A uric acid detection system based on BIC supersurface-enhanced Raman spectroscopy

Abstract

The present invention provides a uric acid detection system based on BIC metasurface-enhanced Raman spectroscopy, comprising a Raman excitation module, a resonant metasurface, a SERS functional layer, a signal acquisition module, and a data processing module. The resonant metasurface is composed of a periodically arranged asymmetric array of dielectric nanopillars, with a filter wavelength matching the excitation wavelength of uric acid Raman spectroscopy, used to excite a high-Q quasi-BIC mode to enhance the optical signal. The SERS functional layer is an array of noble metal nanoparticles modified on the surface of the resonant metasurface, used to synergistically enhance the Raman signal through electromagnetic fields and chemical enhancement. The system supports direct detection of saliva or sweat samples without pretreatment, combining high sensitivity, high specificity, and portability, and can be expanded for non-invasive and rapid detection of multiple biochemical indicators.

Description

Technical Field

[0001] The present invention relates to the field of micro-nano optical technology, and in particular to a uric acid detection system based on BIC supersurface enhanced Raman spectroscopy. Background Art

[0002] Uric acid is the end product of purine metabolism in the human body, and its concentration in the blood is an important indicator of human health. Testing blood uric acid concentrations plays a crucial role in early detection of hyperuricemia, preventing gout attacks, monitoring renal function, and assessing cardiovascular disease risk.

[0003] Traditional uric acid detection methods, such as the uricase-peroxidase coupling method and the uricase-ultraviolet spectrophotometry method, can meet clinical detection needs to a certain extent, but still have some limitations, such as dependence on biochemical reactions, high equipment complexity, inability to perform non-invasive detection, susceptibility to interference, and the need to improve sensitivity.

[0004] Raman spectroscopy, with its advantages of molecular fingerprint recognition, high specificity, and the absence of complex sample pretreatment, can rapidly and accurately detect uric acid concentrations with high reliability, showing promising prospects for uric acid concentration detection. However, it is limited by its weak Raman signal, making it difficult to detect uric acid in low-concentration samples. To address this, some researchers have employed surface-enhanced Raman scattering (SERS) technology, using metal nanoparticles to enhance the Raman signal. However, this method still suffers from random hotspot distribution and poor reproducibility.

[0005] Therefore, there is an urgent need for a uric acid detection system with high detection sensitivity to achieve efficient and accurate detection of uric acid concentration and provide a powerful tool for clinical disease diagnosis and treatment. Summary of the Invention

[0006] To solve the above problems, the present invention provides a uric acid detection system based on BIC supersurface enhanced Raman spectroscopy, comprising:

[0007] A resonant metasurface for enhancing optical signals; the resonant metasurface is composed of a periodically arranged asymmetric array of dielectric nanopillars, and the filtering wavelength of the resonant metasurface matches the excitation wavelength of uric acid Raman spectroscopy;

[0008] The resonant metasurface comprises a plurality of periodically arranged metasurface units; each of the metasurface units comprises four nanopillars; the nanopillars are rectangular parallelepipeds with a square bottom surface; the nanopillars are subwavelength structures; and the material of the nanopillars is a high refractive index material greater than 1.3;

[0009] A SERS functional layer is used to enhance Raman signals; the SERS functional layer is a noble metal nanoparticle array modified on the surface of the resonant metasurface; the diameter of the noble metal nanoparticles is 20-50 nm, and the spacing between the noble metal nanoparticles in the array is ≤5 nm;

[0010] A Raman excitation module, configured to emit laser light to illuminate the resonant metasurface and the sample to be tested thereon, thereby exciting uric acid molecules in the sample to be tested to generate Raman signals on the resonant metasurface, wherein the wavelength of the laser light is consistent with the filter wavelength;

[0011] A signal acquisition module is used to collect the Raman signal through a semiconductor photodetector or a photomultiplier tube, and convert it into an electrical signal to transmit to the data processing module;

[0012] The data processing module is used to calculate the uric acid concentration in the sample to be tested based on the Raman signal provided by the signal acquisition module.

[0013] In some embodiments, the material of the nanorods is silicon, titanium dioxide, or silicon nitride.

[0014] In some embodiments, the filtering wavelength of the resonant metasurface is 785 nm.

[0015] In some more specific embodiments, the period length of the metasurface unit is 680 nm, the height of the nanopillar is 140 nm, and the bottom side length is 220 nm; the four nanopillars in the metasurface unit are moved toward the center position, and the lateral and longitudinal movement distances are both 40 nm.

[0016] In some embodiments, the method of modifying the noble metal nanoparticles on the resonant metasurface is any one of electron beam sputtering, self-assembly, chemical bonding, and nanosphere lithography.

[0017] In some embodiments, the noble metal is gold, silver, or a gold-silver alloy.

[0018] In some embodiments, the Raman excitation module includes a semiconductor laser, a collimating lens, an objective lens, a reflector, and a polarization controller; the polarization controller includes a linear polarizer and a quarter-wave plate.

[0019] In some embodiments, the calculation of the uric acid concentration in the sample based on the Raman signal provided by the signal acquisition module is implemented by a Raman spectroscopy analysis algorithm, and the Raman spectroscopy analysis algorithm includes:

[0020] A baseline correction algorithm is used to remove the fluorescence background by using an adaptive iterative weighted penalized least squares method;

[0021] Noise reduction algorithm, used to filter out high-frequency noise using wavelet transform;

[0022] Smoothing algorithm for smoothing spectra based on Savitzky-Golay filter;

[0023] Characteristic peak extraction algorithm for determining uric acid 1330 cm based on the second-order derivative after smoothing with Savitzky-Golay filter -1 and 640 cm -1 Characteristic peak integrated intensity;

[0024] A convolutional neural network concentration inversion algorithm is used to determine the uric acid concentration based on the integral intensity of the characteristic peak.

[0025] In some more specific embodiments, the convolutional neural network concentration inversion algorithm is trained based on the ResNet-18 architecture, and the training sample features are pre-processed 1330 cm -1 and 640 cm -1 Characteristic peak integrated intensity matrix, labeled as uric acid concentration value.

[0026] In some embodiments, the sample to be tested is saliva or sweat that has not been pretreated.

[0027] The uric acid detection system provided by this invention significantly enhances the Raman scattering efficiency and signal repeatability in uric acid detection by combining a BIC metasurface with a SERS substrate, enabling efficient and accurate detection of uric acid concentration. This system combines high sensitivity, high specificity, and portability, overcoming many limitations of existing uric acid detection technologies and promoting the development of the field. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 This is a schematic diagram of the structure of the uric acid detection system provided by the present invention;

[0030] Figure 2 An optical path diagram of a Raman excitation module provided by the present invention;

[0031] Figure 3 A schematic diagram of a metasurface unit structure provided by the present invention;

[0032] Figure 4 A top view of a metasurface unit provided by the present invention;

[0033] Figure 5A schematic diagram of the BIC position of a resonant metasurface provided by the present invention;

[0034] Explanation of symbols: 1-metasurface unit, 2-nanopillar. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings.

[0036] When light waves interact with certain dielectric subwavelength structures that meet certain size conditions, electric or magnetic resonance phenomena will occur (electromagnetic resonance may also exist simultaneously). Common types of resonance include Mie resonance, Fano resonance, bound state in the continuum (BIC), etc.

[0037] Optical metasurfaces are highly sensitive optical biosensors based on the BIC principle. As a novel morphological material with a two-dimensional artificial periodic structure, it possesses subwavelength-scale characteristic dimensions and, through design, can manipulate information such as the phase, amplitude, and polarization of light waves. When the resonant spectrum overlaps with the absorption fingerprint, the enhanced molecule-resonator coupling causes a shift in the resonant frequency or intensity, enabling the extraction of the molecular fingerprint. This overlap can be achieved by varying the metasurface's structural parameters and displacement parameters, significantly enhancing the spectral signal. Compared to traditional optical devices, metasurfaces offer smaller dimensions, higher resolution, and ultra-thin thickness. Furthermore, they are compatible with microelectronics manufacturing technologies and are easier to integrate into miniaturized optoelectronic devices. With the recent development of nanotechnology and micro-nano optics, metasurfaces, as artificial micro-nano structures with unique electromagnetic properties, have provided new insights and methods for bioassays. They have shown great potential in a variety of applications, including biological sciences, clinical medicine, and environmental monitoring, opening up new possibilities for breaking bottlenecks in bioassays and other industries.

[0038] Applicants have discovered that by disrupting the symmetry of a metasurface unit, a symmetrically protected quasi-BIC mode with high-quality (Q) resonance can be generated. By combining the BIC metasurface with a SERS substrate, the high-Q localized field of the BIC is utilized to enhance Raman scattering efficiency. Simultaneously, signal repeatability is improved through the ordered nanostructure, enabling noninvasive detection of uric acid with picosecond sensitivity. Based on this, the present invention utilizes the high-Q characteristics of a quasi-BIC metasurface in conjunction with an ordered noble metal array to enhance Raman signal repeatability and achieve high-sensitivity noninvasive detection of uric acid.

[0039] Based on the above concept, the present invention designs a uric acid detection system based on BIC super surface enhanced Raman spectroscopy signal. The following is a detailed description of the functions and structures of the various parts of the uric acid detection system provided by the present invention in conjunction with the accompanying drawings. Figure 1 As shown, the uric acid detection system provided by the present invention includes a Raman excitation module, a resonant metasurface, a SERS functional layer, a signal acquisition module, and a data processing module. To perform uric acid testing using this system, simply place the sample on the resonant metasurface, place it in the system's detection area, and turn on the switch to complete the uric acid concentration test. Furthermore, the system supports direct testing of saliva or sweat samples without pretreatment, making it very convenient.

[0040] The uric acid detection system provided by the present invention detects uric acid concentration based on Raman spectroscopy. Therefore, the system uses the excitation wavelength of uric acid Raman spectroscopy as the working wavelength. The excitation wavelength of uric acid Raman spectroscopy is 532 nm or 785 nm. For the convenience of description, 785 nm is selected as the working wavelength in the following.

[0041] First, let's introduce the Raman excitation module. The Raman excitation module is used to emit laser light to illuminate the resonant metasurface and the sample on it, thereby exciting the uric acid molecules in the sample to generate Raman signals on the resonant metasurface.

[0042] The optical path diagram of the Raman laser module is as follows Figure 2 As shown, it includes a semiconductor laser, a collimating lens, an objective lens, a reflector, and a polarization controller. The output wavelength of the semiconductor laser is 785 nm. The collimating lens is a parabolic mirror or an aspheric lens, which is used to compress the laser beam diameter to 2 nm. The reflectors are used to adjust and optimize the optical path, and there are two of them. The polarization controller includes a linear polarizer and a quarter-wave plate to match the TE polarization requirements of the BIC mode.

[0043] Having introduced the Raman excitation module above, we will now introduce the resonant metasurface, which is used to amplify optical signals of a target wavelength.

[0044] The resonant metasurface includes a plurality of periodically arranged metasurface units 1, each of which has the same structure. The structure of the metasurface unit 1 is as follows: Figure 2 and Figure 3As shown, each metasurface unit 1 includes four nanopillars 2 arranged in a 2×2 array. Each nanopillar 2 is a rectangular parallelepiped with a square bottom and has identical structural parameters. The nanopillars 2 are subwavelength structures, and their specific size and spacing are used to adjust the filtering wavelength. The filtering wavelength of the resonant metasurface is 785nm. The nanopillars 2 are made of a high-refractive-index material with a refractive index greater than 1.3, such as silicon, titanium dioxide, or silicon nitride. The nanopillars 2 are made of a high-refractive-index, low-loss dielectric material within the operating band, which facilitates processing and facilitates the generation of resonant modes within nanostructures with sufficiently low aspect ratios.

[0045] The above describes the resonant metasurface. Next, we will introduce the SERS functional layer, which is used to enhance the Raman signal.

[0046] The SERS functional layer is an array of noble metal nanoparticles modified on the surface of the resonant metasurface. These modified gold nanoparticles form ordered SERS hotspots on the resonant metasurface, enhancing the Raman signal. The noble metal can be gold, silver, or a gold-silver alloy; the diameter of the noble metal nanoparticles is 20–50 nm, and the spacing between the noble metal nanoparticles in the array is ≤5 nm. The noble metal nanoparticles can be modified on the resonant metasurface by electron beam sputtering, self-assembly, chemical bonding, or nanosphere photolithography.

[0047] The above introduces the SERS functional layer. The following briefly introduces how the resonant metasurface and the SERS functional layer synergistically enhance the Raman signal. When the metasurface unit 1 of the metasurface interacts with the 785 nm incident light wave output by the Raman excitation module, the resonance peak position corresponds to a lower transmittance, and both sides of the resonance peak have higher transmittances, thereby amplifying the light signal of the target wavelength; and when the SERS functional layer is formed by modifying the gold nanoparticles on the surface of the metasurface, surface enhanced Raman scattering can be achieved, making the Raman signal excited by the incident light stronger.

[0048] The above introduces the Raman excitation module, resonant metasurface and SERS functional layer. The signal acquisition module is introduced below.

[0049] The signal acquisition module collects the Raman signal after the light source passes through the BIC+SERS metasurface reaction system and converts it into an electrical signal for transmission to the data processing module. The signal acquisition module includes a semiconductor photodetector or photomultiplier tube and a narrowband bandpass filter.

[0050] The above introduces the signal acquisition module, and the following introduces the data processing module.

[0051] The data processing module is used to calculate the uric acid concentration in the sample to be tested based on the Raman signal provided by the signal acquisition module.

[0052] The data processing module uses a Raman spectroscopy analysis algorithm to calculate the uric acid concentration in the sample to be tested. The Raman spectroscopy analysis algorithm includes:

[0053] A baseline correction algorithm is used to remove the fluorescence background by using an adaptive iterative weighted penalized least squares method;

[0054] Noise reduction algorithm, used to filter out high-frequency noise using wavelet transform (Daubechies 4 basis functions);

[0055] Smoothing algorithm for smoothing spectra based on Savitzky-Golay filter;

[0056] The characteristic peak extraction algorithm was used to determine the 1330 cm-1 peak of uric acid based on the second-order derivative after smoothing with a Savitzky-Golay filter. -1 (CN bond) and 640 cm -1 (Purine ring deformation) characteristic peak integrated intensity;

[0057] The convolutional neural network concentration inversion algorithm is used to determine the uric acid concentration based on the integral intensity of the characteristic peak. The convolutional neural network concentration inversion algorithm is a deep learning algorithm trained based on the ResNet-18 architecture. The training sample features are pre-processed 1330 cm -1 and 640 cm -1 Characteristic peak integrated intensity matrix, labeled as uric acid concentration value.

[0058] In order to make the specific implementation and effects of the present invention clearer, a specific example of the practical application of the resonant metasurface in a uric acid detection system is provided below.

[0059] In this embodiment, the device operates in the near-infrared band, with 785 nm selected as the operating wavelength. The incident light is linearly polarized along the x-direction. The height H of the metasurface is set to 140 nm. The metasurface unit 1 is composed of four rectangular nanorods 2 of the same size. Figure 4 As shown, the period length of the metasurface unit is denoted as P, and the cross-sectional side lengths of the nanopillar 2 are denoted as a and b.

[0060] The four nanopillars 2 in the metasurface unit 1 move closer to the center position, with a horizontal movement distance of g1 and a vertical movement distance of g2. The displacement parameters The purpose of introducing the displacement parameter is to break the symmetry of the metasurface unit structure, allowing the incident light wave to excite the symmetry-protected continuum bound states, or BICs, within it. The magnitude of the displacement parameter Δg determines the quality factor, or Q, of the electromagnetic BICs resonance generated when the light wave interacts with the metasurface. The asymmetry parameter α = Δg / g, and the asymmetry parameter α and Q satisfy the following relationship:

[0061] Q∝α-2

[0062] From this relationship, it can be seen that the smaller the Δg value, the larger the Q value. In order to make the resonance peak of the metasurface overlap with the sensitive wavelength of uric acid and blood glucose, in this embodiment, g1=g2=40 nm.

[0063] In this embodiment, the material of the nanopillars 2 is single-crystal silicon, which has a refractive index of 3 at the operating wavelength. When using electromagnetic simulation software to scan and optimize the dimensional parameters of the metasurface unit, it is necessary to adjust the structural parameters and displacement parameters so that the position of the resonance peak moves toward the excitation wavelength of uric acid Raman spectroscopy. During the simulation process, it was observed that changes in the parameters P, a, b, H and Δg will cause the resonance wavelength of the electric and magnetic BICs to shift. By optimizing the size of the nanopillars to achieve resonance wavelength matching, the parameters were finally adjusted so that the dimensional parameters of the BICs-based resonant metasurface were H = 140nm, P = 680nm, a = 220nm, b = 220nm, g1 = 40nm, and g2 = 40nm when the resonance peak position was 785nm.

[0064] Finally, the effect of the metasurface system designed in this embodiment for enhancing Raman spectroscopy signal detection of uric acid concentration was obtained through electromagnetic simulation. Figure 5 shown. Figure 5 Figure 2 shows the variation of the metasurface light wave transmission coefficient with wavelength. The results show that the resonance peak is located at 785 nm, and the transmission coefficient is less than 0.01. This indicates that the metasurface system designed in this embodiment can be used to enhance the spectral signal at 785 nm.

[0065] It can be seen that the uric acid detection system provided by the present invention has the following beneficial effects compared with the prior art:

[0066] (1) The high-Q resonance of the metasurface system designed in the present invention for uric acid concentration detection is spectrally clean without additional resonance background, which allows highly spectrally selective enhancement of spectrally rich uric acid concentration signals.

[0067] (2) The metasurface unit of the present invention has a sub-wavelength size, is smaller in size and lighter in weight. When applied to uric acid concentration detection, it has the advantage of high integration compared with existing uric acid detection methods.

[0068] (3) The ingeniously designed BIC metasurface and SERS substrate of the present invention can significantly enhance the Raman scattering efficiency and signal repeatability in uric acid detection, making it easier for detection equipment to detect signal changes, greatly improving the sensitivity of detection, and facilitating early detection of abnormal fluctuations in uric acid levels.

[0069] The uric acid detection system provided by the present invention has high sensitivity, high specificity and portability, can overcome many limitations of existing uric acid detection technology, realize efficient and accurate detection of uric acid concentration, and promote the development of the field of uric acid detection.

[0070] In the description of the embodiments of the present application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of the present application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0071] In the description of the embodiments of this application, the term "and / or" is merely a description of an association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent the following three situations: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the term "plurality" means two or more.

[0072] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly identifying the technical features being referred to. Thus, features specified as "first" or "second" may explicitly or implicitly include one or more of such features. The terms "include," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0073] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A uric acid detection system, characterized in that: include: A resonant metasurface for enhancing optical signals; the resonant metasurface is composed of a periodically arranged asymmetric array of dielectric nanopillars, and the filtering wavelength of the resonant metasurface matches the excitation wavelength of uric acid Raman spectroscopy; The resonant metasurface comprises a plurality of periodically arranged metasurface units; each of the metasurface units comprises four nanopillars; the nanopillars are rectangular parallelepipeds with a square bottom surface; the nanopillars are subwavelength structures; and the material of the nanopillars is a high refractive index material greater than 1.3; A SERS functional layer is used to enhance Raman signals; the SERS functional layer is a noble metal nanoparticle array modified on the surface of the resonant metasurface; the diameter of the noble metal nanoparticles is 20-50 nm, and the spacing between the noble metal nanoparticles in the array is ≤5 nm; A Raman excitation module, configured to emit laser light to illuminate the resonant metasurface and the sample to be tested thereon, thereby exciting uric acid molecules in the sample to be tested to generate Raman signals on the resonant metasurface, wherein the wavelength of the laser light is consistent with the filter wavelength; A signal acquisition module is used to collect the Raman signal through a semiconductor photodetector or a photomultiplier tube, and convert it into an electrical signal to transmit to the data processing module; The data processing module is used to calculate the uric acid concentration in the sample to be tested based on the Raman signal provided by the signal acquisition module.

2. The uric acid detection system according to claim 1, characterized in that: The material of the nanocolumns is silicon, titanium dioxide or silicon nitride.

3. The uric acid detection system according to claim 1, characterized in that: The filtering wavelength of the resonant metasurface is 785 nm.

4. The uric acid detection system according to claim 1 or 3, characterized in that: The period length of the metasurface unit is 680 nm, the height of the nanopillar is 140 nm, and the bottom side length is 220 nm; the four nanopillars in the metasurface unit move closer to the center position, and the horizontal and vertical movement distances are both 40 nm.

5. The uric acid detection system according to claim 1, characterized in that: The method for modifying the noble metal nanoparticles on the resonant super surface is any one of electron beam sputtering, self-assembly, chemical bonding, and nanosphere photolithography.

6. The uric acid detection system according to claim 1, characterized in that: The noble metal is gold, silver or a gold-silver alloy.

7. The uric acid detection system according to claim 1, characterized in that: The Raman excitation module includes a semiconductor laser, a collimating lens, an objective lens, a reflector, and a polarization controller; the polarization controller includes a linear polarizer and a quarter-wave plate.

8. The uric acid detection system according to claim 1, characterized in that: The calculation of the uric acid concentration in the sample based on the Raman signal provided by the signal acquisition module is achieved by a Raman spectroscopy analysis algorithm, which includes: A baseline correction algorithm is used to remove the fluorescence background by using an adaptive iterative weighted penalized least squares method; Noise reduction algorithm, used to filter out high-frequency noise using wavelet transform; Smoothing algorithm for smoothing spectra based on Savitzky-Golay filter; Characteristic peak extraction algorithm for determining uric acid 1330 cm based on the second-order derivative after smoothing with Savitzky-Golay filter -1 and 640 cm -1 Characteristic peak integrated intensity; A convolutional neural network concentration inversion algorithm is used to determine the uric acid concentration based on the integral intensity of the characteristic peak.

9. The uric acid detection system according to claim 8, characterized in that: The convolutional neural network concentration inversion algorithm is trained based on the ResNet-18 architecture, and the training sample features are preprocessed 1330 cm -1 and 640 cm -1 Characteristic peak integrated intensity matrix, labeled as uric acid concentration value.

10. The uric acid detection system according to claim 1, characterized in that: The sample to be tested is saliva or sweat that has not been pretreated.

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