A construction method of a high-throughput array sensor for simultaneous detection of multiple heavy metals

Abstract

The present invention relates to the technical field of the construction of fluorescent chemical array sensors, and discloses a construction method of a high-throughput array sensor for simultaneously detecting multiple heavy metals. The method includes: First, synthesize copper nanocluster materials modified by three different ligands, select a determined excitation wavelength and emission wavelength, and measure the fluorescence intensities of the three copper nanocluster materials at the selected wavelength; Subsequently, construct a fluorescence sensor array, mix the three copper nanoclusters with the target heavy metal ions for incubation, and measure their fluorescence intensities at a specific wavelength to obtain fluorescence response data; Finally, normalize the obtained fluorescence response data, and use machine learning methods such as linear discriminant analysis and hierarchical clustering analysis to analyze and identify different heavy metal ions. By analyzing different heavy metal ions through the high-throughput array and machine learning methods, qualitative and quantitative analysis of different heavy metal ions in a complex environment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of constructing fluorescent chemical array sensors. More specifically, the present invention relates to a method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals. Background Art

[0002] With the rapid development of industrialization and urbanization, a large number of heavy metal ions with characteristics such as high toxicity, strong bioaccumulation, and difficulty in degradation are released into the environment, causing serious pollution to the atmosphere, water bodies, and soil. Common heavy metal ions such as Hg 2+ , Pb 2+ , Cr 3+ , Cd 2+ and Co 2+ etc. can also enter the ecosystem through various exposure pathways and ultimately invade the human body. Research shows that heavy metal pollution is related to a series of diseases such as neurodegenerative diseases, liver damage, and kidney dysfunction. It can be seen that heavy metal pollution has become a major threat to environmental safety and human health. Therefore, to cope with the challenges brought by heavy metal pollution, it is necessary to accurately identify heavy metal ions.

[0003] Currently, some standard instrumental methods including inductively coupled plasma mass spectrometry (ICP-MS), atomic absorption spectrometry (AAS), and atomic emission spectrometry (AES) have been widely used for detecting heavy metal ions, but their further applications are limited by time-consuming, high cost, and complex operation. In addition, researchers are also committed to developing new chemical sensing technologies such as electrochemistry, colorimetry, and fluorescence detection to accurately detect and analyze heavy metals. However, most chemical sensors have strong specificity and usually can only be used to detect one kind of heavy metal ion and cannot cope with the combined pollution of pollutants in the current complex environment. Therefore, how to achieve the simultaneous detection of multiple heavy metal ions is an important research direction in the field of environmental analysis at present.

[0004] In the continuous exploration, inspired by the olfactory and gustatory systems of mammals, the design of array sensors has become an important direction to achieve this goal. There are a large number of cross-reactive receptors in the olfactory and gustatory systems of mammals. They interact with various taste and odor molecules and are the basis of the powerful and sensitive olfactory system possessed by mammals. Therefore, an array sensor composed of a series of sensing units shows different responses to different target analytes through physical or chemical reactions, and these unique responses can constitute a specific fingerprint pattern of the target analyte. Based on the generated fingerprint pattern, methods of machine learning such as linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) are used for analysis, and the discrimination and identification of multiple target analytes can be achieved.

[0005] So far, many research teams have developed high-throughput array sensors for the detection and analysis of multi-component analytes. Among them, array sensors based on metal nanoclusters have received extensive attention in the field of environmental pollutant detection. Metal nanoclusters are usually composed of several to hundreds of metal atoms, with a size close to the Fermi wavelength of electrons, capable of continuous energy level splitting, and having excellent optical stability, large Stokes shift, and low toxicity. As a response material, metal nanoclusters are functionalized with different groups to produce multiple sensing units, and each sensing unit generates different signal responses with the target analyte for further analysis. In recent years, the construction of metal nanocluster sensing elements has mainly focused on gold nanoclusters and silver nanoclusters, which cost relatively high and are not friendly for commercialization. Compared with gold and silver, copper is very inexpensive, and has the advantages of low toxicity, good biocompatibility, and excellent fluorescence. However, there is less attention paid to copper nanoclusters currently.

[0006] To solve the above problems, a technical solution is provided now. Summary of the Invention

[0007] To overcome the above defects of the prior art, the present invention provides a method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals. By synthesizing three different ligand-modified copper nanocluster materials, a fluorescence sensor array is constructed. The three copper nanoclusters are mixed and incubated with the target heavy metal ions, and their fluorescence intensities are measured at specific wavelengths. Machine learning methods such as linear discriminant analysis and hierarchical clustering analysis are used to analyze and identify different heavy metal ions. Through the high-throughput array and machine learning methods, different heavy metal ions are analyzed, realizing the qualitative and quantitative analysis of different heavy metal ions in a complex environment. To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals, including the following specific steps:

[0009] Step 1, synthesize three different ligand-modified copper nanocluster materials, measure the fluorescence spectra of the three copper nanocluster materials, select the determined excitation wavelength and emission wavelength, and measure the fluorescence intensities of the three copper nanocluster materials at the selected wavelength;

[0010] Step 2, construct a fluorescence sensor array, mix and incubate the three copper nanocluster materials with the target analyte, and measure the fluorescence intensities of the three copper nanocluster materials after adding the target analyte at the selected wavelength;

[0011] Step 3, perform normalization processing on the obtained fluorescence response data, and analyze the obtained data based on machine learning methods.

[0012] As a further aspect of the present invention, in Step 1, the three ligands used are lysozyme, cysteine, and ascorbic acid, and the copper nanocluster materials modified by the three different ligands synthesized by the one-pot hydrothermal synthesis method are Lys-CuNCs, Cys-CuNCs, and AA-CuNCs, respectively.

[0013] As a further aspect of the present invention, in Step 1, the fluorescence intensities of the Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials are measured by ultraviolet-visible absorption spectroscopy under the conditions of an excitation wavelength of 300 - 460 nm and an emission wavelength of 400 - 650 nm, the shapes and size ranges of the Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials are measured by transmission electron microscopy, and the presence of Cu, N, S, C, and O components in the Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials is determined by X-ray photoelectron spectroscopy to verify the successful preparation of the three copper nanocluster materials.

[0014] As a further aspect of the present invention, in Step 2, a sensor array is created using the Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials. After mixing 100 μL of the Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials with 10 μL of the target analyte in a 96-well plate and incubating for 10 minutes, the fluorescence intensities of the Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials when the target analyte is added are measured under the conditions of an excitation wavelength of 300 - 460 nm and an emission wavelength of 400 - 650 nm. The target analytes are heavy metal ions such as Hg, Pb, Cr, Co, Cd, As, Se, Zn, and Mn.

[0015] As a further aspect of the present invention, in Step 3, the fluorescence response data is the change in the fluorescence intensity after mixing the three copper nanocluster materials with the target analyte. Among them, the formula for normalizing the obtained fluorescence response data is:

[0016]

[0017] In the formula, F is the fluorescence intensity of the three copper nanocluster materials when the target analyte is added, and F0 is the fluorescence intensity of the three copper nanocluster materials when the target analyte is not added.

[0018] As a further solution of the present invention, in step three, three kinds of copper nanoclusters are used as fluorescence sensors to detect fluorescence response data for nine heavy metal ions. Five repeated experiments are carried out for the detection of each heavy metal ion, and a 15×9 matrix with 135 data is obtained. Linear discriminant analysis (LDA) is used to analyze this data matrix, and the new variables generated by measuring the fluorescence intensities of the copper nanoclusters with and without the target analyte added are identified and extracted as the key discrimination factors for the classification and identification of different heavy metal ions. The key discrimination factors obtained in the linear discriminant analysis are intuitively displayed using a three-dimensional model, and different heavy metal ion species are classified according to the significant factors, and the classification accuracy of the heavy metal ions is determined by hierarchical cluster analysis (HCA).

[0019] The technical effects and advantages of the method for constructing a high-throughput array sensor for simultaneous detection of multiple heavy metals according to the present invention: The present invention utilizes the interaction between different heavy metal ions and copper nanoclusters, and analyzes different metal ions through high-throughput arrays and machine learning methods according to different fluorescence response characteristics, realizing the qualitative and quantitative analysis of different metal ions in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic overview diagram of the method for constructing a high-throughput array sensor for simultaneous detection of multiple heavy metals according to the present invention;

[0021] Figure 2 It is the material characterization of CuNCs modified with different ligands in the method for constructing a high-throughput array sensor for simultaneous detection of multiple heavy metals according to the present invention;

[0022] Figure 3 It is the fluorescence response of the array sensor to nine metal ions in the method for constructing a high-throughput array sensor for simultaneous detection of multiple heavy metals according to the present invention;

[0023] Figure 4 It is the quantitative analysis of Cd in the method for constructing a high-throughput array sensor for simultaneous detection of multiple heavy metals according to the present invention 2+ Hg 2+ and Cr 3+ .

[0024] In the figure, Cu 2+: Cupric ion; Lys: Lysozyme; L-Cys: L-cysteine; AA: Ascorbic acid; CuNCs: Copper nanoclusters; Sensor array: Sensor array; Machine learning: Machine learning; Identification: Identification; Lys-Cu: Lys-CuNCs; Cys-Cu: Cys-CuNCs; AA-Cu: AA-CuNCs; Hg 2+ : Mercuric ion; Pb 2+ : Lead ion; Cr 3+ : Chromium(III) ion; Co 2+ : Cobalt(II) ion; Cd 2+ : Cadmium ion; As 3+ : Arsenic(III) ion; Se6 + : Selenium(VI) ion; Zn 2+ : Zinc ion; Mn 2+ : Manganese ion; LDA: Linear discriminant analysis; HCA: Hierarchical clustering analysis; Normalized Intensity: Normalized intensity; Wavelength: Wavelength; Relative frequency: Relative frequency; Diameter: Diameter; Control: Control; PC1: Principal component 1; PC2: Principal component 2; PC3: Principal component 3; Eucildean Distances: Euclidean distances; The concentration of Cd 2+ : Cadmium ion concentration; The concentration of Hg 2+ : Mercuric ion concentration; The concentration of Cr 3+ : Chromium(III) ion concentration. Detailed implementation mode

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] Example 1

[0027] As Figure 1This is a schematic overview of the method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals in the present invention. First, fluorescent sensors are prepared by synthesizing copper nanocluster materials modified with three different ligands, their fluorescence spectra are measured, the determined excitation wavelength and emission wavelength are selected, and the fluorescence intensities of the three copper nanocluster materials are measured at the selected wavelength. Then, a fluorescence sensor array is constructed, these copper nanoclusters are mixed and incubated with the target heavy metal ions, and the changes in their fluorescence intensities are measured at the selected wavelength. Finally, the obtained fluorescence response data are normalized, and machine learning methods such as linear discriminant analysis and hierarchical clustering analysis are used to analyze and identify different heavy metal ions.

[0028] Example 2

[0029] A method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals, comprising the following specific steps:

[0030] Step 1, synthesize copper nanocluster materials modified with three different ligands, measure the fluorescence spectra of the three copper nanocluster materials, select the determined excitation wavelength and emission wavelength, and measure the fluorescence intensities of the three copper nanocluster materials at the selected wavelength;

[0031] In step 1 of the embodiment of the present invention, the three ligands used are lysozyme, cysteine, and ascorbic acid. Copper nanocluster materials modified with three different ligands, namely Lys-CuNCs, Cys-CuNCs, and AA-CuNCs, are synthesized by a one-pot hydrothermal synthesis method. They have different binding abilities with metal ions, resulting in fluorescence quenching or enhancement, thus generating unique fluorescence change patterns. The fluorescence intensities of Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials are measured by ultraviolet-visible absorption spectroscopy under the conditions of excitation wavelength 300 - 460 nm and emission wavelength 400 - 650 nm, as Figure 2 shown in the material characterization of CuNCs modified with different ligands. It can be seen that Lys-CuNCs has the maximum fluorescence intensity at an excitation wavelength of 350 nm and an emission wavelength of 450 nm, Cys-CuNCs has the maximum fluorescence intensity at an excitation wavelength of 370 nm and an emission wavelength of 500 nm, and AA-CuNCs has the maximum fluorescence intensity at an excitation wavelength of 360 nm and an emission wavelength of 450 nm. Transmission electron microscope images show that the shapes of Lys-CuNC, Cys-CuNC, and AA-CuNC are spherical or ellipsoidal, and the size range is 2.4 - 5.3 mm. At the same time, through X-ray photoelectron spectroscopy characterization, the successful preparation of the three copper nanoclusters is verified by the presence of components Cu, N, S, C, and O;

[0032] Step 2: Construct a fluorescence sensor array, mix and incubate three copper nanocluster materials with the target analyte, and measure the fluorescence intensities of the three copper nanocluster materials after adding the target analyte at selected wavelengths.

[0033] In Step 2 of the embodiments of the present invention, when the target analytes are heavy metal ions such as Hg, Pb, Cr, Co, Cd, As, Se, Zn, and Mn, a sensor array is created using Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials. After mixing 100 μL of Lys-CuNCs, Cys-CuNCs, and AA-CuNCs copper nanocluster materials with 10 μL of the target analyte in a 96-well plate and incubating for 10 minutes, the fluorescence intensities of Lys-CuNCs, Cys-CuNCs, and AA-CuNCs after adding the target analyte are measured under the conditions of an excitation wavelength of 300 - 460 nm and an emission wavelength of 400 - 650 nm. A significant change in the fluorescence response occurs, as Figure 3 shown, which shows the different fluorescence responses of the copper nanoclusters to 9 metal ions. It can be seen that Lys-Cu-NCs have a stronger binding force to heavy metal ions, while AA-Cu-NCs and Cys-Cu-NCs have a weaker binding force to heavy metal ions. It is speculated that the possible reason is that lysozyme, as a protein, has more binding sites, thus generating stronger coordination, electrostatic attraction, and hydrophobic interactions with heavy metal ions.

[0034] Step 3: Normalize the obtained fluorescence response data and analyze the obtained data based on machine learning methods.

[0035] In Step 3 of the embodiments of the present invention, the fluorescence response data is the change in the fluorescence intensity after mixing the three copper nanocluster materials with the target analyte. Among them, the formula for normalizing the obtained fluorescence response data is:

[0036]

[0037] In the formula, F is the fluorescence intensity of the three copper nanocluster materials when adding the target analyte, and F0 is the fluorescence intensity of the three copper nanocluster materials when not adding the target analyte.

[0038] In step three of the embodiments of the present invention, three kinds of copper nanoclusters are used as fluorescence sensors to detect fluorescence response data for nine heavy metal ions. Five repeated experiments are conducted for the detection of each heavy metal ion, obtaining a 15×9 matrix with 135 data. Linear discriminant analysis (LDA) is used to analyze this data matrix to identify and extract key distinguishing factors for the classification and identification of different heavy metal ions. A three-dimensional model is used to visually display the key distinguishing factors obtained from the linear discriminant analysis, and different heavy metal ion types are classified according to the significant factors. Nine groups are established from the 135 training sets, as Figure 3 shown. In the space transformed by LDA, factor 1 explains 69.0% of the total variance of the data, factor 2 explains 24.8%, and factor 3 explains 6.2%. These three factors explain 99.0% of the total variance of all variables. The nine heavy metal ions are distinguished, and the 95% confidence ellipses of each group do not overlap. In addition, hierarchical cluster analysis (HCA) also shows that the classification accuracy of the nine heavy metal ions is 100%. The above results indicate that the array sensor has good feasibility in identifying multi-component heavy metal ions.

[0039] Example 3

[0040] Record the fluorescence response of the array sensor to heavy metal ions at different concentrations and construct a standard curve, as Figure 4 shown is the quantitative analysis of Cd by the array sensor of a method for constructing a high-throughput array sensor for simultaneous detection of multiple heavy metals according to the present invention 2+ , Hg 2+ , and Cr 3+ figure. The array sensor has good linear responses to Cd 2+ , Hg 2+ , and Cr 3+ , with a linear response range of 0.01 μM - 10 μM, and the detection limits are as low as Cd 2+ (0.5 nM), Hg 2+ (0.5 nM), and Cr 3+

[0041] (0.6 nM).

[0042] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0043] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals, characterized in that: The specific steps include: Step 1, synthesizing three copper nanocluster materials modified with different ligands, measuring the fluorescence spectra of the three copper nanocluster materials, selecting excitation and emission wavelengths, and measuring the fluorescence intensity of the three copper nanocluster materials at the selected wavelengths, wherein the three ligands used are lysozyme, cysteine ​​and ascorbic acid respectively; Step 2, constructing a fluorescence sensor array, mixing and incubating the three copper nanocluster materials with the target analyte, and measuring the fluorescence intensity of the three copper nanocluster materials after adding the target analyte at a selected wavelength, and using Lys-CuNCs, Cys-CuNCs and AA-CuNCs to create a sensor array; Step 3, normalizing the obtained fluorescence response data, and analyzing the obtained data based on a machine learning method; The fluorescence response data are the changes in fluorescence intensity after the three copper nanocluster materials are mixed with the target analytes.

2. The method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals according to claim 1, characterized in that: In step 1, the fluorescence intensity of Lys-CuNCs, Cys-CuNCs and AA-CuNCs copper nanocluster materials under the conditions of excitation wavelength of 300-460 nm and emission wavelength of 400-650 nm is measured.

3. The method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals according to claim 2, characterized in that: The shape and size range of Lys-CuNCs, Cys-CuNCs and AA-CuNCs copper nanocluster materials were determined, and the presence of Cu, N, S, C and O components in Lys-CuNCs, Cys-CuNCs and AA-CuNCs copper nanocluster materials were determined by X-ray photoelectron spectroscopy to verify the successful preparation of the three copper nanocluster materials.

4. The method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals according to claim 1, characterized in that: After 100 μL of Lys-CuNCs, Cys-CuNCs and AA-CuNCs copper nanocluster materials were mixed and incubated with 10 μL of target analytes in a 96-well plate for 10 minutes, the fluorescence intensity of Lys-CuNCs, Cys-CuNCs and AA-CuNCs copper nanocluster materials when the target analytes were added was measured under the conditions of excitation wavelength of 300-460 nm and emission wavelength of 400-650 nm. The target analytes were heavy metal ions such as Hg, Pb, Cr, Co, Cd, As, Se, Zn and Mn.

5. The method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals according to claim 1, characterized in that: In step 3, the formula for normalizing the obtained fluorescence response data is: ; Wherein, F is the fluorescence intensity of the three copper nanocluster materials when the target analyte is added, and F0 is the fluorescence intensity of the three copper nanocluster materials when the target analyte is not added.

6. The method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals according to claim 1, characterized in that: In step three, three copper nanoclusters were used as fluorescence sensors to detect fluorescence response data of nine heavy metal ions.

7. The method for constructing a high-throughput array sensor for simultaneously detecting multiple heavy metals according to claim 6, characterized in that: The detection of each heavy metal ion was repeated 5 times to obtain a 15×9 matrix with 135 data. This data matrix was analyzed using linear discriminant analysis (LDA), and new variables generated by the fluorescence intensity measurements of copper nanoclusters with and without the addition of target analytes were identified and extracted as key distinguishing factors for the classification and identification of different heavy metal ions. A three-dimensional model was used to intuitively display the key distinguishing factors obtained from the linear discriminant analysis, and different types of heavy metal ions were classified according to significant factors. The classification accuracy of heavy metal ions was determined by hierarchical cluster analysis (HCA).

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