in the HM UIO-66(Ce) / Cu-adenine composite material, the Cu-adenine component forms a stable interfacial bond with HM UIO-66(Ce), resulting in a more rational spatial distribution of active sites on the material surface or at the pore interface. This improves the substrate conversion efficiency at the interface, ultimately leading to higher laccase-like catalytic activity. Furthermore, the Cu-adenine component in the composite material is confined by the pore structure of HM UIO-66(Ce), limiting its loading (≤20%). This helps avoid masking of active sites, further optimizing catalytic performance and improving system stability.

[0040] Example 7: Identification of different types of chlorophenol at the same concentration by a highly active HM UIO-66(Ce) / Cu-adenine nanozyme sensor array. (1) Taking the detection of various chlorophenols (2-CP, 3-CP, 4-CP, 2, 4-DCP, 3, 4-DCP) at 25 μM as an example, 300 μL of PBS buffer (10 mM) at pH=7, 100 μL of HM UIO-66(Ce) / Cu-adenine solution (1 mg / mL), 50 μL of 4-aminoantipyrine (4-AAP, 4 mM) and 50 μL of different chlorophenols at 250 μM were added to a 2 mL centrifuge tube. Five parallel sets of each chlorophenol were prepared. The absorbance values ​​at 510 nm at 4, 6, 8, 10, 12 and 15 min were measured using an enzyme-linked immunosorbent assay (ELISA) reader. The sample was placed in a self-made smartphone camera dark box device for taking pictures. Finally, the RGB values ​​of the images taken by the phone were read using the color picker APP, and the R / G values ​​were calculated. Finally, a multimodal sensing array of 1 nanozyme × 6 reaction times × 5 target substances × 5 parallel samples × 2 signals was constructed. (2) Identification of chlorophenol at other concentrations: Only change the concentration of each chlorophenol (the final concentrations in the system are 10 μM, 25 μM, 50 μM, 75 μM, 100 μM and 150 μM respectively), and perform the other operations as in step (1).

[0041] The results are as follows Figure 4 As shown, based on the data acquisition and analysis results of both absorbance values ​​and R / G values ​​read by the mobile phone, Principal Component Analysis (PCA) can group the five similar points of each chlorophenol together without any misclassification or error. Figure 4 A and 4C). Hierarchical clustering analysis (HCA) also showed the same analytical results. Figure 4 B and 4D).

[0042] Example 8: Identification of different concentrations of chlorophenol of the same class by a highly active HM UIO-66(Ce) / Cu-adenine nanozyme sensor array Taking 3-CP as an example, a series of concentrations of 3-CP were prepared (the final concentrations in the system were 10 μM, 25 μM, 50 μM, 100 μM, and 150 μM, respectively). 300 μL of pH 7 PBS buffer (10 mM), 100 μL of HMUIO-66(Ce) / Cu-adenine solution (1 mg / mL), 50 μL of 4-aminoantipyrine (4-AAP, 4 mM), and 50 μL of 3-CP at each concentration were added to a 2 mL centrifuge tube. Five replicates of each chlorophenol were prepared. The absorbance at 510 nm was measured using a microplate reader at 4, 6, 8, 10, 12, and 15 min. The samples were then photographed using a self-made smartphone camera dark box. Finally, the RGB values ​​of the images were read using a color picker app, and the R / G values ​​were calculated. Ultimately, a multimodal sensing array was constructed, consisting of 1 nanozyme × 6 reaction times × 5 concentrations × 5 replicates × 2 signals. The results are as follows Figure 5 As shown, based on the data acquisition and analysis results of absorbance values, the PCA method can accurately distinguish 3-CP at various concentrations (within the range of 10~150 μM) without overlap. Figure 5 A), more importantly, there is a strong linear correlation between discriminant factor 1 and 3-CP concentration ( Figure 5 B). Hierarchical cluster analysis (HCA) also showed the same analytical results. Figure 5 C). Meanwhile, the R / G values ​​read from the mobile phone, after being analyzed using PCA, also showed the same analytical results ( Figure 5 D).

[0043] Example 9: Identification of a mixture of dichlorophenols by a highly active HM UIO-66(Ce) / Cu-adenine nanozyme sensor array. (1) Taking a binary mixture of 25% 2,4-DCP and 75% 2-CP (total concentration controlled at 25 μM) as an example, add 300 μL of pH=7 PBS buffer (10 mM), 100 μL of HM Ce-UIO-66 solution (1 mg / mL), 50 μL of 4-aminoantipyrine (4-AAP, 4 mM) and 50 μL of the above binary mixture of 500 μM to a 2 mL centrifuge tube. Perform 5 parallel groups and measure the absorbance at 510 nm at 4, 6, 8, 10, 12 and 15 min using an ELISA reader. Place it in a self-made smartphone camera dark box device for taking pictures, and use the color picker APP to read the RGB values ​​of the mobile phone image and calculate the R / G value. (2) Other dichlorophenol mixtures were: 25% 2-CP and 75% 3-CP, 50% 2-CP and 50% 3,4-DCP, 50% 3-CP and 50% 2,4-DCP, 25% 3-CP and 75% 3,4-DCP, and 25% 4-CP and 75% 2,4-DCP. The concentration of each dichlorophenol mixture was controlled at 25 μM, and other operations were the same as in step (1). Finally, a multimodal sensing array of 1 nanozyme × 6 reaction times × 7 target substances × 5 parallel samples × 2 signals was constructed. The results are as follows Figure 6 As shown, based on the data acquisition and analysis results of both absorbance values ​​and R / G values ​​read by the mobile phone, Principal Component Analysis (PCA) can group the five similar points of each chlorophenol together without any misclassification or error. Figure 6 AD).

[0044] Example 10: Qualitative and quantitative detection of chlorophenol using a highly active HM UIO-66(Ce) / Cu-adenine nanozyme sensor array combined with machine learning. (1) Qualitative identification: The data matrix of the multimodal sensor array with 5 concentrations × 5 types of chlorophenol × 5 parallel × 2 types of signals in Examples 7 and 8 above is divided into training set and test set in a ratio of 7:3, and the weighted random forest classification model is used for analysis; (2) Quantitative detection: Taking 3-CP as an example, the data obtained in Example 4 were analyzed using a multi-model comparison strategy of machine learning.

[0045] The results are as follows Figure 7 As shown in Figure A, this confusion matrix can classify five target categories, including 2-CP and 3-CP, with 100% accuracy and no misclassification. Taking the quantitative analysis of 3-CP as an example ( Figure 7 B), concentration prediction R 2 The value reaches 0.997, demonstrating excellent overall performance.

[0046] In summary, this invention provides a highly active nanozyme for the simultaneous detection of multiple chlorophenols, its preparation method, and its applications. This invention utilizes an in-situ chelation method to form a stable interfacial bond between Cu-adenine and HM UIO-66(Ce), thereby constructing a stable HM UIO-66(Ce) / Cu-adenine nanozyme composite material. Experimental results show that, under the same component conditions, the highly active nanozyme prepared by this invention exhibits significantly higher laccase-like catalytic activity than HM UIO-66(Ce), Cu-adenine, and simple mixtures of the two. Based on the catalytic response of the aforementioned highly active nanozyme, this invention constructs a colorimetric sensor array system for the detection of multiple chlorophenols. By utilizing the differences in the catalytic responses of different chlorophenols in this system, the identification and analysis of multiple target analytes can be achieved. Compared with traditional single detection methods, this invention does not rely on complex electrode systems or multiple buffer systems to achieve simultaneous detection of multiple components, and features simple operation, high detection throughput, and suitability for rapid screening. This invention combines ultraviolet absorption signals with image color signals for data acquisition, enabling the integration of laboratory analysis and on-site testing, and has promising application prospects.

[0047] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural modifications made using the present invention specification, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A highly active nanozyme for the simultaneous detection of multiple chlorophenols, characterized in that, The nanozyme is a highly active nanozyme HMUIO-66(Ce) / Cu-adenine. The highly active nanozyme HMUIO-66(Ce) / Cu-adenine is a composite material formed by in-situ chelation of copper nitrate, adenine and hierarchical porous cerium-based metal-organic framework HMUIO-66(Ce). The copper-adenine formed by the coordination of copper nitrate and adenine is uniformly loaded on the surface or pore interface of the hierarchical porous cerium-based metal-organic framework HMUIO-66(Ce) and forms a stable binding structure.

2. The method for preparing a highly active nanozyme for simultaneous detection of multiple chlorophenols as described in claim 1, characterized in that, Includes the following steps: (1) Preparation of hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce) by template method; (2) Using the hierarchical porous cerium-based metal-organic framework HM UIO-66(Ce) prepared in step (1) as a porous carrier, add an ethanol solution of copper nitrate trihydrate, and centrifuge after reaction; (3) Add an aqueous solution of adenine and react under heating conditions to allow copper ions to coordinate and chelate with adenine, forming a Cu-adenine complex structure on the surface or pore interface of HMUIO-66(Ce). (4) Cooling, washing, and freeze-drying yielded the highly active nanozyme HM UIO-66(Ce) / Cu-adenine.

3. The preparation method according to claim 2, characterized in that, The concentration of the copper nitrate trihydrate solution in step (2) is 10-40 mmol / L, the concentration of the adenine aqueous solution in step (3) is 10-80 mmol / L, and the reaction time in step (3) is 20-60 min.

4. A method for detecting multiple chlorophenols based on the dual-modal characteristics of the highly active nanozyme for simultaneous detection of multiple chlorophenols as described in claim 1, or the highly active nanozyme prepared by the preparation method described in claim 2 or 3, characterized in that... include: (1) The single nanozyme HM UIO-66(Ce) / Cu-adenine was mixed with the chromogenic substrate and the sample to be tested to form a reaction system; (2) Collect the ultraviolet absorption signal of the reaction system described in step (1) at multiple time points during the reaction process, and construct a sensor array based on the absorbance signal; (3) Use a smartphone to simultaneously acquire image data of the reaction system described in step (1) at multiple time points, and perform color processing to obtain color feature values, and construct a sensor array based on the image R / G value signal; (4) Perform feature fusion processing on the sensor array based on absorbance signal described in step (2) and the sensor array based on image R / G value signal described in step (3) and establish a machine learning recognition model; (5) Based on the two data acquisition and analysis results of the obtained absorbance value and R / G value, the category and / or concentration of the chlorophenol to be tested are visualized and output.

5. The detection method as described in claim 4, characterized in that, The reaction system described in step (1) includes reaction systems of different types of chlorophenol at the same concentration and different concentrations of the same type of chlorophenol.

6. The detection method as described in claim 4, characterized in that, The color feature value obtained in step (3) is obtained by using a color picker APP to read the RGB values ​​of the image captured by the mobile phone, calculate the R / G value, and establish the corresponding R / G information of the sensor array based on absorbance signal in step (2) and the sensor array based on image R / G value signal in step (3).

7. The detection method as described in claim 4, characterized in that, The chlorophenol mentioned in step (5) is one or more of 2-chlorophenol, 3-chlorophenol, 4-chlorophenol, 2,4-dichlorophenol, and 3,4-dichlorophenol.

8. The detection method as described in claim 4, characterized in that, The visualization output described in step (5) combines two types of data provided by a UV-Vis spectrophotometer and a smartphone. The resulting spectra are obtained through principal component analysis, hierarchical cluster analysis, and machine learning, which complement each other to distinguish the types and concentrations of chlorophenol in the sample to be tested.

9. The application of the highly active nanozyme for simultaneous detection of multiple chlorophenols as described in claim 1, or the highly active nanozyme prepared by the preparation method described in claim 2 or 3, in the simultaneous detection of multiple chlorophenols.

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