1
|
Zhang X, Shang Y, Xie Q, Hu X, Wu K, Qu LL, Gu Y. Recyclable Au@R-Fe 3O 4/g-C 3N 4 substrates for rapid SERS detection and degradation of multiple pollutants. Talanta 2024; 276:126291. [PMID: 38776774 DOI: 10.1016/j.talanta.2024.126291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/14/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
Developing a Surface-enhanced Raman spectroscopy (SERS) method with excellent detecting ability, good recyclability and analyzing multiple pollutants rapidly are critical for evaluation of water quality in emergency pollution affairs. While constructing a multifunctional substrate with these characteristics to realize the application of SERS in water quality monitoring remains a challenge. In this work, a reusable Au@R-Fe3O4/g-C3N4 SERS substrate is prepared by loading Au nanoparticles (Au NPs) on Fe3O4 nanorings (R-Fe3O4) and the formed Au@R-Fe3O4 is further combined with g-C3N4 nanosheets through a simple electrostatic assembly method. The Au@R-Fe3O4/g-C3N4 nanocomposite presents multifunction of magnetic enrichment, SERS signal enhancement, multiple pollutants analyzing, and photocatalytic activity, which achieves quantitative detection of rhodamine B (RhB), tetracycline hydrochloride (TC), and 4-chlorophenol (4-CP), with detection limits of 5.30 × 10-9, 7.50 × 10-8, 7.69 × 10-8 mol/L, respectively. Furthermore, the recyclable detection capability of Au@R-Fe3O4/g-C3N4 for multi components is demonstrated by the strong SERS signal after 9 cycles of "detection-degradation" processes. Combined with good uniformity and stability, this SERS method based on Au@R-Fe3O4/g-C3N4 substrate provides a new strategy for the multi-pollutants detection and degradation in water environment.
Collapse
Affiliation(s)
- Xue Zhang
- School of Chemistry & Materials Science, Jiangsu Normal University, Xuzhou, 221116, China
| | - Yunsheng Shang
- School of Chemistry & Materials Science, Jiangsu Normal University, Xuzhou, 221116, China
| | - Qi Xie
- School of Chemistry & Materials Science, Jiangsu Normal University, Xuzhou, 221116, China
| | - Xingzhe Hu
- School of Chemistry & Materials Science, Jiangsu Normal University, Xuzhou, 221116, China
| | - Ke Wu
- School of Chemistry & Materials Science, Jiangsu Normal University, Xuzhou, 221116, China
| | - Lu-Lu Qu
- School of Chemistry & Materials Science, Jiangsu Normal University, Xuzhou, 221116, China.
| | - Yingqiu Gu
- School of Chemistry & Materials Science, Jiangsu Normal University, Xuzhou, 221116, China.
| |
Collapse
|
2
|
Wang Z, Li Y, Zhai J, Yang S, Sun B, Liang P. Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach. Talanta 2024; 275:126138. [PMID: 38677164 DOI: 10.1016/j.talanta.2024.126138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 04/29/2024]
Abstract
Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.
Collapse
Affiliation(s)
- Zilong Wang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China; Xiamen Palantier Technology Co., Ltd., Xiamen, 361000, China
| | - Yunfeng Li
- College of Information Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Jinglei Zhai
- School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, 300072, China
| | - Siwei Yang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Biao Sun
- School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, 300072, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China; Xiamen Palantier Technology Co., Ltd., Xiamen, 361000, China.
| |
Collapse
|
3
|
Lee S, Dang H, Moon JI, Kim K, Joung Y, Park S, Yu Q, Chen J, Lu M, Chen L, Joo SW, Choo J. SERS-based microdevices for use as in vitro diagnostic biosensors. Chem Soc Rev 2024; 53:5394-5427. [PMID: 38597213 DOI: 10.1039/d3cs01055d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.
Collapse
Affiliation(s)
- Sungwoon Lee
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hajun Dang
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Joung-Il Moon
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Kihyun Kim
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Younju Joung
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Sohyun Park
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Qian Yu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Jiadong Chen
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Mengdan Lu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Lingxin Chen
- School of Pharmacy, Binzhou Medical University, Yantai, 264003, China
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Yantai 264003, China.
| | - Sang-Woo Joo
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul 06978, South Korea.
| | - Jaebum Choo
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| |
Collapse
|
4
|
Zhang X, Li M, Meng G, Huang Z, Zhu S, Chen B. Ag Nanoparticles@Au Nanograting Array as a 3D Flexible and Effective Surface-Enhanced Raman Scattering Substrate. Anal Chem 2024; 96:6112-6121. [PMID: 38554137 DOI: 10.1021/acs.analchem.3c02710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
Surface-enhanced Raman scattering (SERS) is a powerful analytical technique for chemical identification, but it remains a great challenge to realize the large-scale and well-controlled fabrication of sensitive and repeatable SERS substrates. Here, we report a facile strategy to fabricate centimeter-sized periodic Au nanograting (Au-NG) decorated with well-arranged Ag nanoparticles (Ag-NPs) (denoted as Ag-NPs@Au-NG) as a three-dimensional (3D) flexible hybrid SERS substrate with high sensitivity and good reproducibility. The Au-NG patterns with periodic ridges and grooves are fabricated through nanoimprint lithography by employing a low-cost digital versatile disc (DVD) as a master mold, and the Ag-NPs are assembled by a well-controlled interface self-assembly method without any coupling agents. Multiple coupling electromagnetic field effects are created at the nanogaps between the Ag-NPs and Au-NG patterns, leading to high-density and uniform hot spots throughout the substrate. As a result, the Ag-NPs@Au-NG arrays demonstrate an ultrahigh SERS sensitivity as low as 10-13 M for rhodamine 6G with a high average enhancement factor (EF) of 1.85 × 108 and good signal reproducibility. For practical applications, toxic organic pollutants including crystal violet, thiram, and melamine have been successfully detected with high sensitivity at a low detection limit, showing a good perspective in the rapid detection of toxic organic pollutants.
Collapse
Affiliation(s)
- Xiang Zhang
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Mingtao Li
- School of Mechanical and Resource Engineering, Wuzhou University, Wuzhou 543002, China
| | - Guowen Meng
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Zhulin Huang
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
| | - Shuyi Zhu
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Bin Chen
- Key Laboratory of Materials Physics and Anhui Key Laboratory of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| |
Collapse
|
5
|
Feng J, Zhou P, Qin C, Chen R, Chen Q, Li L, Chen J, Cheng H, Huang W, Cao J. Magnetic solid-phase extraction-based surface-enhanced Raman spectroscopy for label-free therapeutic drug monitoring of carbamazepine and clozapine in human serum. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123924. [PMID: 38262293 DOI: 10.1016/j.saa.2024.123924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/19/2024] [Accepted: 01/19/2024] [Indexed: 01/25/2024]
Abstract
Determination of antiepileptic drugs and antipsychotics in human serum is significant in individualized drug administration and therapeutic drug monitoring (TDM). In this study, we developed a rapid label-free TDM method for the antiepileptic drug carbamazepine (CBZ) and the antipsychotic clozapine (CLO) in human serum. This detection strategy is based on the combination of surface-enhanced Raman scattering (SERS) and magnetic solid-phase extraction (MSPE). Initially, Fe3O4@SiO2@MIL-101(Fe) nanocomposites were synthesized by the layer-by-layer self-assembly method and characterized using scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Brunauer-Emmett-Teller, ultraviolet-visible, and Fourier transform infrared analyses. Subsequently, CBZ and CLO were detected in human serum using Fe3O4@SiO2@MIL-101(Fe) as the solid-phase extraction adsorbent and Ag nanoparticles as SERS substrates. The potential of the MSPE-SERS method for the label-free TDM of CBZ and CLO was then investigated. Fe3O4@SiO2@MIL-101(Fe) prevents magnetic particle aggregation and demonstrates rapid magnetic separation capability that simplifies the pretreatment process and reduces interference from complex matrices. Its large surface area can effectively enrich targets in complex matrices, thereby improving the SERS detection sensitivity. The linearity between CBZ and CLO was excellent over the concentration range of 0.1-100 µg/mL (calculated as the intensity of the SERS characteristic peaks of CBZ and CLO at 728 cm and 1054 cm-1, respectively), with correlation coefficients (R2) of 0.9987 and 0.9957, and detection limits of 0.072 and 0.12 µg/mL, respectively. The recoveries of CBZ with CLO ranged from 94.0 % to 105.0 %, and their relative standard deviations were <6.8 %. Compared to other assays, the developed MSPE-SERS method has the advantages of simple sample pretreatment, rapid detection, and good reproducibility, which provides a novel approach for the TDM of other drugs.
Collapse
Affiliation(s)
- Jun Feng
- Department of Medicine, Guangxi University of Science and Technology, Liuzhou 545005, Guangxi, PR China
| | - Pei Zhou
- Guangxi Key Laboratory of Green Processing of Sugar Resources, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, Guangxi, PR China
| | - Chunli Qin
- Department of Medicine, Guangxi University of Science and Technology, Liuzhou 545005, Guangxi, PR China
| | - Ruijue Chen
- Guangxi Key Laboratory of Green Processing of Sugar Resources, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, Guangxi, PR China
| | - Qiying Chen
- Guangxi Key Laboratory of Green Processing of Sugar Resources, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, Guangxi, PR China
| | - Lina Li
- Department of Medicine, Guangxi University of Science and Technology, Liuzhou 545005, Guangxi, PR China
| | - Jun Chen
- Department of Medicine, Guangxi University of Science and Technology, Liuzhou 545005, Guangxi, PR China
| | - Hao Cheng
- Guangxi Key Laboratory of Green Processing of Sugar Resources, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, Guangxi, PR China
| | - Wenyi Huang
- Guangxi Key Laboratory of Green Processing of Sugar Resources, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, Guangxi, PR China
| | - Jinru Cao
- Dongguan Key Laboratory of Precision Molecular Diagnostics, Prenatal Diagnosis Center, Dongguan Songshan Lake Central Hospital, Dongguan 523200, Guangdong, PR China.
| |
Collapse
|