Photoelectrochemical sensor and biochemical multi-molecular concentration detection method

Abstract

The application provides a photoelectrochemical sensor and a biochemical multi-molecule concentration detection method. The photoelectrochemical sensor comprises a working electrode, the surface of the working electrode is configured with a photoelectric probe, the photoelectric probe is composed of a photoelectric conversion material, an aptamer and a photoelectric enhancement material, and the photoelectric conversion material and the photoelectric enhancement material are connected through the aptamer; wherein after specific binding of a to-be-detected biochemical molecule and the aptamer, a spatial structure change is caused, so that the distance between the photoelectric conversion material and the photoelectric enhancement material changes, and then the photoelectric enhancement material enhances the photocurrent signal of the photoelectric conversion material; wherein the photocurrent change caused by the change of the photocurrent enhancement amplitude can be used for concentration detection of the to-be-detected biochemical molecule. The application can realize high-sensitivity molecule concentration detection.

Description

Technical Field

[0001] This application relates to the field of biochemical sensing and detection, specifically to a photoelectrochemical sensor and a method for detecting the concentration of multiple biochemical molecules. Background Technology

[0002] Biochemical sensing detection is an analytical technique that identifies and responds to measured biochemical quantities and converts them into usable signals according to certain rules. It involves numerous disciplines such as physics, chemistry, biology, materials science, and nanotechnology, and is widely used in important fields such as environmental monitoring, medical diagnosis, and food safety. Traditional biochemical sensing detection methods mainly include optical methods and electrochemical methods.

[0003] Photoelectrochemical (PEC) detection is a newly developed method following the methods mentioned above. It utilizes the change in photocurrent generated by exciting electrode materials under light to detect target molecules. PEC detection methods offer several advantages over traditional optical and electrochemical biochemical detection methods. Firstly, compared to traditional optical methods, PEC detection methods are simpler, less expensive, and easier to miniaturize. Secondly, unlike electrochemical methods where both input and output are electrical signals, PEC detection uses light sources of different wavelengths as excitation signals and current or voltage as detection signals. These two signals are separate and different in form, avoiding mutual interference and thus improving the signal-to-noise ratio for higher sensitivity. Furthermore, PEC detection methods also have their own unique advantages, such as wide applicability and strong scalability.

[0004] Traditional photoelectrochemical sensing methods utilize redox reactions to detect small biomolecules, readily yielding an increasing photocurrent response. However, due to the insulating properties of the biomolecule surface, redox reactions are difficult to occur. Therefore, specific recognition is typically employed for the detection of large biomolecules. Generally, specific recognition is achieved through the specific binding of antibodies or aptamers to the target biomolecule. This process suppresses electron transfer in the photoelectrochemical reaction through steric hindrance, resulting in a decreasing photocurrent response. However, detection methods relying on decreasing photocurrent responses suffer from poor interference resistance, compromising detection accuracy.

[0005] Therefore, this application provides a photoelectrochemical sensor and a method for detecting biochemical multi-molecule concentrations to solve one of the aforementioned technical problems. Summary of the Invention

[0006] The purpose of this application is to provide a photoelectrochemical sensor and a method for detecting the concentration of multiple biochemical molecules, which can solve at least one of the technical problems mentioned above. The specific solution is as follows:

[0007] According to a specific embodiment of this application, in a first aspect, this application provides a photoelectrochemical sensor, comprising: a working electrode, wherein a photoelectric probe is disposed on the surface of the working electrode, the photoelectric probe being composed of a photoelectric conversion material, an aptamer, and a photoelectric enhancement material, and the photoelectric conversion material and the photoelectric enhancement material being connected through the aptamer; wherein, after the target biochemical molecule specifically binds to the aptamer, it causes a change in spatial structure, thereby changing the distance between the photoelectric conversion material and the photoelectric enhancement material, and thus enhancing the photocurrent signal of the photoelectric enhancement material to the photoelectric conversion material; wherein, the change in photocurrent caused by the change in the photocurrent enhancement amplitude can be used to detect the concentration of the target biochemical molecule.

[0008] In one embodiment, the photoelectric conversion material is selected from one of the following materials: inorganic semiconductor nanomaterials with a separated band structure; organic semiconductor nanomaterials with a separated band structure; fluorescent nanomaterials with photoelectric properties; and carbon nanomaterials with photoelectric properties.

[0009] In one embodiment, the photoelectric conversion material is selected from one of the following materials: cadmium sulfide; lead sulfide; zinc sulfide; titanium dioxide; ferric oxide; zinc oxide; copper oxide; aluminum oxide; copper sulfide; cadmium antimonide; cadmium selenide; gold clusters; carbon dots; carbon nanotubes; wherein the photoelectric conversion material is further selected from one of the following materials: titanium dioxide; ferric oxide; zinc oxide; cadmium sulfide; gold clusters.

[0010] In one embodiment, the photoelectric enhancement material is selected from one of the following materials: gold nanoparticles; gold nanorods; silver nanoparticles; platinum nanoparticles; copper nanoparticles; graphene oxide nanosheets; molybdenum disulfide nanosheets; tungsten disulfide nanosheets; wherein the photoelectric enhancement material is further selected from one of the following materials: gold nanoparticles; silver nanoparticles; platinum nanoparticles; copper nanoparticles.

[0011] In one embodiment, the aptamer is selected from one of the following materials: an arbitrary length nucleic acid aptamer composed of DNA; an arbitrary length nucleic acid aptamer composed of RNA; or an arbitrary length polypeptide aptamer composed of oligopeptides; wherein the aptamer is further selected from one of the following materials: an arbitrary length nucleic acid aptamer composed of DNA; or an arbitrary length nucleic acid aptamer composed of RNA.

[0012] In one embodiment, the aptamer is selected to specifically bind only to the biochemical molecule to be tested, and not to other biochemical molecules.

[0013] According to a specific embodiment of this application, in a second aspect, this application provides a biochemical multi-molecule concentration detection method, applied to the photoelectrochemical sensor of the first aspect, the method comprising:

[0014] The photoelectrochemical sensor is used to perform photoelectrochemical detection on a mixture containing at least two analyte biochemical molecules; wherein the aptamer of the photoelectrochemical sensor is configured to specifically bind only to each of the analyte biochemical molecules; the dataset generated during the photoelectrochemical detection process is input into a pre-trained machine learning model to obtain the biochemical molecule concentration detection result output by the machine learning model; wherein the biochemical molecule concentration detection result includes the molecular concentration corresponding to each of the at least two analyte biochemical molecules.

[0015] In one embodiment, the dataset is a three-dimensional dataset including time data, bias voltage data, and photocurrent amplitude data, used to characterize the relationship between bias voltage data and photocurrent amplitude data over time; the machine learning model is pre-trained using the training datasets of each of the at least two biochemical molecules to be tested; wherein, the training dataset of each biochemical molecule to be tested includes: multiple photocurrent amplitude curves over time corresponding to the biochemical molecule at different concentrations and / or when different bias voltages are applied.

[0016] In one implementation, the training dataset of the machine learning model is normally distributed, and the top 20% of data that deviates most from the center value are removed.

[0017] In one embodiment, the algorithm of the machine learning model is either a quadratic rational Gaussian process regression algorithm or a three-layer wide neural network algorithm, with the quadratic rational Gaussian process regression algorithm being selected.

[0018] Compared with the prior art, the above-described solution of this application has at least the following beneficial effects: This application provides a photoelectrochemical sensor, the core of which lies in the photoelectric probe structure configured on the surface of the working electrode. This structure consists of a photoelectric conversion material, an aptamer, and a photoelectric enhancement material, and the photoelectric conversion material and the photoelectric enhancement material are connected through the aptamer. When the target biochemical molecule specifically binds to the aptamer, it causes a change in the spatial structure, resulting in a change in the distance between the photoelectric conversion material and the photoelectric enhancement material, thereby changing the photoelectric enhancement effect of the photoelectric enhancement material on the photoelectric conversion material. This change is ultimately reflected in the change of photocurrent, which can be used to detect the concentration of the target biochemical molecule. This design significantly improves the sensitivity and specificity of the sensor because it utilizes the principle of photocurrent response caused by a small distance change, enabling even trace amounts of target molecules to be accurately detected. Attached Figure Description

[0019] Figure 1A structural block diagram of a photoelectrochemical sensor is shown;

[0020] Figure 2 The photoelectric conversion principle diagram of the photoelectrochemical sensor in this application is shown;

[0021] Figure 3 A schematic diagram of the photoelectric probe of this application is shown;

[0022] Figure 4 The schematic diagram shows the photocurrent response effect before and after a single detection of biochemical molecules and after cleaning the sensor.

[0023] Figure 5 A schematic diagram of the fabrication and self-assembly process of the photoelectric probe is shown;

[0024] Figure 6 A schematic diagram of a hybrid sensing electrode suitable for the detection of multiple biochemical molecules is shown.

[0025] Figure 7 A flowchart of the biochemical multi-molecule concentration detection method of this application is shown;

[0026] Figure 8 This diagram illustrates the variation of photocurrent amplitude over time at different time points.

[0027] Figure 9 A schematic diagram of the training data constructed for VEGF and Ang-2 is shown. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0030] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0031] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0032] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0033] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0034] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0035] Photoelectrochemical detection is a sensor technology that integrates optical and electrochemical signals. It utilizes the change in photocurrent generated by exciting electrode materials under light to detect target molecules. Photoelectrochemical detection methods offer several advantages over traditional optical and electrochemical biochemical detection methods. Firstly, compared to traditional optical methods, photoelectrochemical detection methods are simpler, less expensive, and easier to miniaturize. Secondly, unlike electrochemical methods where both input and output are electrical signals, photoelectrochemical detection methods use light sources of different wavelengths as excitation signals and current or voltage as detection signals. These two signals are separate and different in form, avoiding mutual interference and thus improving the signal-to-noise ratio for higher sensitivity. Furthermore, photoelectrochemical detection methods have their own unique advantages, such as wide applicability and strong scalability. Traditional photoelectrochemical sensing methods use redox reactions to detect small biomolecules, easily obtaining an increased photocurrent response. However, because the insulating substances on the surface of large biomolecules make redox reactions difficult, specific recognition principles are typically used to detect them. Generally, specific recognition is achieved through the specific binding of antibodies or nucleic acid aptamers to the target biochemical macromolecules. This process suppresses electron transfer in the photoelectrochemical reaction through steric hindrance, resulting in a decreased photocurrent response. However, detection methods relying on decreased photocurrent response have poor anti-interference capabilities, affecting detection accuracy. In summary, no effective photoelectrochemical detection method has yet been proposed to achieve efficient detection of biochemical molecules of both large and small sizes.

[0036] Therefore, this application aims to develop a new method for detecting biochemical molecules, which enables the sensor to have high sensitivity to biochemical molecule detection, strong anti-interference ability, and high repeatability, thereby reducing detection costs.

[0037] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0038] Figure 1 A structural block diagram of a photoelectrochemical sensor 100 is shown, as follows: Figure 1 As shown, the photoelectrochemical sensor 100 includes a working electrode 101 and a photoelectric probe 102 disposed on the surface of the working electrode 101.

[0039] In some embodiments, the photoelectric probe 102 is composed of a photoelectric conversion material, an aptamer, and a photoelectric enhancement material, wherein the photoelectric conversion material and the photoelectric enhancement material are connected by the aptamer.

[0040] In some embodiments, sensors formed by self-assembly are more stable, and the nanostructures are less prone to detachment. Therefore, the nanostructure of photoelectric conversion material-aptamer-photoelectric enhancement material can be linked to the electrode surface through self-assembly, resulting in a more stable photocurrent response and higher repeatability during sensing.

[0041] In this embodiment, the specific binding of the biochemical molecule to be tested with the aptamer causes a change in spatial structure, which in turn changes the distance between the photoelectric conversion material and the photoelectric enhancement material, thereby enhancing the photocurrent signal of the photoelectric conversion material by the photoelectric enhancement material.

[0042] In some embodiments, the change in photocurrent caused by the change in the amplitude of photocurrent enhancement can be used to detect the concentration of the biochemical molecule to be tested.

[0043] The photoelectrochemical sensor 100 provided in this application has a photoelectric probe 102 structure disposed on the surface of the working electrode 101. This structure consists of a photoelectric conversion material, an aptamer, and a photoelectric enhancement material, with the photoelectric conversion material and the photoelectric enhancement material connected by the aptamer. When the target biochemical molecule specifically binds to the aptamer, it causes a change in the spatial structure, resulting in a change in the distance between the photoelectric conversion material and the photoelectric enhancement material. This alters the photoelectric enhancement effect of the photoelectric enhancement material on the photoelectric conversion material, and this change is ultimately reflected in a change in photocurrent, which can be used to detect the concentration of the target biochemical molecule. This design significantly improves the sensitivity and specificity of the sensor because it utilizes the principle of photocurrent response caused by minute distance changes, enabling the accurate detection of even trace amounts of target molecules.

[0044] Figure 2 The schematic diagram of the photoelectric conversion principle of the photoelectrochemical sensor in this application is shown.

[0045] For example, such as Figure 2 As shown, e- represents photogenerated electrons, Ke represents the process of photogenerated electrons tunneling from the lowest unoccupied molecular orbital to the electrode Fermi level, Ka represents the process of photogenerated electrons tunneling from the low unoccupied molecular orbital to the Fermi level of the photoelectric enhancement material, and Kb represents the process of photogenerated electrons tunneling from the electrode Fermi level to the highest occupied molecular orbital.

[0046] In this embodiment, after the electrode is illuminated, electrons in the highest occupied molecular orbitals transition to the lowest unoccupied molecular orbitals, generating photogenerated electron-hole pairs. These photogenerated electrons can tunnel to the electrode, allowing the corresponding excitation photocurrent to be detected in the external circuit. Furthermore, the photoelectro-enhancing material can improve the separation efficiency of photogenerated electron-hole pairs in the photoelectric conversion material, further increasing the photocurrent amplitude. The aptamer between the photoelectric conversion material and the photoelectro-enhancing material specifically binds to the target molecule, causing a reduction in the distance between them and altering the enhancement amplitude of the photocurrent by the photoelectro-enhancing material. Since the photocurrent changes induced by specific molecules follow a fixed pattern, the change in photocurrent amplitude during this process can be used to achieve molecule concentration detection.

[0047] In this embodiment, the photoelectric enhancement material and the photoelectric conversion material are linked by an aptamer. The length of the aptamer affects the enhancement of the photoelectric separation efficiency of the photoelectric conversion material on the photoelectric enhancement material, thus affecting the enhancement of the photocurrent of the photoelectric enhancement material. Initially, with a length of D1, the photoelectric separation efficiency of the photoelectric conversion material on the photoelectric enhancement material is enhanced, for example, the photocurrent enhancement is 150%. Later, the aptamer deforms due to the influence of the molecular content in the solution, changing its length to D2. This reduces the distance between the fluorescent photoelectric enhancement material and the metallic photoelectric conversion material. Therefore, the photoelectric separation efficiency of the photoelectric conversion material on the photoelectric enhancement material is further improved, resulting in a greater enhancement of the photocurrent of the photoelectric enhancement material, such as increasing to 200%. When the molecular concentration decreases or competing biochemicals appear in the solution and drive away the molecules, the aptamer gradually returns to D1, at which point the enhancement of the photocurrent of the photoelectric conversion material on the photoelectric enhancement material decreases back to 150%. By observing the biochemical reactions and competition between the analyte molecules and the aptamer, the distance between the photoelectric conversion material and the photoelectric enhancement material can be controlled, thereby changing the enhancement of the photocurrent of the photoelectric conversion material on the photoelectric enhancement material. Finally, by detecting the amplitude change of the photocurrent of the photoelectrochemical enhancement material in the fluorescent nanomaterial, the photoelectrochemical sensing measurement of the molecular marker can be completed.

[0048] In this embodiment, the photoelectrochemical sensor 100 detects molecules by detecting changes in the amplitude of photocurrent enhancement caused by distance changes during the detection process. Tiny distance changes caused by molecules are more noticeably reflected in changes in photocurrent. Therefore, compared to traditional photoelectrochemical sensors 100, the photoelectrochemical sensor 100 provided in this application has higher sensitivity and can better meet the needs of detecting ultra-low concentration molecules in the test solution.

[0049] Figure 3 A schematic diagram of the photoelectric probe of this application is shown.

[0050] For example, such as Figure 3As shown, the chain-like structure between the photoelectric conversion material and the photoelectric enhancement material represents an aptamer. A specific aptamer can specifically bind to a specific molecule. When the photoelectrochemical sensor 100 is excited, the aptamer begins to specifically bind to the specific molecule or small molecule, causing the chain-like structure to shrink. Based on this, the distance between the photoelectric conversion material and the photoelectric enhancement material linked at both ends of the aptamer begins to decrease.

[0051] Figure 4 The diagram shows the photocurrent response before and after a single detection of biochemical molecules and after cleaning the sensor.

[0052] For example, such as Figure 4 As shown, the photocurrent amplitude increases when the aptamer specifically binds to the molecule. Upon completion of detection, the detected biochemical molecule can be eluted from the sensor surface, restoring the photoelectric probe 102 to its structure before specific binding. Correspondingly, the photocurrent amplitude decreases. Since the photoelectric probe 102 can be eluted back to its original structural state, the photoelectrochemical sensor 100 provided in this application is reusable, reducing its usage cost.

[0053] As some feasible embodiments, the photoelectric conversion material can be selected from one of the following four types of materials:

[0054] I. Inorganic semiconductor nanomaterials with separated band structures;

[0055] II. Organic semiconductor nanomaterials with separated band structures;

[0056] III. Fluorescent nanomaterials with photoelectric properties;

[0057] IV. Carbon nanomaterials with photoelectric properties.

[0058] In some embodiments, the photoelectric conversion material is selected from cadmium sulfide, lead sulfide, zinc sulfide, ferric oxide, zinc oxide, copper oxide, aluminum oxide, copper sulfide, cadmium antimony, cadmium selenide, gold clusters, carbon dots, and carbon nanotubes. Further, the photoelectric conversion material is selected from titanium dioxide, ferric oxide, zinc oxide, cadmium sulfide, and gold cluster materials.

[0059] In this embodiment, each selected material conforms to one of the four categories of optional materials mentioned above, and is represented as a specific embodiment selected from the four categories of optional materials.

[0060] As some feasible embodiments, the photoelectric enhancement material may be selected from one of the following: gold nanoparticles, gold nanorods, silver nanoparticles, platinum nanoparticles, copper nanoparticles, graphene oxide nanosheets, molybdenum disulfide nanosheets, and tungsten disulfide nanosheets. Further, the photoelectric enhancement material may be selected from one of the following: gold nanoparticles, silver nanoparticles, platinum nanoparticles, and copper nanoparticles.

[0061] As some feasible embodiments, the aptamer is selected from one of the following materials:

[0062] I. Nucleic acid aptamers of arbitrary length composed of DNA;

[0063] II. Nucleic acid aptamers of arbitrary length composed of RNA;

[0064] III. Polypeptide aptamers of arbitrary length composed of oligopeptides.

[0065] The aptamer can be further selected as a nucleic acid aptamer of any length composed of DNA, or a nucleic acid aptamer of any length composed of RNA.

[0066] In this embodiment, the aptamer is selected to specifically bind only to the biochemical molecule to be tested, and not to other biochemical molecules.

[0067] In some embodiments, the photoelectric probe 102 is prepared according to the following steps: a photoelectric enhancement material is covalently connected to one end of the aptamer to obtain a nanostructure of aptamer-photoelectric enhancement material, and then a photoelectric conversion material is covalently connected to the other end of the aptamer to obtain a nanostructure of photoelectric conversion material-aptamer-photoelectric enhancement material.

[0068] In some embodiments, the preparation process of the nanostructure of photoelectric conversion material-aptamer-photoelectric enhancement material is as follows: An aptamer solution with a concentration of 10-100 nM is mixed with a photoelectric enhancement material solution with a concentration of 1-10 nM at a certain volume ratio (1:1, 2:1, 3:1, 4:1, etc.), and the mixture is shaken at room temperature for 2-4 hours. If multiple biochemical molecules need to be detected, the corresponding aptamer solutions should be mixed accordingly. The mixed solution is then centrifuged at 10000-20000 rpm for 15-45 minutes to remove the supernatant. The precipitate is then dispersed in a 10 mM Tris-HCl buffer solution with a pH of 7.4. Subsequently, the mixture is centrifuged again at 10000-20000 rpm for 15-45 minutes, and the final precipitate is dispersed in PBS to obtain a stable aptamer-photoelectric enhancement material structure. The nanostructure solution of the aptamer-photoelectric enhancement material is mixed with the photoelectric conversion material solution at a certain volume ratio (1:1, 2:1, 3:1, 4:1, etc.), and the mixture is sonicated at room temperature for 15-45 minutes. After centrifuging the mixed solution at 10,000-20,000 rpm for 15-45 minutes to remove the supernatant, the obtained precipitate was dispersed with PBS to obtain a stable solution of nanostructured photoelectric conversion material-aptamer-photoelectric enhancement material.

[0069] In some embodiments, the process of self-assembling the photoelectric conversion material-aptamer-photoelectric enhancement material nanostructure onto the electrode is as follows: First, the electrode is immersed in acetone solution and ultrasonically cleaned for 5-15 minutes. Next, the electrode is placed in anhydrous ethanol solution and ultrasonically cleaned for 5-15 minutes. Then, the electrode is placed in deionized water and ultrasonically cleaned for 5-15 minutes. Finally, the electrode surface is dried with nitrogen gas to obtain a cleaned electrode. The cleaned and dried electrode is immersed in an ethanol solution with a concentration of 5-15 mg / mL BDT for 2-6 hours, then the electrode surface is gently rinsed with anhydrous ethanol and dried with nitrogen gas. 50-150 μL of the photoelectric conversion material-aptamer-photoelectric enhancement material nanostructure solution is uniformly dropped onto the electrode surface, and after standing for 0.5-1.5 hours, a spin coater is started to remove excess nanomaterials. After rinsing the surface with deionized water and drying, the self-assembly of the photoelectric conversion material-aptamer-photoelectric enhancement material nanostructure on the electrode surface is completed.

[0070] To facilitate understanding, the following section uses a specific set of fabrication materials to illustrate a selection process for photoelectric probes, from fabrication to self-assembly into electrodes.

[0071] Figure 5 A schematic diagram of the fabrication and self-assembly process of the photoelectric probe is shown.

[0072] In this embodiment, as Figure 5As shown, the following materials are selected for preparing photoelectric probes: aptamer solution, silver nanoparticle (AgNPs) solution, and gold nanocluster (AuNCs) solution. Here, the silver nanoparticle (AgNPs) solution represents a specific photoelectric enhancement material solution, and the gold nanocluster (AuNCs) solution represents a specific photoelectric conversion material solution.

[0073] For the preparation of photoelectric probes, a 40 nM aptamer solution and a 6 nM silver nanoparticle solution were mixed at a volume ratio of 1:1 and shaken at room temperature for 3 hours. The mixture was then centrifuged at 15,000 rpm for 30 minutes to remove the supernatant, and the precipitate was dispersed in 10 mM Tris-HCl buffer (pH 7.4). The mixture was then centrifuged again at 15,000 rpm for 30 minutes, and the final precipitate was dispersed in PBS to obtain a stable aptamer-AgNP nanostructure. The aptamer-AgNP nanostructure solution was then mixed with a gold nanocluster solution at a volume ratio of 1:1 and sonicated at room temperature for 30 minutes. The mixture was then centrifuged at 15,000 rpm for 30 minutes to remove the supernatant, and the precipitate was dispersed in PBS to obtain a stable AuNCs-aptamer-AgNP nanostructure solution.

[0074] For the self-assembly process of the photoelectric probe, the electrode was first immersed in acetone solution and ultrasonically cleaned for 10 minutes. Next, the electrode was placed in anhydrous ethanol solution and ultrasonically cleaned for 10 minutes. Then, the electrode was placed in deionized water and ultrasonically cleaned for 10 minutes. Finally, the electrode surface was dried with nitrogen gas to obtain a cleaned electrode. The cleaned and dried electrode was then immersed in an ethanol solution with a concentration of 7.5 mg / mL BDT for 4 hours, followed by gentle rinsing of the electrode surface with anhydrous ethanol and drying with nitrogen gas. A solution of a photoelectric conversion material-aptamer-photoelectric enhancement material nanostructure was uniformly dropped onto the electrode surface. After standing for 1 hour, a spin coater was used to remove excess nanomaterials. The surface was then rinsed with deionized water and dried to complete the self-assembly of the photoelectric conversion material-aptamer-photoelectric enhancement material nanostructure on the electrode surface.

[0075] In some embodiments, each photodetector connected to the electrode surface is configured with a consistent aptamer. Correspondingly, when performing photoelectrochemical detection using this working electrode, the photodetectors on the electrode surface will only be affected by one type of biochemical molecule, causing a change in photocurrent. This biochemical molecule is one that can specifically bind to the aptamer of the photodetector. Based on this, the change in photocurrent is only related to the specific binding of the biochemical molecule, and the concentration of the biochemical molecule determines the degree of photocurrent change. Furthermore, the influence of biochemical molecule concentration on photocurrent can be used to establish a mapping relationship between photocurrent amplitude and biochemical molecule concentration, thereby enabling biochemical molecule concentration detection through this mapping relationship. For example, given the photocurrent amplitude, the biochemical molecule concentration can be obtained based on the mapping relationship.

[0076] Furthermore, in some other embodiments, to address detection scenarios involving multiple biochemical molecules, a hybrid sensing electrode can be fabricated by configuring various different photoelectric probes on the working electrode. These different photoelectric probes differ in the use of different aptamers, and each probe can specifically bind to a specific biochemical molecule via its aptamer. Based on this, the hybrid sensing electrode can achieve the ability to specifically bind to several specific biochemical molecules by utilizing multiple different photoelectric probes.

[0077] For example, such as Figure 6As shown, assuming aptamer A specifically binds to biomolecule A, aptamer B specifically binds to biomolecule B, and aptamer C specifically binds to biomolecule C, if it is necessary to detect the concentration of each of the three biomolecules separately in a mixed solution containing biomolecules A, B, and C, photoelectric probes can be prepared according to the binding characteristics of the biomolecules and aptamers. For example, taking the AuNCs-aptamer-AgNPs nanostructure as an example, three photoelectric probes can be prepared for biomolecules A, B, and C respectively: AuNCs-aptamer A-AgNPs nanostructure, AuNCs-aptamer B-AgNPs nanostructure, and AuNCs-aptamer C-AgNPs nanostructure. These three photoelectric probes can then be configured on the working electrode to obtain a hybrid sensing electrode capable of detecting multiple biomolecules. Based on this, a mixed sensing electrode can be used to detect mixed solutions containing biochemical molecules A, B, and C. Specifically, during the photoelectrochemical detection process, the mixed sensing electrode is illuminated, and the three aptamers specifically bind to their corresponding three biochemical molecules, thereby causing changes in photocurrent. Furthermore, in some additional embodiments, the concentrations of the three biochemical molecules can be identified by analyzing the degree of influence of the three biochemical molecules on the photocurrent change. For details, please refer to the relevant embodiments of the biochemical multi-molecule concentration detection method described below.

[0078] In some embodiments, the cyclic detection process for biochemical molecules based on a photoelectrochemical sensor is as follows: The baseline photocurrent of the developed sensor in PBS buffer is measured. Then, the sensor is placed in PBS buffer containing the target biochemical molecule for 5-15 minutes to allow sufficient reaction time. After this, the photocurrent is measured again, completing a single detection of the target biochemical molecule. Subsequently, the test solution is replaced with PBS buffer containing 0 ng / L of the target molecule. The electrode is rinsed 1-3 times and allowed to stand for 5-15 minutes to allow the dissociation of the target molecule within the nanostructure of the photoelectric conversion material-aptamer-photoelectric enhancement material. The photocurrent response of the electrode in PBS buffer is then measured and compared with the baseline photocurrent before measurement to verify the completion of the dissociation process. This yields a reusable sensor.

[0079] In the aforementioned cyclic detection process of this application, the selected procedure is as follows: First, the baseline photocurrent of the sensor in PBS buffer is measured. Then, the sensor is placed in PBS buffer containing the target biochemical molecule and allowed to stand for 10 minutes to allow sufficient reaction. After this, the photocurrent of the sensor is measured, completing a single detection of the target biochemical molecule. Subsequently, the test solution is replaced with PBS buffer containing 0 ng / L of the target molecule. The sensor is rinsed twice and allowed to stand for 10 minutes to allow the dissociation process of the target molecule within the nanostructure of the photoelectric conversion material-aptamer-photoelectric enhancement material. The photocurrent response of the sensor in PBS buffer is then measured and compared with the baseline photocurrent before measurement to verify the completion of the dissociation process. This yields a reusable sensor.

[0080] This application employs a nanostructure of photoelectric conversion material-aptamer-photoelectric enhancement material. When detecting biochemical molecules, it generates an enhanced photocurrent signal through minute distance changes, eliminating reliance on steric hindrance and thus exhibiting extremely high sensitivity. Compared to commonly used photoelectrochemical sensors that detect the decreased photocurrent signal due to steric hindrance in biochemical molecules, this application detects the enhanced photocurrent signal, significantly improving the accuracy of the photocurrent signal and enhancing the sensor's anti-interference capability. Furthermore, the nanostructure of photoelectric conversion material-aptamer-photoelectric enhancement material used in this application allows the distance between nanostructures to return to its pre-detection level after a single biochemical molecule detection, enabling multiple measurements with the same sensor in a short time and minimizing errors, thus significantly reducing sensor operating costs. In addition, linking the nanostructure of photoelectric conversion material-aptamer-photoelectric enhancement material to the electrode surface through self-assembly results in a more stable sensor, with the nanostructure less prone to detachment, leading to a more stable photocurrent response and higher repeatability during sensing.

[0081] In summary, the photoelectrochemical sensor provided in this application is used for the cyclic detection of biochemical molecules. While meeting the requirements for detecting small biochemical molecules, it can provide an increased photocurrent signal when detecting biochemical molecules. This increased photocurrent signal can largely avoid the influence of interfering substances. Furthermore, the sensor has extremely high sensitivity and reusability.

[0082] Based on the same concept, this application also provides an embodiment of a biochemical multi-molecule concentration detection method that follows the above embodiments. This method is applied to the photoelectrochemical sensor in the above embodiments. The interpretation of the same name is the same as that in the above embodiments, and it has the same technical effects as the above embodiments. It will not be repeated here.

[0083] Figure 7 A flowchart of the biochemical multi-molecule concentration detection method of this application is shown, such as... Figure 7As shown, the procedure includes steps S701 to S702.

[0084] Step S701: The mixture containing at least two biochemical molecules to be tested is detected by photoelectrochemical sensor.

[0085] In this embodiment, the working electrode of the photoelectrochemical sensor can be selected as the hybrid sensing electrode provided in the above embodiments.

[0086] In this hybrid sensing electrode, multiple photoelectric probes are configured to specifically bind to each of the target biochemical molecules. For example, if the mixture contains two target biochemical molecules and one other molecule, the aptamer of the photoelectric probe can specifically bind to the two target biochemical molecules, but not to the other molecule.

[0087] Step S702: Input the dataset generated during the photoelectrochemical detection process into the pre-trained machine learning model to obtain the biochemical molecule concentration detection results output by the machine learning model.

[0088] The biochemical molecule concentration detection results include the molecular concentration of each of the at least two types of biochemical molecules to be tested.

[0089] In the method provided in this application, machine learning algorithms are used to model and analyze photoelectrochemical response signals. The multi-channel signals collected by the sensor are input as a dataset into a trained machine learning model, which outputs the corresponding concentrations of multiple analyte biochemical molecules. This method combines the data-driven capabilities of artificial intelligence with the physical sensing capabilities of a photoelectrochemical platform, significantly improving the efficiency and accuracy of multi-component identification and effectively addressing the limitations of photoelectrochemical sensors in terms of poor selectivity, signal overlap, and quantification difficulties.

[0090] The biochemical multi-molecule concentration detection method provided in this application not only meets the requirements for detecting the concentration of multiple biochemical molecules, but also takes into account the requirements for detecting the concentration of a single biochemical molecule. For example, when a photoelectrochemical sensor carries a single biochemical molecule, and this biochemical molecule is one of the types that allows the aptamers in the photoelectrochemical sensor to specifically bind, the change in the enhancement amplitude of the photocurrent generated by this single biochemical molecule can be input into a specific machine learning model, so that the machine learning model outputs the concentration detection result of this biochemical molecule.

[0091] Furthermore, the concentration detection results of a single biochemical molecule can be obtained through direct mapping; the methods for detecting the concentration of a single biochemical molecule are not detailed here. Based on this, the method for detecting the concentration of a single biochemical molecule can be combined with the multi-molecule biochemical concentration detection described in this application to achieve molecular concentration detection of one or more biochemical molecules in practical applications.

[0092] In some embodiments, the machine learning model is pre-trained using training datasets for at least two different biochemical molecules to be tested.

[0093] While it's feasible to determine the concentration of a single molecule by fitting a relationship, its anti-interference ability, stability, and accuracy are not excellent, and it cannot predict the concentration of multiple molecules using a single channel. To address this issue, this application proposes using a supervised machine learning algorithm to separate the current curves of mixed sensor data and identify the concentration of each molecule in the mixed data one by one.

[0094] In this application, feature values ​​are first selected based on the characteristics of the sensor output signal. Since the photocurrent amplitude changes differently over time when the photoelectrochemical sensor detects different concentrations of different biochemical molecules, i.e., the photocurrent amplitude change curve exhibits different shapes and amplitudes over time, this application uses time and different bias voltages as feature values ​​when constructing the training set. The dataset can be a three-dimensional dataset containing time data, bias voltage data, and photocurrent amplitude data, used to characterize the relationship between bias voltage data and photocurrent amplitude data over time. Secondly, the detection target, detection accuracy, and detection range are determined based on the actual application scenario. Then, a concentration gradient for the dataset is designed based on this. Finally, the data is cleaned to eliminate data redundancy, clutter, and incompleteness, thereby improving data quality and reliability. Specifically, data cleaning addresses outliers and missing values ​​in the original data, including deleting duplicate data and filling in missing data and erroneous data that does not conform to common understanding using the median or mean.

[0095] As some feasible implementations, the algorithm can be built on a three-dimensional dataset consisting of time, bias voltage, and photocurrent amplitude. The following uses VEGF and Ang-2 as examples to illustrate the process of constructing training data and training machine learning models.

[0096] Figure 8 The diagram shows the variation of photocurrent amplitude over time at different time points.

[0097] For example, when constructing a dataset, data can be collected separately for different concentrations of biochemical molecules. In other words, during a single data acquisition, experiments are conducted with a fixed concentration of biochemical molecules, observing the relationship between photocurrent amplitude and time. Simultaneously, throughout the entire acquisition cycle, data can be collected at specific time intervals. On one hand, at a given time point after the time interval is met, the effect of different bias voltages on the photocurrent amplitude can be investigated by applying different bias voltages. On the other hand, by using different data points with the same bias voltage applied between consecutive time intervals, the relationship between photocurrent amplitude and time when the bias voltage is fixed can be investigated.

[0098] For example, as Figure 8 shown, in this embodiment, for a single experiment, the test can be performed at intervals of two minutes, and (a) to (d) respectively represent the time points "0 min", "2 min", "4 min", and "6 min" for data collection. Among them, for each test interval, the optoelectrochemical detection method is used to turn on the light, and the light is turned off after the photocurrent amplitude reaches the peak value under this test, and the tables corresponding to "0 min", "2 min", "4 min", and "6 min" as shown in Figure 8 are obtained. Since the photocurrent amplitude is zero when the light is not turned on, the photocurrent amplitude increases during the light-on period and slowly decays after reaching the peak value, and quickly returns to zero after the light is turned off, so the photocurrent amplitude data under a single test shows a "V" shape. For a single test, the peak value of the photocurrent amplitude during the light-on period can be used as the photocurrent amplitude data obtained from this test.

[0099] On this basis, to explore the influence of different biases on the photocurrent amplitude, it can be achieved by dividing each time interval into multiple time sub-intervals and performing a test in each different time sub-interval. For example, when the time reaches "2 min", the time sub-interval can be set to ten seconds, and then three biases are applied within two minutes to two minutes and ten seconds, two minutes and ten seconds to twenty seconds, and two minutes and twenty seconds to thirty seconds for three tests. It should be noted that the application does not specifically limit the duration of the sub-interval setting and the number of sub-intervals.

[0100] Finally, in the Figure 8 data collection process, a peak value of the photocurrent amplitude obtained from each test can be used as the photocurrent amplitude value of a data point shown in the chart in Figure 9 (a) or (b). For ease of understanding, the following will describe the training data in combination with Figure 9 the training data.

[0101] Figure 9 shows a schematic diagram of the training data constructed for VEGF and Ang-2.

[0102] Among them, Figure 9 the table in (a) shows the training data of VEGF, Figure 9 and the table in (b) shows the training data of Ang-2. For example, after the above data collection is completed, the training data set of each biochemical molecule to be measured includes multiple curves of the photocurrent amplitude changing with time corresponding to the biochemical molecule at different concentrations and / or different applied biases. For example, as shown in Figure 9As shown in (a), the table represents the photocurrent amplitude versus time curves for VEGF concentrations of 1 pg / ml, 10 pg / ml, 20 pg / ml, 40 pg / ml, and 60 pg / ml under a single applied bias voltage. The table on the lower right represents the photocurrent amplitude versus time curves for Ang-2 concentrations of 1 pg / ml, 10 pg / ml, 20 pg / ml, 40 pg / ml, and 60 pg / ml under a single applied bias voltage. For any given table, each data point in each curve was collected when the same bias voltage was applied. In other words, a single table contains multiple curves collected under a single bias voltage, with different curves corresponding to different molecular concentrations. In this application, a table containing photocurrent amplitude versus time curves at multiple concentrations can be generated for different bias voltages. This is equivalent to further constructing a three-dimensional data relationship on top of the two-dimensional linear relationship between photocurrent amplitude and time using different bias voltage values, and using this as the three-dimensional dataset used in this application. Based on this, the dataset covers photocurrent amplitudes corresponding to different molecules, different bias voltages, and different concentrations. Since the tables indicate the mapping relationship between the molecular concentration and the molecular concentration, the three-dimensional dataset can effectively train machine learning algorithms to recognize molecular concentrations.

[0103] Furthermore, for mixed sensing data corresponding to mixed solutions, AI algorithms can be used to extract the individual sensing data of each biochemical molecule. In this process, multiple concentration combinations can be simulated based on the constructed dataset to generate thousands of data sets, which are then used to train the model until the algorithm converges. Based on this, the model can achieve individual molecule concentration detection when faced with multi-molecule mixed sensing data, according to the training set and pre-training results. For example, for a model trained using VEGF and Ang-2 to construct a training dataset, it can extract the concentration curve of a single molecule from the mixed sensing data provided by the photoelectrochemical sensor when faced with a mixed solution containing VEGF and Ang-2, outputting the photocurrent amplitude variation curves of VEGF and Ang-2 respectively over time, and then combining the curve features to determine the corresponding molecule concentration.

[0104] As some other feasible embodiments, in addition to time data, bias voltage data, and photocurrent amplitude data, data such as temperature, light intensity, and humidity can also be used to construct the dataset. These data can replace time data, bias voltage data, and photocurrent amplitude data to construct different types of three-dimensional datasets, or serve as new data dimensions to construct more dimensional datasets based on the three-dimensional dataset. This application does not specifically limit the data dimensions of the dataset or the types of data used in the dataset.

[0105] In some embodiments, the training dataset for the machine learning model is normally distributed, and the top 20% of data that deviates most from the center value are removed. This method can improve the quality and reliability of the training data.

[0106] As some feasible embodiments, the algorithm for the machine learning model is either the quadratic rational Gaussian process regression algorithm or the three-layer wide neural network algorithm, with the quadratic rational Gaussian process regression algorithm being selected.

[0107] This application constructs a specific dataset and then uses machine learning algorithms to predict multi-molecule concentrations, which is a typical supervised regression learning approach. In preliminary experiments, the most suitable algorithms for predicting molecular concentrations using the photoelectrochemical sensor in this application were quadratic rational Gaussian process regression (QR-GPR) and a three-layer wide neural network. These two types of algorithms not only have high recognition accuracy but are also suitable for handling nonlinear relationships. The appropriate algorithm is selected based on the size of the dataset, the dimensionality of the features, the data type, whether there are labels, the type of task, and the distribution of the data. This application ultimately chose QR-GPR, which is also related to the characteristics of QR-GPR. QR-GPR is an extension of Gaussian process regression, employing a quadratic rational kernel to characterize the latent structure of the data. QR-GPR can capture more complex nonlinear relationships in the data, especially when there are proportional relationships or large relative changes between data points, exhibiting good fitting ability, flexibility, and scalability. Therefore, QR-GPR is suitable for datasets with complex nonlinear relationships, proportional changes in data, complex data distribution, irregular fluctuations, and small sample sizes. After training the model on the computer, we used MATLAB's Embedded Coder to convert the model and its trained built-in parameters into C code for deployment on the chip of the detection instrument. Ultimately, the concentration detection of single molecules achieved an error of ±5 pg / mL.

[0108] Although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0109] The methods, apparatus, devices, and storage media of this application can be implemented using standard programming techniques, and various method steps can be implemented using rule-based logic or other logic. It should also be noted that the terms "apparatus" and "module" as used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0110] Any step, operation, or procedure described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product comprising a computer-readable medium containing computer program code, which is executable by a computer processor to perform any or all of the described steps, operations, or procedures.

[0111] The foregoing description of implementations of this application has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this application to the exact forms disclosed. Various modifications and variations may exist in accordance with the foregoing teachings, or may arise from practice of this application. These embodiments were chosen and described to illustrate the principles of this application and its practical application, enabling those skilled in the art to utilize this application in various implementations and modifications to suit the specific purpose of the concept.

[0112] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0113] It can be further understood that, unless otherwise specified, "connection" includes both direct connections where no other components exist between the two parties and indirect connections where other components exist between them.

[0114] It is further understood that although the operations are described in a specific order in the accompanying drawings in the embodiments of this application, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0115] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the field of this application that are not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0116] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

[0117] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such 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 this application.

Claims

1. A biochemical method for detecting the concentration of multiple molecules, characterized in that, The method includes: A photoelectrochemical sensor is used to perform photoelectrochemical detection on a mixture containing at least two target biochemical molecules; wherein the aptamer of the photoelectrochemical sensor is configured to specifically bind only to each target biochemical molecule. The dataset generated during the photoelectrochemical detection process is input into a pre-trained machine learning model to obtain the biochemical molecule concentration detection results output by the machine learning model. The biochemical molecule concentration detection result includes the molecular concentration of each of the at least two biochemical molecules to be tested. The algorithm used in the machine learning model is either a quadratic rational Gaussian process regression algorithm or a three-layer wide neural network algorithm. The photoelectrochemical sensor includes: The working electrode has a photoelectric probe disposed on its surface. The photoelectric probe is composed of a photoelectric conversion material, an aptamer, and a photoelectric enhancement material, and the photoelectric conversion material and the photoelectric enhancement material are connected through the aptamer. Specifically, the specific binding of the biochemical molecule to be tested with the aptamer causes a change in spatial structure, which in turn changes the distance between the photoelectric conversion material and the photoelectric enhancement material, thereby enhancing the photocurrent signal of the photoelectric enhancement material to the photoelectric conversion material. The change in photocurrent caused by the change in the amplitude of the photocurrent enhancement can be used to detect the concentration of the biochemical molecule to be tested.

2. The biochemical multi-molecule concentration detection method according to claim 1, characterized in that, The aptamer is made of one of the following materials: Nucleic acid aptamers of arbitrary length composed of DNA; Nucleic acid aptamers of arbitrary length composed of RNA; A polypeptide aptamer of arbitrary length composed of oligopeptides.

3. The method according to claim 1, characterized in that, The dataset is a three-dimensional dataset containing time data, bias voltage data, and photocurrent amplitude data, used to characterize the relationship between bias voltage data and photocurrent amplitude data and time. The machine learning model is pre-trained using the training datasets of each of the at least two biochemical molecules to be tested. The training dataset for each type of biochemical molecule to be tested includes: Multiple photocurrent amplitude curves over time corresponding to different concentrations of biochemical molecules and / or when different bias voltages are applied.

4. The method according to claim 1, characterized in that, The training dataset for the machine learning model is normally distributed, and the top 20% of data that deviates most from the center value are removed.

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