Optical response nano-enzyme array sensor and preparation method and application thereof

Abstract

The application discloses a kind of light response nanoenzyme array sensor and its preparation method and application, belong to neurotransmitter detection technical field.The kind of light response nanoenzyme array sensor, including MOF1, MOF2 and MOF3, the organic ligand of MOF1, MOF2 and MOF3 are all ZnTCPP, and metal ions are zinc ion, titanium ion and aluminum ion respectively.Besides the larger specific surface area of MOFs, the interaction of reaction substrate and catalytic active site in material is enhanced, the response sensitivity of sensor is significantly improved, and its light response characteristics can be regulated by light catalytic activity, realize the dynamic signal amplification of neurotransmitter.The array sensor can detect various neurotransmitters, realize accurate distinction and quantification within a few minutes.Even in complex biological samples, the light response nanoenzyme array sensor can still maintain high precision.

Description

Technical Field

[0001] This invention belongs to the field of neurotransmitter detection technology, specifically relating to a photoresponsive nanoenzyme array sensor, its preparation method, and its application. Background Technology

[0002] The information disclosed in this background section is intended only to enhance understanding of the overall background of the invention and is not necessarily to be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

[0003] Neurological diseases, including Alzheimer's disease (AD), Parkinson's disease, and multiple sclerosis (MS), pose a significant health challenge due to their complex pathogenesis and widespread prevalence. These diseases are often associated with dysregulation of neurotransmitter regulation, leading to progressive cognitive and motor dysfunction. Abnormal neurotransmitter concentrations are closely related to diseases such as Parkinson's, AD, and MS. However, traditional diagnostic methods for neurological diseases are time-consuming and lack sufficient sensitivity, failing to effectively detect neurotransmitters in their early stages. Therefore, developing 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, tetracarboxyphenyl zinc porphyrin (ZnTCPP), as a photoactive ligand, forms MOFs that coordinate with metal ions, exhibiting both photoresponsive properties and peroxidase-like activity, and can enhance signal output through photomodulation of catalytic reactions. However, current applications of ZnTCPP-based MOFs are mostly concentrated in photocatalytic degradation of pollutants or tumor therapy, and have not yet been applied to the field of neurotransmitter detection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a photoresponsive nanozyme array sensor, its preparation method, and its application. This invention proposes for the first time a novel photoresponsive nanozyme array sensor, combined with metal-organic frameworks (MOFs), for rapid, sensitive, and multiplex detection of neurotransmitters.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention provides a photoresponsive nanoenzyme 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 in a specific pattern. Its core lies in using spatially distributed multiple sensing units, combined with signal integration and analysis techniques, to achieve multi-target detection, high-sensitivity identification, or complex signal interpretation. It is widely used in environmental monitoring, medical diagnosis, food safety, and industrial process control. This invention uses MOFs constructed from Zn(II) porphyrin compounds (ZnTCPP) as sensing units to form an array sensor. The large specific surface area of ​​MOFs enhances the interaction between the reaction substrate and the catalytically active sites in the material, significantly improving the sensor's response sensitivity. Furthermore, its photoresponse characteristics can be used to modulate catalytic activity through light (e.g., photocontrolled enzyme reactions), enabling dynamic signal amplification of neurotransmitters. The porous structure of MOFs can selectively adsorb target molecules (e.g., dopamine, serotonin), allowing detection based on changes in the optical signals of catalytic reaction products. In addition, photo-driven catalysis greatly accelerates the response speed of MOFs. Mimicking the mammalian olfactory system, this array sensor responds to various neurotransmitters in a specific mode, achieving accurate differentiation and quantification within minutes. This photoresponsive nanozyme array sensor maintains high accuracy even in complex biological samples such as serum and cerebrospinal fluid. It can detect neurotransmitter signatures associated with neurological diseases, such as Alzheimer's disease. This platform shows significant potential for the early diagnosis and continuous monitoring of neurological disorders.

[0009] Furthermore, photoresponsive nanozyme array sensors composed of different MOF units can simultaneously detect multiple neurotransmitters. By comparing the signal differences of different units, interference from complex matrices in biological samples can be eliminated, demonstrating high anti-interference capability.

[0010] In some embodiments of the present invention, MOF1 is a two-dimensional layered structure, while MOF2 and MOF3 are both rod-like cluster structures. The two-dimensional layered structure significantly increases the specific surface area of ​​the MOF, thereby enhancing the interaction between the reactant substrate and the catalytically active sites within the material and improving detection sensitivity. MOF1, MOF2, and MOF3 all possess photoresponsive oxidase activity, and the three MOF nanozymes exhibit significant differences in free energy barriers during catalysis, which is beneficial for establishing an array sensor.

[0011] This invention integrates three nanozymes, MOF1, MOF2 and MOF3, into a photoresponsive nanozyme array sensor. By utilizing specific binding interactions and catalytic activities, cross-reaction signals are generated when detecting neurotransmitters, thereby achieving rapid and sensitive detection of neurotransmitters and enabling the simultaneous detection of multiple neurotransmitters.

[0012] Understandably, the term rod-like cluster structure refers to both the rod-like structure of MOF materials and the cluster structure formed by the aggregation of rod-like MOF materials.

[0013] In a second aspect, the present invention provides a method for preparing the above-mentioned photoresponsive 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 photoresponsive nanozyme array sensor.

[0014] This invention synthesizes three MOF materials using a simple solvent method and arranges them into an array to obtain a photoresponsive nanozyme array sensor. The preparation method provided by this invention is simple to operate and easy to scale up for industrial production.

[0015] In some embodiments of the present invention, 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. The present invention prepares MOF1, i.e., ZnTCPP-Zn MOF, through a simple solution-thermal method, which is simple to implement.

[0016] The zinc source includes any one of zinc nitrate, zinc chloride, and zinc acetate. Because the type of zinc source significantly affects the structure and properties of the resulting MOF, compared to chloride and acetate ions, nitrate is a weakly coordinating anion, easily replaced by the carboxylic acid groups 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 higher specific surface area. Therefore, zinc nitrate is preferred as the zinc source.

[0017] The organic solvent includes at least one of N,N-dimethylformamide (DMF) and dimethyl sulfoxide (DMSO). It is understood that the type of organic solvent significantly affects the structure, morphology, and function of the resulting MOF. Highly polar solvents, such as DMF and DMSO, promote the deprotonation of ligands and the complete dissolution of metal ions, which is beneficial for forming MOFs with high crystallinity and high specific surface area, and thus improves the detection sensitivity of the photoresponsive nanozyme array sensor. To further increase the specific surface area of ​​MOF1, the organic solvent is preferably DMF.

[0018] In order to assist in the dissolution of the zinc source and ligands and regulate crystal growth, benzoic acid (BA) is 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+ The coordination rate of ZnTCPP controls the pore size and morphology of MOFs. It also helps ZnTCPP disperse in DMF, guiding the orderly assembly of the MOF framework to form a hierarchical 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 exhibits superior performance.

[0020] After the reaction is complete, the mixture is cooled to room temperature, centrifuged, and washed to obtain MOF1. Washing effectively removes unreacted porphyrins and zinc sources, reducing the impurity content in MOF1.

[0021] In some embodiments of the present invention, 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. The present invention prepares MOF2, i.e., ZnTCPP-Al MOF material, via a one-step hydrothermal method, which is a simple preparation process.

[0022] The aluminum source includes any one of aluminum chloride, aluminum nitrate, and aluminum sulfate. Using the above aluminum source can produce highly crystalline MOF2, thereby improving the detection sensitivity of the photoresponsive 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 exhibits high crystallinity.

[0024] After the reaction was completed, the reaction system was cooled at a rate of 1-2℃ / min, centrifuged, washed, and dried to obtain MOF2. Washing was performed to thoroughly remove unreacted porphyrins and pyrophyll sources, thereby improving the purity of MOF2.

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

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

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

[0028] Acetic acid is added during the preparation of MOF3 to dissolve the intermediate, ZnTCPP, and acetic acid in an organic solvent. The addition of acetic acid can adjust the pH, promote ligand deprotonation, inhibit the excessively rapid hydrolysis of the titanium precursor, help metal ions bind to ligands, regulate crystal growth, and optimize the crystallinity and stability of MOF.

[0029] The organic solvent is a mixture of acetonitrile and tetrahydrofuran at a volume ratio of 2-4:1. This mixed solvent promotes uniform dispersion of the metal source and ligands, slows down the hydrolysis rate of titanium, avoids the formation of amorphous TiO2, and prevents excessive interference with metal-ligand bonding. It also regulates coordination, guides crystallization, and significantly optimizes 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 structure and photoelectric properties.

[0031] A third aspect of the present invention provides an application of the above-described photoresponsive nanozyme array sensor or the photoresponsive nanozyme array sensor prepared by the above-described preparation method in neurotransmitter detection.

[0032] The photoresponsive nanoenzyme array sensor based on ZnTCPP MOF provided by this invention can rapidly and sensitively detect neurotransmitters, and has broad application prospects for the 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, adrenaline, noradrenaline, serotonin, histamine, and acetylcholine.

[0034] The neurological diseases mentioned 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-described photoresponsive nanozyme array sensor or the photoresponsive nanozyme array sensor prepared by the above-described preparation method.

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

[0037] When using the neurotransmitter detection kit provided by this invention for neurotransmitter detection, the combination with machine learning algorithms enables the kit to perform multiple detections simultaneously, allowing for the analysis of various neurotransmitters. This ability to differentiate and quantify neurotransmitters in real time represents a breakthrough in diagnostic technology, providing a rapid, non-invasive, and cost-effective alternative to traditional methods. Furthermore, this photoresponsive nanozyme array sensor demonstrates excellent performance in distinguishing normal samples from Alzheimer's disease (AD) samples, validating its potential for clinical applications. Advanced machine learning algorithms further enhance the sensor's diagnostic capabilities, providing a powerful tool for early disease identification in clinical settings. This technology not only holds immense potential for the early detection of neurological diseases but also provides a practical and scalable solution for continuous monitoring, opening up new avenues in healthcare. The combination of MOF photoresponsive nanozymes and machine learning algorithms sets a new standard in the field of biosensing, pushing the technological boundaries of disease diagnosis, particularly significant for large-scale screening of low-abundance biomarkers.

[0038] The beneficial effects of this invention are as follows:

[0039] This invention provides a photoresponsive nanozyme array sensor. ZnTCPP is combined with metal ions to form a photo-driven catalytic MOF. 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 precise detection of neurotransmitter characteristics in complex biological samples such as serum and cerebrospinal fluid. This photoresponsive nanozyme array sensor can respond to various neurotransmitters in specific modes, achieving accurate differentiation and quantification within minutes.

[0040] This photoresponsive nanoenzyme array sensor can also be combined with machine learning to detect neurotransmitter characteristics associated with nervous system diseases, and can be applied to the early diagnosis and continuous monitoring of nervous system diseases. Attached Figure Description

[0041] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0042] Figure 1 ZnTCPP used in the embodiments of the present invention 1 H NMR;

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

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

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

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

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

[0048] Figure 7 Steady-state dynamics analysis of ZnTCPP-Zn(A), ZnTCPP-Al(C), and ZnTCPP-Ti(B) with TMB as substrate; Lineweaver-Burk plots of ZnTCPP-Zn(D), ZnTCPP-Al(F), and ZnTCPP-Ti(G) with TMB as substrate;

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

[0050] Figure 9 A schematic diagram of the structure of neurotransmitters (A); colorimetric response patterns of the array sensor for six neurotransmitters (B); violin plot of the distribution of characteristic data of neurotransmitter colorimetric response patterns (C); box plot of the distribution of colorimetric responses of the six neurotransmitters in ZnTCPP-Zn (D), ZnTCPP-Ti (E), and ZnTCPP-Al (F); clustering heatmap of array signal response changes based on the six neurotransmitters (G); comparison of the accuracy of neurotransmitters in training and prediction using different machine learning algorithms (H); LDA typical score plot using the first two factors obtained from the colorimetric response patterns (I) (each point represents the response pattern of a single neurotransmitter. Elliptical regions represent 95% confidence intervals);

[0051] Figure 10 Colorimetric response patterns [(A-A0) / A0] of six neurotransmitters at concentrations of 0.1 μM (A) and 1 μM (C) were obtained from the array sensor; linear discriminant analysis (LDA) typical score plots were performed using the first two factors obtained from the colorimetric response patterns at concentrations of 0.1 μM (B) and 1 μM (D).

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

[0053] Figure 12 For visualization of six neurotransmitters at different concentrations, clustering heatmaps generated from array signal response changes are presented, targeting the changes of DA(A), EP(B), 5-HT(C), NE(D), HA(E), and ACh(F) at different concentrations; typical LDA score maps of ratio array sensors at different concentrations are presented, targeting DA(G), EP(H), 5-HT(I), NE(J), HA(K), and ACh(L).

[0054] Figure 13 Colorimetric reaction patterns [(A-A0) / A0] of the array sensor for DA / EP mixture (A) and DA / NE mixture (B); typical LDA score plots were generated using the first two factors obtained from the colorimetric reaction patterns of DA / EP mixture (C) and DA / NE mixture (D);

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

[0056] Figure 15 A schematic diagram of the detection of normal mice and AD mice (A); a heatmap derived from the array signal response changes of normal samples and AD samples (11 normal samples and 11 AD samples) (B); principal component analysis (PCA) score plot used to identify AD samples and normal sample individuals (C); linear discriminant analysis (LDA) receiver operating characteristic (ROC) curve used to identify disease samples (D); and the distribution of differential results for actual samples (E). Detailed Implementation

[0057] To enable those skilled in the art to better 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 commercially available products that can be purchased.

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

[0060] Table 1. Experimental reagents and their sources

[0061]

[0062] All reagents and materials are analytical grade and can be used without further purification.

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

[0064]

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

[0066] First, 6.9 g of methyl p-formaldehyde benzoate was completely dissolved in 100 mL of propionic acid and heated to 60 °C. Then, 3 mL of pyrrole was slowly added to the solution, and the mixture was reacted under reflux for 24 hours. After the reaction mixture was cooled to room temperature, it was collected by vacuum filtration to obtain a 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, and 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 evaporated to dryness to obtain the purple solid.

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

[0069] This invention successfully synthesized ZnTCPP monomers using methyl paraformylbenzoate and pyrrole as starting materials through a multi-step reaction, utilizing Fourier transform infrared spectroscopy (FT-IR) and proton nuclear magnetic resonance spectroscopy (NMR). 1 H-NMR, Figure 1 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] The preparation of ZnTCPP-Zn (i.e., MOF1) includes 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 and dissolved in 12 mL of DMF using ultrasound. The mixture was then heated and stirred at 150 °C for 3 hours. The mixture was then cooled to room temperature and ZnTCPP-Zn was obtained by centrifugation and washing five times with ethanol.

[0075] Example 2

[0076] The preparation of ZnTCPP-Al (i.e., MOF2) includes 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 heating, 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] The preparation of ZnTCPP-Ti (i.e., MOF3) includes the following steps:

[0080] 192 mg of p-aminobenzoic acid, 5.0 mL of isopropanol, and 104 μL of titanium(IV) isopropanol 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 isopropanol and then 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, then heated at 160 °C for 48 hours. The resulting solid was washed with acetonitrile and DMF to remove unreacted ligands 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 photoresponsive nanoenzyme array sensor comprising ZnTCPP-Zn, ZnTCPP-Al, and 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 photoresponsive nanozyme array sensor.

[0085] Example 5

[0086] A photoresponsive nanozyme array sensor differs from Example 4 in that: each MOF material is used in 6 replicates, while the remaining steps are the same as in Example 4, to form a photoresponsive nanozyme array sensor.

[0087] Performance verification

[0088] 1. Structural characterization

[0089] The MOFs prepared in Examples 1, 2, and 3 of this invention used ZnTCPP as a ligand to synthesize ZnTCPP-Zn, ZnTCPP-Ti, and ZnTCPP-Al, respectively, via a simple solvothermal method. The synthesis methods are as follows: Figure 2 As shown in (A), ZnTCPP ligands were reacted with zinc ions, aluminum ions, and titanium ions via a solvothermal method to prepare MOFs with different structures.

[0090] like Figure 2 As shown in (B), the ZnTCPP-Zn prepared in Example 1 exhibits a typical two-dimensional layered structure. This structural configuration typically provides a significantly increased specific surface area, thereby enhancing the interaction between the reactant substrate and the catalytically 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 Figure (E), compared with the X-ray diffraction (XRD) pattern of (4-carboxyphenyl)porphyrin (TCPP), ZnTCPP-Zn, ZnTCPP-Ti, and ZnTCPP-Al exhibit distinct characteristic peaks, indicating their good crystallinity. Further FT-IR spectroscopy analysis was used to determine the functional groups of these MOFs, such as... Figure 2 As shown in (F), at 1686cm -1 The disappearance of the C=O stretching vibration peak and at 1600 cm⁻¹ -1The presence of MO bonds indicates that the carboxyl groups in ZnTCPP have undergone metal coordination modification to form MO nodes. Furthermore, the 952 cm⁻¹ in TCPP... -1 The drug lord peak corresponds to vibrations in the NH plane, while the 990 cm⁻¹ peak in ZnTCPP-Zn, ZnTCPP-Ti, and ZnTCPP-Al... -1 The nearby drug lord peaks indicate the presence of Zn at the center of the porphyrin ring. 2+ Due to Zn 2+ The introduction of this element reduces the Q band of all ZnTCPP MOFs to two peaks, which increases the symmetry of porphyrins, bringing the energy levels closer together and resulting in a reduction in the Q band. Meanwhile, as... Figure 2 As shown in (G), a blue shift was observed in the fluorescence emission peaks of these ZnTCPP MOFs. This blue shift is due to the reduction of electron delocalization in the porphyrin molecule caused by metal ligand coordination, thereby increasing the energy level of the lowest excited state and widening the band gap of the S→S transition. Finally, X-ray photoelectron spectroscopy (XPS) confirmed the ZnTCPP-Zn( Figure 3 ZnTCPP-Ti Figure 5 ) and ZnTCPP-Al( Figure 4 The successful synthesis of ).

[0092] 2. Research on the impact of MOFs on TMB

[0093] To determine the effect of the MOFs prepared in Examples 1-3 on 3,3',5,5'-tetramethylbenzidine (TMB) under light conditions, three control experiments were set up, each containing 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), with the same amount of MOF added in each group. The control group repeated the above steps, while the blank group had its TMB (50 μL) replaced with DMF (50 μL), and the above steps were repeated. The light-treated and blank groups were illuminated with a 30W flashlight for five minutes to observe the color changes of the samples.

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

[0095] A UV-Vis spectrophotometer was used to measure the scanning spectra of each sample in the 500-750 nm range, using DMF (50 μL), TMB (3.84 mM, 200 μL), and HAc-NaAc buffer solution (650 μL, 0.1 M) (pH = 4.0). Simultaneously, the absorbance of each sample at 652 nm was measured. Figure 6 As shown in (C), the TMB sample alone did not exhibit a significant absorption peak under illumination, while the ZnTCPP-Zn and TMB mixture showed a distinct oxTMB absorption peak at 652 nm after illumination. In contrast, the absorption at 652 nm of the unilluminated sample was negligible, further confirming the photoresponsive activity of ZnTCPP-Zn. Figure 6 As shown in (D) and (E), ZnTCPP-Ti and ZnTCPP-Al also exhibit excellent photoresponsive oxidase activity due to the addition of the same ZnTCPP photoactive ligand.

[0096] To maximize the photoresponsive oxidase activity of MOF-based nanozymes, this embodiment systematically explored the pH-dependent activity profile under light irradiation. Photoresponsive oxidase activity was detected at different pH values, such as... Figure 6 As shown in Figure (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 various reactive oxygen species, including hydroxyl radicals, singlet oxygen, hydrogen peroxide, and superoxide anions. To elucidate the main catalytic pathway of ZnTCPP-Zn, targeted scavenging experiments were conducted using predetermined quenchers: mannitol, tryptophan, catalase, superoxide dismutase, and ethanol. These substances were added separately during the catalytic process, and then the absorbance was measured. The experimental results are as follows: Figure 6 As shown in (G), the signal decreased significantly after the addition of mannitol, which may be due to the oxidation of TMB by hydroxyl radicals (·OH).

[0098] To verify the mechanistic involvement of reactive oxygen species (ROS) in the catalytic cycle, electron paramagnetic resonance (EPR) spectroscopy was employed, using 5,5-dimethyl-1-pyrrolline N-oxide (DMPO) as a spin trapping agent. ZnTCPP-Zn (20 μL, 1 mg / mL) and DMPO (100 mM) were added to 200 μL of acetate buffer (0.1 M, pH = 4), and the signal was measured after 5 minutes of illumination. The detection results are as follows: Figure 6As shown in (H), the ZnTCPP-Zn photoresponsive nanozyme exhibits characteristic EPR signals corresponding to the DMPO-OOH adduct, which are diagnostic markers of hydroxyl radical (·OH) generation. These results indicate that ·OH is a major ROS mediator in the photocatalytic oxidase mimicry process.

[0099] 3. Dynamics of MOFs

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

[0101] Steady-state kinetics were used to characterize the oxidase-like activity of ZnTCPP MOFs. Figure 7 These are typical Michaelis-Menten curves for ZnTCPP-Zn, ZnTCPP-Al, and ZnTCPP-Ti. Based on the function v = v max [S] / (K m +[S]) calculated the dynamic parameter v max and K m Where v represents the initial velocity, [S] is the substrate concentration, and v max and K m The values ​​represent the maximum reaction rate and the Michaelis constant, respectively (Table 2). ZnTCPP-Zn exhibits the highest reaction rate and the lowest Km. m The value indicates that its catalytic effect is most significant, 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 implemented using the Vienna First Principles Calculation Package (VASP). The exchange-correlation potential energy was approximated using the Perdew-Burke-Ernzerhof generalized gradient approximation (GGA-PBE). Projected enhanced wave (PAW) was employed to handle the interaction between the ion nucleus and valence electrons. The plane wave cutoff energy was fixed at 500 eV. The given structural model was relaxed until the Hellmann–Feynman force was less than 500 eV. Energy change less than 10 -6 eV. Vacuum layer thickness set to To minimize interlayer interactions, the Brillouin zone is represented by a 7×7×1k-point grid with a Γ center during relaxation. Grimme's DFT-D3 method is used to describe the dispersive interactions of all atoms in the adsorption model.

[0106] The potential mechanism of the oxidase-mimicking activity of MOFs was investigated using DFT calculations. O2 was catalyzed by MOFs to generate ·OH, which further oxidized colorless TMB to produce the blue oxTMB product. Figure 8 In the middle (A)), free O2 molecules readily adsorb to the Zn center of ZnTCPP, activating O2(*)( Figure 8 In (B) and (C) sequences, 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 the two O(*) atoms, forming two OH(*) radicals, one of which absorbs energy to transform into a free ·OH radical. The energy barriers for 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 process, the free energy required for the rate-determining step of the Zn node based on 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 atom sites (C). Figure 8(D) This indicates that the introduction of Zn cations significantly enhances the electron cloud density around the central Zn atom in the Zn-TCPP framework. The increased electron transfer enhances the interaction between the Zn atom and the ORR intermediates, making adsorption and activation more efficient. Furthermore, the enhanced electron-donating capacity of the Zn cations better supports the electron-intensive steps in the ORR process. The introduction of Zn cations may modulate the electronic state of the central Zn atom in Zn-TCPP, optimizing the adsorption strength of ORR intermediates to achieve a balance—neither too strong (hindering desorption) nor too weak (impeding reaction progress). This optimization promotes rapid adsorption and desorption of intermediate species, avoiding reaction bottlenecks. The reduced free energy barrier means that ZnTCPP-Zn can undergo electron transfer and oxygen reduction more efficiently, resulting in faster reaction kinetics. In contrast, the differential charge density and free energy pathways of Zn-TCPP-Al and Zn-TCPP-Ti indicate lower electron transfer at the central Zn atom. This may be because the Al and Ti cations exhibit stronger electron sharing or shielding effects, weakening the activity of the central Zn atom in the Zn-TCPP framework. Furthermore, the significant differences in the free energy barriers of the three materials throughout the process are beneficial for the establishment of the array sensor.

[0107] 5. Detection of different types of neurotransmitters

[0108] The photoresponsive nanozyme array sensor prepared in Example 5 identifies six neurotransmitters (DA, EP, 5-HT, NE, HA, ACh, with structures as shown in Example 5). Figure 9 (A) 20 μL of neurotransmitter solution was added to each well of a 96-well MOF plate, resulting in a final neurotransmitter concentration of 10 μM. The samples were then exposed to light at room temperature for 5 minutes. The absorbance was measured at 652 nm, with each sample repeated 6 times. The change in absorbance is expressed as (A-A0) / A0, where A and A0 represent the absorbance with and without added neurotransmitter, respectively. The detection procedure for low-concentration samples (0.1 μM and 1 μM) was the same as described above.

[0109] Test results as follows Figure 9As shown in Figure (B), each MOF in the array exhibits a different colorimetric signal response to the six neurotransmitters, and different neurotransmitters have different effects on the same MOF. The colorimetric signal response of ZnTCPP-Zn nanozyme significantly decreased after 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 structure and chemical properties of different neurotransmitters, which affect their interactions with the metal nodes and porphyrin ligands of MOFs. These interactions can inhibit or enhance the oxidase-like catalytic activity of MOFs, leading to changes in the oxidation rate of the colorimetric substrate (TMB), and consequently, differences in color intensity. Neurotransmitters with electron-donating groups may reduce the electron density of the catalytic center, thereby reducing oxidation efficiency and resulting in a weakened color response, as observed in ZnTCPP-Zn. Conversely, neurotransmitters with electron-attracting groups can promote electron transfer, enhance catalytic activity, and generate stronger colorimetric signals, as seen in ZnTCPP-Ti and ZnTCPP-Al. The diverse modulation of MOF activity by different neurotransmitters is crucial for array sensors to generate unique response patterns and achieve precise differentiation.

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

[0111] To improve the array sensor's ability to distinguish six neurotransmitters and predict unknown samples, various machine learning algorithms were used for model training, including Bernoulli Naive Bayes (BNB), Gaussian Process Classifier (GPC), K-Nearest Neighbors (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 testing accuracies exceeding 95%. A visual classification model was created using the LDA algorithm. The training matrix (3 ZnTCPP MOFs × 6 neurotransmitters × 6 repeated experiments) was converted to typical scores using the LDA algorithm. In the LDA plot, each factor represents its contribution to the overall classification, showing that factor 1 and factor 2 account for 48.1% and 27.2% of the variance, respectively. The six neurotransmitters were classified into six distinct groups. All neurotransmitters were clearly classified without any misclassification, achieving a classification accuracy of 100%. 32 unknown neurotransmitters were correctly identified with 100% accuracy. Furthermore, the detection of low-concentration neurotransmitters was investigated; the array sensor still showed significant responses to different types of neurotransmitters at low concentrations (0.1 μM and 1 μM). Notably, the array sensor was able to accurately distinguish six neurotransmitters at these low concentrations, with both model accuracy and prediction accuracy obtained through cross-classification reaching 100%. Figure 3-10 This array sensor performs exceptionally well in neurotransmitter detection.

[0112] 6. Detection of neurotransmitters at different concentrations

[0113] After successfully identifying different types of neurotransmitters, the array sensor's ability to distinguish different concentrations was further evaluated. Six neurotransmitters were diluted with deionized water to different concentrations. The final concentrations of the neurotransmitters were 0.5 μM, 2 μM, 3.5 μM, 5 μM, 6.5 μM, 8 μM, and 9.5 μM. Next, the above procedure was repeated. 20 μL of neurotransmitter was mixed with the sensor element in a 96-well plate (6 replicates). The sample was exposed to light at room temperature for 5 minutes, with the instrument settings unchanged.

[0114] Test results as follows Figure 11 As shown, the array sensor exhibits different colorimetric responses to individual neurotransmitters at various concentrations. The radius transformation of the radar plot reflects the relative changes in colorimetric response. The radar plot shape and size differ for each neurotransmitter at the seven concentrations, indicating that these seven concentrations can be easily distinguished by their different three-signal radii.

[0115] In the clustering heatmap, 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 distinct clusters, with samples of the same concentration grouped together, and no misclassification occurred. 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 different groups. Although NE and HA showed slight overlap on factors 1 and 2, they were well distinguishable on factors 1 and 3. The classification matrix using interpolation showed a 100% discrimination accuracy. The prediction accuracies for the six neurotransmitters at different concentrations were 97%, 100%, 100%, 100%, 96%, and 100%, respectively. This indicates that the array sensor can achieve semi-quantitative detection of various neurotransmitters.

[0116] 7. Detection of mixtures of DA analogues

[0117] To evaluate the array's ability to distinguish neurotransmitter mixtures, two binary solutions with different ratios were investigated: DA and NE, and DA and Ep. Taking DA and NE as an example, five different ratios of mixed solutions were prepared. 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. 20 μL of each neurotransmitter mixture was mixed with sensor elements in a 96-well plate (6 replicates). The samples were exposed to light at room temperature for 5 minutes, with the instrument settings identical to those described above. The recognition process for the different ratios of DA and Ep mixed solutions was also the same as described above.

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

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

[0120] Different types of neurotransmitters were added to real samples (cerebrospinal fluid and serum). The identification process for these samples followed the same procedure described above, and the absorbance changes at 652 nm were evaluated using the same instrument settings.

[0121] The shape and size of each neurotransmitter in the complex matrix, such as Figure 14 (B) and Figure 14 As shown in (E), these six neurotransmitters can be easily distinguished by their different three-signal radii. In the clustering heatmap ( Figure 14 (C) and Figure 14In the study (F), the six neurotransmitters had varying degrees of influence on the three enzyme-like activities in cerebrospinal fluid and serum. The neurotransmitters were clearly classified into six distinct clusters, with no misclassification observed in either cerebrospinal fluid or serum. LDA results showed that the six neurotransmitters were significantly separated from each other in different matrices. Figure 14 China (D) and Figure 14 (G)). The cross-validated bootstrap classification matrix showed 100% accuracy in real samples. Blind testing also demonstrated 100% accuracy. These results indicate that the array sensor is robust to interference and successfully enables the detection of neurotransmitters in complex samples.

[0122] 9. Machine Learning for the Diagnosis of Neurological Diseases

[0123] To demonstrate its practical application, an array of sensors combined with machine learning was used for serum testing in healthy mice and AD model mice, with the aim of achieving clinical diagnosis. Schematic diagrams of the testing in normal mice and AD mice are shown below. Figure 15 As shown in (A). Serum samples were collected from 11 Alzheimer's disease (AD) model mice and 11 normal mice. Serum was segregated into disease and normal groups, and each serum was homogeneously diluted to prepare a serum diluent. For the disease group, 20 mL of the disease group serum diluent was mixed with the sensor element in a 96-well plate. The samples were then exposed to light at room temperature for 5 minutes. Instrument settings were consistent with those previously described. In the normal group, the disease group serum diluent was replaced with the normal group serum diluent, while all other procedures remained unchanged from the disease group.

[0124] Serum samples were obtained from 22 mice, and a dataset (3 sensor elements × 22 samples) was generated for AD diagnosis. Heatmaps generated from the array sensor responses clearly showed significant changes in signal patterns between the two groups, demonstrating the sensor's ability to detect subtle differences indicated by AD. Figure 15 (B) Independent principal components were generated using principal component analysis (PCA). The PCA score plot effectively distinguished between normal and AD samples, and the first two principal components explained the significant variance, further enhancing the discriminative power of the array sensor. Figure 15 (C)). To evaluate the performance of this array sensor in diagnosing AD, several machine learning algorithms were employed. A comparison of training and prediction accuracy showed that LDA and Decision Tree (DT) achieved the highest performance, with 100% accuracy in distinguishing between normal and AD samples. This result was further supported by Receiver Operating Characteristic (ROC) analysis, where LDA exhibited the largest area under the curve (AUC), indicating its superior sensitivity and specificity. Figure 15(D)). These results highlight the effectiveness of combining array sensor technology with machine learning for AD diagnosis. Furthermore, this embodiment explores the distribution of differential results in real-world samples, further validating the high accuracy and robustness of the model in practical applications. Figure 15 (E) Array sensors combined with machine learning algorithms provide a reliable and efficient method for early detection, which is crucial for improving the care and treatment outcomes of AD patients. This approach not only demonstrates the potential of array sensors in neurodiagnostics but also opens new avenues for real-time, non-invasive monitoring of diseases such as Alzheimer's.

[0125] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A light-responsive nanozyme array sensor, characterized in that, The MOF1, the MOF2 and the MOF3 all have ZnTCPP as the organic ligand, and the metal ions are zinc ions, aluminum ions and titanium ions respectively.

2. The light-responsive nanosensor array of claim 1, wherein, The MOF1 is a two-dimensional layered structure, and the MOF2 and the MOF3 are both rod-like cluster structures.

3. A method for preparing the light-responsive nanosensor array of claim 1 or 2, characterized in that, The application further discloses a preparation method of the light-responsive nano-enzyme array sensor. The MOF1, the MOF2 and the MOF3 are prepared by mixing zinc source, aluminum source and titanium source with ZnTCPP respectively through a solvothermal method; the MOF1, the MOF2 and the MOF3 are arranged into an array to obtain the light-responsive nano-enzyme array sensor.

4. The production method according to claim 3, wherein The preparation method of the MOF1 comprises the following steps: dissolving zinc source and ZnTCPP in an organic solvent, stirring and reacting at 140-160 DEG C for 1-5 h to obtain the MOF1.

5. The production method according to claim 4, wherein The zinc source comprises any one of zinc nitrate, zinc chloride and zinc acetate.

6. The production method according to claim 4, wherein The organic solvent comprises at least one of N, N-dimethylformamide and dimethyl sulfoxide.

7. The production method according to claim 4, wherein In the preparation process of the MOF1, benzoic acid is further added, and the mass ratio of the zinc source, ZnTCPP, benzoic acid and organic solvent is (4-6):(1-3):(8-12):(10-13).

8. The production method according to claim 4, wherein After the reaction is completed, the mixture is cooled to room temperature, centrifuged and washed to obtain the MOF1.

9. The production method according to claim 3, wherein The preparation method of the MOF2 comprises the following steps: dissolving aluminum source and ZnTCPP in water, stirring and reacting at 160-200 DEG C for 15-20 h to obtain the MOF2.

10. The production method according to claim 9, wherein The aluminum source comprises any one of aluminum chloride, aluminum nitrate and aluminum sulfate.

11. The production method according to claim 9, wherein The mass ratio of the aluminum source, ZnTCPP and water is (8-12):(8-12):(3-5).

12. The production method according to claim 9, wherein After the reaction is completed, the reaction system is cooled at a speed of 1-2 DEG C / min, centrifuged, washed, dried to obtain the MOF2.

13. The production method according to claim 3, wherein The preparation method of the MOF3 comprises the following steps: mixing titanium acid tetraisopropyl ester, p-aminobenzoic acid and isopropyl alcohol, stirring and reacting at 80-120 DEG C for 75-80 h to obtain an intermediate; dissolving the intermediate and ZnTCPP in an organic solvent, reacting at 150-170 DEG C for 45-50 h to obtain the MOF3.

14. The production method according to claim 13, wherein The use amount ratio of the titanium acid tetraisopropyl ester, the p-aminobenzoic acid and the isopropyl alcohol is (90-110 μL):(190-200 mg):(4-6 mL).

15. The production method according to claim 13, wherein In the preparation process of the MOF3, acetic acid is further added, and the intermediate, ZnTCPP and acetic acid are dissolved in the organic solvent, the organic solvent is a mixed solvent obtained by mixing acetonitrile and tetrahydrofuran at a volume ratio of 2-4:1, and the use 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).

16. Application of the light-responsive nano-enzyme array sensor of claim 1 or 2 or the light-responsive nano-enzyme array sensor prepared by the preparation method of any one of claims 3-15 in detection of a neurotransmitter and / or preparation of a diagnostic product for a neurological disease. The neurotransmitter comprises any one or more of dopamine, adrenaline, norepinephrine, serotonin, histamine and acetylcholine. The neurological disease includes any one or more of Alzheimer's disease, Parkinson's disease, and multiple sclerosis.

17. A neurotransmitter detection kit comprising, The light-responsive nanoenzyme array sensor of claim 1 or 2 or prepared by the preparation method of any one of claims 3-15; The neurotransmitter includes any one or more of dopamine, adrenaline, noradrenaline, serotonin, histamine, and acetylcholine.

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