Preparation method and application of a multi-channel colorimetric sensor array

Abstract

The application discloses a preparation method and application of a multi-channel colorimetric sensor array, and the preparation method comprises the following steps: taking seaweed as raw material, and carbonizing to obtain algal-based biochar; and taking the algal-based biochar as a sensing unit to construct the multi-channel colorimetric sensor array. The algal-based biochar is highly aromatic, rich in surface functional groups, has strong adsorption performance, and has excellent peroxidase-like activity. When the analyte is adsorbed on the algal-based biochar, the active sites of the algal-based biochar are covered, and the peroxidase-like activity changes. By combining and analyzing the colorimetric response of the sensing unit to the analyte, the detection and identification of various substances can be realized. When the application is applied to pesticide detection, the various pesticides or pesticide compositions can be distinguished and identified, the detection limit is as low as 1 muM, the detection method is simple, and the successful distinction of five pesticides from actual samples such as soil can be realized, and the application has a wide application prospect in the field of biological analysis technology.

Description

Technical Field

[0001] This application belongs to the field of analytical chemistry technology, specifically relating to a method for preparing and applying a multi-channel colorimetric sensor array. Background Technology

[0002] Pesticides are widely used in agriculture to protect crops from pests and regulate plant growth. Some pesticides are difficult to break down, accumulating in the soil and leaving residues on the surface of fruits and vegetables. Long-term consumption of pesticide-contaminated crops can harm human health. Currently, methods such as gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and surface-enhanced Raman scattering (SERS) can detect various pesticides with high sensitivity and specificity. However, these methods involve expensive instruments, complex operations, and long detection cycles. Other methods, such as enzyme-linked immunosorbent assay (ELISA) and biosensors, are also used for pesticide detection; however, the required natural enzymes and antibodies are expensive, easily inactivated, and have poor reproducibility. Therefore, developing a simple, economical, and efficient pesticide detection method is of great significance. Summary of the Invention

[0003] To address the shortcomings of existing pesticide detection technologies, this application aims to provide a low-cost, simple, and highly sensitive multi-channel colorimetric sensor array. This application also provides a method for preparing the sensor and its applications.

[0004] To solve the above problems, one technical solution adopted in this application is:

[0005] A method for fabricating a multi-channel colorimetric sensor array is provided, comprising:

[0006] Algae-based biochar is obtained by carbonization using seaweed as raw material;

[0007] The multi-channel colorimetric sensor array was constructed using the algae-based biochar as the sensing unit.

[0008] In one or more embodiments, the seaweed includes one or more combinations of green algae, blue algae, and brown algae.

[0009] In one or more embodiments, the step of carbonization to obtain algae-based biochar includes:

[0010] After washing and drying the seaweed, grind and sieve it to obtain seaweed powder;

[0011] The seaweed powder was placed in an inert gas atmosphere, heated and carbonized, and then ground and sieved to obtain the seaweed-based carbon material.

[0012] In one or more embodiments, in the step of washing, drying, grinding, and sieving seaweed, the drying temperature is 50-70°C, the drying time is greater than or equal to 12 hours, and the sieve mesh size is 50-150 mesh.

[0013] In one or more embodiments, the heating and carbonization step includes: heating to a carbonization temperature, maintaining the carbonization temperature for carbonization, wherein the heating rate is 4.5 to 5.5 °C / min, the carbonization temperature is 600 to 800 °C, and the carbonization time is 2.5 to 3.5 h.

[0014] To solve the above problems, another technical solution adopted in this application is:

[0015] A multi-channel colorimetric sensor array prepared by the preparation method described in any of the above embodiments is provided.

[0016] In one or more embodiments, at least one of the sensing units is included, the sensing unit being prepared from one or more combinations of Spirulina, Chlorella, and Ulva as raw materials.

[0017] In one or more embodiments, the sensing unit has peroxidase activity, and the sensing unit detects the analyte by testing the change in peroxidase activity before and after the addition of the analyte.

[0018] To address the aforementioned problems, another technical solution adopted in this application is:

[0019] An application of the multi-channel colorimetric sensor array described in any of the above embodiments in pesticide detection is provided.

[0020] Specifically, the algae-based biochar is combined with pesticides, and the pesticides are identified by changes in peroxidase activity.

[0021] In one or more embodiments, the pesticide includes one or more combinations of buprofen, bensulfuron-methyl, flusulfanilamide, quizalofop-P-ethyl, and isooctyl clopyralid.

[0022] In one or more embodiments, the detection limit of the pesticide is 1 μM.

[0023] The advantages of this application, which differ from existing technologies, are:

[0024] The sensing unit algae-based biochar of this application uses seaweed as raw material to make resource utilization of seaweed. The preparation method is simple and low cost. The algae-based biochar is highly aromatized, rich in surface functional groups, has excellent peroxidase-like activity, and good stability.

[0025] The multi-channel colorimetric sensor array of this application is low in cost, simple in method, and highly sensitive, making it suitable for rapid trace detection and having broad application prospects in the field of bioanalytical technology.

[0026] The multi-channel colorimetric sensor array of this application is applied to pesticide detection, which can distinguish and identify multiple pesticides or pesticide combinations with a detection limit as low as 1 μM. It can also successfully distinguish five pesticides in actual samples such as soil, making it an economical and efficient pesticide detection method. Attached Figure Description

[0027] Figure 1 This is a schematic flowchart of the fabrication method of the multi-channel colorimetric sensor array of this application;

[0028] Figure 2 This is a scanning electron microscope image of the seaweed from Embodiment 1 of this application;

[0029] Figure 3 This is a scanning electron microscope image of the algae-based biochar prepared in Example 1 of this application;

[0030] Figure 4 This is an X-ray diffraction pattern of the *Ulva prolifera* species and the prepared algal-based biochar from Example 1 of this application;

[0031] Figure 5 This is the absorbance test result graph of Example 2 of this application;

[0032] Figure 6 This is the linear discriminant analysis graph for Example 3 of the effect of this application;

[0033] Figure 7 This is the linear discriminant analysis graph for Example 4 of the effect of this application;

[0034] Figure 8 This is the linear discriminant analysis graph for Example 5 of the effect of this application. Detailed Implementation

[0035] The present application will now be described in detail with reference to the embodiments shown in the accompanying drawings. However, these embodiments do not limit the present application, and any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the protection scope of the present application.

[0036] Nanozymes are a class of functional nanomaterials with enzymatic activity. Compared with natural enzymes, nanozymes have advantages such as ease of preparation, low cost, good stability, and tunable catalytic activity, attracting great interest. In recent years, based on the properties of nanozymes, they have been widely used to construct sensors for the detection of proteins, heavy metal ions, and organic compounds. However, existing sensors are mostly used to detect organophosphorus and organochlorine pesticides, with limited detection of other pesticides. Due to the similar chemical properties of pesticides, they lack sufficient selectivity to distinguish between different pesticides or pesticide mixtures.

[0037] In recent years, sensor arrays have become a powerful tool for detecting and identifying various analytes. Sensor arrays have multiple cross-response sensing units that produce different response patterns to different analytes. By using array data of these cross-response patterns, various analytes can be classified and distinguished, providing a powerful tool for identifying analytes with similar structures and properties. Colorimetric sensor arrays have advantages such as low cost, simple method, and visualization; however, existing colorimetric sensor arrays are still relatively few used for pesticide detection, and their sensitivity is not yet high enough.

[0038] Biochar is a carbon-rich product of biomass pyrolysis under oxygen-limited conditions. It boasts advantages such as wide availability of raw materials, low cost, stability, large specific surface area, and abundant surface functional groups, demonstrating environmental sustainability and attracting significant attention in environmental, energy, materials, and biological fields. Seaweed, with its vast production volume, is an abundant biomass material. However, some seaweeds can trigger red tides, harming marine life. Others, such as *Ulva prolifera* and *Leptochloa crus-galli*, are marine pollutants. Currently, research on algae-based biochar is limited both domestically and internationally, primarily focusing on its use as an adsorbent, for example, to remove heavy metal ions and dyes from water.

[0039] To address the existing problems in pesticide detection, the applicant has developed a multi-channel colorimetric sensor array using seaweed as raw material. This multi-channel colorimetric sensor array is environmentally friendly, low-cost, simple to use, and can perform pesticide detection efficiently and quickly.

[0040] Specifically, please refer to Figure 1 , Figure 1 This is a schematic flowchart of the fabrication method of the multi-channel colorimetric sensor array of this application.

[0041] The preparation method includes:

[0042] S100: Algae-based biochar is obtained by carbonizing seaweed.

[0043] Specifically, in one application scenario, seaweed can include one or more combinations of green algae, cyanobacteria, and brown algae, where green algae refer to various algae under the Chlorophyta phylum, cyanobacteria refer to various algae under the Cyanobacteria phylum, and brown algae refer to various algae under the Brown Algae phylum.

[0044] In one application scenario, the steps for carbonizing algae-based biochar include: washing and drying seaweed, grinding and sieving it to obtain seaweed powder; placing the seaweed powder in an inert gas atmosphere, heating and carbonizing it, grinding and sieving it to obtain algae-based carbon material.

[0045] The drying temperature can be 50–70°C, and the time can be greater than or equal to 12 hours. In other application scenarios, other drying temperatures and times can also be used, as long as the seaweed can be dried. The mesh size of the sieve used for grinding and sieving can be 100 mesh. In other application scenarios, other mesh sizes can also be used based on actual working conditions, such as 50 mesh, 150 mesh, etc., all of which can achieve the effect of this embodiment.

[0046] In one application scenario, the inert gas can be argon.

[0047] In one application scenario, heating carbonization can specifically involve: heating to the carbonization temperature and maintaining that temperature for carbonization. The heating rate can be 4.5–5.5 °C / min, the carbonization temperature can be 600–800 °C, and the carbonization time can be 2.5–3.5 h.

[0048] S200, using algae-based biochar as the sensing unit, a multi-channel colorimetric sensor array was constructed.

[0049] Specifically, algae-based biochar is used as a sensing unit to test the changes in the activity of algae-based biochar peroxidases before and after the addition of analytes.

[0050] The sensing unit algae-based biochar of this application uses seaweed as raw material to make resource utilization of seaweed. The preparation method is simple and low cost. The algae-based biochar is highly aromatized, rich in surface functional groups, has excellent peroxidase-like activity, and good stability.

[0051] The multi-channel colorimetric sensor array of this application has the advantages of low cost, simple method, and high sensitivity, and is suitable for rapid trace detection.

[0052] This application also provides a multichannel colorimetric sensor array prepared according to any of the above embodiments.

[0053] Specifically, in one embodiment, the multi-channel colorimetric sensor array may include at least one sensing unit, which may be prepared from one or more of Spirulina, Chlorella and Ulva as raw materials.

[0054] The sensing unit has peroxidase activity and detects analytes by testing the changes in peroxidase activity before and after the addition of the analyte.

[0055] This application also provides an application of a multichannel colorimetric sensor array prepared according to any of the above embodiments in pesticide detection.

[0056] Specifically, because the algae-based biochar of this application is highly aromatized and has abundant surface functional groups, it has excellent peroxidase-like activity. The algae-based biochar has abundant pores and a large specific surface area, which can adsorb pesticides. When pesticides are adsorbed on the algae-based biochar, the active sites of the algae-based biochar are masked, and the peroxidase-like activity changes.

[0057] Therefore, by detecting changes in the peroxidase-like activity of algal-based biochar, and combining and analyzing the colorimetric responses of several sensing units to the analyte, pesticide detection can be achieved.

[0058] In one application scenario, the multi-channel colorimetric sensor array of this application may include three colorimetric sensor units, the raw materials of which may be Spirulina, Chlorella and Ulva, respectively, thereby ensuring the accuracy of pesticide differentiation and identification.

[0059] In one application scenario, the pesticides identified by the multi-channel colorimetric sensor array may include one or a combination of two of the following: buprofen, bensulfuron-methyl, flumetsulam, quizalofop-P-ethyl, and isooctyl clopyralid. Specifically, the detection limit for pesticides can be as low as 1 μM.

[0060] In one application scenario, the peroxidase activity assay can be performed by first mixing and incubating algal-based biochar and pesticides, then adding hydrogen peroxide and TMB colorimetric solution, and measuring the absorbance at 652 nm to determine peroxidase activity. In other application scenarios, any other known or unknown peroxidase activity assay method can be used to achieve the same effect as described in this embodiment.

[0061] Due to the vast variety of seaweed and pesticides, the embodiments of this application cannot exhaustively list all types of seaweed and seaweed combinations, as well as pesticides and pesticide combinations. It is understood that, based on the principles of this application, any technical solution using algal-based biochar prepared from any type of seaweed and utilizing changes in peroxidase activity to detect and identify pesticides should be within the scope of protection of this application.

[0062] The technical solution of this application will be further described in detail below with reference to specific embodiments.

[0063] Example 1:

[0064] An algae-based biochar is prepared using the following method:

[0065] Wash the seaweed with water to remove the mud and salt from its surface, then dry it in a 60℃ oven for 12 hours. After drying, cut the seaweed into small pieces, grind it into powder using a mortar and pestle, and pass it through a 100-mesh sieve to obtain seaweed powder.

[0066] The seaweed powder was placed in a corundum ark and then placed in a tube furnace, and heated at 5°C / min under an argon atmosphere. -1 The temperature was rapidly increased to 700℃ and maintained at 700℃ for 3 hours for carbonization. The carbonized product was then ground into powder using a mortar and passed through a 100-mesh sieve to obtain algae-based biochar.

[0067] Example 2:

[0068] An algae-based biochar was prepared using a method that is basically the same as in Example 1, except that the raw material is spirulina.

[0069] Example 3:

[0070] An algae-based biochar was prepared using a method that is basically the same as in Example 1, except that the raw material is Chlorella.

[0071] Example 1: Characterization Analysis

[0072] The raw material *Ulva prolifera* from Example 1 and the algal-based biochar prepared in Example 1 were characterized and analyzed to obtain... Figures 2 to 4 ,in, Figure 2 This is a scanning electron microscope image of *Ulva prolifera* from Embodiment 1 of this application. Figure 3 This is a scanning electron microscope image of the algae-based biochar prepared in Example 1 of this application. Figure 4 This is an X-ray diffraction pattern of the *Ulva prolifera* and the prepared algal-based biochar from Example 1 of this application.

[0073] Please see Figure 2 and Figure 3 The surface of *Ulva prolifera* is smooth and the adhering particles are small; while the surface of algal biochar prepared from *Ulva prolifera* has many pores and folds, and a large number of particles are adhering to the surface, thus increasing the specific surface area.

[0074] As can be seen from the above, the algae-based biochar of this application has a porous structure and abundant surface functional groups, thus exhibiting good adsorption performance.

[0075] Please see Figure 4 In the image, 'a' represents algal-based biochar, and 'b' represents *Ulva prolifera*. From... Figure 4 It can be seen that the algae-based biochar exhibits broad and low-intensity dispersed diffraction peaks at 25° and 43°, corresponding to the (002) and (100) diffraction peaks of graphitic carbon, indicating that pyrolysis promotes the formation of graphitic carbon structure, giving it a graphitized aromatic structure.

[0076] As can be seen from the above, the algae-based biochar of this application is highly aromatized.

[0077] Example 2:

[0078] The peroxidase activity of the algae-based biochar prepared in Examples 1, 2, and 3 was tested. The specific test method was as follows:

[0079] (1) Prepare a pH 4.0, 100mM NaOAc-HOAc buffer solution, 50mM H2O2 and 10mM TMB aqueous solution;

[0080] (2) Add 1 mg of algal biochar and 900 μL of NaOAc-HOAc buffer to a 2 mL centrifuge tube, incubate at 37 °C for 10 min, then add 50 μL of H2O2 and 50 μL of TMB aqueous solution, react at 37 °C for 5 min, then transfer 350 μL to a 1 mm cuvette and use a UV-Vis spectrophotometer to detect the absorbance in the range of 400-800 nm.

[0081] In addition, 900 μL of NaOAc-HOAc buffer was added to a 2 mL centrifuge tube and incubated at 37 °C for 10 min. Then, 50 μL of H2O2 and 50 μL of TMB aqueous solution were added and reacted at 37 °C for 5 min. 350 μL was then transferred to a 1 mm cuvette and the absorbance in the range of 400-800 nm was measured using a UV-Vis spectrophotometer. This was used as control group 1.

[0082] Summarizing the above test data, we obtain Figure 5 , Figure 5 This is a graph showing the absorbance test results of Example 2 of this application. In the graph, a, b, c, and d correspond to the absorbance test results of Examples 2, 3, 1, and Control Group 1, respectively.

[0083] As shown in the figure, the absorbance of the algae-based biochar prepared in Examples 1, 2, and 3 was greater than that of the H2O2-TMB system in the control group 1 in the peroxidase activity test, and the absorbance reached its maximum value at 652 nm, indicating that the algae-based biochar has peroxidase activity. Among them, the algae-based biochar prepared from Spirulina showed the best peroxidase activity.

[0084] Example 3: Single-component pesticide detection experiment

[0085] The design includes a colorimetric sensor array with three colorimetric sensor units, which are algae-based biochar prepared in Examples 1, 2, and 3, respectively.

[0086] The specific experimental procedure is as follows:

[0087] (1) Prepare a pH 4.0, 100mM NaOAc-HOAc buffer solution, 50mM H2O2 and 2mM TMB aqueous solution;

[0088] 2) Dissolve a certain amount of butyl urea, bensulfuron-methyl, flusulfanilamide, quizalofop-P-ethyl, and clopyralid isooctyl ester in dimethyl sulfoxide to prepare five pesticide solutions with a concentration of 1 μM.

[0089] 3) Add 0.5 mg of algal-based biochar, 890 μL of NaOAc-HOAc buffer and 10 μL of pesticide solution prepared in step (2) to a 2 mL centrifuge tube, incubate at 37 °C for 10 min, then add 50 μL of H2O2 and 50 μL of TMB aqueous solution, react at 37 °C for 5 min, transfer 200 μL to a 96-well plate, and detect the absorbance at 652 nm using an ELISA reader. Repeat the experiment 6 times.

[0090] The above experiments were repeated with the algal-based biochars prepared in Examples 1, 2, and 3, respectively, against five pesticide solutions. Thirty absorbance data points were obtained for each type of algal-based biochar mixed with the five pesticide solutions, totaling 90 absorbance data points for the three types of algal-based biochars. The original data matrix was subjected to linear discriminant analysis using SPSS software, yielding... Figure 6 , Figure 6 This is a linear discriminant analysis plot for Example 3 of the effect of this application. In the plot, 1, 2, 3, 4, and 5 correspond to the data areas of butyl urea, bensulfuron-methyl, flusulfanilamide, quizalofop-P-ethyl, and isooctyl clopyralid, respectively.

[0091] like Figure 6 As shown, the data points of the five pesticides each form a non-overlapping region, thus indicating that the five pesticides at a concentration of 1 μM can be successfully identified and distinguished by the colorimetric sensor array.

[0092] Example 4: Two-component pesticide detection experiment

[0093] A certain amount of buprofen and flumetsulam were mixed and dissolved in dimethyl sulfoxide to prepare pesticide mixtures with different proportions at a concentration of 10 μM, namely 100% buprofen, 75% buprofen + 25% flumetsulam, 50% buprofen + 50% flumetsulam, 25% buprofen + 75% flumetsulam, and 100% flumetsulam, resulting in five pesticide mixtures with different proportions.

[0094] The experimental process of Example 3 was repeated for the algal-based biochars prepared in Examples 1, 2, and 3, showing their effects on mixtures of five pesticides. Thirty absorbance data points were obtained for each type of algal-based biochar mixed with the five pesticide mixtures, totaling 90 absorbance data points for the three types of algal-based biochars. The original data matrix was then subjected to linear discriminant analysis using SPSS software. Figure 7 , Figure 7 This is a linear discriminant analysis plot for Example 4 of the effect of this application. In the plot, 1, 2, 3, 4, and 5 correspond to the data areas of 100% butyl urea, 75% butyl urea + 25% flusulfanilamide, 50% butyl urea + 50% flusulfanilamide, 25% butyl urea + 75% flusulfanilamide, and 100% flusulfanilamide, respectively.

[0095] like Figure 7As shown, the data points of the five pesticide mixtures in different proportions each form a non-overlapping region, thus demonstrating that the colorimetric sensor array can successfully identify and distinguish mixtures of different proportions of buprofen and flufenoxuron. In other words, the colorimetric sensor array of this application can identify and detect multi-component pesticides.

[0096] Example 5: Soil Pesticide Detection Experiment

[0097] Soil was selected from the grassland near Building 52 of Tianjin University, dried and ground through a 100-mesh sieve, and then ultrasonically dispersed in water. The supernatant was centrifuged and mixed with five pesticides: buprofen, bensulfuron-methyl, flusulfanilamide, quizalofop-P-ethyl, and isooctyl chlorpyrifos, to obtain five soil samples containing 5 μM of pesticides.

[0098] The experimental procedure of Example 5 was repeated for the algae-based biochar prepared in Examples 1, 2, and 3 on five soil samples. Thirty absorbance data points were obtained after mixing each type of algae-based biochar with the five soil samples, totaling 90 absorbance data points for the three types of algae-based biochar. The original data matrix was subjected to linear discriminant analysis using SPSS software. Figure 8 , Figure 8 This is a linear discriminant analysis plot for Example 5 of the application. In the plot, 1, 2, 3, 4, and 5 correspond to the data regions of soil samples of butyl urea, bensulfuron-methyl, flusulfanilamide, quizalofop-P-ethyl, and isooctyl clopyralid, respectively.

[0099] like Figure 8 As shown, the data points of the five soil samples each form a non-overlapping region, indicating that the five soil samples can be successfully identified and distinguished by the colorimetric sensor array. This means that the colorimetric sensor array of this application can identify and detect different types of pesticides in the soil.

[0100] The foregoing description of this disclosure is provided to enable any person skilled in the art to implement or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles applicable herein can be applied to other variations without departing from the scope of this disclosure. Therefore, this disclosure is not limited to the examples and designs described herein, but is consistent with the widest scope of the principles and novel features disclosed herein.

Claims

1. A method for fabricating a multi-channel colorimetric sensor array, characterized in that, include: Algae-based biochar is obtained by carbonization of seaweed, wherein the seaweed includes one or more combinations of green algae, blue algae and brown algae. The multi-channel colorimetric sensor array was constructed using the algae-based biochar as the sensing unit. The carbonization step includes: heating to a carbonization temperature and maintaining the carbonization temperature for carbonization, wherein the heating rate is 4.5~5.5℃ / min, the carbonization temperature is 600~800℃, and the carbonization time is 2.5~3.5 h.

2. The preparation method according to claim 1, characterized in that, The carbonization process for obtaining algae-based biochar includes: After washing and drying the seaweed, grind and sieve it to obtain seaweed powder; The seaweed powder was placed in an inert gas atmosphere, heated and carbonized, and then ground and sieved to obtain the algae-based biochar.

3. The preparation method according to claim 2, characterized in that, In the step of washing, drying, grinding, and sieving seaweed, the drying temperature is 50-70℃, the drying time is greater than or equal to 12 hours, and the sieve mesh size is 50-150 mesh.

4. A multi-channel colorimetric sensor array prepared by any one of the preparation methods described in claims 1 to 3.

5. The multi-channel colorimetric sensor array according to claim 4, characterized in that, It includes at least one of the aforementioned sensing units, which are prepared from one or more of Spirulina, Chlorella, and Ulva as raw materials.

6. The multi-channel colorimetric sensor array according to claim 4, characterized in that, The sensing unit has peroxidase activity, and the sensing unit detects the analyte by testing the change in peroxidase activity before and after the analyte is added.

7. The application of a multi-channel colorimetric sensor array according to any one of claims 4 to 6 in pesticide detection.

8. The application according to claim 7, characterized in that, The pesticide includes one or more combinations of buprofen, bensulfuron-methyl, flusulfanilamide, quizalofop-P-ethyl, and isooctyl clopyralid, and the detection limit of the pesticide is 1 μM.

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