Construction method and application of organophosphorus pesticide detection sensor assisted by smart phone

Abstract

The invention discloses a construction method and application of a smartphone-assisted organophosphorus pesticide detection sensor, and belongs to the technical field of analysis and detection. The method comprises the following steps: firstly, synthesizing UIO-66 (at) MnO2 nano-enzyme which has relatively high oxide-like enzyme activity so as to catalytically oxidize 3, 3 ', 5, 5'-tetramethyl benzidine (TMB) to develop color; acetylcholin esterase is utilized to catalyze hydrolysis of acetylthiocholine to generate reducing substances, the chromogenic reaction of TMB is inhibited, and the organophosphorus pesticide can specifically inhibit the activity of acetylcholin esterase, so that the chromogenic reaction is recovered; the reaction solution is shot through Color Grab software of the smart phone, an RGB value is output, a gray value is calculated, a linear relation between the gray value and the pesticide concentration is established, and quantitative detection of the organophosphorus pesticide is achieved. The sensor constructed by the invention is high in sensitivity, good in selectivity, simple and convenient to operate and low in cost, can be successfully applied to rapid detection of organophosphorus pesticides in food and environmental samples, and has a good practical application prospect.

Description

Technical Field

[0001] This invention relates to a method for constructing and applying a smartphone-assisted organophosphorus pesticide detection sensor. Background Technology

[0002] In recent years, researchers have developed various instrumental analytical techniques for detecting organophosphorus pesticides (OPs), including gas chromatography, high-performance liquid chromatography, liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and chemiluminescence. Although these traditional techniques have achieved sensitive detection of OPs, a number of insurmountable limitations remain, such as the need for complex and costly equipment, cumbersome sample preparation, excessively long analysis times, and high detection costs. Therefore, exploring simple, rapid, and visualized sensing methods for detecting OPs in food is of great significance.

[0003] Among numerous methods for pesticide residue analysis, colorimetric analysis stands out due to its simplicity, rapid response, portability, miniaturization, and low cost. However, colorimetric methods are inherently insensitive, making it difficult to detect target concentrations under low illumination, which limits their practical applications. In recent years, nanozyme-based colorimetric biosensors have attracted significant attention due to their stability, simplicity, tunability, and low cost. The high catalytic activity of nanozymes can convert target analytes into more obvious color-changing output signals, enabling visual detection. Moreover, the catalytic activity of nanozymes can be modulated by adjusting their size, morphology / shape, composition, molecular weight, or the formation of complexes or hybrids. Therefore, it holds promise for rapid, visual, and sensitive analysis of pesticide residues (OPs) by constructing nanozyme-based colorimetric sensors.

[0004] Point-of-care testing (POCT) plays a crucial role in food safety testing, environmental monitoring, disease identification, and diagnosis. POCT devices typically combine sensors and miniaturized components to achieve integrated "sampling-input-response-output." Smartphones possess many advanced features, including light sensors, high-definition cameras, Wi-Fi and Bluetooth communication, large-capacity storage, and touchscreens. Therefore, smartphones are not only common communication tools but also convenient digital processing centers that can act as sensors to meet the limited space requirements of other devices. Thus, utilizing smartphones for POCT analysis of operational hazards (OPs) has significant scientific and practical application value.

[0005] To date, several smartphone-assisted colorimetric sensors have been reported for detecting organophosphorus pesticides, such as:

[0006] Chinese patent CN202410538185.5 discloses a smartphone-assisted instant detection test strip for organophosphorus pesticides, using a polyvinyl chloride substrate card, a glass fiber film, and a composite nanomaterial. Based on the inhibitory effect of organophosphorus pesticides on the biocatalytic activity of acetylcholinesterase (AChE), and utilizing AChE as a signal amplifier, organophosphorus pesticides can reduce the yield of thioacetylcholine (ATCh) hydrolyzed by AChE to produce thiocholine (TCh), thereby reducing the inhibitory effect of TCh on the peroxidase activity of the composite nanomaterial. Visual detection of organophosphorus pesticides is achieved through the TMB oxidation method.

[0007] The article Wang, Y.; Chen, J.; Liu, Y.; Zhang, R.; Hong, J.; Zhao, Y. Microchim. Acta 2025, 192 (11), 714, synthesized a novel paper-based colorimetric sensor based on a core-shell metal-organic framework (MOF) coated with a virtual molecularly imprinted polymer (DMIP) for the detection of organophosphorus pesticides. The MOFs, as the core, enhance the sensor's sensitivity due to their inherent advantages such as high catalytic activity and stability; simultaneously, the introduction of the MIP effectively overcomes the limitations of MOFs in single-substrate detection. This sensor, combining color response and quantitative analysis via a smartphone, successfully detected six OPs in agricultural products.

[0008] The article, Jing, W.; Qiang, S.; Jia, Z.; Shi, QH; Meng, X.; Yu, M.; Ma, H.; Zhao, K.; Dai, Y. Sens. Actuators B, Chem. 2023, 389, 133857, describes the development of a nanozyme array sensor based on Ag₂O nanospheres. The sensor utilizes Ag₂O, which exhibits oxidase-like activity, to catalyze the reaction of TMB, o-phenylenediamine, and 2,2'-azido-bis-3-ethylbenzothiazoline-6-sulfonic acid. A smartphone with a camera is used to record and analyze the colorimetric results of the sensor array for OPs detection.

[0009] The article, published in *Food Chem.*, 2023, 424, 136477, by Song, D.; Tian, ​​T.; Yang, X.; Wang, L.; Sun, Y.; Li, Y.; Huang, H., describes a novel nanozyme sensor array based on four copper nanomaterials with laccase-like activity. This nanozyme catalyzes the colorimetric reaction of 2,4-dichlorophenol with 4-aminoantipyrine. Upon the addition of OPs (opioids), the laccase activity of the nanozyme changes to varying degrees, resulting in significant color changes. This array achieves selective and individual identification of OPs while effectively overcoming interference from carbamate pesticides. Finally, a portable method for detecting OP residues in fruits and vegetables was established using a smartphone and a self-made dark box.

[0010] Traditional methods for detecting organophosphorus pesticides rely on expensive, large-scale instruments (such as UV-Vis spectrometers and chromatographs), which are complex to operate, costly, and slow. Among the currently reported colorimetric sensing methods based on smartphones, nanozymes exhibit limited catalytic activity, and the sensitivity for detecting organophosphorus pesticide content needs improvement. Summary of the Invention

[0011] The first technical problem to be solved by the present invention is to provide a method for constructing a smartphone-assisted organophosphorus pesticide detection sensor. Compared with the prior art, the organophosphorus pesticide detection sensor constructed by this method has high sensitivity, is simple and fast, and has a low cost, and can be used for rapid on-site detection of a large number of samples.

[0012] The second technical problem to be solved by the present invention is to provide the application of the above-mentioned smartphone-assisted organophosphorus pesticide detection sensor in the detection of organophosphorus pesticides in food and environmental samples.

[0013] To address the first technical problem mentioned above, this invention provides a method for constructing a smartphone-assisted organophosphorus pesticide detection sensor, comprising the following steps:

[0014] (1) Synthesis of UIO-66@MnO2 nanozyme:

[0015] Zirconium tetrachloride (ZrCl4), terephthalic acid (BDC), and 2-aminoterephthalic acid (NH2-BDC) were mixed and dispersed in 6-10 mL of N,N-dimethylformamide (DMF) at a molar ratio of 1:11.1:0.8. 60-100 μL of hydrochloric acid was added, and the mixture was stirred vigorously before being transferred to a polytetrafluoroethylene-lined stainless steel autoclave. The autoclave was calcined at 120 °C for 12-48 hours. The product was washed alternately with DMF and water, and then dried in an oven to obtain UIO-66 powder. UIO-66 was dispersed in 2-morpholinoethanesulfonic acid (MES) buffer solution, and 2-5 mM potassium permanganate (KMnO4) was added. The mixture was ultrasonically reacted for 30-40 min. The product was washed with water and dried in an oven to obtain brown UIO-66@MnO2 nanozyme.

[0016] Catalytic activity test of nanozymes:

[0017] A 10–20 μg / mL aqueous dispersion of UIO-66@MnO2, a 0.1–1 mM TMB concentration, and a 0.1 M HAc-NaAc buffer solution at pH 4.0 were mixed and reacted at 35 °C for 5–10 min. The UV-Vis absorption spectra were measured at 600–800 nm, and the absorbance of the oxidized TMB (oxTMB) at 652 nm was recorded. Steady-state kinetic analysis was performed to evaluate the catalytic performance of the nanozyme, and a series of initial reaction rates at different TMB concentrations were calculated.

[0018] v = A / εbt

[0019] Where ε is the molar absorptivity of TMB, b is the optical path length, and t is the colorimetric reaction time;

[0020] And fit it to the Michaelis equation:

[0021] v=V max [S] / ( K m+ [S])

[0022] Where v is the initial velocity, V max [S] is the maximum reaction rate, [S] is the substrate concentration, and K is the maximum reaction rate. m It is the Michaelis constant;

[0023] The specific activity of the material is calculated using the formula: SA = V / (ε×b)×(ΔA / Δt)×[m].

[0024] SA is the specific activity value of the material, measured in U·mg. -1 V is the total volume of the reaction solution in μL, ε is the molar absorptivity of TMB, b is the optical path length, and ΔA / Δt is the initial rate of change of absorbance at 652 nm in nm·min.-1 [m] is the unit of mass of the nanozyme, which is mg;

[0025] (2) Construction of a smartphone-assisted nanozyme colorimetric sensor:

[0026] Mix 10–20 μL of organophosphorus pesticides of different concentrations, 100 mU / mL of acetylcholinesterase (AChE), and 2 mM of thioacetylcholine (ATCh), and incubate at 37 °C for 30–60 min. Then, add 10–20 μg / mL of UIO-66@MnO2 aqueous dispersion, 0.1–1 mM of TMB, and 0.1 M HAc-NaAc buffer solution at pH 4.0, and react at 35 °C for 5–10 min. Finally, photograph the reaction product using the Color Grab software on a smartphone, outputting R, G, and B values. Calculate the grayscale value using the formula Grayscale = 0.299R + 0.587G + 0.114B. Establish a linear relationship between the grayscale value y and the organophosphorus pesticide concentration x, and fit the linear equation y = kx + b; Calculate the detection limit (LOD) of the smartphone-assisted colorimetric sensor method according to the formula LOD=3s / k, where s is the standard deviation of 11 blank tests and k is the slope of the linear equation.

[0027] Analytical performance testing of organophosphorus pesticide sensors based on smartphone assistance:

[0028] Selectivity test: A certain concentration (10 times the concentration of organophosphorus pesticides) of interfering substances (cationic, anionic, other types of pesticides), 100 mU / mL AChE, and 2 mM ATCh were mixed and incubated at 37 °C for 30–60 min; then, 10–20 μg / mL UIO-66@MnO2 aqueous dispersion, 0.1–1 mM TMB, and 0.1 M HAc-NaAc buffer solution at pH 4.0 were added, and the reaction was carried out at 35 °C for 5–10 min; finally, the reaction product was photographed using the Color Grab software of a smartphone, and the R, G, and B values ​​were output. The formula Grayscale = 0.299R + 0.587G + 0.114B Calculation of grayscale value; Repeatability test: Perform colorimetric reaction with a fixed concentration of organophosphorus pesticide according to the above steps, record the R, G, and B values, calculate the grayscale value, perform 6 parallel operations, and calculate the relative standard deviation (RSD) of the results; Stability test: Store the nanozyme at room temperature, and every 5 days, perform the reaction with a fixed concentration of organophosphorus pesticide according to the above steps, record the R, G, and B values, calculate the grayscale value, and calculate the RSD of the grayscale value within 30 days.

[0029] To address the second technical problem mentioned above, this invention provides the application of the smartphone-assisted organophosphorus pesticide detection sensor constructed using the above method in the detection of organophosphorus pesticides in food and environmental samples, specifically including the following steps:

[0030] First, the sample is pretreated;

[0031] For solid samples: Weigh 0.5~1.0 g of solid sample, chop it, immerse it in a methanol / acetone mixed solution with a volume ratio of 1:1, vortex mix in the solution for 2~5 min, centrifuge for 5~10 min, and then dilute the supernatant with 2% ethanol-water solution 50~200 times for later use.

[0032] For liquid samples: measure 0.5~1.0 mL of liquid sample, filter it through a 0.22~0.45 μm microporous membrane, and dilute it 50~200 times for later use;

[0033] Take 10-20 μL of pretreated food and environmental samples, mix 100 mU / mL AChE with 2 mM ATCh, and incubate at 37°C for 30-60 min; then, add 10-20 μg / mL UIO-66@MnO2 aqueous dispersion, 0.1-1 mM TMB, and 0.1 M HAc-NaAc buffer solution with pH 4.0 and react at 35°C for 5-10 min; finally, take a picture of the reaction product using the Color Grab software on a smartphone, output the R, G, and B values, calculate the grayscale value using the formula Grayscale = 0.299R + 0.587G + 0.114B, and substitute it into the linear fitting equation y = kx + b in step (2) to calculate the concentration x of organophosphorus pesticides in the food sample;

[0034] For actual samples where organophosphorus pesticides were not detected, the accuracy of the method was verified using the spiked recovery method.

[0035] Advantages of this invention:

[0036] This invention uses zirconium ions as the central ion, terephthalic acid / aminoterephthalic acid as the organic ligand, and N,N-dimethylformamide as the solvent to synthesize zirconium-based metal-organic framework UIO-66 via a one-step solvothermal method. Then, on its surface, potassium permanganate is reduced by 2-morpholine ethanesulfonic acid using an in-situ reduction method, followed by ultrasonic reaction to synthesize UIO-66@MnO2 nanozyme.

[0037] This invention uses TMB (colorless) as a substrate and measures the absorbance of the catalytic oxidation product oxTMB (blue) in an HAc-NaAc buffer solution to evaluate the catalytic activity of the nanozyme. Subsequently, AChE and ATCh are introduced into the catalytic system. AChE catalyzes the production of reducing thiocholine from ATCh, which reduces oxTMB, causing the blue color of the solution to fade. The inhibitory effect of organophosphorus pesticides on AChE further weakens this fading effect. All these color changes can be recorded using a smartphone. Color processing software outputs the R, G, and B values ​​of the reaction solution, and the solution's grayscale color is calculated, enabling integrated real-time detection of organophosphorus pesticides through "sample input - color recognition - concentration output".

[0038] Utilizing the large specific surface area and good affinity of MOF materials, the synthesized nanozymes possess high oxidase-like activity and a Michaelis constant K. m With a concentration as low as 0.0504 mM, the sensitivity of the colorimetric sensor can be significantly improved, resulting in a detection limit of 0.18 ng / mL for organophosphorus pesticides. Furthermore, the use of smartphone-assisted sensing makes experimental results more intuitive, reduces reliance on large instruments, lowers detection costs, and increases detection speed. This enables integrated "sampling-input-response-output" rapid detection of food safety and environmental samples, suitable for point-of-care testing (POCT) of large numbers of food and environmental samples. In summary, compared with common colorimetric sensing methods, the sensing method developed in this invention offers high sensitivity, simplicity, speed, and lower cost, making it applicable to rapid on-site detection of large numbers of samples. Attached Figure Description

[0039] Figure 1 The images show the morphological characteristics of the nanozymes, including: (A) scanning electron microscope images of UIO-66 and (B) UIO-66@MnO2; (C) transmission electron microscope images of UIO-66 and (D) UIO-66@MnO2; and (E) high-angle annular dark-field (HAADF) STEM image of UIO-66@MnO2 and the corresponding (a)-(d) elemental mapping images.

[0040] Figure 2 The structure characterization diagrams of the nanozyme are shown below, including: (A) XRD patterns of UIO-66, UIO-66@MnO2 and simulated XRD pattern of UIO-66; (BD) Infrared absorption spectra of UIO-66, MnO2 and UIO-66@MnO2 (B), UV absorption spectra (C) and Zeta potential diagram (D); (E) XPS spectra of UIO-66@MnO2 and (F) Mn element.

[0041] Figure 3The figure shows the peroxidase-like activity test results of the UIO-66@MnO2 nanozyme prepared in Example 1, where: (A) Michaelis-Menten curves of UIO-66@MnO2 and (B) MnO2 (inset: corresponding Lineweaver-Burk plot).

[0042] Figure 4 Linear fitting graph of the smartphone-assisted dichlorvos (Dip) colorimetric sensor prepared in Example 1 (inset: smartphone photos of reaction solutions at different concentrations).

[0043] Figure 5 The selective test results of the smartphone-assisted Dip colorimetric sensor prepared in Example 1 are shown in the figure.

[0044] Figure 6 This is a repeatability test result diagram of the smartphone-assisted Dip colorimetric sensor prepared in Example 1.

[0045] Figure 7 This is a graph showing the stability test results of the smartphone-assisted Dip colorimetric sensor prepared in Example 1.

[0046] Figure 8 The linear fitting graph of the smartphone-assisted dichlorvos (Dic) colorimetric sensor prepared in Example 2 is shown in the inset: photos of the reaction solution at different concentrations taken with a mobile phone.

[0047] Figure 9 The linear fitting graph of the smartphone-assisted phoxim (Pho) colorimetric sensor prepared in Example 3 is shown in the inset: photos taken with a mobile phone of the reaction solution at different concentrations.

[0048] Figure 10 The linear fitting graph of the smartphone-assisted malathion (Mal) colorimetric sensor prepared in Example 4 is shown in the inset: smartphone images of reaction solutions at different concentrations.

[0049] Figure 11 Linear fitting graph of the smartphone-assisted Dim colorimetric sensor prepared in Example 5 (inset: mobile phone photos of reaction solutions at different concentrations).

[0050] Figure 12 Linear fitting graph of the smartphone-assisted chlorpyrifos (Chl) colorimetric sensor prepared in Example 6 (inset: smartphone photos of reaction solutions at different concentrations).

[0051] Figure 13 This is a schematic diagram of the overall solution of the present invention. Detailed Implementation

[0052] Example 1 (see Figure 13 ):

[0053] (1) Synthesis of UIO-66@MnO2 nanozyme:

[0054] 0.330 mmol ZrCl4, 3.672 mmol BDC, and 0.265 mmol NH2-BDC were dispersed in 6 mL DMF, and 60 μL hydrochloric acid was added. After vigorous stirring, the mixture was transferred to a PTFE-lined stainless steel autoclave and calcined at 120 °C for 48 h. The product was washed three times alternately with DMF and water, and dried in a 60 °C oven to obtain UIO-66 powder. 10 mg of UIO-66 was dispersed in 10 mL MES buffer solution, and 3 mM KMnO4 was added. The mixture was sonicated for 30 min. The product was washed three times with water and dried in a 60 °C oven to obtain UIO-66@MnO2 nanozyme.

[0055] like Figure 1 As shown in A and C, UIO-66 exhibits a spherical morphology with an average diameter of approximately 47 nm. After in-situ growth of MnO2, a transparent wrinkled film was observed on the surface of the spheres. Figure 1 (B and 1D). Furthermore... Figure 1 Scanning transmission electron microscopy (STEM) combined with elemental mapping images in E confirmed the uniform distribution of C, O, Zr, and Mn elements in the UIO-66@MnO2 nanocomposite. Figure 2 The XRD diffraction peaks shown in Figure A match well with the simulated diffraction pattern of UIO-66. After modification with MnO2, the XRD pattern shows that the crystal structure of the MOF remains intact. Its functional groups were observed using Fourier transform infrared spectroscopy (FT-IR). Figure 2 As shown in Figure B, the FT-IR spectrum of UIO-66@MnO2 simultaneously exhibits values ​​at 662, 550, and 486 cm⁻¹. -1 Peaks corresponding to Zr-O bonds nearby, and at 589, 499, and 434 cm⁻¹. -1 The absorption peak at this location corresponds to the Mn-O bond. Furthermore, absorption peaks at approximately 250 nm and 380 nm, attributed to UIO-66 and MnO2 respectively, can be observed in the UV-Vis absorption spectrum of UIO-66@MnO2. Figure 2 C) indicates that MnO2 was successfully loaded onto UIO-66. Figure 2D shows that the Zeta potentials of UIO-66, MnO2, and UIO-66@MnO2 are -11.0 mV, -17.9 mV, and -19.4 mV, respectively. X-ray photoelectron spectroscopy (XPS) characterization from UIO-66 to UIO-66@MnO2 reveals the signals of the Zr3d, C1s, O1s, and Mn2p peaks. Figure 2 E), which is Figure 1 The element mapping results in F are consistent. Furthermore, Figure 2 F shows the XPS spectra of Mn2p with characteristic peaks at 642.0 eV and 653.6 eV. These results validate the successful synthesis of the UIO-66@MnO2 nanozyme.

[0056] Catalytic activity test of nanozymes:

[0057] 20 μL of 1.0 mg / mL UIO-66@MnO2 aqueous dispersion, 100 μL of 1 mM TMB, and 800 μL of HAc-NaAc buffer (0.1 M, pH 4.0) were mixed and reacted at 35 °C for 5 min. The UV-Vis absorption spectra were measured at wavelengths of 600–800 nm, and the absorbance of oxTMB at 652 nm was recorded. Steady-state kinetic analysis was performed to evaluate the catalytic performance of the nanozyme. A series of initial reaction rates were calculated at different TMB concentrations (0.05, 0.06, 0.07, 0.08, 0.09, 0.1 mM) and fitted to the Michaelis-Menten equation v = V max [S] / (Km+[S]) is in [S] / (Km+[S]). According to Figure 3 The Michaelis-Menten curve and Lineweaver-Burk plot in A show that UIO-66@MnO2 exhibits extremely low K0. m Value (0.0504 mM) and higher V max (12.2×10) -8 M / s). Compared to individual MnO2 nanosheets (K m = 0.0716 mM, V max = 11.5 × 10 -8 M / s ( Figure 3 B) Compared with other nanozymes (Table 1), UIO-66@MnO2 exhibits a higher affinity for TMB. The inherent properties of UIO-66, such as its large specific surface area, porous structure, and abundant active sites, contribute to enhancing the substrate's affinity for TMB. Figure 3 As shown in C, the specific activity (SA) of UIO-66@MnO2 was calculated to be 0.28 U / mg, which is 1.56 times that of MnO2 nanosheets alone, exhibiting good oxidase-like activity.

[0058] Table 1. Comparison of kinetic parameters of oxidase-like activities of UiO-66@MnO2 and other nanozymes

[0059]

[0060] Numerical source, references:

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[0070]

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[0071] Construction of a smartphone-assisted nanozyme colorimetric sensor:

[0072] A sensing platform for the organophosphorus pesticide dichlorvos (Dip) was constructed. 20 μL of different concentrations of Dip, 40 μL of 100 mU / mL AChE, and 40 μL of 4 mM ATCh were mixed and incubated at 37 °C for 30 min. Then, 20 μL of 1 mg / mL UIO-66@MnO2 aqueous dispersion, 100 μL of 1 mM TMB, and 800 μL of HAc-NaAc buffer solution (0.1 M, pH 4.0) were added, and the reaction was carried out at 35 °C for 5 min. Finally, the reaction product was photographed using the Color Grab software on a smartphone, and the R, G, and B values ​​were output. The grayscale value was calculated using the formula Grayscale = 0.299R + 0.587G + 0.114B. A linear relationship between the grayscale value y and the organophosphorus pesticide concentration x was established, and the linear equation y = kx + b was fitted. The limit of detection (LOD) of this smartphone-assisted colorimetric sensor method was calculated using the formula LOD = 3s / k (where s is the standard deviation of 11 blank tests and k is the slope of the linear equation). The results are as follows: Figure 4 As shown, the grayscale value and the Dip concentration between 0.5 ng / mL and 50 ng / mL conform to the linear equation y = -2.11x + 194.66, with a correlation coefficient R. 2 The value is 0.996. The calculated LOD is 0.18 ng / mL.

[0073] Analytical performance testing of organophosphorus pesticide sensors based on smartphone assistance:

[0074] Testing the selectivity performance of the DIP sensor:

[0075] 500 ng / mL Na + K + Ca 2+ Mg 2+ Glucose (Glu), HSO4 - HPO4 2- H2PO4 -Quinoline (Imi) and imidacloprid (Qui) were used to replace organophosphorus pesticides, mixed with 40 μL of 100 mU / mL AChE and 40 μL of 4 mM ATCh, and incubated at 37 °C for 30 min. Then, 20 μL of 1 mg / mL UIO-66@MnO2 aqueous dispersion, 100 μL of 1 mM TMB, and 800 μL of HAc-NaAc buffer (0.1 M, pH 4.0) were added, and the reaction was carried out at 35 °C for 5 min. Finally, the reaction product was photographed using the Color Grab software on a smartphone, and the R, G, and B values ​​were output. The grayscale value was calculated using the formula Grayscale = 0.299R + 0.587G + 0.114B. The results are as follows. Figure 5 As shown, compared with the blank experiment, the grayscale change value of Dip is significantly higher than the detection results of other interfering substances, proving that the sensing method has good selectivity for organophosphorus pesticides.

[0076] Testing the repeatability performance of the DIP sensor:

[0077] 50 ng / mL Dip was mixed with 40 μL of 100 mU / mL AChE and 40 μL of 4 mM ATCh, and incubated at 37 °C for 30 min. Then, 20 μL of 1 mg / mL UIO-66@MnO2 aqueous dispersion, 100 μL of 1 mM TMB, and 800 μL of HAc-NaAc buffer (0.1 M, pH 4.0) were added, and the reaction was carried out at 35 °C for 5 min. Finally, the reaction product was photographed using the Color Grab software on a smartphone, and the R, G, and B values ​​were output. The grayscale value was calculated using the formula Grayscale = 0.299R + 0.587G + 0.114B. Six parallel operations were performed, and the relative standard deviation (RSD) of the results was calculated. The results are as follows. Figure 6 As shown, the RSD value of the 6 parallel tests was 2.4%, proving that the sensing method has good repeatability.

[0078] Testing the stability performance of the DIP sensor:

[0079] Using nanozymes stored at room temperature, 50 ng / mL Dip was mixed with 40 μL of 100 mU / mL AChE and 40 μL of 4 mM ATCh every 5 days and incubated at 37 °C for 30 min. Then, 20 μL of 1 mg / mL UIO-66@MnO2 aqueous dispersion, 100 μL of 1 mM TMB, and 800 μL of HAc-NaAc buffer (0.1 M, pH 4.0) were added, and the reaction was carried out at 35 °C for 5 min. Finally, the reaction product was photographed using the Color Grab software on a smartphone, and the R, G, and B values ​​were output. The grayscale value was calculated using the formula Grayscale = 0.299R + 0.587G + 0.114B. The RSD of the grayscale value over 30 days was also calculated. The results are as follows. Figure 7 As shown, the RSD value of the 30-day grayscale results is 1.5%, which proves that the sensing method has good stability.

[0080] Example 2:

[0081] Similar to Example 1, in the construction of the smartphone-assisted nanozyme colorimetric sensor, Dip was replaced with dichlorvos (Dic). The results are as follows... Figure 8 As shown, the gray value y and the Dic concentration x conform to the linear equation y = -1.42x + 160.57 between 0.5 ng / mL and 50 ng / mL, with a correlation coefficient R. 2 The value is 0.998, and the LOD of the smartphone-assisted Dic colorimetric sensor is calculated to be 0.26 ng / mL.

[0082] Example 3:

[0083] Similar to Example 1, in the construction of the smartphone-assisted nanozyme colorimetric sensor, Dip was replaced with phoxim (Pho). The results are as follows... Figure 9 As shown, the gray value y and the Dim concentration x conform to the linear equation y = -1.31x + 162.81 between 0.5 ng / mL and 50 ng / mL, with a correlation coefficient R. 2 The value is 0.998, and the LOD of the smartphone-assisted Pho colorimetric sensor is calculated to be 0.29 ng / mL.

[0084] Example 4:

[0085] Similar to Example 1, in the construction of the smartphone-assisted nanozyme colorimetric sensor, Dip was replaced with malathion (Mal). The results are as follows... Figure 10 As shown, the gray value y and the Mal concentration x conform to the linear equation y = -0.66x + 172.41 between 1.0 ng / mL and 50 ng / mL, with a correlation coefficient R. 2The value is 0.996, and the LOD of the smartphone-assisted Mal colorimetric sensor is calculated to be 0.57 ng / mL.

[0086] Example 5:

[0087] Similar to Example 1, in the construction of the smartphone-assisted nanozyme colorimetric sensor, Dip was replaced with Dim. The results are as follows... Figure 11 As shown, the gray value y and the Dim concentration x conform to the linear equation y = -1.07x + 181.45 between 0.5 ng / mL and 50 ng / mL, with a correlation coefficient R. 2 Given a value of 0.999, the LOD of the smartphone-assisted Dim colorimetric sensor was calculated to be 0.35 ng / mL.

[0088] Example 6:

[0089] Similar to Example 1, in the construction of the smartphone-assisted nanozyme colorimetric sensor, Dip was replaced with chlorpyrifos (Chl). The results are as follows... Figure 12 As shown, the gray value y and Chl concentration x conform to the linear equation y = -1.00x + 167.53 between 0.5 ng / mL and 50 ng / mL, with a correlation coefficient R. 2 The value is 0.999, and the LOD of the smartphone-assisted Chl colorimetric sensor is calculated to be 0.38 ng / mL.

[0090] Example 7:

[0091] The application of the smartphone-assisted Dip colorimetric sensor prepared according to Example 1 in the detection of Dip residues in two foods (cabbage and apple);

[0092] Chop the food (cabbage, fruit peel, etc.), immerse it in a methanol / acetone mixed solution (V:V = 1:1) and vortex mix for 2 min. After centrifuging at 10000 rpm for 5 min, dilute the supernatant 50 times with 2% ethanol-water solution for later use.

[0093] Take 20 μL of the food sample to be tested and mix it with 40 μL of 100 mU / mL AChE and 40 μL of 4 mM ATCh, and incubate at 37 °C for 30 min. Then, add 20 μL of 1 mg / mL UIO-66@MnO2 aqueous dispersion, 100 μL of 1 mM TMB, and 800 μL of HAc-NaAc buffer solution (0.1 M, pH 4.0), and react at 35 °C for 5 min. Finally, take a picture of the reaction product using the ColorGrab software on a smartphone, output the R, G, and B values, and calculate the grayscale value using the formula Grayscale = 0.299R + 0.587G + 0.114B. Then, substitute it into the linear equation y = -2.11x + 194.66 to calculate the concentration of Dip. The accuracy of the method was verified by the spiked recovery method, and the results are shown in Table 2. The spiked recoveries of this method ranged from 95.2% to 105%, and the relative standard deviations ranged from 0.25% to 3.6%, demonstrating that the method has high accuracy and can perform point-of-care testing (POCT) analysis of organophosphorus pesticide content in food samples.

[0094] Table 2. Detection results of Dip content in food samples

[0095]

[0096] Note: NA: Not applicable, ND: Not detected.

[0097] Example 8:

[0098] The application of the smartphone-assisted Dip colorimetric sensor prepared according to Example 1 in the detection of Dip residues in two water samples (Huaihe River water and Longzihu Lake water);

[0099] Take 0.5~1.0 mL of water sample, filter it through a 0.22~0.45 μm microporous membrane, and dilute it 100 times for later use.

[0100] Take 20 μL of the treated water sample and mix it with 40 μL of 100 mU / mL AChE and 40 μL of 4 mM ATCh, and incubate at 37 °C for 30 min. Then, add 20 μL of 1 mg / mL UIO-66@MnO2 aqueous dispersion, 100 μL of 1 mM TMB, and 800 μL of HAc-NaAc buffer solution (0.1 M, pH 4.0), and react at 35 °C for 5 min. Finally, take a picture of the reaction product using the Color Grab software on a smartphone, output the R, G, and B values, and calculate the grayscale value using the formula Grayscale = 0.299R + 0.587G + 0.114B. Then, substitute the values ​​into the linear equation y = -2.11x + 194.66 to calculate the concentration of Dip. The accuracy of the method was verified by the spiked recovery method, and the results are shown in Table 3. The spiked recoveries of this method ranged from 99.3% to 102%, and the relative standard deviations ranged from 0.30% to 4.2%, demonstrating that the method has high accuracy and can achieve point-of-care testing (POCT) analysis of organophosphorus pesticide content in environmental samples.

[0101] Table 3. Detection results of Dip content in water samples

[0102]

[0103] Note: NA: Not applicable, ND: Not detected.

Claims

1. A method for constructing a smartphone-assisted organophosphorus pesticide detection sensor, characterized in that, Includes the following steps: (1) Synthesis of UIO-66@MnO2 nanozyme: Zirconium tetrachloride, terephthalic acid, and 2-aminoterephthalic acid were mixed and dispersed in 6-10 mL of N,N-dimethylformamide at a molar ratio of 1:11.1:0.

8. 60-100 μL of hydrochloric acid was added, and the mixture was stirred vigorously before being transferred to a polytetrafluoroethylene-lined stainless steel autoclave and calcined at 120 °C for 12-48 hours. The product was washed alternately with N,N-dimethylformamide and water, and then dried in an oven to obtain UIO-66 powder. UIO-66 was dispersed in 2-morpholine ethanesulfonic acid buffer solution, and 2-5 mM potassium permanganate was added. The mixture was ultrasonically reacted for 30-40 min. The product was washed with water and dried in an oven to obtain brown UIO-66@MnO2 nanozyme. (2) Construction of a smartphone-assisted nanozyme colorimetric sensor: Mix 10–20 μL of organophosphorus pesticides of different concentrations, 100 mU / mL of acetylcholinesterase, and 2 mM of thioacetylcholine, and incubate at 37 °C for 30–60 min. Then, add 10–20 μg / mL of UIO-66@MnO2 aqueous dispersion, 0.1–1 mM of TMB, and 0.1 M HAc-NaAc buffer solution at pH 4.0, and react at 35 °C for 5–10 min. Finally, photograph the reaction product using the Color Grab software on a smartphone, outputting R, G, and B values. Calculate the grayscale value using the formula Grayscale = 0.299R + 0.587G + 0.114B. Establish a linear relationship between the grayscale value y and the organophosphorus pesticide concentration x, and fit the linear equation y = kx + b; Calculate the detection limit (LOD) of the smartphone-assisted colorimetric sensor method according to the formula LOD=3s / k, where s is the standard deviation of 11 blank tests and k is the slope of the linear equation.

2. The application of the smartphone-assisted organophosphorus pesticide detection sensor constructed according to claim 1 in the detection of organophosphorus pesticides in food and environmental samples.

3. The application according to claim 2, characterized in that, Includes the following steps: First, the sample is pretreated; For solid samples: Weigh 0.5~1.0 g of solid sample, chop it, immerse it in a methanol / acetone mixed solution with a volume ratio of 1:1, vortex mix in the solution for 2~5 min, centrifuge for 5~10 min, and then dilute the supernatant with 2% ethanol-water solution 50~200 times for later use. For liquid samples: measure 0.5~1.0 mL of liquid sample, filter it through a 0.22~0.45 μm microporous membrane, and dilute it 50~200 times for later use; Take 10-20 μL of pretreated food and environmental samples, mix 100 mU / mL acetylcholinesterase with 2 mM thioacetylcholine, and incubate at 37 °C for 30-60 min; then add 10-20 μg / mL UIO-66@MnO2 aqueous dispersion, 0.1-1 mM TMB, and HAc-NaAc buffer solution with a concentration of 0.1 M and a pH of 4.0, and react at 35 °C for 5-10 min; finally, take a picture of the reaction product using the Color Grab software of a smartphone, output the R, G, and B values, calculate the grayscale value using the formula Grayscale = 0.299R + 0.587G + 0.114B, and substitute it into the linear fitting equation y = kx + b in step (2) to calculate the concentration x of organophosphorus pesticides in the food sample; For actual samples where organophosphorus pesticides were not detected, the accuracy of the method was verified using the spiked recovery method.

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