Photoresponse nano-enzyme array sensor as well as preparation method and application thereof

Abstract

The invention discloses a photoresponse nano-enzyme array sensor as well as a preparation method and application thereof, and belongs to the technical field of neurotransmitter detection. The photoresponse nano enzyme array sensor comprises MOF1, MOF2 and MOF3, organic ligands of the MOF1, the MOF2 and the MOF3 are all ZnTCPP, and metal ions of the MOF1, the MOF2 and the MOF3 are respectively zinc ions, titanium ions and aluminum ions. By means of the large specific surface area of MOFs, the interaction between a reaction substrate and catalytic activity sites in the material is enhanced, the response sensitivity of the sensor is remarkably improved, the catalytic activity can be regulated and controlled through illumination according to the light response characteristic of the sensor, and dynamic signal amplification of neurotransmitters is achieved. The array sensor can be used for detecting various neurotransmitters and realizing accurate distinguishing and quantification within a few minutes. Even in a complex biological sample, the photoresponse nano-enzyme array sensor can still keep high precision.

Description

Technical Field

[0001] The present invention belongs to the technical field of neurotransmitter detection, and specifically relates to a light-responsive nanoenzyme array sensor and a preparation method and application thereof. Background Art

[0002] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not necessarily be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art.

[0003] Neurological diseases, including Alzheimer's disease (AD), Parkinson's disease and multiple sclerosis (MS), pose a major health challenge due to their complex pathogenesis and wide prevalence. These diseases are often associated with disorders of neurotransmitter regulation, leading to progressive cognitive and motor dysfunction. Abnormal concentrations of neurotransmitters are closely related to diseases such as Parkinson's disease, AD, and MS. Traditional diagnostic methods for neurological diseases are time-consuming and lack sufficient sensitivity, making it impossible to effectively detect neurotransmitters in the early stages. Therefore, the development of highly sensitive and selective neurotransmitter detection technologies is of great significance for disease diagnosis and drug development.

[0004] In recent years, metal-organic framework (MOF) materials have shown great potential in the field of biosensing due to their tunable pore structure, high specific surface area, and enzyme-like catalytic activity. Among them, zinc tetracarboxyphenylporphyrin (ZnTCPP), a photoactive ligand, coordinates with metal ions to form a MOF that combines photoresponsive properties with peroxidase-like activity, enabling enhanced signal output through photoregulated catalytic reactions. However, existing applications of ZnTCPP-based MOFs have primarily focused on photocatalytic degradation of pollutants or tumor treatment, and have not yet been applied to the field of neurotransmitter detection. Summary of the Invention

[0005] In order to address the deficiencies of the prior art, the purpose of the present invention is to provide a light-responsive nanozyme array sensor and its preparation method and application. The present invention proposes for the first time a new type of light-responsive nanozyme array sensor combined with metal-organic frameworks (MOFs) for rapid, sensitive and multiplex detection of neurotransmitters.

[0006] In order to achieve the above object, the technical solution of the present invention is:

[0007] The first aspect of the present invention provides a light-responsive nanozyme array sensor, comprising MOF1, MOF2 and MOF3, wherein the organic ligands of MOF1, MOF2 and MOF3 are all ZnTCPP, and the metal ions are zinc ions, titanium ions and aluminum ions, respectively.

[0008] An array sensor is a detection system composed of multiple independent or collaborative sensing units arranged according to a specific pattern. Its core concept is to achieve multi-target detection, high-sensitivity identification, or complex signal analysis through spatially distributed multiple sensing units combined with signal integration and analysis technologies. It is widely used in environmental monitoring, medical diagnosis, food safety, and industrial process control. The present invention uses MOFs constructed from Zn(II) porphyrin compounds (ZnTCPP) as sensing units to form an array sensor. The MOFs' large specific surface area enhances the interaction between reaction substrates and catalytic active sites within the material, significantly improving the sensor's response sensitivity. Furthermore, their photoresponsive properties allow for the regulation of catalytic activity through light (e.g., photo-controlled enzyme reactions), enabling dynamic signal amplification of neurotransmitters. The porous structure of MOFs selectively adsorbs target molecules (e.g., dopamine and 5-hydroxytryptamine), which are then detected by combining changes in the optical signal of the catalytic reaction products. Furthermore, light-driven catalysis significantly accelerates the response speed of MOFs. Mimicking the mammalian olfactory system, the array sensor responds to various neurotransmitters in a specific pattern, enabling precise differentiation and quantification within minutes. The light-responsive nanozyme array sensor maintains high accuracy even in complex biological samples such as serum and cerebrospinal fluid. The light-responsive nanozyme array sensor is capable of detecting neurotransmitter signatures associated with neurological diseases, such as Alzheimer's disease. This platform has significant potential for early diagnosis and continuous monitoring of neurological diseases.

[0009] Furthermore, light-responsive nanozyme array sensors composed of different MOF units can simultaneously detect multiple neurotransmitters. By comparing the signal differences between different units, the interference of complex matrices in biological samples can be eliminated, and the anti-interference ability is high.

[0010] In some embodiments of the present invention, MOF1 is a two-dimensional layered structure, and MOF2 and MOF3 are both rod-shaped cluster structures. The two-dimensional layered structure can significantly increase the specific surface area of the MOF, thereby enhancing the interaction between the reactant substrate and the catalytic active sites within the material and improving detection sensitivity. MOF1, MOF2, and MOF3 all have light-responsive oxidase activity, and the three MOF nanozymes have significantly different free energy barriers during the catalytic process, which is conducive to the establishment of array sensors.

[0011] The present invention integrates three nanozymes, MOF1, MOF2 and MOF3, into a light-responsive nanozyme array sensor, which utilizes specific binding interactions and catalytic activity to generate cross-reaction signals when detecting neurotransmitters, thereby achieving rapid and sensitive detection of neurotransmitters and capable of simultaneously detecting multiple neurotransmitters.

[0012] It can be understood that the term rod-like cluster structure refers to the MOF material having a rod-like structure and a cluster structure formed by aggregation of rod-like MOF materials.

[0013] The second aspect of the present invention provides a method for preparing the above-mentioned light-responsive nanozyme array sensor, comprising: mixing a zinc source, a titanium source and an aluminum source with ZnTCPP respectively, and preparing MOF1, MOF2 and MOF3 by a solvothermal method; arranging MOF1, MOF2 and MOF3 into an array to obtain a light-responsive nanozyme array sensor.

[0014] The present invention uses a simple solvent method to synthesize three MOF materials and arrange them into an array to obtain a light-responsive nanozyme array sensor. The preparation method provided by the present invention is simple to operate and can be easily industrialized and mass-produced.

[0015] In some embodiments of the present invention, the preparation method of MOF1 includes dissolving a zinc source and ZnTCPP in an organic solvent, and reacting the mixture at 140-160°C with stirring for 1-5 hours to obtain MOF1. The present invention prepares MOF1, namely ZnTCPP-Zn MOF, through a simple solution thermal method, which is simple to prepare.

[0016] The zinc source includes any one of zinc nitrate, zinc chloride, and zinc acetate. The type of zinc source significantly affects the structure and performance of the resulting MOF. Compared to chloride and acetate ions, nitrate is a weakly coordinating anion and is easily replaced by the carboxylic acid group of ZnTCPP, promoting homogeneous nucleation and forming a regular pore structure. MOF materials prepared using zinc nitrate as the zinc source have high crystallinity, uniform pore size, and a higher specific surface area. Therefore, the zinc source is preferably zinc nitrate.

[0017] Wherein, the organic solvent includes at least one of N,N-dimethylformamide (DMF) and dimethyl sulfoxide (DMSO). It is understandable that the type of organic solvent will significantly affect the structure, morphology and function of the resulting MOF. Highly polar solvents such as DMF and DMSO will promote the deprotonation of the ligand and the full dissolution of the metal ions, which is conducive to the formation of MOFs with high crystallinity and high specific surface area, and is conducive to improving the sensitivity of detection of the light-responsive nanozyme array sensor. In order to further increase the specific surface area of MOF1, the organic solvent is preferably DMF.

[0018] Among them, in order to assist the dissolution of zinc source and ligand and regulate crystal growth, benzoic acid (BA) is also added during the preparation of MOF1. After the addition of benzoic acid, its hydroxyl group (-OH) can regulate the metal node (Zn) through weak coordination. 2+ ) coordination rate, controlling the MOF pore size and morphology. It also helps ZnTCPP disperse in DMF, guiding the orderly assembly of the MOF skeleton to form a multi-level porous structure.

[0019] The mass ratio of the zinc source, ZnTCPP, benzoic acid, and organic solvent is (4-6):(1-3):(8-12):(10-13). At this ratio, the resulting MOF1 has better performance.

[0020] After the reaction is completed, the mixture is cooled to room temperature, centrifuged, and washed to obtain MOF1. Washing can fully remove unreacted porphyrin and zinc source, thereby reducing the impurity content in MOF1.

[0021] In some embodiments of the present invention, the MOF2 preparation method includes dissolving an aluminum source and ZnTCPP in water, and stirring the mixture at 160-200°C for 15-20 hours to obtain MOF2. The present invention prepares MOF2, i.e., the ZnTCPP-Al MOF material, via a one-step hydrothermal method, resulting in a simple preparation process.

[0022] Among them, the aluminum source includes any one of aluminum chloride, aluminum nitrate and aluminum sulfate. The use of the above aluminum source can produce highly crystalline MOF2, thereby improving the detection sensitivity of the light-responsive nanozyme array sensor.

[0023] The mass ratio of the aluminum source, ZnTCPP, and water is (8-12):(8-12):(3-5). At this ratio, the resulting MOF2 has high crystallinity.

[0024] After the reaction is completed, the reaction system is cooled at a rate of 1-2°C / min, centrifuged, washed, and dried to obtain MOF2. The washing is performed to fully remove unreacted porphyrin and rate source to improve the purity of MOF2.

[0025] In some embodiments of the present invention, the MOF3 preparation method includes: mixing tetraisopropyl titanate, p-aminobenzoic acid, and isopropyl alcohol, stirring and reacting at 80-120°C for 75-80 hours to obtain an intermediate; and dissolving the intermediate and ZnTCPP in an organic solvent, reacting at 150-170°C for 45-50 hours to obtain MOF3. The MOF3 provided by the present invention, namely ZnTCPP-Ti, is also prepared by a simple solvothermal synthesis method, which has a simple preparation process.

[0026] The usage ratio of tetraisopropyl titanate, p-aminobenzoic acid and isopropyl alcohol is (90-110 μL):(190-200 mg):(4-6 mL).

[0027] The intermediate is washed with isopropyl alcohol to remove unreacted tetraisopropyl titanate and p-aminobenzoic acid, and then dissolved in an organic solvent with ZnTCPP to improve the purity of MOF3.

[0028] Acetic acid is also added during the MOF3 preparation process, dissolving the intermediate, ZnTCPP, and acetic acid in an organic solvent. The addition of acetic acid adjusts the pH, promotes ligand deprotonation, inhibits excessive hydrolysis of the titanium precursor, facilitates the binding of metal ions to ligands, and regulates crystal growth, optimizing the crystallinity and stability of the MOF.

[0029] The organic solvent is a mixture of acetonitrile and tetrahydrofuran in a volume ratio of 2-4:1. This mixed solvent promotes uniform dispersion of the metal source and ligand, slows the hydrolysis rate of titanium, prevents the formation of amorphous TiO2, and avoids excessive interference with metal-ligand bonding, thereby regulating coordination and guiding crystallization, significantly optimizing the structure and photoelectric properties of MOF3.

[0030] The ratio of the intermediate, ZnTCPP, acetic acid, and organic solvent is (3-5 mg): (15-25 mg): (0.5-1.5 mL): (1.5-2.5 mL). At this ratio, the resulting MOF3 exhibits excellent structural and photoelectric properties.

[0031] The third aspect of the present invention provides an application of the above-mentioned light-responsive nanozyme array sensor or the light-responsive nanozyme array sensor prepared by the above-mentioned preparation method in neurotransmitter detection.

[0032] The ZnTCPP MOF-based light-responsive nanozyme array sensor provided by the present invention can quickly and sensitively detect neurotransmitters, and has broad application prospects for early diagnosis and monitoring of neurological diseases such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis.

[0033] The neurotransmitters include, but are not limited to, any one or more of dopamine, epinephrine, norepinephrine, serotonin, histamine and acetylcholine.

[0034] The neurological diseases include any one or more of Alzheimer's disease, Parkinson's disease and multiple sclerosis.

[0035] A fourth aspect of the present invention provides a neurotransmitter detection kit, comprising the above-mentioned light-responsive nanozyme array sensor or the light-responsive nanozyme array sensor prepared by the above-mentioned preparation method.

[0036] The neurotransmitters include any one or more of dopamine, epinephrine, norepinephrine, serotonin, histamine and acetylcholine.

[0037] When the neurotransmitter detection kit provided by the present invention is used for neurotransmitter detection, it can be combined with a machine learning algorithm. These characteristics enable the neurotransmitter detection kit to have multiple detection capabilities and to analyze multiple neurotransmitters simultaneously. This ability to distinguish and quantify neurotransmitters in real time represents a breakthrough in diagnostic technology and provides a fast, non-invasive and cost-effective alternative to traditional methods. In addition, the light-responsive nanozyme array sensor performed well in distinguishing normal samples from Alzheimer's disease (AD) samples, verifying its potential in clinical applications. Advanced machine learning algorithms further enhance the diagnostic capabilities of the sensor and provide a powerful tool for the identification of early diseases in clinical settings. This technology not only has great potential for the early detection of neurological diseases, but also provides a practical and scalable solution for continuous monitoring, opening up a new field of medical health. The combination of MOF light-responsive nanozymes and machine learning algorithms has set a new standard in the field of biosensing, pushed the technical boundaries of disease diagnosis, and is particularly important in the large-scale screening of low-abundance biomarkers.

[0038] The beneficial effects of the present invention are:

[0039] This invention provides a light-responsive nanozyme array sensor. ZnTCPP and metal ions are combined to form a MOF with light-driven catalytic properties. The integration of ZnTCPP into the MOF significantly enhances the array sensor's response speed, sensitivity, and specificity, enabling rapid detection of multiple neurotransmitters and accurate detection of neurotransmitter signatures in complex biological samples such as serum and cerebrospinal fluid. This light-responsive nanozyme array sensor can respond to various neurotransmitters in specific patterns, enabling precise differentiation and quantitative detection within minutes.

[0040] This light-responsive nanozyme array sensor can also be combined with machine learning to detect neurotransmitter characteristics associated with neurological diseases, and can be used for early diagnosis and continuous detection of neurological diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0042] Figure 1 The ZnTCPP used in the embodiment of the present invention is 1 H NMR;

[0043] Figure 2The synthesis methods of ZnTCPP-Zn, ZnTCPP-Al, and ZnTCPP-Ti obtained in Examples 1, 2, and 3 of the present invention (A); TEM images of ZnTCPP-Zn (B), ZnTCPP-Ti (C), and ZnTCPP-Al (D); XRD spectra (E), FT-IR spectra (F), and UV-visible absorption and emission spectra (G) of TCPP, ZnTCPP-Zn, ZnTCPP-Ti, and ZnTCPP-Al;

[0044] Figure 3 The full XPS spectrum (A), O 1s (B), N 1s (C), and Zn 2p (D) of ZnTCPP-Zn prepared in Example 1 of the present invention;

[0045] Figure 4 The full XPS spectrum of ZnTCPP-Al prepared in Example 2 of the present invention (A), O 1s (B), N 1s (C), Zn 2p (D), and Al 2p (E);

[0046] Figure 5 The full XPS spectrum of ZnTCPP-Ti prepared in Example 2 of the present invention (A), O 1s (B), N 1s (C), Zn 2p (D), and Ti 2p (E);

[0047] Figure 6 Schematic diagram of the light-responsive catalytic process (A); step-like behavior of oxidase-like activity when the light source is turned on and off (B); absorption spectra of ZnTCPP-Zn (C), ZnTCPP-Al (D), and ZnTCPP-Ti (E) in 0.10 M acetate buffer containing TMB, under light, MOFs+TMB without light, and MOFs+TMB with light (the insets are the corresponding photos of the three samples, from left to right: TMB+light, TMB+MOFs+light, and TMB+MOFs); effect of pH on the mimetic oxidase activity of ZnTCPP-Zn (F) (the insets are the corresponding photos of the seven samples); effect of different scavengers on the catalytic oxidation of ZnTCPP-Zn under light (G); EPR spectra of a mixed solution of ZnTCPP-Zn and DMPO before and after irradiation with a xenon lamp (300 W) for 0 and 5 minutes (H);

[0048] Figure 7 Steady-state kinetic analysis of ZnTCPP-Zn (A), ZnTCPP-Al (C) and ZnTCPP-Ti (B) when TMB was used as substrate; Lineweaver-Burk plot of ZnTCPP-Zn (D), ZnTCPP-Al (F) and ZnTCPP-Ti (G) when TMB was used as substrate;

[0049] Figure 8 Schematic diagram of the oxidase-mimicking activity of MOFs (A); Schematic diagram of the proposed catalytic mechanism of MOFs (B); Free energy diagram of the catalytic process of MOFs in an acidic environment (C); Charge density difference between ZnTCPP-Zn, ZnTCPP-Ti, and ZnTCPP-Al (D);

[0050] Figure 9 Schematic diagram of the structure of neurotransmitters (A); colorimetric response patterns of array sensors to six neurotransmitters (B); distribution violin plot of characteristic data of neurotransmitter colorimetric response patterns (C); distribution box plot of colorimetric responses of six neurotransmitters in ZnTCPP-Zn (D), ZnTCPP-Ti (E), and ZnTCPP-Al (F); cluster heat map of changes in array signal responses based on six neurotransmitters (G); comparison of neurotransmitter accuracy by training and prediction using different machine learning algorithms (H); LDA typical score plot using the first two factors obtained from the colorimetric response pattern (I) (each point represents the response pattern of a single neurotransmitter. The elliptical area represents the 95% confidence interval);

[0051] Figure 10 Figure 3: Colorimetric response patterns [(A-A0) / A0] of the array sensor to six neurotransmitters at 0.1 μM (A) and 1 μM (C) concentrations; representative scores of linear discriminant analysis (LDA) using the first two factors obtained from the colorimetric response patterns at 0.1 μM (B) and 1 μM (D) concentrations, respectively.

[0052] Figure 11 Schematic diagram of the neurotransmitter sensing mechanism at different concentrations (A); radar plots of the ratiometric fluorescence response patterns to DA (B), EP (C), 5-HT (D), NE (E), HA (F), and ACh (G) at different concentrations;

[0053] Figure 12 Cluster heat maps generated from array signal response changes for visualization of six neurotransmitters at different concentrations, for DA (A), EP (B), 5-HT (C), NE (D), HA (E), and ACh (F) at different concentrations; LDA typical score plots of ratiometric array sensors at different concentrations, for DA (G), EP (H), 5-HT (I), NE (J), HA (K), and ACh (L);

[0054] Figure 13 Figure 2 shows the colorimetric response patterns [(A-A0) / A0] of the array sensor to the DA / EP mixture (A) and the DA / NE mixture (B); and the typical LDA score plots using the first two factors obtained from the colorimetric response patterns of the DA / EP mixture (C) and the DA / NE mixture (D).

[0055] Figure 14 Schematic diagram of the neurotransmitter detection process in real samples (A); radar plots of the colorimetric response patterns for different types of neurotransmitters in cerebrospinal fluid (B) and serum (E); clustered heat maps of changes in array signal responses based on different types of neurotransmitters in cerebrospinal fluid (C) and serum (F); LDA typical score plots of ratiometric array sensors for different types of neurotransmitters in cerebrospinal fluid (D) and serum (G);

[0056] Figure 15 Schematic diagram of the detection of normal mice and AD mice (A); heat map derived from the changes in array signal responses of normal samples and AD samples (11 normal samples and 11 AD samples) (B); principal component analysis (PCA) score plot for identifying AD samples and normal samples (C); linear discriminant analysis (LDA) receiver operating characteristic (ROC) curve for identifying disease samples (D); distribution of difference results of actual samples (E). DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0058] The raw materials and equipment used in the following examples and comparative examples are all conventional commercially available products and can be purchased.

[0059] The experimental reagents and their sources are shown in Table 1.

[0060] Table 1 Experimental reagents and sources

[0061]

[0062] All reagents and materials were of analytical grade and used without further purification.

[0063] The tetracarboxyphenyl zinc porphyrin (ZnTCPP) ligand used was synthesized via the following route.

[0064]

[0065] The specific synthesis method comprises the following steps:

[0066] First, 6.9 g of methyl paraformaldehyde benzoate was completely dissolved in 100 mL of propionic acid and heated to 60°C. Subsequently, 3 mL of pyrrole was slowly added to the solution, and the mixture was reacted under reflux conditions for 24 hours. After the reaction mixture was cooled to room temperature, it was collected by vacuum filtration to obtain the purple crystalline product.

[0067] 0.5 g of the purple crystalline product was refluxed with 2 g of Zn(NO₃)₂·6H₂O in 100 mL of N,N-dimethylformamide (DMF) for 6 hours. After cooling the mixture to room temperature, 200 mL of water was added. The precipitate was collected by centrifugation and washed twice with 100 mL of water. The obtained solid was dissolved in ethyl acetate, and the solution was washed three times with water. The organic layer was dried over anhydrous magnesium sulfate and then spin-dried to obtain a purple solid.

[0068] 0.7 g of the purple solid was dissolved in 25 mL of THF and 25 mL of methanol, and a KOH solution (2.5 g dissolved in 20 mL of water) was added, and the mixture was reacted under reflux overnight. After the reaction, the mixture was cooled to room temperature, THF and methanol were removed under reduced pressure, additional water and ethyl acetate were added to the aqueous phase, and the solution was then acidified with 1 M HCl. The porphyrin was transferred to the organic phase, which was then washed three times with water. The organic phase was dried over anhydrous magnesium sulfate and then spin-dried to obtain a purple solid ZnTCPP.

[0069] The present invention uses methyl p-formylbenzoate and pyrrole as starting materials, and successfully synthesizes ZnTCPP monomer through multi-step reaction. The results show that the ZnTCPP monomer has good thermal conductivity and good thermal conductivity, and has good thermal conductivity. 1 H-NMR, Figure 1 ) to characterize it.

[0070] FT-IR(KBr): v=1681(s),1606(s),1562(m),1500(m),1403(s),1314(m),1178(m),993(s),865(m),793(s),764(m),719(m)cm -1 .

[0071] 1 H NMR (400MHz, DMSO-d6, ppm) 8.81 (s, 8H), 8.38-8.31 (m, 16H).

[0072] Example 1

[0073] A preparation method of ZnTCPP-Zn (i.e. MOF1) comprises the following steps:

[0074] 2.0 mg of ZnTCPP, 10 mg of benzoic acid and 5 mg of Zn(NO3)2·6H2O (dispersed in 2 mL of water) were dissolved in a 25 mL Schlenk tube, dissolved in 12 mL of DMF using ultrasound, and then heated and stirred at 150°C for 3 hours. Then, the mixture was cooled to room temperature and ZnTCPP-Zn was obtained by centrifugation and washing with ethanol five times.

[0075] Example 2

[0076] A preparation method of ZnTCPP-Al (i.e. MOF2) comprises the following steps:

[0077] 10 mg of ZnTCPP and 10 mg of AlCl3·6H2O were added to 4 mL of deionized water and stirred for 30 minutes to ensure complete dissolution. The suspension was then transferred to a 10 mL Teflon-lined autoclave and heated at 180°C for 16 hours. At the end of the heating period, the solution was slowly cooled at a rate of 1.5°C per minute. The solid product was recovered by centrifugation and washed three times with 10 mL of DMF and ethanol to remove unreacted porphyrin and AlCl3·6H2O. After drying, ZnTCPP-Al was obtained.

[0078] Example 3

[0079] A preparation method of ZnTCPP-Ti (i.e. MOF3) comprises the following steps:

[0080] 192 mg of p-aminobenzoic acid, 5.0 mL of isopropyl alcohol, and 104 μL of titanium (IV) isopropyl oxide were mixed and placed in a Teflon-lined autoclave. The mixture was stirred for 30 minutes and then heated in an oven at 100°C for 78 hours. The resulting yellow crystals were thoroughly washed with isopropyl alcohol and dried at 60°C for 12 hours.

[0081] 4 mg of dried yellow crystals, 20 mg of ZnTCPP ligand, and 1 mL of acetic acid were dissolved in 2 mL of a mixed solvent (acetonitrile / tetrahydrofuran = 3:1, volume ratio). The resulting solution was stirred in a Teflon autoclave for 15 minutes and then heated at 160°C for 48 hours. The resulting solid was washed with acetonitrile and DMF to remove unreacted ligand and metal clusters. Finally, the dark purple rod-shaped crystals were dried at 60°C for 12 hours to obtain ZnTCPP-Ti.

[0082] Example 4

[0083] A light-responsive nanozyme array sensor comprises ZnTCPP-Zn, ZnTCPP-Al, ZnTCPP-Ti and a 96-well plate.

[0084] 10 μL ZnTCPP-Zn (1 mg / mL), 40 μL TMB (3.84 mM) and 130 μL HAc-NaAc buffer solution (0.1 mM, pH = 4.0) were added to three wells of a 96-well plate, 10 μL ZnTCPP-Al, 40 μL TMB (3.84 mM) and 130 μL HAc-NaAc buffer solution (0.1 mM, pH = 4.0) were added to the other three wells, and 10 μL ZnTCPP-Ti, 40 μL TMB (3.84 mM) and 130 μL HAc-NaAc buffer solution (0.1 mM, pH = 4.0) were added to the other three wells to form a light-responsive nanozyme array sensor.

[0085] Example 5

[0086] A light-responsive nanozyme array sensor is different from Example 4 in that 6 repetitions are set for each MOF material, and the remaining steps are consistent with Example 4 to form a light-responsive nanozyme array sensor.

[0087] Performance Verification

[0088] 1. Structural characterization

[0089] The MOFs prepared in Examples 1, 2, and 3 of the present invention use ZnTCPP as a ligand, and ZnTCPP-Zn, ZnTCPP-Ti, and ZnTCPP-Al are synthesized by a simple solvent thermal method, respectively. Figure 2 As shown in (A), ZnTCPP ligands were reacted with zinc ions, aluminum ions, and titanium ions respectively through solvothermal methods to prepare MOFs with different structures.

[0090] like Figure 2 As shown in (B), the ZnTCPP-Zn prepared in Example 1 has a typical two-dimensional layered structure. This structural configuration generally provides a greatly expanded specific surface area, thereby enhancing the interaction between the reactant substrate and the catalytic active sites within the material. Figure 2 As shown in (C) and (D), ZnTCPP-Ti and ZnTCPP-Al exhibit rod-like cluster structures.

[0091] like Figure 2 As shown in (E), compared with the X-ray diffraction (XRD) pattern of (4-carboxyphenyl)porphyrin (TCPP), ZnTCPP-Zn, ZnTCPP-Ti and ZnTCPP-Al have obvious characteristic peaks, indicating that they have good crystallinity. The functional groups of these MOFs were further analyzed by FT-IR spectroscopy, such as Figure 2 As shown in (F), at 1686cm -1 The disappearance of the C=O stretching vibration peak at 1600 cm -1The appearance of MO bond at 952cm in TCPP indicates that the carboxyl group of ZnTCPP is modified by metal coordination to form MO node. -1 The peak at 990 cm-1 in ZnTCPP-Zn, ZnTCPP-Ti and ZnTCPP-Al corresponds to the in-plane vibration of NH. -1 The nearby drug lord peak indicates the presence of Zn in the porphyrin ring center. 2+ , due to Zn 2+ With the introduction of , the Q bands of all ZnTCPP MOFs are reduced to two peaks, which increases the symmetry of porphyrin and brings the energy levels closer, resulting in a decrease in the Q band. Figure 2 As shown in (G), a blue shift was observed in the fluorescence emission peak of these ZnTCPP MOFs. This blue shift is due to the fact that the metal ligand coordination leads to a decrease in the electron delocalization in the porphyrin molecule, thereby increasing the energy level of the lowest excited state and increasing the energy gap of the S→S transition. Finally, X-ray photoelectron spectroscopy (XPS) confirmed that the ZnTCPP-Zn( Figure 3 )、ZnTCPP-Ti( Figure 5 ) and ZnTCPP-Al( Figure 4 ) was successfully synthesized.

[0092] 2. Study on the Effect of MOFs on TMB

[0093] To determine the effects of the MOFs prepared in Examples 1-3 on 3,3',5,5'-tetramethylbenzidine (TMB) under illumination, three control experiments were conducted. Each experiment consisted of a mixture of MOF (50 μL), TMB (3.84 mM, 200 μL), and HAc-NaAc buffer solution (650 μL, 0.1 M) (pH = 4.0). The MOFs added to each group were identical. The control group repeated the above steps. A blank group replaced the TMB (50 μL) in the culture medium with DMF (50 μL) and repeated the above steps. Both the illuminated and blank groups were illuminated with a 30W flashlight for five minutes to observe the color changes in each sample.

[0094] The above-mentioned photoresponsive catalytic process is as follows Figure 6 As shown in (A), under light irradiation, MOF catalyzes the oxidation of colorless TMB to generate blue oxidized TMB (oxTMB) product. Figure 6 As shown in (B), further experiments using alternating photoperiods revealed the stepwise behavior of the oxidase-like activity of the MOFs, indicating that they possess light-controllable oxidase-like activity.

[0095] Using a UV-visible spectrophotometer, the scanning spectrum of each sample was measured in the range of 500-750 nm, using DMF (50 μL), TMB (3.84 mM, 200 μL), HAc-NaAc buffer solution (650 μL, 0.1 M) (pH = 4.0), and the absorbance of each sample at 652 nm was measured simultaneously. Figure 6 As shown in (C), the TMB sample alone has no obvious absorption peak under light irradiation, while the ZnTCPP-Zn and TMB mixture observes a clear oxTMB absorption peak at 652nm after light irradiation; in contrast, the absorption of the unirradiated sample at 652nm is negligible, which further confirms the light-responsive activity of ZnTCPP-Zn. Figure 6 As shown in (D) and (E), ZnTCPP-Ti and ZnTCPP-Al also exhibited excellent light-responsive oxidase-like activity due to the addition of the same ZnTCPP photoactive ligand.

[0096] In order to maximize the light-responsive oxidase activity of MOF-based nanozymes, this example systematically explored the pH-dependent activity spectrum under light. The light-responsive oxidase activity was detected at different pH values, such as Figure 6 As shown in (F), the optimal catalytic performance was achieved at pH 4.0, demonstrating the unique proton-coupled electron transfer mechanism inherent in this photoresponsive system.

[0097] The oxidase-like catalytic mechanism of photoactive ZnTCPP MOFs may involve multiple reactive oxygen species, including hydroxyl radicals, singlet oxygen, hydrogen peroxide, and superoxide anions. To clarify the main catalytic pathway of ZnTCPP-Zn, this example conducted targeted scavenging experiments using established quenchers: mannitol, tryptophan, catalase, superoxide dismutase, and ethanol. These substances were added during the catalytic process, and the absorbance was measured. The experimental results are shown in Figure 2. Figure 6 As shown in (G), the signal decreased significantly after adding mannitol, so it is possible that hydroxyl radicals (·OH) oxidized TMB and changed its color.

[0098] In order to verify the mechanism of the involvement of reactive oxygen species (ROS) in the catalytic cycle, electron paramagnetic resonance (EPR) spectroscopy was used, using 5,5-dimethyl-1-pyrroline N-oxide (DMPO) as a spin trap. ZnTCPP-Zn (20 μL, 1 mg / mL) and DMPO (100 mM) were added to 200 μL acetate buffer solution (0.1 M, pH = 4), and the signal was measured after 5 minutes of illumination. The detection results are shown in Figure 2. Figure 6As shown in (H), the ZnTCPP-Zn photoresponsive nanozyme exhibits characteristic EPR signals corresponding to DMPO-OOH adducts, which are diagnostic markers for the generation of hydroxyl radicals (·OH). These results indicate that ·OH is the main ROS mediator in the photocatalytic oxidase simulation process.

[0099] 3. MOFs kinetics research

[0100] To determine the oxidation of TMB by MOFs at varying concentrations, TMB solutions of varying concentrations were prepared as follows: 200 μL of TMB of varying concentrations, 50 μL of MOF, and 650 μL of HAc-NaAc buffer (0.1 M, pH 4.0) were thoroughly mixed to a total volume of 900 μL. After 5 minutes of illumination, 200 μL of the test solution was placed in a 96-well plate, and the absorbance of each solution at 652 nm was measured in triplicate in a microplate reader.

[0101] Steady-state kinetics were used to characterize the oxidase-like activity of ZnTCPP MOFs. Figure 7 The typical Michaelis-Menten curves of ZnTCPP-Zn, ZnTCPP-Al and ZnTCPP-Ti are shown. According to the function v=v max [S] / (K m +[S]) calculated the kinetic parameter v max and K m , where v represents the initial velocity, [S] is the substrate concentration, and v max and K m The maximum reaction rate and Michaelis constant are shown in Table 2. ZnTCPP-Zn exhibits the highest reaction rate and the lowest K m value, indicating that it has the most significant catalytic effect, which may be attributed to its unique two-dimensional morphology.

[0102] Table 2 Kinetic parameters of ZnTCPP-Zn, ZnTCPP-Al and ZnTCPP-Ti

[0103] MOFs <![CDATA[v max (M / s)]]> <![CDATA[K m (mM)]]> ZnTCPP-Zn <![CDATA[2.384×10 -6 ]]> 0.219 ZnTCPP-Al <![CDATA[2.752×10 -6 ]]> 1.041 ZnTCPP-Ti <![CDATA[7.388×10 -7 ]]> 0.089

[0104] 4. Theoretical calculation

[0105] Density functional theory (DFT) was performed using the Vienna First Principles Package (VASP). The exchange-correlation potential was calculated using the Perdew-Burke-Ernzerhof generalized gradient approximation (GGA-PBE). To treat the interaction between the ion core and the valence electrons, the projected augmented wave (PAW) method was used. The plane wave cutoff energy was fixed at 500 eV. The given structural model was relaxed until the Hellmann–Feynman force was less than Energy change is less than 10 -6 eV. The vacuum layer thickness is set to To minimize interlayer interactions. During the relaxation process, the Brillouin zone is represented by a 7 × 7 × 1 k-point grid centered at Γ. To describe the dispersion interactions of all atoms in the adsorption model, Grimme's DFT-D3 method was used.

[0106] The potential mechanism of the oxidase-mimicking activity of MOFs was investigated by DFT calculations. O2 was catalyzed by MOF to generate ·OH, which further oxidized the colorless TMB to generate the blue oxTMB product ( Figure 8 In (A), free O2 molecules are easily adsorbed to the Zn center of ZnTCPP, activating O2(*)( Figure 8 In (B) and (C), the activated O2(*) undergoes uniform cleavage at the Zn site under photocatalysis, generating two O(*) bound to the Zn site. In this rate-determining step, the energy barriers for ZnTCPP-Ti, ZnTCPP-Al, and ZnTCPP-Zn are 3.68 eV, 2.16 eV, and 1.85 eV, respectively. Figure 8 (C)). Subsequently, the protonated hydrogen atom approaches two O(*) atoms, forming two OH(*) radicals, one of which absorbs energy and transforms into a free ·OH radical. The energy barriers of this process are 2.02 eV, 1.66 eV, and 0.93 eV for ZnTCPP-Ti, ZnTCPP-Al, and ZnTCPP-Zn, respectively. Figure 8 (C)). Finally, the protonated hydrogen atoms combine with OH(*) radicals to generate H2O(*) and release H2O, restoring ZnTCPP to its initial state. Throughout the entire process, the free energy required for the rate-determining step based on the Zn node of ZnTCPP-Zn is significantly lower than that of the other two MOFs. Differential charge density calculations show that Zn-TCPP-Zn exhibits the highest electron transfer at the active Zn atomic site ( Figure 8(D)). This indicates that the introduction of the Zn cation group significantly enhances the electron cloud density around the central Zn atom in the Zn-TCPP framework. The increased electron transfer strengthens the interaction between the Zn atom and the ORR intermediate, making adsorption and activation more efficient. In addition, the enhanced electron donation ability of the Zn cation better supports the electron-intensive steps in the ORR process. The introduction of the Zn cation may have adjusted the electronic state of the central Zn atom in Zn-TCPP and optimized the adsorption strength of the ORR intermediate to achieve a balance state - neither too strong (hindering desorption) nor too weak (hindering reaction progress). This optimization promotes the rapid adsorption and desorption of intermediate species and avoids reaction bottlenecks. The reduced free energy barrier means that ZnTCPP-Zn can carry out electron transfer and oxygen reduction more efficiently, resulting in faster reaction kinetics. In contrast, the differential charge density and free energy paths of Zn-TCPP-Al and Zn-TCPP-Ti indicate that the electron transfer of the central Zn atom is lower. This is likely due to the stronger electron sharing or shielding effect exhibited by Al and Ti cations, which weakened the activity of the central Zn atom in the Zn-TCPP framework. In addition, the significant difference in the free energy barriers of the three materials during the entire process is beneficial for the establishment of array sensors.

[0107] 5. Detection of different types of neurotransmitters

[0108] The light-responsive nanozyme array sensor prepared in Example 5 was used to identify six neurotransmitters (DA, EP, 5-HT, NE, HA, ACh, and the structure is shown in FIG. Figure 9 (A)). 20 μL of neurotransmitter solution was added to each MOF in a 96-well plate, resulting in a final concentration of 10 μM. Next, the samples were illuminated at room temperature for 5 minutes. The absorbance at 652 nm was measured six times for each sample. The change in absorbance is expressed as (A-A0) / A0. A and A0 represent the absorbance with and without the addition of neurotransmitter, respectively. The detection process for low-concentration samples (0.1 μM and 1 μM) was the same as described above.

[0109] Test results such as Figure 9As shown in (B), each MOF in the array exhibited a different colorimetric signal response to the six neurotransmitters, indicating that different neurotransmitters had different effects on the same MOF. The colorimetric signal response of the ZnTCPP-Zn nanozyme significantly decreased upon the addition of neurotransmitters, while the colorimetric signal responses of ZnTCPP-Ti and ZnTCPP-Al increased or decreased to varying degrees, indicating that different neurotransmitters have different effects on MOF activity. This phenomenon can be attributed to the unique molecular structures and chemical properties of different neurotransmitters, which influence their interactions with the metal nodes and porphyrin ligands of MOFs. These interactions can inhibit or enhance the oxidase-like catalytic activity of MOFs, resulting in changes in the oxidation rate of the colorimetric substrate (TMB) and, in turn, differences in color intensity. Neurotransmitters with electron-donating groups may reduce the electron density of the catalytic center, thereby reducing oxidation efficiency and leading to a weakened color response, as observed in ZnTCPP-Zn. Conversely, neurotransmitters with electron-withdrawing groups can promote electron transfer, enhance catalytic activity, and produce stronger colorimetric signals, as seen in ZnTCPP-Ti and ZnTCPP-Al. The diverse regulation of MOF activity by different neurotransmitters is crucial for generating unique response patterns and achieving precise discrimination in array sensors.

[0110] A violin plot of the original data is generated, such as Figure 9 As shown in (C), the wide variability of the data in the array is visually demonstrated and the significant responsiveness and variability of the colorimetric reaction is highlighted. In order to gain a deeper understanding of the data distribution, a box plot of the raw data was generated, as shown in Figure 9 (D), (E), and (F) reveal significant differences in the colorimetric responses between different analytes. In the clustered heat map, each MOF in the array sensor exhibits a unique colorimetric change in response to neurotransmitters ( Figure 9 Middle (G). The colorimetric response patterns of neurotransmitters were converted to Euclidean distances using a hierarchical clustering algorithm (HCA), which successfully classified all six neurotransmitters without misclassification, demonstrating the array's robust ability to discriminate between different types of neurotransmitters.

[0111] In order to improve the ability of the array sensor to distinguish six neurotransmitters and predict unknown samples, a variety of machine learning algorithms were used for model training, including Bernoulli Naive Bayes (BNB), Gaussian Process Classifier (GPC), K Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Logistic Regression (LR) ( Figure 9(H)). Only the BNB algorithm had an accuracy below 95%, while most algorithms had training and test accuracies exceeding 95%. The LDA algorithm was used to create a visual classification model. The training matrix (3ZnTCPP MOFs × 6 neurotransmitters × 6 repeated experiments) was converted into a typical score by the LDA algorithm. In the LDA plot, each factor represents the degree of contribution to the overall classification, showing that factor 1 and factor 2 accounted for 48.1% and 27.2% of the variation, respectively. The six neurotransmitters were classified into six different groups. All neurotransmitters were clearly classified without any misclassification, and the classification accuracy was 100%. 32 unknown neurotransmitters were correctly identified with 100% accuracy. In addition, the detection of low concentrations of neurotransmitters was also studied, and the array sensor still showed a clear response to different types of neurotransmitters at lower concentrations (0.1μM and 1μM). It is worth noting that the array sensor was able to accurately distinguish the six neurotransmitters at these lower concentrations, and the model accuracy and the prediction accuracy obtained by cross-classification method both reached 100% ( Figure 3-10 ). The array sensor performed well in neurotransmitter detection.

[0112] 6. Detection of different concentrations of neurotransmitters

[0113] After successfully identifying different types of neurotransmitters, the ability of the array sensor to distinguish different concentrations was further evaluated. The six neurotransmitters were diluted to different concentrations with deionized water. The final concentrations of the neurotransmitters were 0.5μM, 2μM, 3.5μM, 5μM, 6.5μM, 8μM and 9.5μM. Next, the above operation was repeated. 20μL of neurotransmitter was mixed with the sensor elements in a 96-well plate (6 replicates). The samples were exposed to light for 5 minutes at room temperature, and the instrument setting parameters were the same as above.

[0114] Test results such as Figure 11 As shown, the array sensor exhibits distinct colorimetric responses to individual neurotransmitters at various concentrations. The shift in the radius of the radar plot reflects the relative colorimetric response. The radar plots for each neurotransmitter at the seven concentrations exhibit distinct shapes and sizes, indicating that these seven concentrations can be easily distinguished by their distinct three-signal radii.

[0115] In the cluster heat map, each channel exhibits a unique response to the colorimetric changes of the six neurotransmitters at different concentrations ( Figure 12 The different concentrations of each neurotransmitter were clearly classified into seven different clusters, and samples with the same concentration were grouped into one cluster, with no misclassification. The data matrix consisted of 7 concentrations × 3 sensor elements × 6 replicates and was further analyzed using the LDA algorithm. Figure 12As shown, the seven concentrations of each neurotransmitter were successfully distinguished and clearly clustered into distinct groups. Although NE and HA overlapped slightly in factors 1 and 2, they were well distinguished in factors 1 and 3. The classification matrix using the interpolation method showed 100% discrimination accuracy. The prediction accuracy of the six neurotransmitters at different concentrations was 97%, 100%, 100%, 100%, 96%, and 100%, respectively. This demonstrates that the array sensor is capable of semiquantitative detection of various neurotransmitters.

[0116] 7. Detection of DA analog mixtures

[0117] To evaluate the array's ability to discriminate neurotransmitter mixtures, two binary solutions with varying ratios were investigated: DA and NE, and DA and Ep. Using DA and NE as an example, mixed solutions were prepared at five different ratios. The final concentration ratios of the two neurotransmitters in the mixed solutions were DA:NE = 5 μM:0 μM, 3 μM:1.5 μM, 2.5 μM:2.5 μM, 1.5 μM:3.5 μM, and 0 μM:5 μM. Twenty μL of the neurotransmitter mixtures at varying ratios were mixed with sensor elements in a 96-well plate (six replicates). The samples were exposed to light at room temperature for 5 minutes. The instrument settings were the same as described above. The identification process for mixed solutions of DA and Ep at varying ratios was the same as described above.

[0118] like Figure 13 As shown, the array sensor displayed a unique colorimetric response pattern for each mixture, which could be clearly distinguished using LDA. The model successfully classified the mixture without any misclassification, achieving 100% classification accuracy. Furthermore, the prediction accuracy for the DA analog mixture was 100% across all tested concentrations, confirming the robustness and precision of the array sensor in distinguishing between different mixtures. This high performance was validated across different machine learning models, further solidifying the reliability of the array sensor for real-time multiplexed detection of neurotransmitter mixtures.

[0119] 8. Detect different types of neurotransmitters in real samples

[0120] Different types of neurotransmitters were spiked into real samples (cerebrospinal fluid and serum). The identification process for these samples followed the same procedure as described above, and the change in absorbance at 652 nm was assessed using the same instrument settings.

[0121] The shape and size of each neurotransmitter in the complex matrix Figure 14 Middle (B) and Figure 14 As shown in (E), these six neurotransmitters can be easily distinguished by their different three-signal radii. Figure 14 Middle (C) and Figure 14In (F), the six neurotransmitters affected the three enzyme-like activities to varying degrees in both CSF and serum. The neurotransmitters were clearly classified into six distinct clusters, with no misclassification observed in either CSF or serum. LDA results showed that the six neurotransmitters in different matrices were clearly separated from each other ( Figure 14 Middle (D) and Figure 14 Middle (G). A cross-validated bootstrap classification matrix demonstrated 100% accuracy in real samples. A blind test also demonstrated 100% accuracy. These results demonstrate the array sensor's robustness to interference and its successful detection of neurotransmitters in complex samples.

[0122] 9. Machine Learning for Neurological Disease Diagnosis

[0123] To demonstrate its practical application, the array sensor was combined with machine learning to detect serum from healthy mice and AD model mice, in order to achieve clinical diagnosis. Figure 15 As shown in (A). Serum samples were collected from 11 Alzheimer's disease (AD) model mice and 11 normal mice. The serum was divided into disease group and normal group, and each serum was evenly diluted to prepare serum dilution. For the disease group, 20 mL of the disease group serum dilution was mixed with the sensor element in a 96-well plate. The sample was then exposed to light for 5 minutes at room temperature. The instrument setting parameters were the same as previously described. In the normal group, the disease group serum dilution was replaced by the normal group serum dilution, while all other procedures remained the same as the disease group.

[0124] Serum samples were obtained from 22 mice, and a dataset (3 sensor elements × 22 samples) was generated for AD diagnosis. The heat map generated from the array sensor responses clearly demonstrated significant changes in signal patterns between the two groups, illustrating the sensor's ability to detect subtle differences indicative of AD. Figure 15 (B)). Principal component analysis (PCA) was used to generate independent principal components. The PCA score plot effectively distinguished normal samples from AD samples. The first two principal components explained a significant portion of the variance, further enhancing the discriminative ability of the array sensor ( Figure 15 To evaluate the performance of the array sensor in diagnosing AD, several machine learning algorithms were employed. Comparison of training and prediction accuracy showed that LDA and decision tree (DT) achieved the highest performance, with 100% accuracy in distinguishing normal samples from AD samples. This result was further supported by receiver operating characteristic (ROC) analysis, where LDA showed the largest area under the curve (AUC), indicating its superior sensitivity and specificity ( Figure 15These results highlight the effectiveness of combining array sensor technology with machine learning for AD diagnosis. In addition, this example also explores the distribution of differential results of actual samples, further verifying the high accuracy and robustness of the model in practical applications ( Figure 15 Middle (E). Array sensors combined with machine learning algorithms provide a reliable and efficient method for early detection, which is crucial for improving care and treatment outcomes for AD patients. This approach not only demonstrates the potential of array sensors in neurodiagnosis but also opens new avenues for real-time, noninvasive monitoring of diseases such as Alzheimer's disease.

[0125] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A light-responsive nanozyme array sensor, characterized in that: The invention comprises MOF1, MOF2 and MOF3, wherein the organic ligands of MOF1, MOF2 and MOF3 are all ZnTCPP, and the metal ions are respectively zinc ion, titanium ion and aluminum ion.

2. The light-responsive nanozyme array sensor according to claim 1, wherein The MOF1 is a two-dimensional layered structure, and the MOF2 and MOF3 are both rod-shaped cluster structures.

3. A method for preparing the light-responsive nanozyme array sensor according to claim 1 or 2, characterized in that: include: Zinc source, titanium source and aluminum source were mixed with ZnTCPP respectively, and MOF1, MOF2 and MOF3 were prepared by solvothermal method; MOF1, MOF2 and MOF3 were arranged into an array to obtain a light-responsive nanozyme array sensor.

4. The preparation method according to claim 3, wherein The preparation method of MOF1 includes: dissolving a zinc source and ZnTCPP in an organic solvent, stirring and reacting at 140-160° C. for 1-5 hours to obtain MOF1.

5. The preparation method according to claim 4, wherein The zinc source includes any one of zinc nitrate, zinc chloride and zinc acetate; Preferably, the organic solvent comprises at least one of N,N-dimethylformamide and dimethyl sulfoxide; Preferably, benzoic acid is further added during the preparation of MOF1, and the mass ratio of the zinc source, ZnTCPP, benzoic acid and organic solvent is (4-6):(1-3):(8-12):(10-13); Preferably, after the reaction is completed, the mixture is cooled to room temperature, centrifuged, and washed to obtain MOF1.

6. The preparation method according to claim 3, wherein The preparation method of MOF2 includes: dissolving an aluminum source and ZnTCPP in water, stirring and reacting at 160-200°C for 15-20 hours to obtain MOF2.

7. The preparation method according to claim 6, wherein The aluminum source includes any one of aluminum chloride, aluminum nitrate and aluminum sulfate; Preferably, the mass ratio of the aluminum source, ZnTCPP and water is (8-12):(8-12):(3-5); Preferably, after the reaction is completed, the reaction system is cooled at a rate of 1-2°C / min, centrifuged, washed, and dried to obtain MOF2.

8. The preparation method according to claim 3, wherein The preparation method of MOF3 includes: mixing tetraisopropyl titanate, p-aminobenzoic acid and isopropyl alcohol, stirring and reacting at 80-120°C for 75-80 hours to obtain an intermediate; dissolving the intermediate and ZnTCPP in an organic solvent, reacting at 150-170°C for 45-50 hours to obtain MOF3; Preferably, the usage ratio of tetraisopropyl titanate, p-aminobenzoic acid and isopropyl alcohol is (90-110 μL):(190-200 mg):(4-6 mL); Preferably, acetic acid is also added during the preparation of MOF3, and the intermediate, ZnTCPP and acetic acid are dissolved in an organic solvent, wherein the organic solvent is a mixed solvent of acetonitrile and tetrahydrofuran in a volume ratio of 2-4:1, and the amount ratio of the intermediate, ZnTCPP, acetic acid and organic solvent is (3-5 mg): (15-25 mg): (0.5-1.5 mL): (1.5-2.5 mL).

9. Use of the light-responsive nanozyme array sensor according to claim 1 or 2 or the light-responsive nanozyme array sensor prepared by the preparation method according to any one of claims 3 to 8 in neurotransmitter detection and / or preparation of neurological disease diagnostic products; Preferably, the neurotransmitter includes any one or more of dopamine, epinephrine, norepinephrine, serotonin, histamine and acetylcholine; Preferably, the neurological disease includes any one or more of Alzheimer's disease, Parkinson's disease and multiple sclerosis.

10. A neurotransmitter detection kit, characterized in that: A light-responsive nanozyme array sensor comprising the light-responsive nanozyme array sensor according to claim 1 or 2 or a light-responsive nanozyme array sensor prepared by the preparation method according to any one of claims 3 to 8; Preferably, the neurotransmitter includes any one or more of dopamine, epinephrine, norepinephrine, serotonin, histamine and acetylcholine.

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