Surface-enhanced Raman scattering detection platform based on gradient nano 3D structure and preparation method and application thereof
By using gradient nano 3D structure on the SERS detection platform, metal nanosheets with continuous changing areas are formed, the problems of signal variability and inconsistency in SERS technology are solved, and high-precision and high-rolean quantitative analysis is achieved.
Patent Information
- Application Number
- CN202510370715.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-27
- Publication Date
- 2025-06-27
Smart Images
Figure CN120213890A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of micro-nano structures, and relates to a surface-enhanced Raman scattering detection platform based on a gradient nano 3D structure, a preparation method thereof, and an application Background Art
[0002] Surface-enhanced Raman scattering (SERS), as a highly sensitive analytical technique, has attracted much attention because it can detect analytes at low concentrations. SERS enhances the Raman signal by several orders of magnitude through the local surface plasmon resonance (LSPR) effect of metal nanostructures, thus enabling trace detection of molecules. This technique has made significant progress in fields such as chemical sensing, biodiagnostics, environmental monitoring, and forensic science. However, the SERS signal is extremely sensitive to experimental conditions and environmental changes, and has inherent variability, which makes it a challenge to achieve reliable and consistent quantitative analysis. The main source of SERS signal variability lies in the uneven distribution of "hot spots" on the nanostructured substrate - these hot spots are regions of enhanced electromagnetic fields, which are usually unevenly distributed and difficult to reproduce, resulting in signal fluctuations, thus affecting the accuracy and reproducibility of detection. In addition, external factors such as unstable laser power, substrate degradation, and local environmental changes further exacerbate the inconsistency of SERS measurement results. Solving these problems is crucial for transforming SERS from a qualitative tool into a robust quantitative method suitable for practical applications
[0003] To improve the accuracy and reliability of SERS-based quantitative analysis, research has mainly focused on the design and preparation of nanostructured substrates. In recent years, significant progress has been made in substrate engineering. For example, Yoshiki et al. developed an AuNPs / TiO2 / Au thin film substrate, which achieved a 78-fold enhancement of the detection signal for crystal violet through the modal supercoupling of local surface plasmon resonance and Fabry-Perot nanocavities. Similarly, three-dimensional and tunable nanostructures (such as ring cavity arrays and gold nanoring arrays) have been shown to significantly increase the hot spot density and uniformity, while innovative designs (such as the wrinkled nanocone substrate fabricated on a PET film) have achieved ultrasensitive detection of TNT at a concentration as low as 10 -13 mol / L. In addition, methods such as the layered double hydroxide (LDH) porous film prepared on a gold nanorod array or the substrate based on flexible carbon cloth provide scalable and cost-effective solutions, further expanding the diversity of SERS in practical applications
[0004] Despite the significant progress of SERS technology, traditional single-spectrum analysis-based quantitative methods still face many challenges. Single-spectrum prediction is extremely vulnerable to random interference, environmental noise, and the inherent variability of experimental conditions, resulting in inconsistent and unreliable results, thus weakening the accuracy and reproducibility required for practical applications Summary of the Invention
[0005] In view of the deficiencies of the prior art, the present invention proposes a preparation method for a surface-enhanced Raman scattering detection platform based on a gradient nano-3D structure. By creating nanospherical shells with continuously varying areas in a low-cost and efficient parallel manner, the variability in the preparation process is eliminated. This unique substrate design allows for the simultaneous measurement of multiple characteristic spectra under the same experimental conditions, reducing the variability in the data acquisition process. The gradient nanostructure generates position-specific spectral responses, enabling the collection of diverse spectral features for a single analyte concentration, providing new ideas for achieving high-precision and high-robustness SERS quantitative analysis.
[0006] A preparation method for a surface-enhanced Raman scattering detection platform based on a gradient nano-3D structure includes the following steps:
[0007] S1: Set a hexagonal close-packed array of nanospheres self-assembled from polymer nanospheres on a PDMS film to obtain a PDMS film arranged with nanospheres.
[0008] S2: Keeping the hexagonal close-packed array of nanospheres facing outward, place the PDMS film arranged with nanospheres on an arch block with a curved surface, such that the shape of the PDMS film arranged with nanospheres is the same as that of the surface of the arch block, to obtain an arch block with a hexagonal close-packed array of nanospheres having a curved surface structure on its surface.
[0009] S3: Place the arch block with a hexagonal close-packed array of nanospheres having a curved surface structure on a horizontal magnetron sputtering sample stage, place the metal target surface horizontally, and make the metal target surface located directly above the arch block with a hexagonal close-packed array of nanospheres having a curved surface structure on its surface.
[0010] Perform metal deposition on the hexagonal close-packed array of nanospheres having a curved surface structure by magnetron sputtering. Utilize the continuously varying shielding effect of the nanospheres in the hexagonal close-packed array of nanospheres having a curved surface structure on the metal deposition, such that the morphology of the metal nanosheets deposited on the surface of the nanospheres changes continuously along with the continuous gradient of the curved surface, forming a position-specific spectral response region, realizing the controllable evolution of the morphology of the metal nanosheets along the gradient direction, enhancing the Raman signal by the metal nanosheets, and realizing position-dependent plasmon coupling, thereby affecting the optical and SERS performance.
[0011] S4: After the magnetron sputtering is completed, remove the PDMS film arranged with nanospheres from the surface of the arch block and flatten it to obtain a surface-enhanced Raman scattering detection platform based on a gradient nano-3D structure. The gradient nano-3D structure can simultaneously measure multiple characteristic spectra under the same experimental conditions, reducing the variability in the data acquisition process.
[0012] Preferably, in S2, the arch block with a curved surface is specifically an arch block with a spherical surface.
[0013] Preferably, in S3, the specific process of the magnetron sputtering includes: a vacuum degree of 5×10 -4 Pa, argon gas is introduced, the power is set to 10 W, and the deposition time of the deposited metal Ag is 60 s.
[0014] Preferably, in S1, the polymer nanospheres are specifically polystyrene (PS) nanospheres.
[0015] Preferably, the size of the nanospheres and the shape of the surface of the arch block are adjusted to realize the adjustment of the surface structure of the surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure.
[0016] The present invention also provides a surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure prepared by the described preparation method.
[0017] The present invention also provides a method for determining the concentration of 4-mercaptobenzoic acid by applying the surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure, including the following steps:
[0018] Obtain a series of 4-mercaptobenzoic acid solutions with known concentrations as calibrators. For each calibrator:
[0019] Immerse the surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure into the calibrator and then take it out;
[0020] Select several fixed points on the surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure, collect the Raman spectra of these fixed points, and send them into a machine learning model; use the known concentration of the calibrator as a label to perform supervised training on the machine learning model to obtain a trained machine learning model;
[0021] Immerse the surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure into the 4-mercaptobenzoic acid solution with the concentration to be measured and then take it out; collect the Raman spectra of the fixed points and send them into the trained machine learning model to obtain the concentration prediction value of the 4-mercaptobenzoic acid solution with the concentration to be measured.
[0022] In the surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure prepared by the present invention, the order degree is high, the uniformity is good, and the repeatability is strong. The preparation method is relatively simple, the preparation period is short, and it can be effectively replicated and applied on a large scale, broadening the operable space for subsequent nanostructures. The gradient nano structure generates a position-specific spectral response, which can collect diverse spectral features for a single analyte concentration, providing a new idea for realizing high-precision and high-robustness SERS quantitative analysis. Description of the Drawings
[0023] Figure 1Schematic diagram of the preparation process of a surface-enhanced Raman scattering detection platform based on a gradient nano 3D structure. Among them, the gradient nano 3D structure is specifically a gradient nano-spherical shell structure. On the surface of the surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure, there is an array of nano-spherical shells made of Ag material.
[0024] Figure 2 Optical microscope images of the nano-spherical shell arrays at different positions on a single substrate.
[0025] Figure 3 SEM images of the nano-spherical shell arrays at different positions on a single substrate.
[0026] Figure 4 Reflection spectrum of the fabricated surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure.
[0027] Figure 5 Raman spectrum of the fabricated surface-enhanced Raman scattering detection platform based on the gradient nano 3D structure
[0028] Figure 6 Hot spot distribution of the nano-spherical shell arrays at different positions on a single substrate obtained by FDTD simulation. Specific implementation mode
[0029] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation modes.
[0030] The preparation method concept provided in this application is as follows:
[0031] Prepare a highly ordered polystyrene (PS) nano-sphere array on a polydimethylsiloxane (PDMS) substrate by self-assembly method;
[0032] Attach the PDMS film with arranged PS nano-spheres to the surface of a 3D printed arched block;
[0033] Vertically deposit a layer of noble metal on the surface of the arched block attached with the PDMS film with PS nano-spheres by magnetron sputtering. Due to the shadow effect of the PS nano-spheres, noble metal films with different areas are formed on the surfaces of the PS nano-spheres on the PDMS substrate. Based on the template effect of the PS nano-spheres, the noble metal film is actually an array of nano-spherical shells made of noble metal material that continuously changes according to a gradient law;
[0034] Remove the PDMS film deposited with noble metal from the arched block to obtain a gradient nano-spherical shell array on the substrate.
[0035] As Figure 1 shown, the preparation method of the gradient nano-spherical shell array in this embodiment includes the following steps:
[0036] 1) Clean and perform plasma treatment on the polydimethylsiloxane (PDMS) film to make its surface hydrophilic and cut it into a rectangle. Subsequently, use the gas-liquid interface self-assembly technique to arrange a polystyrene (PS) colloidal sphere template array on the PDMS surface to form a hexagonal close-packed structure;
[0037] Use the self-assembly method to form a monolayer hexagonal close-packed array of polystyrene (PS) nanospheres, and then transfer the PS nanospheres to a polydimethylsiloxane (PDMS) substrate; the diameter of the polystyrene nanospheres is 500 nm.
[0038] 2) Attach the PDMS film with the arranged PS nanospheres to the surface of the 3D-printed arch block; the 3D-printed arch block selected is a hemispherical arch block with a radius of 2 cm
[0039] 3) Use magnetron sputtering for deposition. Fix the arch block on a horizontal sample stage, evacuate the vacuum to 5×10 -4 Pa, introduce argon gas, set the power to 10 W, and the deposition time of Ag is 60 s;
[0040] 4) Remove the PDMS film on the arch block and flatten it to obtain a gradient silver nanosphere shell array.
[0041] Quantitative methods based on single-spectrum analysis still have many limitations. Single-spectrum prediction is extremely vulnerable to random interference, environmental noise, and inherent variations in experimental conditions, resulting in inconsistent and unreliable results, thus weakening the accuracy and reproducibility required by SERS technology in practical applications. The present invention creates metal nanosheets with continuously varying areas in a low-cost and efficient parallel manner through a hexagonal close-packed array of nanostructures on a curved surface, eliminating the variability in the preparation process. This unique substrate design allows multiple characteristic spectra to be measured simultaneously under the same experimental conditions, reducing the variability in the data acquisition process. The gradient nanostructure can generate position-specific spectral responses, providing diverse spectral features for a single analyte concentration, thus significantly improving the accuracy and robustness of SERS quantitative analysis.
[0042] Perform optical microscope observations on different positions of the surface-enhanced Raman scattering detection platform based on the gradient nano-3D structure obtained. As Figure 2 shown, color gradient changes on the substrate can be observed; further use SEM to observe different positions on a single substrate. As Figure 3 shown, gradient changes in the area of the silver nanosheets can be seen. Further, we test the reflection spectra of different positions on a single substrate sample. As Figure 4 shown, when x c = 0, an obvious resonance dip is observed at λ = 584 nm, which is attributed to the strong LSPR generated by the complete coverage of the Ag nanosheets. As x cWith the increase of , the resonance decreases and shows a progressive blue shift, reflecting the reduction of plasmon coupling due to the incomplete and asymmetric Ag nanosheet structure. The blue shift of the resonance wavelength is directly related to the decrease of the Ag nanosheet coverage. Raman spectra were measured at different positions of a single substrate sample, such as Figure 5 as shown, it can be observed that the Raman detection signal also shows corresponding intensity gradient changes, realizing multi-spectral acquisition on a single substrate.
[0043] In addition, FDTD was used to simulate different positions of the gradient nanosheet array on a single substrate, such as Figure 6 as shown, it can be seen that high-energy hot spots are generated at the nanosheet gaps, and the electromagnetic field intensities at different positions of the gradient nanosheets also show corresponding gradient changes. This method provides a new idea for realizing high-precision and high-robust SERS quantitative analysis, laying a solid foundation for the further development of SERS technology in practical applications such as chemical sensing, biological diagnosis, and environmental monitoring, and promoting the transformation of SERS from a qualitative analysis tool to a quantitative analysis method.
[0044] A method for determining the concentration of 4-mercaptobenzoic acid using the surface-enhanced Raman scattering detection platform based on the gradient nano-3D structure described above includes the following steps:
[0045] Obtain a series of 4-mercaptobenzoic acid solutions with known concentrations as calibrators. For each calibrator:
[0046] Immerse the surface-enhanced Raman scattering detection platform based on the gradient nano-3D structure into the calibrator and then take it out;
[0047] Select several fixed points on the surface-enhanced Raman scattering detection platform based on the gradient nano-3D structure, collect the Raman spectra of these fixed points, and send them into a machine learning model; use the known concentration of the calibrator as a label to perform supervised training on the machine learning model to obtain a trained machine learning model;
[0048] Immerse the surface-enhanced Raman scattering detection platform based on the gradient nano-3D structure into the 4-mercaptobenzoic acid solution with the concentration to be measured and then take it out; collect the Raman spectra of the fixed points and send them into the trained machine learning model to obtain the concentration prediction value of the 4-mercaptobenzoic acid solution with the concentration to be measured.
[0049] By combining gradient nanostructure design and machine learning (ML), the issues of variability and inconsistency in surface-enhanced Raman spectroscopy (SERS) quantitative analysis are addressed. By fabricating gradient silver (Ag) nanosheets on a flexible PDMS substrate, a reproducible and position-specific spectral response is achieved. The study characterized the morphology of the nanosheets evolving along the gradient, revealing the influence of position-dependent plasmon coupling on optical and SERS properties. Based on the spectral diversity of these gradient nanostructures, machine learning algorithms enable a multi-spectral analysis strategy. Integrating the datasets of several of these fixed positions significantly improves the prediction performance. This method mitigates the impact of batch-to-batch variability, captures subtle spectral changes, and enhances the robustness and accuracy of analyte concentration prediction. By combining gradient nanostructures with advanced data-driven models, this study provides a scalable and cost-effective framework for reliable quantitative SERS analysis. This achievement marks an important step for SERS towards a powerful tool in practical applications, including fields such as chemical sensing, bio-diagnostics, and environmental monitoring, enabling capabilities beyond traditional qualitative methods.
Claims
1. A method for preparing a surface enhanced Raman scattering detection platform based on a gradient nano 3D structure, characterized in that: The following steps are involved: S1: a layer of hexagonal close-packed array of nanospheres formed by self-assembly of polymer nanospheres is arranged on the PDMS film to obtain a PDMS film with nanospheres arranged thereon; S2: Keeping the hexagonal close-packed array of nanospheres facing outward, the PDMS film with nanospheres is placed on the arch block with a curved surface, so that the PDMS film with nanospheres has the same shape as the surface of the arch block, and an arch block with a hexagonal close-packed array of nanospheres with a curved structure is obtained; S3: placing the arch block with the hexagonal close-packed array of nanospheres with curved surface structure on the surface on a horizontal magnetron sputtering sample stage, placing the metal target surface horizontally, and making the metal target surface be located directly above the arch block with the hexagonal close-packed array of nanospheres with curved surface structure on the surface; Metal deposition is performed on the hexagonal close-packed array of nanospheres with a curved surface structure by magnetron sputtering, and the metal deposition is shielded by the nanospheres in the hexagonal close-packed array of nanospheres with a curved surface structure, so that the morphology of the metal nanosheets deposited on the surface of the nanospheres changes with the continuous gradient of the curved surface, thereby forming a spectral response region with a specific position; After S4 magnetron sputtering is completed, the PDMS film with nanospheres is removed from the surface of the arch block and flattened to obtain a surface enhanced Raman scattering detection platform based on a gradient nano 3D structure.
2. The method for preparing a surface enhanced Raman scattering detection platform based on gradient nano 3D structure according to claim 1, characterized in that: In S2, the arch block with a curved surface is specifically an arch block with a spherical surface.
3. The method for preparing a surface enhanced Raman scattering detection platform based on gradient nano 3D structure according to claim 1, characterized in that: In S3, the specific process of magnetron sputtering includes: vacuum degree 5×10 -4 Pa, argon gas was passed, the power was set to 10 W, and the deposition time of metal Ag was 60 s.
4. The method for preparing a surface enhanced Raman scattering detection platform based on a gradient nano 3D structure according to claim 1, characterized in that: In S1, the polymer nanospheres are specifically polystyrene (PS) nanospheres.
5. A surface enhanced Raman scattering detection platform based on a gradient nano 3D structure prepared using the preparation method as described in any one of claims 1 to 4.
6. The concentration of 4-mercaptobenzoic acid is determined by using the surface enhanced Raman scattering detection platform based on gradient nano 3D structure as claimed in claim 5, characterized in that: The following steps are involved: Obtain a series of known concentrations of 4-mercaptobenzoic acid solutions as calibrants. For each calibrant: The surface enhanced Raman scattering detection platform based on the gradient nano 3D structure is immersed in the calibration agent and then taken out; Select several fixed points on the surface enhanced Raman scattering detection platform based on the gradient nano 3D structure, collect the Raman spectra of these fixed points, and send them to the machine learning model; use the known concentration of the calibration agent as a label to perform supervised training on the machine learning model to obtain a trained machine learning model; The surface enhanced Raman scattering detection platform based on the gradient nano 3D structure is immersed in a 4-mercaptobenzoic acid solution of the concentration to be measured and then taken out; the Raman spectrum of the fixed point is collected and sent to the trained machine learning model to obtain the concentration prediction value of the 4-mercaptobenzoic acid solution of the concentration to be measured.