Array sensor based on cerium-based metal organic framework nano-enzyme and detection application of perfluoroalkyl compound

Abstract

The invention belongs to the technical field of sensors, and relates to an array sensor based on cerium-based metal organic framework nano-enzyme and detection application of perfluoroalkyl compounds. The array sensor is a Ce-MOF triple enzyme activity array sensor and is provided with a plurality of mutually independent sensing channels; the sensing channel contains a Ce-MOF nano enzyme and a chromogenic substrate; the chromogenic substrates comprise a first chromogenic substrate, a second chromogenic substrate and a third chromogenic substrate, the first chromogenic substrate comprises 3, 3 ', 5, 5'-tetramethyl benzidine, the second chromogenic substrate comprises 2, 4-diaminophenol and 4-aminoantipyrine, and the third chromogenic substrate comprises xanthine, purine oxidase and nitrogen blue tetrazole. The MOF nano-enzyme array sensor constructed by the invention has relatively high sensitivity and selectivity in identification and distinguishing of PFASs compounds, not only can accurately distinguish single PFASs compounds, but also can perform quantitative detection, and has good response to PFASs with different concentrations.

Description

Technical Field

[0001] This invention belongs to the field of sensor technology, specifically relating to an array sensor based on cerium-based metal-organic framework nanozymes and its application in the detection of perfluoroalkyl compounds. Background Technology

[0002] Perfluoroalkyl substances (PFASs) are widely used in various consumer products such as surfactants, cosmetics, coatings, non-stick cookware, and fire extinguishing foams due to their excellent chemical stability and hydrophobic and oleophobic properties. However, the high stability of the CF bond in PFASs makes them persistent, and their low volatility and high water solubility make water the main accumulation medium, making them difficult to degrade in aquatic environments and prone to accumulation in organisms. This poses potential health risks to wildlife and human health, causing various diseases including thyroid disease, decreased fertility, hepatotoxicity, and immunotoxicity.

[0003] Currently, the commonly used detection methods for PFASs are high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS). While these methods offer high sensitivity and reliability, they typically require expensive equipment, are complex to operate, and demand a high level of operator skill. In recent years, sensors based on various materials and principles, such as fluorescence, colorimetry, and electrochemical methods, have been introduced for PFAS detection. However, these sensors usually only identify a single PFAS and struggle to distinguish between multiple PFASs with similar structures and reactivity. Summary of the Invention

[0004] The purpose of this invention is to provide an array sensor based on cerium-based metal-organic framework nanozymes and its application in the detection of perfluoroalkyl compounds, thereby overcoming the shortcomings of existing technologies and developing a simple and efficient method that can simultaneously achieve quantitative analysis and effective differentiation of multiple PFASs.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: In a first aspect, the present invention provides an array sensor, which is a Ce-MOF triple enzyme activity array sensor. The Ce-MOF triple enzyme activity array sensor has multiple independent sensing channels. The sensing channels contain Ce-MOF nanozymes and chromogenic substrates. The chromogenic substrates include a first chromogenic substrate, a second chromogenic substrate, and a third chromogenic substrate. The first chromogenic substrate includes 3,3',5,5'-tetramethylbenzidine (TMB), the second chromogenic substrate includes 2,4-diaminophenol (DP) and 4-aminoantipyridine (AP), and the third chromogenic substrate includes xanthine, purine oxidase, and nitroblue tetrazolium (NBT).

[0006] Sensor arrays, as an effective method for distinguishing structurally similar analytes, generate unique response spectra for each analyte based on the differentiated responses produced between different sensing elements and the target analyte, and then identify and classify them using statistical analysis tools. Metal-organic framework (MOF) nanozymes possess unique structures and functions, showing great potential in the field of sensing. Their porous structure facilitates rapid proton transfer; tunable pore sizes facilitate selective enrichment and separation of target molecules; and MOFs are easy to construct and modify to impart specific enzyme activities. These advantages enable MOF nanozymes to exhibit excellent sensitivity and selectivity in recognizing structurally similar substances. The cerium-based MOF (Ce-MOF) nanozyme used in this invention possesses three enzyme-mimicking activities: oxidase, laccase, and superoxide dismutase, which can serve as three independent sensing channels. The constructed array sensor exhibits significant recognition capabilities for various PFASs.

[0007] In some other embodiments, the array sensor has three independent sensing channels, namely a first sensing channel, a second sensing channel and a third sensing channel, wherein the first sensing channel contains Ce-MOF nanozyme, 3,3',5,5'-tetramethylbenzidine and acetate-sodium acetate buffer. Alternatively, the second sensing channel contains Ce-MOF nanozyme, 2,4-diaminophenol, 4-aminoantipyrrolidone, and acetate-sodium acetate buffer. Alternatively, the third sensing channel contains Ce-MOF nanozyme, xanthine, purine oxidase, nitroblue tetrazolium, and tris(hydroxymethyl)aminomethane hydrochloride buffer.

[0008] In some other embodiments, the array sensor includes a first sensing channel, a second sensing channel, and a third sensing channel; The first sensing channel includes Ce-MOF nanozyme, 3,3',5,5'-tetramethylbenzidine (TMB), and acetate-sodium acetate buffer; Ce-MOF catalyzes the oxidation of TMB to generate the blue product oxTMB, with a maximum absorption peak at 652 nm, exhibiting properties similar to those of natural oxidases.

[0009] The second sensing channel includes Ce-MOF nanozyme, 2,4-diaminophenol (DP), 4-aminoantipyrrolidone (AP), and acetate-sodium acetate buffer. When Ce-MOF is present with AP and DP, the reaction system shows a significant absorption peak at 510 nm, while no signal is generated when a single component or any two components are mixed, confirming that Ce-MOF has excellent laccase mimicry activity.

[0010] The third sensing channel comprises Ce-MOF nanozyme, xanthine, purine oxidase, nitroblue tetrazolium (NBT), and tris(hydroxymethyl)aminomethane hydrochloride buffer. Xanthine oxidase catalyzes the oxidation of xanthine to O2˙. - The latter can reduce NBT to blue formazan (maximum absorption peak at 560 nm), while Ce-MOF can remove O2˙. - And it inhibits the reduction process.

[0011] In some other embodiments, the volume ratio of Ce-MOF nanozyme, 3,3',5,5'-tetramethylbenzidine, and acetate-sodium acetate buffer in the first sensing channel is (15-25):(10-15):(35-45), the concentration of Ce-MOF nanozyme is 0.4-0.6 mg / mL, the concentration of 3,3',5,5'-tetramethylbenzidine is 4-6 mM, the concentration of acetate-sodium acetate buffer is 90-110 mM, and the pH is 4.0; For example, the volume ratios of Ce-MOF nanozyme, 3,3',5,5'-tetramethylbenzidine, and acetate-sodium acetate buffer in the first sensing channel are 15:10:35, 15:15:45, 20:15:40, 20:10:45, 20, 12, and 40. The concentrations of Ce-MOF nanozyme are 0.4, 0.5, and 0.6 mg / mL, the concentrations of 3,3',5,5'-tetramethylbenzidine are 4, 5, and 6 mM, and the concentrations of acetate-sodium acetate buffer are 90, 100, and 110 mM.

[0012] The first sensing channel prepared within the above-mentioned value range exhibits Ce-MOF oxidase (OXD) mimicking activity, displaying properties similar to those of natural oxidases.

[0013] In the second sensing channel, the volume ratio of Ce-MOF nanozyme, 2,4-diaminophenol, 4-aminoantipyridine, and acetate-sodium acetate buffer is (15-25):(20-30):(5-10):(35-45). The concentration of Ce-MOF nanozyme is 0.4-0.6 mg / mL, the concentration of 2,4-diaminophenol is 4-6 mM, the concentration of 4-aminoantipyridine is 4-6 mM, the concentration of acetate-sodium acetate buffer is 90-110 mM, and the pH is 6.0. For example, the volume ratios of Ce-MOF nanozyme, 2,4-diaminophenol, 4-aminoantipyridine, and acetate-sodium acetate buffer in the second sensing channel are 15:20:5:35, 25:30:10:45, 15:30:10:35, 20:30:8:40, and 20:24:8:40. The concentrations of Ce-MOF nanozyme are 0.4, 0.5, and 0.6 mg / mL, the concentrations of 2,4-diaminophenol are 4, 5, and 6 mM, the concentrations of 4-aminoantipyridine are 4, 5, and 6 mM, and the concentrations of acetate-sodium acetate buffer are 90, 100, and 110 mM.

[0014] The second sensing channel prepared within the above-mentioned value range exhibits excellent laccase (LAC) mimicry activity when Ce-MOF coexists with AP and DP.

[0015] In the third sensing channel, the volume ratio of Ce-MOF nanozyme, xanthine, purine oxidase, nitroblue tetrazolium, and tris(hydroxymethyl)aminomethane hydrochloride buffer is (15-25):(45-55):(45-55):(25-35):(15-25). The concentration of Ce-MOF nanozyme is 0.4-0.6 mg / mL, the concentration of xanthine is 4-6 mM, the concentration of purine oxidase is 0.5-1.5 U / mL, the concentration of nitroblue tetrazolium is 1.5-2.5 mM, the concentration of tris(hydroxymethyl)aminomethane hydrochloride buffer is 15-25 mM, and the pH is 7.0.

[0016] For example, the volume ratios of Ce-MOF nanozyme, xanthine, purine oxidase, nitroblue tetrazolium, and tris(hydroxymethyl)aminomethane hydrochloride buffer in the third sensing channel are 15:45:45:25:15, 25:55:55:35:25, 20:50:50:30:20, and 20:45:50:25:20. The concentrations of Ce-MOF nanozyme are 0.4, 0.5, and 0.6 mg / mL; the concentrations of xanthine are 4, 5, and 6 mM; the concentrations of purine oxidase are 0.5, 1.0, and 1.5 U / mL; the concentrations of nitroblue tetrazolium are 1.5, 2.0, and 2.5 mM; and the concentrations of tris(hydroxymethyl)aminomethane hydrochloride buffer are 15, 20, and 25 mM.

[0017] The third sensing channel obtained within the above-mentioned value range allows superoxide dismutase (SOD) to catalyze O2˙ - It disproportionates into H2O2 and O2, alleviating oxidative stress damage caused by reactive oxygen species.

[0018] It is evident that Ce-MOF can catalyze a variety of reactions, exhibiting excellent multi-enzyme mimicry activities of OXD, LAC, and SOD.

[0019] In a second aspect, the present invention provides the application of the array sensor described in the first aspect in the detection of perfluoroalkyl compounds, wherein the perfluoroalkyl compounds include at least one selected from perfluorononanoic acid, perfluorooctanoic acid, hexafluoropropylene oxide dimer acid, perfluoroheptanoic acid, perfluorohexanoic acid, perfluorovalerate, 2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecyl octanol, perfluorobutyric acid, and trifluoromethanesulfonic acid.

[0020] Thirdly, the present invention provides a method for detecting perfluoroalkyl compounds, which employs the array sensor described in the first aspect. The sample containing perfluoroalkyl compounds is mixed with Ce-MOF nanozyme solution and incubated at room temperature. After adding a chromogenic substrate and replenishing water to make up the volume, the absorbance data is measured and processed to obtain a data matrix. The data matrix is ​​then classified to achieve the differentiation and detection of multiple perfluoroalkyl compounds.

[0021] Functional groups such as -SO3H, -COOH, and -F in the structure of PFASs bind to Ce-MOFs through various non-specific interactions (such as electrostatic adsorption and hydrophobic interactions), leading to changes in substrate accessibility to the active site and thus causing differential changes in enzyme activity. This detection method can simultaneously achieve quantitative analysis and effective differentiation of multiple PFASs, and the operation is simple and efficient.

[0022] In some other embodiments, the volume ratio of the perfluoroalkyl compound to the Ce-MOF solution is (25-35):(15-25); wherein the final concentration of the perfluoroalkyl compound is 7.5-45 μg / mL, and the concentration of the Ce-MOF solution is 0.4-0.6 mg / mL; and the wavelength for measuring the absorbance of the chromogenic substrate is 510-660 nm.

[0023] The absorbance data is processed using the absorbance change rate, and the formula for calculating the absorbance change rate is as follows: Absorbance change rate = (A-A0) / A0, where A is the absorbance value with PFASs added in different channels, and A0 is the absorbance value without PFASs added in different channels.

[0024] Statistical analysis software was used to process and analyze the colorimetric data. Linear discriminant analysis (LDA) was used to convert the colorimetric response patterns into canonical patterns. The Mahalanobis distance from each individual pattern to each group centroid in the multidimensional space was calculated, and all class assignments were based on the shortest Mahalanobis distance.

[0025] Alternatively, the preparation method of Ce-MOF nanozyme is as follows: terephthalic acid is added to N,N-dimethylformamide and stirred until homogeneous, then cerium ammonium nitrate is added and stirred until homogeneous to obtain a mixed solution; the mixed solution is subjected to hydrothermal reaction, cooled to room temperature, and then centrifuged, washed with water, and freeze-dried to obtain Ce-MOF nanozyme; Alternatively, in the preparation method of Ce-MOF nanozymes, the mass ratio of terephthalic acid, N,N-dimethylformamide, and cerium ammonium nitrate is (0.5-0.6):(20-30):(1.5-1.8); the hydrothermal reaction temperature is 95-105℃ and the time is 0.5-1.5h.

[0026] For example, the volume ratio of the perfluoroalkyl compound to the Ce-MOF solution is 25:15, 30:20, 35:25, 30:25, and 25:25. The final concentrations of the perfluoroalkyl compound are 7.5, 10, 15, 20, 25, 30, 35, 40, and 45 μg / mL, and the concentrations of the Ce-MOF solution are 0.4, 0.5, and 0.6 mg / mL. In the preparation method of Ce-MOF nanozymes, the mass ratio of terephthalic acid, N,N-dimethylformamide, and cerium ammonium nitrate is 0.5:20:1.5, 0.6:30:1.8, and 0.53:23.7:1.74; the hydrothermal reaction temperature is 95, 100, and 105℃, and the time is 0.5, 1.0, and 1.5 h.

[0027] The wavelengths for measuring the absorbance of the chromogenic substrate were 5652 nm, 510 nm, and 560 nm.

[0028] Within this ratio range, perfluoroalkyl compounds bind to Ce-MOF through various non-specific interactions (such as electrostatic adsorption and hydrophobic interactions), leading to changes in substrate accessibility to the active site and thus causing differential changes in enzyme activity.

[0029] In some other embodiments, the perfluoroalkyl compound is at least one of perfluorononanoic acid, perfluorooctanoic acid, and 2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecanoic acid.

[0030] For example, the perfluoroalkyl compound is a mixture of perfluorononanoic acid and perfluorooctanoic acid, with a mixing volume ratio of 100%:0%, 75%:25%, 50%:50%, 25%:75%, or 0%:100%. The perfluoroalkyl compound is also a mixture of perfluorononanoic acid, perfluorooctanoic acid, and 2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecanoic acid, with a mixing volume ratio of 25%:50%:25%, 50%:25%:25%, 25%:25%:50%, 12.5%:75%:12.5%, 75%:12.5%:12.5%, or 12.5%:12.5%:75%.

[0031] In some other embodiments, the samples containing perfluoroalkyl compounds include seawater, shrimp, and cod; When the sample containing perfluoroalkyl compounds is shrimp or cod, the shrimp or cod needs to be pretreated. The pretreatment involves crushing the shrimp or cod, soaking it in trichloroacetic acid overnight, vortexing, centrifuging to precipitate the solid, and separating the supernatant.

[0032] For example, seawater is pretreated with a filter membrane, and after shrimp and cod are crushed, chloroacetic acid is added at a solid-liquid ratio of 1g:(2-5)mL and soaked overnight. The mixture is then vortexed for 3-6 minutes, and the solid is precipitated by centrifugation and the supernatant is separated.

[0033] Fourthly, the present invention provides a kit for detecting perfluoroalkyl compounds, comprising the array sensor described in the first aspect.

[0034] For example, the kit is a hydrogel detection kit. The preparation method of the hydrogel detection kit is as follows: dissolve agarose in boiling water, stir evenly, add Ce-MOF when the solution is cooled to 40-45℃, mix thoroughly, add to agarose, and cool and solidify to form a hydrogel.

[0035] The ratio of agarose, boiling water and Ce-MOF is (25-35) mg: (2-4) mL: (2-4) mL, and the concentration of Ce-MOF is 0.2-0.3 mL.

[0036] The beneficial effects of this invention: (1) In the colorimetric sensor array of MOF nanozymes prepared in this invention, Ce-MOF nanozymes show great potential in the field of sensing due to their unique structure and function. Their porous structure is conducive to rapid mass transfer; the tunable pore size facilitates the selective separation of target molecules; and Ce-MOF is easy to construct and modify to endow specific enzyme activities, so that MOF nanozymes exhibit excellent sensitivity and selectivity in recognizing structurally similar substances.

[0037] (2) The Ce-MOF nanozyme in this invention has three enzyme-mimicking activities: oxidase, laccase, and superoxide dismutase, which can be used as three independent sensing channels. The functional groups such as -SO3H, -COOH, and -F in the PFASs structure bind to Ce-MOF through various non-specific interactions (such as electrostatic adsorption and hydrophobic interactions), which leads to changes in the accessibility of the substrate to the active site, thereby causing differential changes in enzyme activity. This enables simultaneous quantitative analysis and effective differentiation of multiple PFASs.

[0038] (3) This invention systematically studies the regulatory effect of PFASs on the activity of these three types of enzymes, and combines linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) to analyze their interaction reaction patterns, thereby evaluating the reliability of the array in recognizing multiple PFASs.

[0039] (4) This invention explores the ability of the array to distinguish between binary and ternary PFAS mixtures at different concentration ratios. Detection and blind sample identification experiments in complex real samples also verified the practical application potential of this method, providing a novel analytical strategy for monitoring PFAS residues in environmental and food systems. Attached Figure Description

[0040] 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.

[0041] Figure 1 The structure and multi-enzyme activities of Ce-MOF were characterized. A is a schematic diagram of Ce-MOF synthesis, B is a SEM image, C is a TEM image, D is an elemental mapping diagram of Ce, C, and O, E is the XRD pattern of simulated Ce-MOF and actual prepared Ce-MOF, F is the FTIR spectrum of Ce-MOF and H2BDC, G is the Zeta potential result of Ce-MOF, H is the XPS spectrum of Ce, I is the triple enzyme activity of Ce-MOF, J is the UV-Vis absorption spectrum of OXD activity: Ce-MOF, TMB, and Ce-MOF+TMB, K is the UV-Vis absorption spectrum of LAC activity: Ce-MOF, AP, DP, AP+DP, and Ce-MOF+AP+DP, and L is the SOD activity diagram: xanthine + xanthine oxidase + nitroblue tetrazolium, and the UV-Vis absorption spectrum of Ce-MOF + xanthine + xanthine oxidase + nitroblue tetrazolium. Figure 2 Here are the structural diagrams of nine typical perfluoroalkyl compounds; Figure 3The image shows the pattern recognition results of PFASs using Ce-MOF-based nanozyme array sensors. (A) Colorimetric response pattern (A-A0) / A0 of the three-channel enzyme-like activity catalyzed by the array sensor under PFASs (70 μg / mL), (B) Violin plot of the colorimetric response pattern distribution data of PFASs, (C) Heatmap of fingerprint pattern, (D) HCA plot used for PFASs identification, (E) Comparison of the accuracy of training and prediction of PFASs using different machine learning algorithms, and (F) Typical LDA score plot using the first two factors obtained from the colorimetric response pattern. Figure 4 Quantitative plots of PFASs at different concentrations, including radar plots of the colorimetric response patterns of (A) PFNA; (B) PFOA; (C) GenX and (D) PFHpA. Clustering heatmaps based on array signal response changes, representing the changes of (E) PFNA; (F) PFOA; (G) GenX and (H) PFHpA at different concentrations. Typical LDA score plots of ratio array sensors at different concentrations: (I) PFNA; (J) PFOA; (K) GenX and (L) PFHpA; linear relationship between factor 1 and (M) PFNA; (N) PFOA; (O) GenX and (P) PFHpA; Figure 5 Radar plots of colorimetric response patterns for mixtures of binary (A) and ternary (D) PFASs. Clustering heatmaps based on array signal response variations, representing variations in mixtures of binary (B) and ternary (E) PFASs, respectively. Typical LDA score plots of ratio array sensors, representing mixtures of binary (C) and ternary (F) PFASs, respectively; Figure 6 Typical score maps of 9 PFAS response patterns obtained by the LDA algorithm, where (A) seawater; (B) shrimp; (C) cod. Unknown sample confusion matrix output by the LDA classifier: (D) seawater; (E) shrimp; (F) cod; Figure 7 Typical score maps of the response modes of 8 PFASs obtained by the LDA algorithm are shown; (A) is the LDA map of the gel array sensor for detecting 8 PFASs in seawater, and (B) is the confusion matrix of unknown seawater samples output by the LDA classifier. Detailed Implementation

[0042] Those skilled in the art will understand that the following embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention. Specific conditions not specified in the embodiments are performed under conventional conditions or conditions recommended by the manufacturer. Components whose manufacturers are not specified are all commercially available conventional products.

[0043] The preparation schematic diagram of Ce-MOF nanozymes is shown below. Figure 1 As shown in section A, the specific steps include: dissolving 0.53 g of terephthalic acid (H2BDC) in 25 mL of diN,N-dimethylformamide (DMF) solution and stirring magnetically until completely dissolved. Then, adding 8 mL of an aqueous solution containing 1.74 g of cerium ammonium nitrate (NH4)2Ce(NO3)6 and stirring continuously for 10 minutes to obtain a mixed solution. The mixture is transferred to a polytetrafluoroethylene-lined high-pressure reactor and sealed, and reacted at 100°C for 1 hour. After the reaction system cools to room temperature, the precipitate is collected by centrifugation at 10000 rpm for 10 minutes. The precipitate is washed repeatedly with water to remove unreacted substances. Finally, Ce-MOF nanozyme powder is obtained by freeze-drying.

[0044] The structure and multi-enzyme activity of the prepared Ce-MOF were characterized, and the results are as follows: Figure 1 As shown, A is a schematic diagram of Ce-MOF synthesis, B is a SEM image, C is a TEM image, D is an elemental mapping diagram of Ce, C, and O, E is the XRD pattern of simulated Ce-MOF and actual prepared Ce-MOF, F is the FTIR spectrum of Ce-MOF and H2BDC, G is the Zeta potential result of Ce-MOF, H is the XPS spectrum of Ce, I is the triple enzyme activity of Ce-MOF, J is the OXD activity: UV-Vis absorption spectra of Ce-MOF, TMB, and Ce-MOF+TMB, K is the LAC activity: UV-Vis absorption spectra of Ce-MOF, AP, DP, AP+DP, and Ce-MOF+AP+DP, and L is the SOD activity: UV-Vis absorption spectra of xanthine, xanthine oxidase, and nitroblue tetrazolium in the presence or absence of Ce-MOF.

[0045] Depend on Figure 1 As indicated by A in the figure, Ce-MOF materials with multi-enzyme activity were synthesized using a simple solvothermal method. Preliminary observations using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) show that Ce-MOF is composed of nanoparticles with a size of approximately 150 nm. Figure 1 (B and C in the original text). The EDS elemental distribution map shows that the elemental composition of Ce-MOF is consistent with that of the raw material. Figure 1 (D in the text). Powder X-ray diffraction (PXRD) analysis showed that the prepared Ce-MOF had high crystallinity, and its characteristic diffraction peaks were consistent with the reference data (CCDC ID: 1036904), with the first main peak located at 7.12° and the second main peak located at 8.23°. Figure 1 (E in the text).

[0046] Ce-MOF was further characterized by Fourier transform infrared spectroscopy (FTIR), and the results showed: 1651 cm⁻¹-1 and 1556cm -1 The characteristic peak at that location is attributed to the carboxylate ion (-COO). - The stretching vibration of 593cm -1 The peak at 3400 cm⁻¹ represents the stretching vibration of the Ce-O bond; -1 The strong OH stretching vibration peak at the point originates from water molecules physically adsorbed on the Ce-MOF surface. Figure 1 The Zeta potential data show that the Ce-MOF surface carries a strong positive charge of +23.95 mV, which is conducive to promoting the enrichment of PFASs in the MOF framework through electrostatic interactions. Figure 1 The valence state changes of Ce were further analyzed using X-ray photoelectron spectroscopy (XPS). Six peaks corresponded to Ce. 4+ The two additional peaks are attributed to Ce ions. 3+ The presence of Ce ions indicates that Ce-MOF nanostructures simultaneously exist. 4+ and Ce 3+ Species ( Figure 1 (H in the text).

[0047] like Figure 1 As shown by I in the figure, the triple enzyme activity of Ce-MOF is demonstrated. The oxidase (OXD) mimicry activity of Ce-MOF was systematically verified by a colorimetric reaction catalyzing TMB. Figure 1 As shown in J, Ce-MOF catalyzes the oxidation of TMB to the blue product oxTMB (characteristic absorption peak at 652 nm), exhibiting properties similar to the natural oxidase. To evaluate the laccase (LAC) mimicry activity of Ce-MOF, a coupling reaction of AP and DP was employed. Figure 1 As shown in K, when Ce-MOF coexists with AP and DP, the reaction system exhibits a significant absorption peak at 510 nm, while no signal is generated when either a single component or any two components are mixed, confirming that Ce-MOF possesses excellent laccase-mimicking activity. Furthermore, superoxide dismutase (SOD) can catalyze O2˙ - It disproportionates into H2O2 and O2, alleviating oxidative stress damage caused by reactive oxygen species.

[0048] SOD activity was determined using the NBT method: xanthine oxidase catalyzes the oxidation of xanthine to O2˙. - The latter can reduce NBT to blue formazan (maximum absorption peak at 560 nm), while SOD can remove O2˙. - And inhibit the reduction process. For example... Figure 1 As shown in L, the absorbance of the reaction system at 560 nm decreased significantly after the addition of Ce-MOF.

[0049] The above experimental results fully demonstrate that Ce-MOF can catalyze a variety of reactions and exhibits excellent multi-enzyme mimicry activities of OXD, LAC and SOD.

[0050] Example 1 Construction of Ce-MOF triple enzyme activity array sensor and PFASs detection procedure (1) Based on the activities of three mimicking enzymes—oxidase (OXD), laccase (LAC), and superoxide dismutase (SOD)—based on Ce-MOF, a three-channel array sensor was constructed for the identification of PFASs. The specific solution composition of each channel is as follows: Channel 1 (OXD): 20 μL Ce-MOF (0.5 mg / mL) + 12 μL 3,3',5,5'-Tetramethylbenzidine (TMB, 5 mM) + 40 μL Acetic acid-sodium acetate (HAc-NaAc buffer, 100 mM, pH 4.0).

[0051] Channel 2 (LAC): 20 μL Ce-MOF (0.5 mg / mL) + 24 μL 2,4-diaminophenol (DP, 5 mM) + 8 μL 4-aminoantipyrrolidone (AP, 5 mM) + 40 μL HAc-NaAc buffer (100 mM, pH 6.0).

[0052] Channel 3 (SOD): 20 μL Ce-MOF (0.5 mg / mL) + 50 μL xanthine (5 mM) + 50 μL xanthine oxidase (1 U / mL) + 30 μL nitroblue tetrazolium (NBT, 2 mM) + 20 μL Tris-HCl buffer (20 mM, pH 7.0).

[0053] (2) PFASs detection procedure: 30 μL of different PFASs samples were incubated with 20 μL of LCe-MOF solution (0.5 mg / mL) in a 96-well plate at room temperature (final PFASs concentration 70 μg / mL). Then, other solutions were added according to the channel requirements, and ultrapure water was added to bring the volume to 200 μL (6 parallel samples). The absorbance of the chromogenic substrate was measured at wavelengths of 652 nm, 510 nm, and 560 nm. The absorbance change rate was calculated as (A-A0) / A0, where, A and A 0 represents the absorbance value with and without PFASs in different channels.

[0054] Due to the high structural similarity of perfluoroalkyl compounds (PFASs) (e.g. Figure 2 (As shown). A single sensor is insufficient for the simultaneous detection of multiple components. This challenge can be overcome by developing an array sensor based on Ce-MOF multi-mimetic enzyme activity.

[0055] This array utilizes the differential regulatory effects of PFASs on the activities of peroxidase (POD), laccase (LAC), and superoxide dismutase (SOD) in Ce-MOF to construct a multi-channel detection system. To verify its feasibility, the study systematically investigated the changes in the colorimetric response of the sensing system after the addition of PFASs. Nine typical PFASs (…) were selected. Figure 2 This encompasses various types of acids: perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), hexafluoropropylene oxide dimer acid (GenX), perfluoroheptanoic acid (PFHpA), perfluorohexanoic acid (PFHxA), perfluorovalerate (PFPeA), 2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptafluorooctyl alcohol (HFO-15), perfluorobutyric acid (PFBS), and trifluoromethanesulfonic acid (TFMSA).

[0056] A multidimensional dataset was obtained by monitoring the colorimetric responses (3 channels) at three wavelengths: 652 nm, 510 nm, and 560 nm. UV-Vis absorption spectroscopy revealed significant differences in the regulation of the three catalytic activities of Ce-MOF by different PFASs. This is mainly attributed to the binding of functional groups such as -SO3H, -COOH, and -F in the PFAS structure to Ce-MOF through various non-specific interactions (e.g., electrostatic adsorption and hydrophobic interactions), leading to changes in substrate accessibility to the active site and thus inducing differential changes in enzyme activity.

[0057] Example 2 Detection of different concentrations of PFASs PFASs were serially diluted with deionized water to different concentrations. 30 μL of PFASs solution was mixed with 20 μL of LCe-MOF (0.5 mg / mL) in a 96-well plate. The final concentration of PFASs was 7.5–45 μg / mL. Reagents were added according to the requirements of each channel, and ultrapure water was added to bring the volume to 200 μL (6 replicates). The detection parameters were the same as the PFASs detection procedure in Example 1.

[0058] Pattern recognition results of PFASs using Ce-MOF-based nanozyme array sensors are as follows: Figure 3As shown, (A) the colorimetric response pattern (A-A0) / A0 of the three-channel enzyme-like activity catalyzed by the array sensor under PFASs (70 μg / mL), (B) a violin plot of the colorimetric response pattern distribution data of PFASs, (C) a heatmap of the fingerprint pattern, (D) an HCA plot used for PFASs identification, (E) a comparison of the accuracy of trained and predicted PFASs using different machine learning algorithms, and (F) a typical LDA score plot using the first two factors obtained from the colorimetric response pattern. Each point represents the response pattern of a single PFAS, and the elliptical region represents the 95% confidence interval. Table 1 shows the LDA bootstrap classification matrix, which classifies nine PFASs using a series of nanozymes. The bootstrap classification matrix showed 100% accuracy through cross-validation.

[0059] like Figure 3 As shown in (A), each channel in the array exhibits a unique colorimetric signal response to the nine PFASs: the signals of channel 1 (OXD activity) and channel 2 (LAC activity) decrease significantly with the addition of PFASs, while the signal of channel 3 (SOD activity) increases, indicating that most PFASs have an inhibitory effect on the activity of the mimic enzyme. A violin plot of the original data was then constructed. Figure 3 (B) visually presents the wide distribution range of the three-channel data, highlighting the high sensitivity and variability of the colorimetric response. Simultaneously, heatmap analysis demonstrates the differentiated response patterns generated by the cross-reaction of the three channels with PFASs. Figure 3 (C)). Hierarchical cluster analysis (HCA) was used to transform the response patterns of the nine PFASs into Euclidean distance plots. Figure 3 (D) shows that all PFASs were accurately classified without any errors. To further improve the array's ability to distinguish PFASs and predict unknown samples, multiple machine learning algorithms were used for model training. Figure 3 (E)). The results show that, except for the Bernoulli Naive Bayes (BNB) algorithm with an accuracy of less than 50%, the training and testing accuracies of the other algorithms (such as LDA, SVM, and Random Forest) all exceed 95%, proving that the multi-enzyme activity multi-channel array sensor has high sensitivity, can effectively distinguish different PFASs, and is suitable for environmental monitoring.

[0060] Linear discriminant analysis (LDA) was used to construct a visual classification model, transforming the training matrix (3 channels × 9 PFASs × 6 repetitions) into typical scores. In the LDA plot, Factor 1 and Factor 2 contributed 60.54% and 39.46% of the classification information, respectively, clearly dividing the 9 PFASs into 9 independent clusters, achieving a classification accuracy of 100%. Validation experiments on 32 unknown PFASs samples also showed a 100% recognition accuracy, demonstrating the superior performance of this array sensor in PFASs detection. Figure 3 (F) in the middle.

[0061] Table 1. Results of cross-validation of the bootstrap classification matrix.

[0062] Figure 4 Radar plots of the colorimetric response patterns of (A) PFNA, (B) PFOA, (C) GenX, and (D) PFHpA for quantification of PFASs at different concentrations. Cluster heatmaps based on array signal response changes represent the variations of (E) PFNA, (F) PFOA, (G) GenX, and (H) PFHpA at different concentrations. Typical LDA scores of ratio array sensors at different concentrations: (I) PFNA, (J) PFOA, (K) GenX, and (L) PFHpA; linear relationship between factor 1 and (M) PFNA, (N) PFOA, (O) GenX, and (P) PFHpA.

[0063] To verify the quantitative capability of the colorimetric array sensor, four common perfluoroalkyl compounds (PFNA, PFOA, GenX, and PFHpA) were selected. Within a concentration range of 7.5–45 μg / mL, the colorimetric response patterns of each analyte and the array sensor were recorded at different concentrations. The results showed that the array exhibited significantly differentiated responses to different concentrations of the same PFASs. The colorimetric signal changes in each channel showed a gradient increase with increasing concentration, indicating a correlation between concentration and simulated enzyme activity. The changes in signal radius at the five concentrations and the differences in radar image morphology and size at different concentrations further demonstrate that the diversity of signal radii across the three channels can easily distinguish between different concentrations. Figure 4 (A)-(D) in the middle.

[0064] Cluster heatmap analysis showed that each channel exhibited a unique colorimetric change pattern for different concentrations of the four PFASs. The five concentrations of the same PFAS were clearly divided into five independent clusters, with no cross-classification. Figure 3-4 EH). Analysis of the data matrix (5 concentrations × 3 channels × 6 repetitions) using the LDA algorithm showed that all five concentrations were successfully distinguished and formed independent clusters. The knife-cut classification matrix showed a classification accuracy of 100%, and the prediction accuracy for all four PFASs at different concentrations was 100%. Figure 4 In the (I)-(L) plot, factor 1 contributes over 64%, which can be used to describe the distribution of different concentrations of PFASs. By establishing standard curves for the four PFASs, a robust linear regression relationship was observed among the concentrations, with a coefficient of determination (R²) of [missing information]. 2 The values ​​are all greater than 0.95, proving that the array can achieve semi-quantitative detection of various PFASs. Figure 4 (M)-(P) in the middle.

[0065] Example 3 Testing of binary and ternary hybrid systems Binary mixture: PFNA and PFOA were mixed in the following proportions (100%:0%, 75%:25%, 50%:50%, 25%:75%, 0%:100%). 30 μL of this mixture was then mixed with 20 μL of Ce-MOF (0.5 g / mL) in a 96-well plate. The final total concentration of PFNA and PFOA was 30 μg / mL. Reagents were added according to the requirements of each channel, and H2O was added to bring the volume to 200 μL (for 6 replicates).

[0066] Ternary mixtures: PFNA, PFOA, and HFO-15 were mixed in different proportions (25%:50%:25%, 50%:25%:25%, 25%:25%:50%, 12.5%:75%:12.5%, 75%:12.5%:12.5%, 12.5%:12.5%:75%). 30 μL of each of these mixed solutions was then mixed with 20 μL of Ce-MOF (0.5 g / mL) in a 96-well plate. The final total concentration of PFNA, PFOA, and HFO-15 was 30 μg / mL.

[0067] The detection method is the same as that for the binary system. Data is obtained by detecting changes in the absorbance of the chromogenic substrate, and the instrument settings and detection parameters are consistent with the PFASs detection conditions in Example 1.

[0068] Figure 5 Radar plots of colorimetric response patterns for binary (A) and ternary (D) PFAS mixtures. Clustered heatmaps based on array signal response variations, representing variations in binary (B) and ternary (E) PFAS mixtures, respectively. Typical LDA score plots of ratio array sensors, representing binary (C) and ternary (F) PFAS mixtures, respectively.

[0069] Distinguishing and identifying PFOS mixtures is an important and challenging task. This study evaluated the ability of this array sensor to distinguish binary and ternary PFOS mixtures (total concentration 30 μg / mL). The experimental setup was as follows: binary mixtures: PFNA / PFOA mass ratios were 100%:0%, 75%:25%, 50%:50%, 25%:75%, and 0%:100%; ternary mixtures: PFNA / PFOA / HFO-15 mass ratios included 25%:50%:25%, 50%:25%:25%, 25%:25%:50%, 12.5%:75%:12.5%, 75%:12.5%:12.5%, and 12.5%:12.5%:75%. Typical radar plots show the colorimetric response patterns of binary and ternary mixtures. The radar plot shapes corresponding to different proportions of binary mixtures (5 types) and ternary mixtures (6 types) showed significant differences. Figure 5 A and Figure 5 D). Cluster heatmap analysis revealed that different proportions of binary and ternary mixtures had varying degrees of influence on the activities of the three Ce-MOF mimic enzymes: the five proportions of binary mixtures were clearly divided into five independent clusters, and the six proportions of ternary mixtures were also clearly divided into six groups, with no misclassifications. Figure 5 (B) and (E) in the text.

[0070] Further visualization of the classification results using linear discriminant analysis (LDA) revealed that mixtures of different proportions were clearly separated in the factor space. Figure 5 (C) and (F) in the diagram). The cross-validation classification matrix shows an accuracy of 100%, indicating that the sensor system can accurately distinguish between binary and ternary mixtures of different proportions. In the test on unknown samples, all binary and ternary mixtures were correctly identified with an accuracy of 100%, fully demonstrating that the array can still effectively identify target PFASs in complex systems.

[0071] Example 4 PFASs identification in actual samples The applicability of the array sensor in real water samples, including seawater, shrimp, and cod, was evaluated. Seawater was collected from the Yellow Sea and pretreated with a 0.22 μm filter membrane for further application. Shrimp and cod were collected from a local supermarket. 1 g of shrimp and cod samples were pulverized, incubated overnight in 2 mL of trichloroacetic acid, vortexed for 5 minutes, centrifuged to precipitate the solids, and the supernatant was used as the sample for further analysis. Nine PFASs were added to the real samples at a final concentration of 65 μg / mL. 30 μL of PFASs and 20 μL of Ce-MOF (0.5 mg / mL) were added, and reagents were replenished according to the requirements of each channel, with ultrapure water added to bring the volume to 200 μL (6 parallel samples). The detection procedures and instrument settings were consistent with the PFASs detection conditions in section 1.

[0072] The sample matrices for the detection of perfluoroalkyl compounds (PFASs) mainly include natural water bodies, seafood, and soil. The complexity of their composition and the presence of numerous interfering substances significantly increase the analytical difficulty. Based on previous experiments, this study further evaluated the applicability of the array sensor in different real-world samples. Seawater, shrimp, and cod samples were selected, and nine PFASs (all at a concentration of 65 μg / mL) were added to each sample. The established detection system was used for analysis. Despite the presence of various interfering substances in the real-world samples, each channel still exhibited stable and differentiated colorimetric responses to PFASs.

[0073] Figure 6 Typical score maps of the 9 PFAS response patterns obtained by the LDA algorithm, where (A) seawater; (B) shrimp; (C) cod. Unknown sample confusion matrix output by the LDA classifier (D) seawater; (E) shrimp; (F) cod.

[0074] The LDA diagram clearly shows that the nine PFASs are divided into independent clusters, proving that they can still be effectively distinguished in complex matrices. Notably, this array can further classify PFASs into three categories based on functional group type (carboxylic acid group, sulfonic acid group, hydroxyl group). Figure 6 (A)-(C)). The cross-validation classification matrix shows that the classification accuracy of marine, shrimp, and cod samples is 98%, 100%, and 100%, respectively, while the prediction accuracy is 97%, 100%, and 100%. Figure 6 (D)-(F)). The above results fully demonstrate that the array sensor has high resolution and can accurately identify PFASs in real samples.

[0075] Example 5 Portable hydrogel detection kit based on Ce-MOF array sensor A portable hydrogel kit based on Ce-MOF was constructed for real-sample detection, with seawater selected as a representative model. 30 mg of agarose was dissolved in 3 mL of boiling water and stirred thoroughly. When the solution was cooled to 45°C, 3 mL of Ce-MOF (0.25 mg / mL) was added and mixed thoroughly. 40 μL of the mixture was quickly injected into a 96-well plate, and the plate was cooled and solidified to form a hydrogel. PFASs identification was performed using seawater samples as a model. The detection procedures and instrument settings were consistent with the PFASs detection conditions in Example 1.

[0076] Building upon the successful detection described above, this study further optimized the detection system to achieve portable, rapid, and accurate detection of PFASs. Hydrogels, with their porous polymer network structure, have attracted considerable attention due to their high mechanical stability, strong loading capacity, and excellent flexibility. By embedding Ce-MOF into agarose hydrogel as a signal indicator and immobilizing it in a microplate, a portable detection kit was constructed. This immobilized matrix not only enhances the stability of the nanomaterials but also allows target molecules to diffuse through the pores, thereby maintaining detection activity.

[0077] Figure 7 Typical score maps of the response modes of 8 PFASs obtained by the LDA algorithm are shown; (A) is the LDA map of the gel array sensor for detecting 8 PFASs in seawater, and (B) is the confusion matrix of unknown seawater samples output by the LDA classifier.

[0078] In the detection application, seawater samples containing PFASs (concentration 65 μg / mL) required no pretreatment and were directly added to the hydrogel kit. Colorimetric response data in three channels (652 nm, 510 nm, and 560 nm) were measured using a microplate reader. The results showed that the hydrogel-embedded Ce-MOF retained the activities of the three mimic enzymes, and different PFASs had varying degrees of influence on each enzyme activity, resulting in differentiated colorimetric responses in the three channels. Linear discriminant analysis (LDA) was used for precise analysis of the fingerprint patterns. Figure 7 In Figure A), all PFASs were accurately classified as independent clusters without any errors. The cross-validation matrix showed a classification accuracy of 100% and a prediction accuracy of 94%. Figure 7 (B in the original text). Compared with free nanozymes, the portable hydrogel kit exhibits comparable nanozyme activity and detection performance. These results indicate that the portable hydrogel kit can be used for rapid qualitative detection of PFASs in seawater.

[0079] Based on the above analysis, the MOF nanozyme array sensor constructed in this invention, based on multiple enzyme activities, exhibits superior performance, demonstrating high sensitivity and selectivity in the identification and differentiation of PFASs compounds. By utilizing the multiple enzyme activities of oxidase, laccase, and superoxide dismutase, the challenge posed by the similarity of PFAS molecular structures is overcome, achieving efficient identification of different types of PFASs. This array sensor can not only accurately distinguish single PFASs compounds but also perform quantitative detection, showing good response to different concentrations of PFASs.

[0080] Furthermore, the sensor's performance in binary and ternary mixtures demonstrates its high resolution. Most importantly, testing with real samples (such as seawater, shrimp, and cod) shows that the sensor can accurately identify PFASs in complex matrices, indicating promising practical applications. To further improve the sensor's convenience and practicality, a portable hydrogel detection kit based on Ce-MOF was developed by integrating the array sensor with a hydrogel. This kit enables rapid on-site detection of PFASs and exhibits detection performance comparable to traditional laboratory equipment. In conclusion, the Ce-MOF-based multi-enzyme activity array sensor provides a reliable, sensitive, and convenient detection platform for environmental monitoring of PFASs, with broad application prospects.

[0081] 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. An array sensor, characterized in that, The array sensor is a Ce-MOF triple enzyme activity array sensor, which has multiple independent sensing channels. Each sensing channel contains a Ce-MOF nanozyme and a chromogenic substrate. The chromogenic substrate includes a first chromogenic substrate, a second chromogenic substrate, and a third chromogenic substrate. The first chromogenic substrate includes 3,3',5,5'-tetramethylbenzidine, the second chromogenic substrate includes 2,4-diaminophenol and 4-aminoantipyridine, and the third chromogenic substrate includes xanthine, purine oxidase, and nitroblue tetrazolium.

2. The array sensor according to claim 1, characterized in that, The array sensor has three independent sensing channels, namely the first sensing channel, the second sensing channel and the third sensing channel. The first sensing channel contains Ce-MOF nanozyme, 3,3',5,5'-tetramethylbenzidine and acetate-sodium acetate buffer. Alternatively, the second sensing channel contains Ce-MOF nanozyme, 2,4-diaminophenol, 4-aminoantipyrrolidone, and acetate-sodium acetate buffer. Alternatively, the third sensing channel contains Ce-MOF nanozyme, xanthine, purine oxidase, nitroblue tetrazolium, and tris(hydroxymethyl)aminomethane hydrochloride buffer.

3. The array sensor according to claim 2, characterized in that, The volume ratio of Ce-MOF nanozyme, 3,3',5,5'-tetramethylbenzidine, and acetate-sodium acetate buffer in the first sensing channel is (15-25):(10-15):(35-45); wherein the concentration of Ce-MOF nanozyme is 0.4-0.6 mg / mL, the concentration of 3,3',5,5'-tetramethylbenzidine is 4-6 mM, and the concentration of acetate-sodium acetate buffer is 90-110 mM. Alternatively, the volume ratio of Ce-MOF nanozyme, 2,4-diaminophenol, 4-aminoantipyridine, and acetate-sodium acetate buffer in the second sensing channel is (15-25):(20-30):(5-10):(35-45); wherein the concentration of Ce-MOF nanozyme is 0.4-0.6 mg / mL; the concentration of 2,4-diaminophenol is 4-6 mM; the concentration of 4-aminoantipyridine is 4-6 mM; and the concentration of acetate-sodium acetate buffer is 90-110 mM. Alternatively, the volume ratio of Ce-MOF nanozyme, xanthine, purine oxidase, nitroblue tetrazolium, and tris(hydroxymethyl)aminomethane hydrochloride buffer in the third sensing channel is (15-25):(45-55):(45-55):(25-35):(15-25); wherein the concentration of Ce-MOF nanozyme is 0.4-0.6 mg / mL, the concentration of xanthine is 4-6 mM, the concentration of purine oxidase is 0.5-1.5 U / mL, the concentration of nitroblue tetrazolium is 1.5-2.5 mM, and the concentration of tris(hydroxymethyl)aminomethane hydrochloride buffer is 15-25 mM.

4. The array sensor according to claim 3, characterized in that, The volume ratio of Ce-MOF nanozyme, 3,3',5,5'-tetramethylbenzidine, and acetate-sodium acetate buffer in the first sensing channel is 20:12:40; wherein the concentration of Ce-MOF nanozyme is 0.5 mg / mL, the concentration of 3,3',5,5'-tetramethylbenzidine is 5 mM, and the concentration of acetate-sodium acetate buffer is 100 mM. Alternatively, the volume ratio of Ce-MOF nanozyme, 2,4-diaminophenol, 4-aminoantipyridine, and acetate-sodium acetate buffer in the second sensing channel is 20:24:8:40; wherein the concentration of Ce-MOF nanozyme is 0.5 mg / mL; the concentration of 2,4-diaminophenol is 5 mM; the concentration of 4-aminoantipyridine is 5 mM; and the concentration of acetate-sodium acetate buffer is 100 mM. Alternatively, the volume ratio of Ce-MOF nanozyme, xanthine, purine oxidase, nitroblue tetrazolium, and tris(hydroxymethyl)aminomethane hydrochloride buffer in the third sensing channel is 20:50:50:30:20; wherein the concentration of Ce-MOF nanozyme is 0.5 mg / mL, the concentration of xanthine is 5 mM, the concentration of purine oxidase is 1.0 U / mL, the concentration of nitroblue tetrazolium is 2.0 mM, and the concentration of tris(hydroxymethyl)aminomethane hydrochloride buffer is 20 mM.

5. The application of the array sensor according to claims 1-4 in the detection of perfluoroalkyl compounds, characterized in that, The perfluoroalkyl compounds include one or more of perfluorononanoic acid, perfluorooctanoic acid, hexafluoropropylene oxide dimer acid, perfluoroheptanoic acid, perfluorohexanoic acid, perfluorovalerate, 2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecanoic acid, perfluorobutyric acid, and trifluoromethanesulfonic acid.

6. A method for detecting perfluoroalkyl compounds, characterized in that, The array sensor described in claims 1-4 includes the following steps: Samples containing perfluoroalkyl compounds were mixed with Ce-MOF nanozyme solution and incubated at room temperature. After adding a chromogenic substrate and making up to a final volume with water, absorbance data were measured and processed to obtain a data matrix. The data matrix was then classified to distinguish and detect various perfluoroalkyl compounds.

7. The method for detecting perfluoroalkyl compounds according to claim 6, characterized in that, The volume ratio of the perfluoroalkyl compound to the Ce-MOF nanozyme solution is (25-35):(15-25); wherein the final concentration of the perfluoroalkyl compound is 7.5-45 μg / mL, and the concentration of the Ce-MOF nanozyme solution is 0.4-0.6 mg / mL. Alternatively, the preparation method of the Ce-MOF nanozyme is as follows: terephthalic acid is added to N,N-dimethylformamide and stirred until homogeneous, then cerium ammonium nitrate is added and stirred until homogeneous to obtain a mixed solution; the mixed solution is subjected to a hydrothermal reaction, cooled to room temperature, and then centrifuged, washed with water, and freeze-dried to obtain the Ce-MOF nanozyme; The wavelength for measuring the absorbance of the chromogenic substrate is 510-660 nm.

8. The method for detecting perfluoroalkyl compounds according to claim 7, characterized in that, The perfluoroalkyl compound is one or more of perfluorononanoic acid, perfluorooctanoic acid, and 2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecanoic acid; Alternatively, in the preparation method of the Ce-MOF nanozyme, the mass ratio of terephthalic acid, N,N-dimethylformamide, and cerium ammonium nitrate is (0.5-0.6):(20-30):(1.5-1.8); the hydrothermal reaction temperature is 95-105℃ and the time is 0.5-1.5h; Alternatively, the wavelengths for measuring the absorbance of the chromogenic substrate are 652 nm, 510 nm, and 560 nm.

9. The method for detecting perfluoroalkyl compounds according to claim 6, characterized in that, The samples containing perfluoroalkyl compounds included seawater, shrimp, and cod; When the sample containing perfluoroalkyl compounds is shrimp or cod, the shrimp or cod needs to be pretreated. The pretreatment involves crushing the shrimp or cod, soaking it in trichloroacetic acid overnight, vortexing, centrifuging to precipitate the solid, and separating the supernatant.

10. A kit for detecting perfluoroalkyl compounds, characterized in that, Includes the array sensor described in claims 1-4.

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