Application of silver crystal branch and tendril nanometer material in SERS (Surface Enhanced Raman Scattering) detection of biological sample and detection method

Abstract

According to the application of the silver crystal branch and tendril nano material in biological sample surface enhanced Raman scattering detection, the silver crystal branch and tendril nano material is used for being in contact with a serum sample to form a surface enhanced Raman scattering detection system, so that Raman scattering signals of endogenous molecules in the serum sample are enhanced, and the detection sensitivity is improved. And analysis is carried out based on the Raman spectrum overall characteristics of the serum sample so as to realize discrimination of disease-related states. The invention also provides a method for detecting surface enhanced Raman scattering of a biological sample by using the silver crystal branch and tendril nano material, the detection process does not need complex sample pretreatment, and a Raman spectrum signal can be stably obtained.

Description

Technical Field

[0001] This invention relates to the field of nanomaterials and surface-enhanced Raman scattering detection technology, specifically to the use of silver crystal dendritic nanomaterials in the preparation of detection systems for surface-enhanced Raman scattering analysis of biological samples, and to a method for detecting biological samples using silver crystal dendritic nanomaterials for enhanced Raman scattering. Background Technology

[0002] Neurodegenerative diseases are a group of common age-related illnesses characterized by progressive neurological dysfunction, with Alzheimer's disease (AD) being one of the representative diseases. These diseases have an insidious onset and slow progression, making early intervention crucial for slowing their progression. However, current clinical diagnosis of neurodegenerative diseases primarily relies on neuropsychological scales and imaging examinations, lacking sensitive and reliable early biomarker detection methods.

[0003] While cerebrospinal fluid biomarker detection can reflect disease-related pathological changes to some extent, its sampling process is highly invasive, complex, and costly, limiting its widespread adoption in routine screening and large-scale clinical applications. In contrast, serum samples offer advantages such as ease of acquisition, minimal trauma, and repeatability, and contain abundant metabolic information, thus gradually becoming an important research subject for non-invasive diagnostic studies of neurodegenerative diseases.

[0004] Surface-enhanced Raman scattering (SERS) technology has been widely used in the field of biomolecular detection and analysis due to its advantages such as high sensitivity, strong fingerprint recognition ability, and small sample volume requirements, and has shown potential for the detection of disease-related biomarkers. However, existing SERS technologies still face many challenges when used for the detection of serum samples for neurodegenerative diseases. For example, the preparation process of high-performance SERS-enhancing substrates is usually complex and costly, making it difficult to meet the stability and reproducibility requirements of clinical applications; the background components in serum samples are complex, often requiring cumbersome pretreatment steps to reduce interference; in addition, serum SERS spectral data are high-dimensional and complex, and their effective interpretation and disease state discrimination need further optimization. Summary of the Invention

[0005] In view of this, the present invention provides the application and detection method of silver crystal dendritic nanomaterials in SERS detection of biological samples, which, combined with efficient data analysis methods, enables rapid and accurate detection of serum samples of neurodegenerative diseases.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows: This invention provides the application of silver crystal dendritic nanomaterials in surface-enhanced Raman scattering detection of biological samples. The silver crystal dendritic nanomaterials are used to contact serum samples to form a surface-enhanced Raman scattering detection system, thereby enhancing the Raman scattering signal of endogenous molecules in the serum sample. Based on the overall Raman spectral characteristics of the serum sample, analysis is performed to determine disease-related states.

[0007] In one implementation, during the construction of the surface-enhanced Raman scattering detection system, silver crystal dendritic nanomaterials are first pretreated by contacting sodium chloride solution, and then calcium chloride solution is introduced after contacting serum samples to regulate the enhanced Raman scattering effect.

[0008] In one implementation, the serum sample is not mixed before the calcium chloride solution is introduced.

[0009] In one embodiment, the silver crystal dendrite nanomaterial has a multi-level branching structure, including a primary trunk formed by assembling short nanorods and secondary branches and tertiary micro-branches branching from the primary trunk.

[0010] In one embodiment, silver crystal dendrites are deposited or distributed on the surface of a solid carrier. The silver crystal dendrites are composed of short nanorods with a length of 150-250 nm, which are orderly assembled into a primary trunk. The primary trunk has a diameter of 80-120 nm and a length of 0.1-0.3 μm. The primary trunk branches into 3-5 secondary branches with a diameter of 40-60 nm and a length of 50-150 nm. The secondary branches branch into 2-3 tertiary micro-branches with a diameter of 20-50 nm. The angle between the secondary branches and the primary trunk is 45°-75°. There are micropores between the short nanorods and between the primary and secondary branches, with a pore size of 50-200 nm, forming a continuous network-like pore structure.

[0011] In one embodiment, the lattice stripe spacing of the silver crystal dendrites is 0.2~0.4 nm, and it is composed of 10~30 nm nanocrystals, exposing grain boundaries and surface defects.

[0012] In one implementation, the disease is a neurodegenerative disease.

[0013] In one implementation, neurodegenerative diseases include Alzheimer's disease.

[0014] This invention also provides a method for detecting surface-enhanced Raman scattering of biological samples using silver crystal dendritic nanomaterials, comprising the following steps: (1) The original solution of silver crystal dendrite nanomaterials was mixed with sodium chloride solution to obtain a pretreated nanomaterial system; (2) Prepare the biological samples to be tested; (3) After the pretreated nanomaterial system is brought into contact with the biological sample to be tested, calcium chloride solution is added and mixed to form a surface-enhanced Raman scattering detection system; (4) The detection system is dropped onto the surface of a solid support, and Raman spectral signals are collected under a Raman spectrometer; (5) Analyze the collected Raman spectral signals to obtain the detection results of the biological samples; In step (3), the biological sample is not mixed before adding calcium chloride solution.

[0015] In one implementation, the biological sample is a serum sample.

[0016] This invention enables the controlled synthesis of silver dendritic nanomaterials in an ethanol-water mixture, yielding a well-dispersed nanomaterial stock solution without additional surface modification. The synthesis method is mild, simplified, and suitable for practical applications. The prepared silver dendritic nanomaterials, after deposition on a solid support, form a multi-level branched and network-like porous structure. Combined with their crystal structure and surface defect characteristics, this facilitates the construction of detection substrates suitable for surface-enhanced Raman scattering (SERS), thus providing a structural basis for the stable acquisition of Raman spectral signals. The enhanced Raman scattering detection method established based on this material can be directly used for biological sample detection. The detection process is relatively simplified, requiring no complex sample pretreatment, providing a feasible technical means for the detection and research of biological samples related to neurodegenerative diseases. Attached Figure Description

[0017] Figure 1 This is a scanning electron microscope (SEM) image of the silver crystal dendritic nanomaterial synthesized in Example 1 of this invention after it has been deposited on a solid support and dried.

[0018] Figure 2 This is a transmission electron microscope (TEM) image of the silver crystal dendritic nanomaterial synthesized in Example 1 of this invention.

[0019] Figure 3 This is a scanning electron microscope (SEM) image of the silver crystal dendritic nanomaterial synthesized in Example 2 of this invention after it has been deposited on a solid support and dried.

[0020] Figure 4 This is a transmission electron microscope (TEM) image of the silver crystal dendritic nanomaterial synthesized in Example 2 of this invention.

[0021] Figure 5 This is a schematic diagram of the average spectrum and average difference spectrum of enhanced Raman scattering spectra of serum samples from the AD group and NA group in this invention.

[0022] Figure 6 This is a principal component analysis score distribution diagram constructed based on enhanced Raman scattering spectroscopy of serum samples according to the present invention.

[0023] Figure 7 This is a schematic diagram showing the variance contribution rate distribution of the top 20 principal components based on the enhanced Raman scattering spectrum of serum samples.

[0024] Figure 8 This is a schematic diagram of the loading distribution corresponding to the first principal component (PC1) obtained by principal component analysis based on enhanced Raman scattering spectroscopy of serum samples.

[0025] Figure 9 This is a schematic diagram of the confusion matrix obtained by grouping five-fold cross-validation using a logistic regression model based on enhanced Raman scattering spectra of serum samples.

[0026] Figure 10 This is a schematic diagram of the receiver operating characteristic (ROC) curves obtained by grouping five-fold cross-validation using a logistic regression model based on enhanced Raman scattering spectra of serum samples.

[0027] Figure 11 This diagram illustrates the classification performance evaluation index obtained by using a logistic regression model for grouped five-fold cross-validation based on enhanced Raman scattering spectra of serum samples.

[0028] Figure 12 This is a schematic diagram of the molecular origin matching results of the enhanced Raman scattering difference spectra of serum samples from groups AD and NA.

[0029] Figure 13 This is a schematic diagram comparing the weighted contribution and intensity contribution of major metabolites based on enhanced Raman scattering differential spectroscopy.

[0030] Figure 14 This is a schematic diagram illustrating the consistency analysis between the characteristic peaks of enhanced Raman scattering difference spectra and the corresponding metabolite variation trends.

[0031] Figure 15 A schematic diagram illustrating the statistical analysis of the proportion of different matching information data sources in the molecular source tracing matching results of Raman scattering differential spectroscopy.

[0032] Figure 16 A statistical diagram illustrating the distribution of matching confidence levels to enhance the molecular origin matching results of Raman scattering differential spectroscopy.

[0033] Figure 17 This is a statistical diagram showing the distribution of shift differences between experimental and reference peak positions in molecular source matching results based on enhanced Raman scattering difference spectroscopy. Detailed Implementation

[0034] 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. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] Example 1: Preparation of Silver Crystal Dendritic Nanomaterials

[0036] (a) Reagents and Instruments

[0037] The reagents used include: silver nitrate, sodium citrate, ascorbic acid, sodium chloride, and calcium chloride.

[0038] The instruments used include: magnetic stirrer, centrifuge, and high-resolution ultrasensitive intelligent Raman spectrometer (HORIBA, LabRAM Soleil™).

[0039] (II) Synthesis of Silver Crystal Dendritic Nanomaterials

[0040] In this invention, comparative experiments were conducted within a certain parameter range by adjusting the concentration of the silver source, the volume fraction of ethanol, and the amount of reducing agent. The results showed that nanomaterials with silver crystal dendrite structures could be stably prepared within the above parameter range. Two representative experiments are described below as examples.

[0041] Before the experiment, the reaction vessel was cleaned by immersing it in aqua regia overnight, followed by ultrasonic cleaning with deionized water for 15 minutes and rinsing several times to remove any possible metallic impurities.

[0042] The following are two representative examples of synthesis: Synthesis example one 40 mg of AgNO3 was added to 20 ml of 50% ethanol solution and placed on a magnetic stirrer to dissolve it. Then, 150 mg of sodium citrate was added. After reacting at 50 °C for 50 minutes, the mixture was cooled to room temperature and 80 mg of ascorbic acid was added as a reducing agent. The reaction was terminated after 30 minutes. The magnetic stirrer was set to a speed of 450 rpm.

[0043] The product obtained from the reaction was centrifuged and the supernatant was discarded. The product was then washed and purified multiple times with anhydrous ethanol and deionized water. Finally, the precipitate was resuspended in 1 ml of deionized water to obtain the original solution of silver crystal dendritic nanomaterials.

[0044] Synthesis Example 2

[0045] 45 mg of AgNO3 was added to 20 ml of 60% ethanol solution and placed on a magnetic stirrer to dissolve it. Then, 170 mg of sodium citrate was added. After reacting at 55 °C for 50 minutes, the mixture was cooled to room temperature and 90 mg of ascorbic acid was added as a reducing agent. The reaction was terminated after 30 minutes. The magnetic stirrer was set to a speed of 450 rpm.

[0046] The purification steps are the same as in Example 1.

[0047] Example 2: Structure and property characterization of silver crystal dendritic nanomaterials

[0048] (I) Morphological and structural characterization

[0049] The stock solutions of the silver crystal dendritic nanomaterials in Examples 1 and 2 were added dropwise to a solid support and dried, then observed using a scanning electron microscope. Figures 1 to 4 As shown, the results indicate that the three-dimensional branch-nanorobot composite cluster structure consists of short nanorods of approximately 200 nm in length, orderly assembled into a primary trunk (approximately 100 nm in diameter and 0.1–0.3 μm in length), which further grows into 3–5 secondary branches (50 nm in diameter and 50–150 nm in length) and 2–3 tertiary micro-branches (20–50 nm in diameter). The angle between the branches and the trunk is 45°–75°, and micropores (50–200 nm in size) exist between the nanorods and branches, forming a continuous network-like pore structure. This structure can simultaneously increase the contact area of ​​target molecules, accelerate the mass transfer of analytes, and improve the capture efficiency.

[0050] (II) Crystal Structure Characterization

[0051] The stock solutions of the silver crystal dendritic nanomaterials in Examples 1 and 2 were centrifuged at 6000 rpm to collect the precipitate, washed with deionized water, and dried at room temperature to obtain powder. Analysis of the powder by conventional X-ray diffraction (XRD) revealed that the silver crystal dendritic nanomaterials in Examples 1 and 2 exhibited a face-centered cubic single-crystal structure of silver with high crystallinity; the interplanar spacing of the lattice stripes was 0.23 nm (corresponding to the (111) crystal plane), and the surface consisted of 10~30... The structure is composed of nanocrystals stacked together, exposing a large number of grain boundaries and surface defects. The XRD spectrum shows diffraction peaks of the (111), (200), and (220) crystal planes (corresponding to approximately 38°, 44°, and 64°, respectively). The intensity ratio of the diffraction peaks of the (111) and (200) planes after baseline subtraction is 3.7 (higher than the standard ratio of 2.5 for silver crystals). This high ratio, combined with surface defect sites, significantly enhances the binding potential of molecules on the silver surface, which is conducive to promoting the binding and local enrichment of endogenous molecules in the sample on the silver surface, thereby forming an enhanced region that is conducive to surface-enhanced Raman scattering effect, and thus improving the SERS detection sensitivity.

[0052] (III) Optical and Colloidal Properties

[0053] The original solutions of the silver crystal dendritic nanomaterials in Examples 1 and 2 were subjected to UV-Vis spectroscopy, which showed that they had a broad UV-Vis absorption band in the 350 nm to near-infrared region (corresponding to a broad localized surface plasmon resonance (LSPR) band in anisotropic structures). This band can match the characteristic spectral range of multiple types of analyte molecules, enabling the Raman scattering signals of multiple types of endogenous molecules in biological samples to be effectively enhanced in the same detection system and form a repeatable overall spectral feature. The average hydrodynamic diameter in the colloidal suspension was 101.4 nm and the average Zeta potential was -18.19 mV, indicating that it can be uniformly and stably dispersed in the aqueous phase, avoiding aggregation and obscuring of active sites, and ensuring consistent detection performance.

[0054] Example 3: Enhanced Raman Scattering Detection Method Based on Silver Crystal Dendritic Nanomaterials

[0055] The silver crystal dendritic nanomaterials prepared by any of the synthesis examples in Example 1 can be used to perform enhanced Raman scattering detection on biological samples. In this example, the stock solution of the silver crystal dendritic nanomaterials prepared by Example 1 was used to detect the serum sample of the subject.

[0056] Serum Sample Collection: Demographic and clinical data, including name, sex, and age, were collected from all participants. All enrolled patients underwent cognitive assessment using the Alzheimer's Disease Assessment Scale (AS-Cog – Cognitive). After a 12-item cognitive task test, those with a total score below 18 were classified as cognitively normal, and those above 18 were classified as Alzheimer's disease (AD). Based on the AS-Cog score, 23 were cognitively normal elderly individuals (CN) and 7 were AD patients. Seven patients with neurodegenerative diseases were diagnosed with the disease after surgical treatment based on assessment of neurodegenerative disease symptoms and markers. Serum samples were collected from these patients as individual experimental samples, resulting in a total of 14 neurodegenerative disease samples. All 30 participants were assessed by neurologists, and none of them were taking any medications related to neurodegenerative diseases.

[0057] The specific basic information of the subjects is shown in Table 1.

[0058] Table 1

[0059] Preparation before testing: Prepare a 10 mM sodium chloride and 2 mM calcium chloride solution. Mix the silver crystal dendrite nanomaterial stock solution with 10 mM sodium chloride at a volume ratio of 1:2. Remove the aliquoted serum from the -80℃ freezer and place it on ice.

[0060] SERS detection: 5 μL of the original solution from the synthesis of silver crystal dendritic nanomaterials (Example 1) was added to a 200 μL EP tube, followed by 5 μL of serum (without mixing). Then, 5 μL of 2 mM calcium chloride was added and mixed thoroughly. 5 μL of this solution was then dropped onto a glass slide and detected using a Raman spectrometer. Detection conditions: laser wavelength 532 nm, laser intensity 0.1 mW, integration time 30 s, integration count 1, range 400–2000 cm⁻¹. -1 Each sample was prepared 3 times, and 3-4 spectra were detected each time, for a total of 10 spectra.

[0061] Analysis of Detection Results in Example 4

[0062] Based on the enhanced Raman scattering spectral signal obtained in Example 3, the spectral data is further processed and analyzed to evaluate the applicability and stability of the method of this application in biological sample detection.

[0063] (I) Spectral data preprocessing and average spectral analysis

[0064] In this embodiment, a total of 176 spectra were obtained from the neurodegenerative disease group (AD as an example) and 259 spectra from the normal control group (NA). Savitzky-Golay smoothing was used for the spectra, followed by baseline removal using asymmetric least squares smoothing. Vector normalization was then applied to standardize the spectra. Based on the normalized spectral data, the mean spectrum and mean difference spectrum of the two groups were calculated. The normalized mean spectrum and mean difference spectrum are shown below. Figure 5 Significant differences in the spectra can be observed, stemming from differences in metabolites between the two groups.

[0065] (II) Principal Component Analysis (PCA) and Spectral Distribution Characteristics

[0066] To investigate the overall distribution and intergroup separation trends of the spectral data, principal component analysis (PCA) was performed on all 435 pretreated serum SERS spectra. Figure 6 As shown, the PCA score plot exhibits a clear trend of separation between groups on the plane formed by the first and second principal components. The first principal component (PC1) and the second principal component (PC2) explain 30.7% and 12.2% of the total variance, respectively. The variance contribution rates of the first 20 principal components are shown below. Figure 7 As shown, PC1 contributed the most significant source of variation.

[0067] (III) Principal component loading analysis and key spectral features

[0068] To further analyze the key spectral features driving this distribution, the loadings corresponding to the first principal component were analyzed, and PC1 was plotted. Figure 8 The results showed that it was located at 626 cm. -1 810 cm -1 882 cm -1 1010 cm -1 1134 cm -1 1202cm -1 The nearby Raman displacement has a high positive load on the first principal component (PC1), while 727 cm -1 1098 cm -1 With 1329cm -1 The nearby bands show higher loading, and these characteristic bands likely correspond to the most significant differences in biochemical molecular vibrational information between groups.

[0069] (iv) Validation of classification analysis based on logistic regression model

[0070] Building upon the unsupervised analysis described above, a supervised classification method was employed to analyze the spectral data to further verify the usability of spectral differences in sample differentiation. This embodiment uses grouped five-fold cross-validation and a logistic regression (LR) model to diagnose neurodegenerative diseases. The results of the mixture matrix are shown below. Figure 9 The area under the receiver operating characteristic curve (AUC) was 1.000 (based on comprehensive evaluation). Figure 10 The mean accuracy was 0.995 ± 0.006, the precision was 1, the recall was 0.987 ± 0.018, and the F1 score was 0.993 ± 0.009. Figure 11 ).

[0071] (v) Molecular origin analysis of differential spectra

[0072] After completing the spectral classification analysis, to further elucidate the molecular basis of the spectral differences, a molecular source tracing analysis was performed on the differential spectra between the neurodegenerative disease group and the normal control group. This embodiment uses the MORE SERSome platform for metabolic analysis, which embeds a multi-source data integration and matching algorithm. This algorithm constructs a triple matching validation system based on metabolomics data, standard spectra, and literature reports, and employs a multi-level threshold detection strategy to effectively identify weak signal peaks. To systematically evaluate the reliability of each match, we established a comprehensive confidence scoring system comprising five dimensions: 1) shift matching degree (1-4 points); 2) peak shape compatibility (-1 to 3 points); 3) intensity correlation (-1 to 2 points); 4) trend consistency (-2 to 2 points); 5) data source weight (2-4 points). For weak peak identification, the algorithm introduces a dynamic tolerance mechanism, appropriately relaxing the matching conditions within a controllable range, and controlling the risk of false positives through confidence scoring. Applying this algorithm, we systematically traced the molecular sources of the differential spectra between the neurodegenerative disease group and the NA group. The final matching results ( Figure 12 The figure shows 634 cm. - The characteristic peak at ¹ originates from the C-C tortuous vibration of uric acid, 726 cm⁻¹ - The strong peak at ¹ corresponds to the circumspiratory vibrations of hypoxanthine and adenine. Furthermore, the analysis revealed several significantly different characteristic peaks (e.g., 494, 533, 810, 886, 1007, 1135, 1203 cm⁻¹). - The negative peak at ¹ has spectral characteristics of uric acid, while another set of characteristic peaks (such as 726, 955, 1330, 1457, and 1585 cm⁻¹) are characteristic of uric acid. - The positive peak at ¹ exhibits spectral characteristics of adenine and hypoxanthine. The algorithm established 151 reliable SERS peak-metabolite associations, involving 22 different metabolites.

[0073] (vi) Assessment of metabolite contribution and matching reliability

[0074] Based on molecular source tracing analysis, the contribution of different metabolites to spectral differences is quantitatively assessed, and the quantitative analysis of metabolite contribution is performed. Figure 13 The analysis revealed that the relationship between the weighted contribution and intensity contribution of the top 10 metabolites showed different levels of matching quality: essentially equal weighted contributions indicated high matching quality and stable assessment; significantly higher weighted contributions indicated extremely high matching quality (high confidence, strong peak); and significantly lower weighted contributions suggested that although the matching peak intensity was high, the matching confidence was low (possibly a weak peak or interference). The analysis showed that the top 5 contributing metabolites accounted for 64.9% of the total differential signal, with uric acid, adenine, and hypoxanthine contributing the strongest contributions of 23.2%, 12.6%, and 12.0%, respectively.

[0075] To further validate the biological rationale for the matching, we assessed the consistency between the SERS spectral change trends and the corresponding metabolite expression changes. Figure 14 The results showed that 64.2% of the matching characteristic peaks exhibited trends completely consistent with metabolite changes, 7.9% were inconsistent, and 27.8% of the characteristic peaks showed no significant differences in their corresponding metabolites in non-target metabolism results. Regarding data sources (…), Figure 15 74.2% of the matching information came from standard spectra, and 25.8% was supported by literature. The confidence distribution of the final matching results ( Figure 16 ) and matching tolerance ( Figure 17 The results showed that 91.4% of the matches were rated as high confidence (≥8 points), with an overall average confidence score of 10.46 points and an average match shift difference of 3.14 cm⁻¹. This indicates that the silver crystal dendritic nanomaterials used in this method effectively ensured the specificity and reliability of the results while achieving high-sensitivity matching. The above molecular source tracing analysis serves as a further explanation of spectral differences and is not a necessary step for sample detection and differentiation.

[0076] The analysis results of this embodiment four verify the effectiveness and stability of the silver crystal dendritic nanomaterials in enhanced Raman scattering detection from multiple perspectives, including spectral repeatability, spatial distribution consistency, enhanced selectivity, and molecular recognition ability.

[0077] In summary, the method for synthesizing silver crystal dendrite nanomaterials presented in this application is rapid and inexpensive. Compared to existing electrochemical methods that require additional surface modification to achieve colloidal stability and are prone to introducing impurities that affect detection performance, the silver crystal dendrites synthesized by this method exhibit strong stability, maintaining good stability for up to 3 months. Its analytical enhancement factor (AEF: used to characterize the enhancement factor of the Raman signal produced by a unit concentration of molecules under SERS conditions compared to the signal produced by a unit concentration of molecules under ordinary Raman conditions) is as high as 2.8 × 10⁻⁶. 8 Formula The SERS enhancement performance of AgNF is analyzed intuitively by calculating AEF. C Raman 10 -2 mol / L, I Raman It is 1072.6 au. C SERS 10 -9 mol / L, I SERS The value is 30028.1 au, and the calculated AEF is 2.8 × 10⁻⁶. 8Based on the silver crystal dendritic nanomaterial substrate prepared in this application, stable SERS spectra can be rapidly obtained from serum samples without pretreatment. No special consumables are required; a simple glass slide is sufficient for detection. A logistic regression model was used to discriminate SERS spectra, with an average accuracy of 0.995 ± 0.006. High-resolution metabolic information of SERS spectra can be obtained using the SOME SERSome platform.

Claims

1. The application of a silver crystal dendritic nanomaterial in surface-enhanced Raman scattering detection of biological samples, characterized in that, The silver crystal dendritic nanomaterial is used to contact serum samples to form a surface-enhanced Raman scattering detection system, thereby enhancing the Raman scattering signal of endogenous molecules in the serum samples and performing analysis based on the overall Raman spectral characteristics of the serum samples to determine disease-related states.

2. The use as described in claim 1, characterized in that, In the construction of the surface-enhanced Raman scattering detection system, the silver crystal dendritic nanomaterial is first pretreated by contacting it with sodium chloride solution, and then calcium chloride solution is introduced after contacting it with serum sample to regulate the enhanced Raman scattering effect.

3. The use as described in claim 2, characterized in that, The serum sample is not mixed before the calcium chloride solution is introduced.

4. The use as described in claim 1, characterized in that, The silver crystal dendrite nanomaterial has a multi-level branching structure, including a primary trunk formed by assembling short nanorods and secondary branches and tertiary micro-branches branching from the primary trunk.

5. The use as described in claim 4, characterized in that, The silver crystal dendrites are deposited or distributed on the surface of a solid carrier. The silver crystal dendrites are composed of short nanorods with a length of 150-250 nm, which are orderly assembled into a primary trunk. The primary trunk has a diameter of 80-120 nm and a length of 0.1-0.3 μm. The primary trunk branches into 3-5 secondary branches with a diameter of 40-60 nm and a length of 50-150 nm. The secondary branches branch into 2-3 tertiary micro-branches with a diameter of 20-50 nm. The angle between the secondary branches and the primary trunk is 45°-75°. There are micropores between the short nanorods and between the primary branches and the secondary branches. The pore size is 50-200 nm, forming a continuous network-like pore structure.

6. The use as described in claim 5, characterized in that, The lattice stripe spacing of the silver crystal dendrites is 0.2~0.4 nm, and they are formed by the accumulation of 10~30 nm nanocrystals, exposing grain boundaries and surface defects.

7. The use as described in claim 1, characterized in that, The disease in question is a neurodegenerative disease.

8. The use as described in claim 7, characterized in that, The neurodegenerative diseases mentioned include Alzheimer's disease.

9. A method for detecting surface-enhanced Raman scattering of biological samples using silver crystal dendritic nanomaterials, characterized in that, Includes the following steps: (1) The original solution of silver crystal dendrite nanomaterials was mixed with sodium chloride solution to obtain a pretreated nanomaterial system; (2) Prepare the biological samples to be tested; (3) After the pretreated nanomaterial system is brought into contact with the biological sample to be tested, calcium chloride solution is added and mixed to form a surface-enhanced Raman scattering detection system; (4) The detection system is dropped onto the surface of a solid carrier, and Raman spectral signals are acquired under a Raman spectrometer; (5) Analyze the collected Raman spectral signals to obtain the detection results of the biological sample; In step (3), the biological sample is not mixed before being added to the calcium chloride solution.

10. The detection method as described in claim 9, characterized in that, The biological sample is a serum sample.

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