A layered structure biosensing membrane for detection of organophosphorus pesticides and a preparation method and application thereof

Abstract

This invention discloses a biosensor membrane for the detection of organophosphorus pesticides, comprising a base membrane on which multiple glucose oxidase catalytic modules and multiple acetylcholinesterase catalytic modules are loaded. Each glucose oxidase catalytic module includes a first carrier immobilized on the base membrane and glucose oxidase loaded thereon. Each acetylcholinesterase catalytic module includes a second carrier immobilized on the base membrane and acetylcholinesterase loaded thereon. The weight ratio of glucose oxidase to acetylcholinesterase is 1:1 to 1:4. This invention reveals the structure-activity relationship of enzyme ratio-enzyme load synergistic regulation of sensing interface performance. By establishing a competitive inhibition advantage through changing the enzyme ratio, the enzyme load is optimized to achieve a balance between high activity and high mass transfer, thereby synergistically improving signal change and inhibition rate, achieving highly sensitive detection of organophosphorus pesticides. The biosensor membrane provided by this invention is suitable for the detection of various organophosphorus pesticides in food (especially crops and dairy products).

Description

Technical Field

[0001] This invention relates to the field of biochemical detection technology, specifically to a layered biosensor membrane for detecting organophosphorus pesticides, its preparation method, and its application. Background Technology

[0002] Organophosphorus pesticides (OPs) are widely used in agriculture due to their high insecticidal efficacy. However, overuse and improper disposal can leave residues in the environment and enter the human body through the food chain, threatening health and the ecosystem. Their toxicity stems from the inhibition of acetylcholinesterase, which can lead to the accumulation of neurotransmitters and trigger toxic reactions. Therefore, developing sensitive, rapid, and reliable detection methods for organophosphorus pesticides is of great significance for ensuring food safety, environmental protection, and public health. Currently, chromatography is commonly used for related detection. This method has high sensitivity and accuracy, but the equipment is expensive, the operation is complex, and it requires professional personnel, making it difficult to achieve rapid on-site detection. To meet the demand for rapid, on-site detection, enzyme-linked immunosorbent assay (ELISA) based on immunological principles and biosensor technology based on enzyme inhibition principles have been widely developed. Among them, the ELISA method has good specificity and high throughput potential, but its performance relies heavily on the preparation of high-quality monoclonal antibodies, which is costly. Furthermore, the stability and reproducibility of antibodies in complex environmental matrices are not stable, thus posing challenges in practical applications.

[0003] Biosensors are widely used due to their advantages such as rapid response, relatively low cost, and ease of miniaturization and integration. The enzyme-nanozyme coupling sensing strategy provides an effective pathway for achieving highly specific detection of opioids (OPs). Its core principle is to utilize acetylcholinesterase (AChE) to catalyze the hydrolysis of acetylthiocholine chloride (ATCh) to generate thiocholine (TCh), which further competitively inhibits the catalytic activity of the nanozyme. OPs, by irreversibly inhibiting AChE activity, interrupt this inhibitory pathway, thereby causing a signal change in the reaction system to achieve quantitative detection of OPs. This strategy involves directly mixing acetylcholinesterase with nanozymes in solution. In pursuit of high sensitivity and response speed, much research at present still focuses on maximizing the catalytic performance of nanozymes through multi-dimensional nanomaterial engineering (“Oxidase-Like Fe-NC Single-Atom Nanozymes for the Detection of Acetylcholinesterase Activity”, Wu Y et al., Small, 15(43): 1903-108; “White Peroxidase-Mimicking Nanozymes: Colorimetric Pesticide Assay without Interferences of O2 and Color”, Liang X et al., Adv. Func. Mater., 2020, 30(28): 2001-933).

[0004] Immobilization strategies can improve system stability through proximity and spatial confinement effects, and create a favorable microenvironment for cascade reactions (“Enzyme-Nanozyme Cascade Flow Reactor Synergy with Deep Learning for Differentiation and Point-of-Care Testing of Multiple Organophosphorus Pesticides”, Bai YJ et al., Adv. Func. Mater., 2024, 35(17): 2419499). Based on this, enzymes and multifunctional nanozymes are co-assembled on the surface of porous membranes to construct cascade sensing systems. Among them, multifunctional nanozymes that simultaneously possess peroxidase-like activity and fluorescence properties offer a unique advantage for achieving dual-mode detection: the absorption peak of the colorimetric oxidation product catalyzed by the nanozyme overlaps with its fluorescence emission peak, leading to fluorescence quenching, thereby achieving dual-mode fluorescence colorimetric detection of OPs. Compared with single-signal detection, multi-mode detection with good anti-interference properties is expected to provide more reliable results.

[0005] It is worth noting that the limited number of active sites on the membrane surface makes it difficult to achieve strong interfacial coupling between the aforementioned sensing materials and the membrane. Furthermore, for layered sensing interfaces, there is an inherent contradiction between improving catalytic activity and maintaining efficient mass transfer, making it difficult to synergistically enhance signal intensity and inhibition rate. Therefore, the core technical challenge this invention aims to address is how to balance metabolic flux and improve the microenvironment through systematic optimization of immobilization parameters within this contradictory framework, thereby revealing a clear structure-activity relationship to guide the rational construction of high-performance interfaces. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a layered biosensor membrane for organophosphorus pesticide detection, its preparation method, and its application.

[0007] This invention provides a biosensor membrane for detecting organophosphorus pesticides, comprising a base membrane on which multiple glucose oxidase catalytic modules and multiple acetylcholinesterase catalytic modules are loaded in random distribution. Each glucose oxidase catalytic module includes a first carrier immobilized on the base membrane and glucose oxidase loaded on the first carrier. Each acetylcholinesterase catalytic module includes a second carrier immobilized on the base membrane and acetylcholinesterase loaded on the second carrier. The weight ratio of glucose oxidase to acetylcholinesterase is 1:1 to 1:4. The first carrier and the second carrier may have the same or different compositions.

[0008] Further, the weight ratio of glucose oxidase to acetylcholinesterase is 1:3 to 1:4, optionally 1:3.

[0009] Furthermore, both the first carrier and the second carrier are iron-doped carbon quantum dots, which are formed from an organic carbon source and iron ions;

[0010] Preferably, the organic carbon source includes at least one of citric acid and polyethyleneimine.

[0011] Furthermore, a fixing layer is attached to the base film. The fixing layer is a mixed coating comprising a phenolic compound and an aminosilane coupling agent, and has quinone groups and / or phenolic hydroxyl groups on its surface, for loading the iron-doped carbon quantum dots.

[0012] Preferably, in the fixing layer, the phenolic compound includes at least one of tannic acid and polydopamine;

[0013] And / or, the aminosilane coupling agent is 3-aminopropyltriethoxysilane;

[0014] And / or, the weight ratio of the phenolic compound to the aminosilane coupling agent is (30~50):(2~18).

[0015] Another aspect of the present invention provides a method for preparing a biosensor membrane for detecting organophosphorus pesticides, comprising the following steps:

[0016] S1: The surface of the base film is treated with a mixed solution containing phenolic compounds and aminosilane coupling agents to obtain a base film with a fixed layer;

[0017] S2: Mix organic carbon source and iron ions in water and react under heating conditions to obtain iron-doped carbon quantum dot dispersion;

[0018] S3: The base film with the fixed layer obtained in S1 is mixed with the iron-doped carbon quantum dot dispersion obtained in S2 and reacted to obtain a sensing film loaded with iron-doped carbon quantum dots.

[0019] S4: The sensing membrane loaded with iron-doped carbon quantum dots obtained in S3 is mixed with the enzyme composition solution and reacted to obtain the biosensor membrane for organophosphorus pesticide detection; the enzyme in the enzyme composition solution is composed of glucose oxidase and acetylcholinesterase, and the weight ratio of glucose oxidase to acetylcholinesterase is 1:1 to 1:4.

[0020] Further, in the enzyme composition solution, the weight ratio of glucose oxidase to acetylcholinesterase is 1:3 to 1:4, optionally 1:3.

[0021] Further, in the enzyme composition solution, the concentration of glucose oxidase is 0.275 g / L, and the concentration of acetylcholinesterase is 0.275~1.1 g / L, preferably 0.825~1.1 g / L, and optionally 0.825 g / L.

[0022] Further, the reaction conditions in step S4 are: pH 6~7, reaction temperature 25~30℃, and reaction time 2~8h; optionally, pH is 6; optionally, the reaction time is 4~6h.

[0023] Further, in step S1, the concentration of the phenolic compound in the mixed solution is 0.5~10 g / L, and the concentration of the aminosilane coupling agent is 0.1~10 g / L;

[0024] And / or, in the mixed solution, the weight ratio of the phenolic compound to the aminosilane coupling agent is (30~50):(5~16) or (40~50):(11~16).

[0025] Further, in step S2, the organic carbon source includes citric acid and polyethyleneimine; the weight ratio of citric acid, polyethyleneimine and iron ions is 1:(0.1~3):(0.1~2) or 1:(1~2):(0.5~1).

[0026] And / or, in step S2, the concentration of iron ions in the mixture is 40~60 g / L or 50 g / L;

[0027] And / or, in step S2, the heating reaction conditions are: 180-250℃ for 8-12 hours.

[0028] Furthermore, the reaction conditions in step S3 are: reaction temperature 25~30℃, reaction time 1~10h.

[0029] The third aspect of the present invention provides the application of the above-mentioned biosensor membrane for detecting organophosphorus pesticides in the detection of organophosphorus pesticide content in food.

[0030] In some embodiments, the food is the edible part of a crop and processed food made using the edible part.

[0031] In some embodiments, the food product is a dairy product.

[0032] Furthermore, the organophosphorus pesticides include parathion, malathion, parathion, and chlorpyrifos.

[0033] The beneficial effects of this invention are as follows: This invention provides a layered structure fluorescence / colorimetric dual-mode biosensor membrane for the detection of organophosphorus pesticides. It achieves precise control over the ratio and amount of immobilized enzyme through systematic optimization of immobilization parameters. Specifically, by optimizing the enzyme ratio to establish a favorable competitive advantage for the inhibitor (TCh), and by changing the enzyme load to create a suitable microenvironment, the preset inhibitory advantage is maximized while ensuring high catalytic activity, resulting in significantly enhanced signal changes and response sensitivity during detection. Therefore, for traditional layered sensing interfaces, performance improvement requires synergistic regulation of the enzyme ratio (establishing the competitive landscape) and the microenvironment (determining the efficiency of landscape realization). This invention not only provides a high-performance sensing membrane but also establishes a universal method through structure-activity relationship studies to guide the rational design and controllable preparation of high-performance interfaces. The biosensor membrane provided by this invention is suitable for the detection of various organophosphorus pesticides in food (especially crops and dairy products), exhibiting high sensitivity, strong detection performance, and universal detection capability, and has broad application prospects. Attached Figure Description

[0034] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative examples are not intended to limit the embodiments. The term "illustrative" as used herein means "serving as an example, embodiment, or illustration." Any embodiment illustrated herein as "illustrative" is not necessarily to be construed as superior to or better than other embodiments.

[0035] Figure 1 The results of zeta potential detection of the DBSM-FeNCQDs membrane at different pH values ​​in Test Example 1 are shown.

[0036] Figure 2 To test the competitive inhibition response of DBSM-FeNCQDs-GOx&AChE membranes under different enzyme immobilization pH in Example 2; where A represents the absorbance change (ΔA) and TCh inhibition rate of each group, and B represents the inhibition efficiency.

[0037] Figure 3 To test the absorbance change (ΔA) of the DBSM-FeNCQDs-GOx&AChE membrane under different enzyme immobilization ratios in Example 3.

[0038] Figure 4 To test the competitive inhibition reaction of DBSM-FeNCQDs-GOx&AChE membranes under different enzyme immobilization times in Example 4; where A represents the absorbance change (ΔA) and TCh inhibition rate of each group, and B represents the inhibition efficiency.

[0039] Figure 5 The enzyme loading of the DBSM-FeNCQDs-GOx&AChE membrane was measured at different enzyme immobilization pH values ​​in Example 5.

[0040] Figure 6 The enzyme loading of the DBSM-FeNCQDs-GOx&AChE membrane was measured under different enzyme immobilization ratios in Example 5.

[0041] Figure 7 The enzyme loading of the DBSM-FeNCQDs-GOx&AChE membrane was measured under different enzyme immobilization times in Example 5.

[0042] Figure 8 To test the activity of the immobilized AChE enzyme on the DBSM-FeNCQDs-GOx&AChE membrane in Example 5; where A represents the activity of the immobilized AChE enzyme at different enzyme immobilization pH, and B represents the activity of the immobilized AChE enzyme at different enzyme immobilization pH.

[0043] Figure 9 To test the absorbance change (ΔA) of the DBSM-FeNCQDs-GOx&AChE membrane under different glucose concentrations in Example 6.

[0044] Figure 10 To test the absorbance change (ΔA) of the DBSM-GOx&AChE-FeNCQDs membrane under different concentrations of acetylthiocholine chloride in Example 7.

[0045] Figure 11To test the absorbance change (ΔA) of the DBSM-FeNCQDs-GOx&AChE membrane under different acetylthiocholine chloride reaction times in Example 8.

[0046] Figure 12 To test the change (ΔA) of absorbance of DBSM-FeNCQDs-GOx&AChE membrane under different OPs and reaction times in Example 9.

[0047] Figure 13 To test the inhibition rate of DBSM-FeNCQDs-GOx&AChE membrane against different OPs in Example 10.

[0048] Figure 14 To test the changes in absorbance and TCh inhibition rate of the enzyme immobilization system DBSM-FeNCQDs-GOx&AChE membrane and the enzyme free system DBSM-FeNCQDs-GOx+AChE in Example 11.

[0049] Figure 15 The results show the colorimetric sensing sensitivity and detection limit of the DBSM-FeNCQDs-GOx&AChE membrane in Test Example 12.

[0050] Figure 16 To test the absorbance change (ΔA) and TCh inhibition rate of the DBSM-FeNCQDs-GOx&AChE membrane under different glucose concentrations in Example 13.

[0051] Figure 17 The results show the fluorescence sensing sensitivity and detection limit of the DBSM-FeNCQDs-GOx&AChE membrane in Test Example 14. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprising of," etc., will be understood to include the stated elements or components, and does not exclude other elements or other components.

[0053] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can still be practiced even without certain specific details. In some embodiments, materials, elements, methods, and means well known to those skilled in the art are not described in detail in order to highlight the spirit of the invention.

[0054] Unless otherwise specified, all reagents or instruments used in this invention are conventional products that can be purchased through legitimate channels.

[0055] The base membrane used in this invention can be any commercially available microfiltration membrane, and there are no special restrictions on the material; the following examples use nylon microfiltration membranes.

[0056] In the following implementation, "DBSM" stands for "Dual-mode Biosensing-Membrane".

[0057] Example 1: Preparation of DBSM-FeNCQDs-GOx & AChE membrane

[0058] A biosensing membrane for detecting organophosphorus pesticides is prepared by the following method:

[0059] S1: Tannic acid (TA) and 3-aminopropyltriethoxysilane (APTES) were dissolved in 10 mM Tris-HCl buffer (pH 8.5) and ethanol, respectively, to prepare a tannic acid Tris-HCl buffer solution with a concentration of 2 g / L and a 3-aminopropyltriethoxysilane ethanol solution with a concentration of 10 g / L. Then, the tannic acid Tris-HCl buffer solution and the 3-aminopropyltriethoxysilane ethanol solution were mixed at a volume ratio of 8:1 to obtain a mixed solution. The base membrane was treated with the mixed solution and reacted at 150 rpm for 24 h at room temperature to form a mixed coating of tannic acid and 3-aminopropyltriethoxysilane (TA-APTES immobilization layer) on the surface of the base membrane.

[0060] S2: Dissolve 2.5g citric acid, 2.5g polyethyleneimine (MW600Da) and 1.25g ferric chloride in 50mL of deionized water, mix with ultrasonication, and then pour into a polytetrafluoroethylene-lined reactor. Heat to 230℃ and react for 12h. After the reaction is complete, cool to room temperature. Centrifuge the above solution and collect the supernatant to obtain an iron-doped carbon quantum dot dispersion.

[0061] S3: The iron-doped carbon quantum dot dispersion obtained in step S2 is diluted 5 times and mixed with the TA-APTES film obtained in step S1. The TA-APTES immobilization layer on the film and the iron-doped carbon quantum dots are reacted at 25℃ and 150rpm for 1h to obtain the DBSM-FeNCQDs film.

[0062] S4: Mix 20 mL of 0.55 g / L glucose oxidase (GOx) acetate buffer solution (pH=6.0, 10 mM) and 20 mL of 1.65 g / L acetylcholinesterase (AChE) acetate buffer solution (pH=6.0, 10 mM) to prepare an enzyme composition solution (at this time, the concentration of GOx is 0.275 g / L and the concentration of AChE is 0.825 g / L), and mix it with the DBSM-FeNCQDs membrane obtained in step S3. React at pH 6.0 for 4 h to obtain a fluorescence / colorimetric dual-mode biosensor membrane for organophosphorus pesticide detection (hereinafter referred to as DBSM-FeNCQDs-GOx&AChE membrane).

[0063] Example 2

[0064] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1, except that the pH of the acetic acid buffer solution in step S4 is 5.0.

[0065] Example 3

[0066] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1, except that the pH of the acetic acid buffer solution in step S4 is 4.0.

[0067] Example 4

[0068] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1. The difference is that the buffer solution in step S4 is a phosphate buffer solution with a pH of 7.

[0069] Example 5

[0070] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1. The difference is that the concentration of AChE in the acetylcholinesterase acetate buffer solution in step S4 is 0.55 g / L (at this time, the concentration of AChE in the enzyme composition solution is 0.275 g / L).

[0071] Example 6

[0072] This embodiment provides a biosensing membrane prepared using the method provided in Example 1. The difference is that the concentration of AChE in the acetylcholinesterase acetate buffer solution in step S4 is 1.1 g / L (at this time, the concentration of AChE in the enzyme composition solution is 0.55 g / L).

[0073] Example 7

[0074] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1. The difference is that the concentration of AChE in the acetylcholinesterase acetate buffer solution in step S4 is 2.2 g / L (at this time, the concentration of AChE in the enzyme composition solution is 1.1 g / L).

[0075] Example 8

[0076] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1, except that the enzyme immobilization time in step S4 is 2 hours.

[0077] Example 9

[0078] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1, except that the enzyme immobilization time in step S4 is 6 hours.

[0079] Example 10

[0080] This embodiment provides a biosensing membrane, which is prepared using the method provided in Example 1, except that the enzyme immobilization time in step S4 is 8 hours.

[0081] Example 11

[0082] This embodiment provides a sensing membrane prepared using the method provided in Example 1, except that step S4 is omitted; that is, the sensing membrane is the DBSM-FeNCQDs membrane prepared in step S3.

[0083] Test Example 1: Zeta potential of DBSM-FeNCQDs membrane at different pH values

[0084] The zeta potential of the DBSM-FeNCQDs membrane was measured at pH = 3, 4, 5, 6, 7, and 8, and the results are as follows: Figure 1 As shown.

[0085] Test Example 2: Competitive inhibition reaction of DBSM-FeNCQDs-GOx & AChE membranes under different enzyme immobilization pH conditions.

[0086] The competitive inhibition reaction of the biosensor membranes prepared in Examples 1-4 was tested, and the test methods are as follows:

[0087] Before competitive inhibition, the biosensor membranes prepared in Examples 1-4 were immersed in 4 mL of Tris-HCl buffer (10 mM, pH=8.0) for 20 min. A substrate solution was prepared by dissolving glucose and 2,2-azido-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt in acetate buffer (10 mM, pH=3.0), with concentrations of 100 mM and 1 mM, respectively. Then, 16 mL of the substrate solution was added to the 4 mL system using a magnetic stirrer, and the absorbance A at 420 nm was recorded. After competitive inhibition, 10 mM thioacetylcholine chloride was dissolved in Tris-HCl buffer (10 mM, pH=8.0), and the membranes prepared in Examples 1-4 were placed in 4 mL of the solution and reacted for 20 min. Subsequently, 100 mM glucose and 1 mM 2,2-azino-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (0.1 M, pH=3.0) to prepare the substrate solution; then, 16 mL of the above substrate solution was added to the above system with the help of a magnetic stirrer, and the absorbance value A0 at 420 nm was recorded. The absorbance change (ΔA), TCh inhibition rate, and inhibition efficiency of each group were calculated according to the following formulas, and the results are as follows. Figure 2 As shown.

[0088] ;

[0089] ;

[0090] ;

[0091] Figure 2 As shown in Figure A, both the TCh inhibition rate and ΔA were lowest at pH 5. Subsequently, the TCh inhibition rate increased significantly with increasing pH, but the maximum ΔA occurred at pH 6. When the pH increased to 7, ΔA decreased to its lowest level. It can be predicted that even if the pH continues to increase, the TCh inhibition rate will not show a significant further increase. Figure 2 As shown in B, the highest inhibition efficiency is achieved at pH 7. Therefore, the preferred pH range in step S4 is 6-7.

[0092] Test Example 3: Competitive inhibition reaction of DBSM-FeNCQDs-GOx & AChE membranes with different enzyme immobilization ratios.

[0093] The biosensor membranes prepared in Examples 1 and 5-7 were used to test the competitive inhibition reaction. The test method was the same as in Test Example 2, and the results are as follows: Figure 3 As shown.

[0094] Figure 3The results showed that as the dosage of acetylcholinesterase (AChE) increased, the TCh inhibition rate gradually increased, but the maximum ΔA occurred at a GOx:AChE ratio of 1:3, while ΔA decreased to its lowest level when the GOx:AChE ratio reached 1:4. It can be predicted that even if the dosage of AChE continues to increase, the TCh inhibition rate will not show a significant improvement. Therefore, the preferred ratio of GOx to AChE in step S4 is 1:3 to 1:4.

[0095] Test Example 4: Competitive inhibition reaction of DBSM-FeNCQDs-GOx & AChE membranes under different enzyme immobilization times.

[0096] The membranes prepared in Examples 1 and 8-10 were tested for competitive inhibition of the reaction using the same method as in Example 2. The results are as follows. Figure 4 As shown.

[0097] Figure 4 The results showed that when the reaction time was 4 hours, both the TCh inhibition rate and ΔA reached their maximum levels. Subsequently, as the reaction time increased, both the TCh inhibition rate and ΔA decreased significantly. In particular, when the reaction time reached 8 hours, both the TCh inhibition rate and ΔA were lower than those when the reaction time was only 2 hours. Figure 4 As shown in B, the inhibition rate gradually decreases with increasing reaction time, and the decrease is greatest when the reaction time is 4 hours. Therefore, the preferred reaction time in step S4 is 4-6 hours.

[0098] Test Example 5: Enzyme Activity and Enzyme Loading Capacity Test

[0099] The membranes prepared in Examples 1-10 were subjected to enzyme loading tests. The test method was as follows: the enzyme activities of the original enzyme solution, the remaining enzyme solution, and the elution enzyme solution were calculated respectively; the amount of immobilized enzyme was calculated based on the principle of activity balance. The results are as follows. Figures 5-8 As shown.

[0100] GOx activity assay: Glucose, 2,2-azino-bis(3-ethyl-benzothiazol-6-sulfonic acid) diammonium salt and horseradish peroxidase were dissolved in acetate buffer (10 mM, pH=5.0) to prepare a substrate solution. The enzyme composition solution was then added to 20 mL of the above substrate solution with the help of a magnetic stirrer, and the absorbance of the solution at 420 nm was recorded.

[0101] AChE activity assay: Acetylthiocholine chloride and 5,5'-dithiobis(2-nitrobenzoic acid) were dissolved in Tris-HCl buffer (10 mM, pH=8.0) to prepare a substrate solution. Then, the enzyme composition solution or the membranes prepared in Examples 1 and 8-10 were added to 20 mL of the above substrate solution with the help of a magnetic stirrer, and the absorbance of the solution at 412 nm was recorded.

[0102] Figure 5 The results showed that at pH 6, both the AChE loading capacity and the AChE / GOx loading ratio reached their maximum levels, significantly higher than in the other groups. At pH 7, the AChE / GOx loading ratio was also high, but the actual loading capacity of both enzymes decreased significantly. Meanwhile, Figure 8 As shown in A, the activity of AChE at pH 6 is significantly higher than that at pH 7, by approximately three times. Therefore, the optimal pH value in step S4 is 6.

[0103] Figure 6 The results show that as the amount of AChE increases, the AChE loading gradually increases, while the GOx loading gradually decreases, and the loading ratio of the two gradually increases. However, when the GOx:AChE ratio is 1:4, the GOx loading is too low, which is not conducive to the reaction. Therefore, the optimal ratio of GOx to AChE in step S4 is 1:3.

[0104] Figure 7 The results show that as the immobilization reaction time increases, the loading rates of both AChE and GOx gradually increase, and their ratio basically matches the optimized ratio described above. Meanwhile, Figure 8 As shown in B, the activity of AChE gradually increases with increasing immobilization reaction time, but the increase is significantly reduced at a reaction time of 8 hours. Therefore, considering the experimental results of Test Example 4 and Test Example 5, the optimal reaction time in step S4 is 4–6 hours.

[0105] Based on the experimental results of tests 1-5, and according to the Zeta potential ( Figure 1 ) and enzyme loading results ( Figure 5Different pH values ​​selectively affect the immobilization efficiency of GOx and AChE through electrostatic interactions, thus altering their final ratio on the membrane. This ratio presupposes the relative generation flux of reactants H2O2 and TCh; a higher AChE proportion results in a stronger theoretical inhibition effect. At pH 5, the immobilization efficiency of GOx is significantly higher than that of AChE, leading to a severe imbalance in their ratio. The concentration of the inhibitor TCh relative to the substrate H2O2 is too low, putting it at a significant disadvantage in competing for the active sites of FeNCQDs, resulting in a very low inhibition rate. pH 6 provides a balanced immobilization environment, enabling both GOx and AChE to achieve efficient immobilization, achieving optimal suppression of the signal generation pathway by the inhibition pathway, thereby generating the maximum signal change. Figure 2 (A) By changing the enzyme feed ratio under pH 6 conditions, the core role of the enzyme immobilization ratio was further revealed. When the GOx to AChE ratio was 1:3, high initial activity and deep inhibition were simultaneously achieved. The inhibition rate was the result of the combined effect of AChE activity (TCh generation) and inhibition efficiency (TCh accessibility). When the AChE ratio was increased to 1:4 or the enzyme immobilization time was extended, although the local inhibitor concentration increased, which was expected to further delay the establishment of competitive equilibrium, it also exacerbated the interfacial mass transfer resistance. The thicker enzyme layer masked the FeNCQDs active sites, and the effective concentration of TCh around them did not increase significantly. Ultimately, this resulted in a limited increase in inhibition rate, while the inhibition efficiency and overall catalytic activity and signal changes were significantly reduced. Figures 3-7 Furthermore, comparing pH 6 and pH 7, it was found that pH 7 exhibited a higher inhibition rate and inhibition efficiency when both had the same immobilized enzyme ratio. Figure 2 (B) This is mainly due to its open interface structure, which ensures that TCh molecules can quickly and unimpededly reach and occupy the active sites of FeNCQDs during the inhibition phase, thereby establishing a stronger competitive advantage. Therefore, whether this theoretical inhibition strength can be fully converted into a high inhibition rate also depends on whether the interfacial mass transfer kinetics can ensure that the generated TCh efficiently occupies the active sites. However, given that the sensing membrane needs a sufficiently strong signal to ensure reliable detection in practical applications, pH 6 achieves the best balance between interfacial inhibition efficiency and overall signal output capability, while the high inhibition efficiency exhibited at pH 7 provides valuable guidance for further optimization based on improving background activity.

[0106] It is noteworthy that comparing the pH 6 and pH 7 groups, and the pH 4 and 1:2 groups, despite having the same immobilized enzyme ratio, they exhibited drastically different functional outputs. This is primarily because in systems with significantly different mass transfer resistances (pH 6 and pH 7), kinetic processes dominate apparent performance, masking the inherent thermodynamic equilibrium presupposition. When interfacial mass transfer efficiencies are similar (pH 4 and 1:2), the thermodynamic equilibrium presupposition becomes apparent, and the immobilized enzyme ratio becomes the decisive factor for performance. In the pH 7 vs. pH 6 comparison, the lower enzyme loading, while reducing the total potential yield of TCh, significantly reduced interfacial mass transfer resistance. The results indicate that the reduction in transfer resistance primarily contributes to performance improvement, thus the pH 7 condition exhibits superior inhibition. In the 1:2 vs. pH 4 comparison, the TCh yield advantage from the high enzyme loading (1:2) and the mass transfer advantage from the low pH (pH 4) reach a performance equilibrium, resulting in comparable inhibition rates and signal changes. This further demonstrates that the two factors mentioned above work together to influence the final performance.

[0107] Test Example 6: Competitive inhibition of the DBSM-FeNCQDs-GOx & AChE membrane reaction at different glucose concentrations

[0108] Using the method of Test Example 2, the competitive inhibition reaction of the membrane prepared in Example 1 was tested at different glucose concentrations. The glucose solution concentrations were set to 20, 50, 100, and 150 mM, respectively. All other raw materials, steps, and parameters were the same as in Test Example 2. The absorbance change ΔA results are shown below. Figure 9 As shown.

[0109] Figure 9 The results show that ΔA increases with increasing glucose concentration, reaching its maximum at a glucose concentration of 100 mM. Subsequently, ΔA decreases as the glucose concentration continues to increase. Therefore, in the test method of Test Example 2, the optimal glucose concentration is 100 mM.

[0110] Test Example 7: Competitive inhibition of DBSM-GOx & AChE-FeNCQDs membrane reaction under different concentrations of acetylthiocholine chloride

[0111] Using the method of Test Example 2, the competitive inhibition reaction of the membrane prepared in Example 1 was tested at different concentrations of acetylcholinesterase chloride. The concentrations of acetylthiocholine chloride (ATCh) were set to 1, 2, 5, and 10 mM, respectively. All other raw materials, steps, and parameters were the same as in Test Example 2. The absorbance change ΔA results are shown below. Figure 10 As shown.

[0112] Figure 10The results showed that ΔA increased with increasing ATCh concentration, but when the ATCh concentration reached 10 mM, the increase in ΔA decreased significantly. At this point, ΔA was very close to that at 5 mM ATCh concentration, with no significant difference. Therefore, considering both experimental effectiveness and cost, the optimal ATCh concentration in the test method of Test Example 2 was 5 mM.

[0113] Test Example 8: Competitive inhibition of the DBSM-FeNCQDs-GOx & AChE membrane reaction under different acetylthiocholine chloride reaction times.

[0114] Using the method of Test Example 2, the competitive inhibition reaction of the membrane prepared in Example 1 was tested under different acetylcholinesterase (ATCh) reaction times. The reaction times for acetylthiocholine chloride (ATCh) were set to 5, 10, 20, and 30 min, respectively. All other raw materials, steps, and parameters were the same as in Test Example 2. The absorbance changes are shown below. Figure 11 As shown.

[0115] Figure 11 The results show that ΔA increases with the extension of ATCh reaction time, but when the reaction time reaches 30 min, the increase in ΔA decreases significantly. At this point, ΔA is very close to ΔA at a reaction time of 20 min, with no significant difference. Therefore, considering both experimental effectiveness and cost factors, the optimal reaction time for ATCh in the test method of Test Example 2 is 20 min.

[0116] Test Example 9: Inhibition of DBSM-FeNCQDs-GOx & AChE Membrane under Different OPs and Reaction Times

[0117] Using paraoxon as the research object, the inhibition of paraoxon reaction at different reaction times was tested on the membrane prepared in Example 1. The test method is as follows:

[0118] The membrane prepared in Example 1 was placed in 4 mL of Tris-HCl buffer (10 mM, pH = 8.0) containing different concentrations of paraoxonium, and reacted for 5-20 min. Then, 10 mM acetylthiocholine chloride was added to the above solution and reacted for 20 min. Subsequently, glucose and 2,2-azido-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (10 mM, pH = 3.0) to prepare substrate solutions, with concentrations of 100 mM and 1 mM, respectively. Then, 16 mL of the above substrate solutions were added to the above reaction system with the help of a magnetic stirrer. The absorbance value A1 of the solution at 420 nm was recorded. The OPs inhibition rate results are as follows: Figure 12 As shown.

[0119] ;

[0120] Figure 12 The results showed that when the reaction time of OPs was 10 min, the inhibition rate of OPs was significantly improved compared with the reaction time of 5 min. However, when the reaction time was further extended, the inhibition rate of OPs did not increase significantly. Therefore, considering both experimental effectiveness and cost factors, the optimal reaction time for OPs is 10 min.

[0121] Based on the experimental results of tests 6-9, in order to generate a larger signal response while controlling experimental costs, the optimal glucose concentration was determined to be 100 mM, the acetylthiocholine chloride concentration to be 10 mM, the acetylthiocholine chloride reaction time to be 20 min, and the OPs inhibition time to be 10 min.

[0122] Test Example 10: Inhibition of DBSM-FeNCQDs-GOx & AChE Membrane by Different Optics

[0123] Using the method provided in Test Example 9, the DBSM-FeNCQDs-GOx&AChE membrane prepared in Example 1 was used to test the inhibition of four OPs: paraoxon, malathion, parathion, and chlorpyrifos. The reaction time for OPs was 10 min, and other raw materials, steps, and parameters were the same as in Test Example 9. The OPs inhibition rate results are as follows: Figure 13 As shown.

[0124] Figure 13 The results show that the DBSM-FeNCQDs-GOx&AChE membrane prepared in Example 1 can achieve high inhibition rates against all four OPs, indicating that the DBSM-FeNCQDs-GOx&AChE membrane of the present invention has good detection effect on a variety of organophosphorus pesticides and has universality for the detection of organophosphorus pesticides.

[0125] Test Example 11: Competitive Inhibition of Enzyme in Free System

[0126] The membrane prepared in Example 11 was used to form an enzyme-free system DBSM-FeNCQDs-GOx+AChE. The competitive inhibition reaction was tested and compared with the DBSM-FeNCQDs-GOx&AChE membrane provided in Example 1. The specific methods are as follows:

[0127] Before competitive inhibition, the membrane prepared in Example 11 was immersed in 4 mL of Tris-HCl buffer (10 mM, pH=8.0) for 20 min. GOx, glucose, and 2,2-azido-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (10 mM, pH=3.0) to prepare a substrate solution with concentrations of 0.0075 g / L, 100 mM, and 1 mM, respectively. 16 mL of the substrate solution was added to the 4 mL system, and the absorbance (A) at 420 nm was recorded. After competitive inhibition, AChE and thioacetylcholine chloride were dissolved in Tris-HCl buffer (10 mM, pH=8.0) with concentrations of 0.1962 g / L and 10 mM, respectively. The membrane prepared in Example 11 was then placed in 4 mL of the solution and reacted for 20 min. Subsequently, 100 mM glucose and 1 mM 2,2-azino-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (0.1 M, pH=3.0) to prepare a substrate solution; then, 16 mL of the above substrate solution was added to a system with different concentrations of inhibitor under magnetic stirring, and the absorbance value A0 at 420 nm was recorded. The change in absorbance and the inhibition rate were calculated respectively. The results are as follows: Figure 14 As shown.

[0128] Figure 14 The results show that the absorbance change and TCh inhibition rate of the enzyme-immobilized DBSM-FeNCQDs-GOx&AChE membrane provided in Example 1 are significantly higher than those of the enzyme-free system DBSM-FeNCQDs-GOx+AChE, indicating that the enzyme immobilization treatment in this application can effectively improve the detection effect.

[0129] Based on its optimized interface structure, the enzyme immobilization system can increase the concentration of substrates and inhibitors near the active site through the proximity effect, while amplifying both cascade signal generation and competitive inhibition functions, ultimately giving it a signal change and inhibition rate that are much higher than those of the free system.

[0130] Test Example 12: Colorimetric Sensor Sensitivity and Detection Limit Test

[0131] The colorimetric sensing sensitivity and detection limit of the membrane prepared in Example 1 were tested. The test method was as follows: The membrane prepared in Example 1 was placed in 4 mL of Tris-HCl buffer (10 mM, pH=8.0) containing different concentrations of OPs and reacted for 10 min. Then, 10 mM acetylthiocholine chloride was added to the above solution and reacted for 20 min. Subsequently, glucose and 2,2-azido-bis(3-ethyl-benzothiazol-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (10 mM, pH=3.0) to prepare substrate solutions, with concentrations of 100 mM and 1 mM, respectively. Then, 16 mL of the above substrate solutions were added to the above reaction system with different concentrations of OPs under the action of a magnetic stirrer. The absorbance value A1 of the solution at 420 nm was recorded. The results are as follows. Figure 15 As shown.

[0132] The results show that the DBSM-FeNCQDs-GOx&AChE membrane prepared in Example 1 has a colorimetric detection limit of 0.488 ng / mL and a linear detection range of 1-10000 ng / mL. It can be seen that the DBSM-FeNCQDs-GOx&AChE membrane provided in this application has a low colorimetric detection limit and a large detection range, and has high colorimetric detection sensitivity and strong detection capability.

[0133] Test Example 13: Competitive inhibition of the DBSM-FeNCQDs-GOx & AChE membrane reaction at different glucose concentrations

[0134] The competitive inhibition reaction of the membrane prepared in Example 1 was tested under different glucose concentrations. The test method was as follows: Before competitive inhibition, the membrane prepared in Example 1 was immersed in 4 ml of Tris-HCl buffer (10 mM, pH=8.0) for 30 min, and then 3.2 ml of the above solution was discarded. Glucose and 2,2-azido-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (10 mM, pH=3.0) to prepare substrate solutions, with their concentrations of 0.5-10 mM and 1 mM, respectively. Then, 3.2 mL of the above substrate solutions were added to the above system under the action of a magnetic stirrer to make the glucose concentration in the substrate 2 mM, 5 mM, 10 mM and 20 mM, respectively, and the absorbance value A0 of the solution at 420 nm was recorded. After competitive inhibition, the membrane prepared in Example 1 was first immersed in 4 ml of Tris-HCl buffer (10 mM, pH=8.0) for 10 min, then 10 mM thioacetylcholine chloride was added and reacted for 20 min. 3.2 ml of the solution was then discarded. Subsequently, 100 mM glucose and 1 mM 2,2-azido-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (0.1 M, pH=3.0) to prepare the substrate solution. Then, 3.2 mL of the substrate solution was added to the above system with a magnetic stirrer, and the absorbance A at 420 nm was recorded. The absorbance changes are shown below. Figure 16 As shown.

[0135] Figure 16 The results showed that when the glucose concentration was 5 mM or 10 mM, the TCh inhibition rate was significantly higher than that when the glucose concentration was 2 mM or 20 mM, and there was no significant difference between the two. However, the absorbance gradually decreased with increasing glucose concentration. Since fluorescence detection must be performed in a 4 mL cuvette, reducing the reaction volume, while keeping the catalyst loading constant, would lead to a significant increase in the local catalyst concentration, thus altering the reaction kinetics. Based on this, the glucose concentration in the 4 mL fluorescence detection system was optimized, and 5 mM was selected as the optimal glucose concentration based on the combined experimental results.

[0136] Test Example 14: Fluorescence Sensor Sensitivity and Detection Limit Test

[0137] The membrane prepared in Example 1 was placed in 4 mL of Tris-HCl buffer (10 mM, pH=8.0) containing different concentrations of OPs and reacted for 10 min. Then, 10 mM acetylthiocholine chloride was added to the above solution and reacted for 20 min. Subsequently, 3.2 mL of the above solution was discarded. Glucose and 2,2-azido-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt were dissolved in acetate buffer (10 mM, pH=3.0) to prepare substrate solutions, with concentrations of 5 mM and 1 mM, respectively. Then, 3.2 mL of the above substrate solution was added to the above reaction system with different concentrations of OPs, and the fluorescence intensity F1 of the solution at approximately 468 nm was recorded. The results are as follows. Figure 17 As shown.

[0138] ;

[0139] The results showed that the fluorescence detection limit of the DBSM-FeNCQDs-GOx&AChE membrane prepared in Example 1 was 0.293 ng / mL, and the linear detection range was 1-10000 ng / mL. It can be seen that the DBSM-FeNCQDs-GOx&AChE membrane provided in this application has a low fluorescence detection limit and a large detection range, and has high fluorescence detection sensitivity and strong detection ability.

[0140] In summary, by constructing the DBSM-FeNCQDs-GOx&AChE sensing interface and systematically optimizing the preparation conditions, we revealed the intrinsic laws governing its performance regulation: the performance of the competitive sensing interface is jointly shaped by the immobilized enzyme ratio and the interfacial microenvironment. The enzyme ratio predetermines the theoretical relative flux of the reactants, while the microenvironment, dominated by the enzyme load, simultaneously regulates the reactant generation rate (reactant generation kinetics) and the interfacial mass transfer resistance (mass transfer kinetics), both of which jointly determine the actual effective concentration near the active site. Studies show that, under current structural constraints, there is an inherent contradiction between enhancing intrinsic activity and maintaining efficient mass transfer. Therefore, it is necessary to find the "kinetic optimum" within this contradiction to ensure the accessibility of the active site while maintaining sufficient signal generation capacity. This creates a more favorable initial occupancy state for the inhibitor (TCh) before competitive initiation, thereby achieving precise regulation of the relative strength of the two metabolic fluxes of signal generation and inhibition. Ultimately, this synergistically yields considerable signal output and inhibition depth, thus improving the sensitivity of ops (active sites). This study establishes the crucial role of mass transfer dynamics in this type of competitive system, providing an important theoretical foundation for further breaking through mass transfer limitations through interface structure design and developing next-generation high-performance sensing technologies.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A biosensing membrane for detecting organophosphorus pesticides, comprising a base membrane, characterized in that, The base membrane is loaded with a plurality of randomly distributed glucose oxidase catalytic modules and a plurality of acetylcholinesterase catalytic modules. The glucose oxidase catalytic module includes a first carrier fixed on the base membrane and glucose oxidase loaded on the first carrier. The acetylcholinesterase catalytic module includes a second carrier fixed on the base membrane and acetylcholinesterase loaded on the second carrier. The weight ratio of glucose oxidase to acetylcholinesterase is 1:3 to 1:

4. Both the first carrier and the second carrier are iron-doped carbon quantum dots, which are formed from organic carbon sources and iron ions.

2. The biosensing membrane for organophosphorus pesticide detection according to claim 1, characterized in that, The organic carbon source includes at least one of citric acid and polyethyleneimine.

3. The biosensing membrane for organophosphorus pesticide detection according to claim 2, characterized in that, A fixing layer is attached to the base film. The fixing layer is a mixed coating comprising a phenolic compound and an aminosilane coupling agent, and has quinone groups and / or phenolic hydroxyl groups on its surface, for loading the iron-doped carbon quantum dots. And / or, in the fixed layer, the phenolic compound includes at least one of tannic acid and polydopamine; And / or, the aminosilane coupling agent is 3-aminopropyltriethoxysilane; And / or, the weight ratio of the phenolic compound to the aminosilane coupling agent is (30~50):(2~18).

4. A method for preparing a biosensor membrane for detecting organophosphorus pesticides, characterized in that, Includes the following steps: S1: The surface of the base film is treated with a mixed solution containing phenolic compounds and aminosilane coupling agents to obtain a base film with a fixed layer; S2: Mix organic carbon source and iron ions in water and react under heating conditions to obtain iron-doped carbon quantum dot dispersion; S3: The base film with the fixed layer obtained in S1 is mixed with the iron-doped carbon quantum dot dispersion obtained in S2 and reacted to obtain a sensing film loaded with iron-doped carbon quantum dots. S4: The sensing membrane loaded with iron-doped carbon quantum dots obtained in S3 is mixed with the enzyme composition solution and reacted to obtain the biosensor membrane for organophosphorus pesticide detection; the enzyme in the enzyme composition solution is composed of glucose oxidase and acetylcholinesterase, and the weight ratio of glucose oxidase to acetylcholinesterase is 1:3 to 1:

4.

5. The method according to claim 4, characterized in that, In the enzyme composition solution, the concentration of glucose oxidase is 0.275 g / L, and the concentration of acetylcholinesterase is 0.825~1.1 g / L.

6. The method according to claim 4, characterized in that, The reaction conditions in step S4 are: pH 6~7, reaction temperature 25~30℃, and reaction time 2~8h.

7. The method according to claim 6, characterized in that, In step S4, the reaction time is 4-6 hours.

8. The method according to any one of claims 4 to 7, characterized in that, In step S1, the concentration of the phenolic compound in the mixed solution is 0.5-10 g / L, and the concentration of the aminosilane coupling agent is 0.1-10 g / L.

9. The method according to claim 8, characterized in that, In the mixed solution, the weight ratio of the phenolic compound to the aminosilane coupling agent is (30~50):(5~16).

10. The method according to claim 9, characterized in that, In the mixed solution, the weight ratio of the phenolic compound to the aminosilane coupling agent is (40~50):(11~16).

11. The method according to claim 8, characterized in that, In step S2, the organic carbon source includes citric acid and polyethyleneimine; the weight ratio of citric acid, polyethyleneimine and iron ions is 1:(0.1~3):(0.1~2).

12. The method according to claim 11, characterized in that, The weight ratio of citric acid, polyethyleneimine, and iron ions is 1:(1~2):(0.5~1).

13. The method according to claim 11, characterized in that, In step S2, the concentration of iron ions in the mixture is 40~60 g / L.

14. The method according to claim 13, characterized in that, In step S2, the concentration of iron ions in the mixture is 50 g / L.

15. The method according to claim 11, characterized in that, In step S2, the heating reaction conditions are: 180~250℃ for 8~12 hours.

16. The method according to claim 11, characterized in that, The reaction conditions in step S3 are: reaction temperature 25~30℃, reaction time 1~10h.

17. The application of the biosensing membrane for organophosphorus pesticide detection according to any one of claims 1 to 3 in the detection of organophosphorus pesticide content in food, characterized in that, The food refers to the edible parts of crops and processed foods made using the edible parts; And / or, the food is a dairy product; The organophosphorus pesticides include paraoxon, malathion, parathion, and chlorpyrifos.

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