Periodic Surface-Enhanced Raman Scattering-Encoded Magnetic Beads for Reliable Quantitative Surface-Enhanced Raman Scattering-Based Multiplex Bioassay

Recyclable Au@R-Fe3O4/g-C3N4 substrates for rapid SERS detection and degradation of multiple pollutants

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.

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

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.

SERS-based microdevices for use as in vitro diagnostic biosensors

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.

Ag Nanoparticles@Au Nanograting Array as a 3D Flexible and Effective Surface-Enhanced Raman Scattering Substrate

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.

Magnetic solid-phase extraction-based surface-enhanced Raman spectroscopy for label-free therapeutic drug monitoring of carbamazepine and clozapine in human serum

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.