15 March 2024, Volume 36 Issue 1
    

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  • Recent advances in surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms in biomedical fields
    WANG Jiaqi, XU Weiqing, XU Shuping, ,
    Chinese Journal of Light Scattering. 2024, 36(1): 1-15. https://doi.org/10.13883/j.issn1004-5929.202401001
    Abstract ( ) Download PDF ( ) Knowledge map Save

    In recent years, the use of surface-enhanced Raman spectroscopy (SERS) technology to detect biological samples has become a hot topic. Significantly, the application of SERS technology combined with machine learning methods in clinical sample diagnosis has become increasingly mature. Machine learning methods based on unsupervised and supervised algorithms to solve complex samples and large, high-dimensional data, have received high attention. This review describes the relevant applications of SERS technology combined with machine learning methods, especially in the biomedical field. SERS can detect fingerprint information of biological samples using label-free strategies, or indirect SERS detections for tracking biomarkers such as proteins. This review summarizes SERS technology combined with machine learning for disease diagnosis in clinical samples such as blood, urine, and biotissues. In addition, we also summarize their applications on many cellular samples and other complex samples. An overview of the latest advances in this field is provided and this study offers a reference that can be followed by researchers working in SERS bioanalysis.

  • Metal-organic Framework/Nanozyme-composite SERS Substrates for Biomedical Analysis and Prospects

    Hu Sen, Jiang Yuning, Cao Jiaying, Zheng Qiangting, Wu Yiping, Guo Xiaoyu, Ying Ye, Liu Xinling, Wen Ying, Yang Haifeng
    Chinese Journal of Light Scattering. 2024, 36(1): 16-22.
    Abstract ( ) Download PDF ( ) Knowledge map Save

    Surface-enhanced Raman Scattering (SERS) is an analytical method based on molecular vibration fingerprint information, which has high sensitivity, good selectivity, nondestructive and on-site detection, and is free from the interference of water system. It has potential application in the fields of biological analysis and early diagnosis. Compared with natural enzyme, nanoenzyme as an artificial mimic enzyme presents the advantages of easy and scalable preparation, diversity of enzymatic activity via rational design as well as long-term environmental stability. Metal-organic framework (MOF) with mesoporous crystal structure, large specific surface area could be easily synthesized, which is beneficial to immobilization of other nanomaterials to improve stability and realize the multifunctional performance. This short review concerns on nano-enzyme, MOF and SERS substrate assembly to advance the figure of analytical merit, and focuses on the current explorations of MOF/nanozyme/SERS platform in the field of biomedical analysis and prospects.

  • Construction of AuNRs/MoS2/Silica gel thin-layer chromatographic plates combined with SERS technology for point-array detection of benzo(a)pyrene in complex aqueous bodies
    Ding Yan#, Cai Lingchen#, Qin Haiyue, Fang Junjie, Zhu Zhe, Liu Qingyu, Chen Feng, Cao Yue
    Chinese Journal of Light Scattering. 2024, 36(1): 23-27. https://doi.org/10.13883/j.issn1004-5929.202401003
    Abstract ( ) Download PDF ( ) Knowledge map Save
    Benzo(a)pyrene (Bap) is a typical organic pollutant in the environment, which is enriched in the human body through continuous biological cycles and can cause chronic damage to the organism. In order to rapidly detect benzopyrene and complete trace analysis, a new disposable sensor for the separation and detection of poisons was constructed by combining thin layer chromatography plates (TLC) with surface-enhanced Raman scattering (SERS) to detect benzopyrene. This method has high sensitivity, easy operation and other advantages. It can complete the on-site rapid analysis and detection of poisons, and effectively overcome the problems of difficult operation and high technical requirements of the current detection method for benzopyrene, which has certain practical significance.

  • Parallel-laser nanofabrication of surface-enhanced Raman spectroscopy (SERS) substrates by microsphere-lens array for ultrasensitive trace-detection

    Wang Mengyuan, Zhao Chen, Yan Yinzhou, Zhao Yan, Jiang Yijian
    Chinese Journal of Light Scattering. 2024, 36(1): 28-37. https://doi.org/10.13883/j.issn1004-5929.202401004
    Abstract ( ) Download PDF ( ) Knowledge map Save
    Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for fingerprinting analysis of substances, which has been widely used in biosensing and chemical analysis. The fabrication of SERS substrates with well-ordered nanostructures is crucial for high reliability and stability in Raman trace-detection. The development of precise, large-scale, and efficient fabrication methods for nanostructured substrates is still a challenge to SERS used in practice so far. In this work, we propose a parallel-laser nanofabrication method for nanostructure arrays, combining microsphere-lenses with gold nanoholes (ML/AuNHs) for ultrasensitive hybrid SERS substrates. The AuNHs are fabricated on the bottom surfaces of ML by the photonic nanojets for the hybrid structures. The effect of process parameters, i.e., the pulsed laser energy, the ML diameter, and the thickness of gold film, on the quality of the nanoholes is investigated. The parameters of laser fabrication are optimized for the diameter of AuNHs down to 215 nm. Furthermore, the optical regulation in ML/AuNHs is theoretically investigated, including self-aligned focusing to the hotspots, optical whispering-gallery modes (WGMs), and directional antenna. The mechanism of Raman enhancement via the ML/AuNHs is therefore revealed. The limit of detection by the ML/AuNHs down to 10-10 M for 4-nitrobenzenethiol (4-NBT) molecules is also demonstrated, which is two orders of magnitude lower than using the AuNHs without ML coupling. This work presents a simple and efficient parallel-laser nanofabrication method for the highly-sensitive SERS substrates in the future applications.
  • Label-free SERS Srategy for Detection of DNA Base Damage in Stimulus-Induced Cell Apoptosis by Plasmonic SERS Au Nanosunflowers
    Qi Guohua, Jin Yongdong
    Chinese Journal of Light Scattering. 2024, 36(1): 38-43. https://doi.org/10.13883/j.issn1004-5929.202401005
    Abstract ( ) Download PDF ( ) Knowledge map Save
    Molecular profiling and accurate damage analysis of complex biomolecular events in tumor cells are critical to the diagnosis and treatment of cancer. However, due to sensitivity limitation and dynamic and complex nature of apoptosis process, label-free detection of cellular DNA damage during cancer therapeutic treatment, at the DNA bases level, is still a huge challenge. Herein, by designed preparation of novel and uniform plasmonic sunflower-like assembly gold (Au) nanostructure that capable of efficient DNA capture and providing high-density “hot spots” for SERS enhancement, we succeeded in sensitive and reliable SERS detection of DNA damage in apoptotic cancer cells at the DNA bases level. The chemical structure damage of cellular DNA caused by electrostimulus-induced cell apoptosis was revealed and discriminated at the bases level, for the first time, by label-free SERS detection with the nano-sunflowers. The SERS results showed that the external electrostimulus (at 1.2 V, for 5 min) was almost harmless to normal healthy cells but it caused pronounced double strands break and Adenine (A) base damage in cancer cell DNAs, which effectively destroyed the reproduction and transcribe of DNA to harass cancer cell mitosis and ultimately induce cell apoptosis. The finding provides deep insights into molecular genomic DNA damages of cancer cells during theranostic process, and the method would open a new avenue for the study of genetically related diseases.
  • Non-destructive detection of soluble solid content based on visible-near infrared spectroscopy
    WU Hongzhang, CAI Hongxing, REN Yu, WANG Tingting, ZHOU Jianwei, LI Dongliang, QU Guannan
    Chinese Journal of Light Scattering. 2024, 36(1): 44-51. https://doi.org/10.13883/j.issn1004-5929.202401006
    Abstract ( ) Download PDF ( ) Knowledge map Save
    The Soluble solid content in grapes is an important indicator for evaluating grape ripeness, and this paper explores the quantitative analysis of soluble solid content (SSC) content in several varieties of grapes (Hongti, Jufeng, and Liaofeng) based on visible/near-infrared (NIR) spectroscopic techniques. The transmission spectra of three grape varieties in the wavelength range of 550-960 nm were collected separately, and Savitzky-Golay convolutional smoothing (S-G), standard normal variate (SNV), wavelet transform (WT), and the combination of first-order derivation + S-G convolutional smoothing (1stDer+S-G) were used to analyze the soluble solids content of the grapes. Preprocessing methods, and compare and analyze the most suitable preprocessing methods for each variety; then under the optimal preprocessing methods, we used the continuous projection algorithm (SPA) and competitive adaptive reweighting (CARS) to select the characteristic wavelengths of the spectra; and combined with chemometrics methods to establish the partial least squares regression (PLSR) for multi-species and single-species, and the lossless prediction model for the content of the SSC of the BP neural network, respectively. The results showed that the SSC content model based on BP-SPA was optimal, and the prediction set correlation coefficient (Rp2) of the generalized SSC content prediction model for multiple varieties was 0.85, which indicated that the non-destructive detection of SSC content in multiple grape varieties based on visible/near-infrared spectroscopy was feasible.
  • Laser-induced electron coherence based on spatial self-phase modulation in quantum materials
    Yixuan Huang, and Jimin Zhao,
    Chinese Journal of Light Scattering. 2024, 36(1): 52-62. https://doi.org/10.13883/j.issn1004-5929.202401007
    Abstract ( ) Download PDF ( ) Knowledge map Save
    Spatial self-phase modulation (SSPM) is a third-order nonlinear optical response, also known as optical Kerr effect. The physics mechanism of SSPM is laser-induced nonlocal electron coherence, which is a collective excitation behavior, nonlinear optical response, and emergence phenomenon. Electrons in the matters move with optical frequency driven by light field and obtain phase determined by the external field. Electrons in separated domains preserve fixed phase difference and nonlocal coherence emerges. The parallel components of diffractive light form a group of concentric conical emissions, resulting in coherence rings in the far field screen. This phenomenon is SSPM. All-optical switching can be achieved based on laser-induced electron coherence in quantum materials, functioning as a “transistor” in photonics, because it can realize using a weak light to control a strong light. This article briefly reviews the investigations towards SSPM physics mechanism, as well as its potential applications in all-optical switching, emphasizing on the new progress and microscopic mechanism of SSPM.
  • The application of snowflake-shaped Fe2O3-AgNPs SERS substrate in the detection of furfural in natural ester insulating oil based on soybeans
    ZHANG ying, DENG Baojia, LEI Yu, WANG Mingliang, LI Shufan, ZHU Lei, CHEN Weigen, WAN Fu
    Chinese Journal of Light Scattering. 2024, 36(1): 63-71. https://doi.org/10.13883/j.issn1004-5929.202401008
    Abstract ( ) Download PDF ( ) Knowledge map Save
    In this paper, snowflake ferric oxide (α-Fe2O3) nanomaterials were prepared by one-step hydrothermal synthesis, and silver nanoparticles (AgNPs) were in-situ grown on the surface of the nanomaterials by chemical reduction method. AgNPs was used as surface enhanced Raman scattering substrate to detect furfural concentration in soybean based natural ester oil. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffractometer (XRD), electrochemical workstation and Raman spectrometer were used for the characterization. The results showed that the lowest detection limit of RhB was 10-11 mol/L (M), and α-Fe2O3-AgNPs had better SERS performance than a single AgNPs substrate. The α-Fe2O3-AgNPs substrate was functionalized to modify 4-ATP molecules, and was used to detect furfural in soya-based natural ester insulating oil with the lowest detection limit of 0.05 mg/L. The test results fully meet the aging standards of insulation oil in the power industry (severe aging, 4 mg/L; moderate aging, 0.5 mg/L; mild aging, 0.1 mg/L).
  • Application of gold film-silver nanowire substrate prepared based on finite element analysis in furfural detection in transformer oil
    ZHU Qilong, LI Shizhen, LI Fu, GUO Tao, YANG Lu, ZHANG Rui, YU Wei, YANG Tao
    Chinese Journal of Light Scattering. 2024, 36(1): 72-76. https://doi.org/10.13883/j.issn1004-5929.202401009
    Abstract ( ) Download PDF ( ) Knowledge map Save
    In order to improve the performance of surface-enhanced Raman scattering(SERS) substrate, silver nanowire substrate was used in this paper. The substrate and diameter of silver nanowire were optimized based on finite element analysis software. The results show that when the substrate is gold film, the hot spot electric field modulus of the substrate is the largest, indicating that it has the best enhancement effect. At the same time, the enhancement effect of silver nanowires with different diameters on the gold substrate was designed and studied. The results show that when the radius is from the 10 nm to 75 nm, the enhancement effect is first enhanced and then weakened. When the radius of the silver nanowires is 45 nm, the enhancement effect is the best. Based on the above research results, a gold film-silver nanowire substrate was prepared, and the prepared substrate was applied to the detection of furfural concentration in transformer oil. The minimum detection limit of the prepared substrate for furfural in transformer mineral oil can reach 1.5 mg/L. It is of great significance to realize the monitoring of transformer operation status.
  • Quantitative detection of dissolved furfural in oil based on Surface-enhanced Raman spectroscopy and machine learning
    LI Fu, ZHU Qilong, LI Shizhen, GUO Tao, YU Yunguang, QING Yan, YANG Lu, WANG Yunguang
    Chinese Journal of Light Scattering. 2024, 36(1): 77-85. https://doi.org/10.13883/j.issn1004-5929.202401010
    Abstract ( ) Download PDF ( ) Knowledge map Save
    We have developed a detection platform that combines Surface-Enhanced Raman Scattering (SERS) with Machine Learning Algorithms (MLA) for the rapid and sensitive detection of furfural in transformer oil. This is crucial for assessing the degree of aging in transformer oil-paper insulation. Firstly, we synthesized silver nanoparticles (AgNPs) with size-dependent properties using a hydrothermal method. These were then spin-coated onto a gold-plated polycrystalline silicon substrate (Si@Au) to form the Si@Au-Ag SERS substrate. With this substrate, we successfully detected furfural in transformer oil solutions of different concentrations, obtaining the corresponding Raman spectroscopic dataset. Subsequently, we employed two different MLAs, namely PCA+ANN and ANN, to construct quantitative calibration curves, enabling the conversion of detected Raman spectroscopic data into corresponding furfural concentrations. The regression model we established achieved a correlation coefficient (R) of 0.958, indicating high accuracy of the model.
  • Study on spectral similarity analysis and identification of green pigments
    Fan Xinyu, Pan Guoxiang, He Guiping, Xu Bo, Qiu chengcong, Zhou Mengyu, Xu minhong, Li Jinhua
    Chinese Journal of Light Scattering. 2024, 36(1): 86-94. https://doi.org/10.13883/j.issn1004-5929.202401011
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    Green pigments are often used in jungle camouflage. This article compares the similarity between 10 commonly used green pigments and plant leaves, including chromium green, alkaline magenta green, alizarin green, iron green, blue pigments(cobalt blue, ultramarine, cobalt sulfonated phthalocyanine, phthalocyanine blue, iron blue), and yellow pigments (bismuth yellow, iron yellow) mixtures, using two spectral similarity evaluation methods. Among them, the similarity between chrome green pigment and plant leaves is the highest, and the similarity in sub bands is good. The types of pigments are distinguished by the first order differential method of spectroscopy, and chromium green pigments with high similarity to plants can be identified by the spectral recognition index method. The spectral recognition index method can quickly extract target information, greatly improving the recognition speed.
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