High Uniformity and Enhancement Au@AgNS 3D Substrates for the Diagnosis of Breast Cancer

Surface-Enhanced Raman Spectroscopy Sensor Integrated with Ag@ZIF-8@Au Core-Shell-Shell Nanowire Membrane for Enrichment, Ultrasensitive Detection, and Inactivation of Bacteria in the Environment

A core-shell-shell sandwich material is developed with silver nanowires as the core, ZIF-8 as an inner shell, and gold nanoparticles as the outer shell, namely, Ag@ZIF-8@Au nanowires (AZA-NW). Then, the synthesized AZA-NW is transformed into a surface-enhanced Raman spectroscopy (SERS) sensor (named M-AZA) by the vacuum filtration method and used to enrich, detect, and inactivate traces of bacteria in the environment. The M-AZA sensor has three main functions: (1) trace bacteria are effectively enriched, with an enrichment efficiency of 91.4%; (2) ultrasensitive detection of trace bacteria is realized, with a minimum detectable concentration of 1 × 101 CFU/mL; (3) bacteria are effectively killed up to 92.4%. The shell thickness of ZIF-8 (5-75 nm) is controlled by adjusting the synthesis conditions. At an optimum shell thickness of 15 nm, the effect of gold nanoparticles and ZIF-8 shell on the sensor's stability, SERS activity, and antibacterial performance is investigated. The simulation of the SERS sensor using the finite difference time domain (FDTD) method is consistent with the experimental results, theoretically demonstrating the role of the gold nanoparticles and the ZIF-8 shell. The sensor also shows excellent stability, safety, and generalizability. The campus water sample is then tested on-site by the M-AZA SERS sensor, indicating its potential for practical applications.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer

Liquid biopsy is currently a non-destructive and convenient method of cancer screening, due to human blood containing a variety of cancer-related biomolecules. Therefore, the development of an accurate and rapid breast cancer screening technique combined with breast cancer serum is crucial for the treatment and prognosis of breast cancer patients. In this study, the surface enhanced Raman spectroscopy (SERS) technique is used to enhance the Raman spectroscopy (RS) signal of serum based on a high sensitivity thermally annealed silver nanoparticle/porous silicon bragg mirror (AgNPs/PSB) composite substrate. Compared with RS, SERS reflects more and stronger spectral peak information, which is beneficial to discover new biomarkers of breast cancer. At the same time, to further explore the diagnostic ability of SERS technology for breast cancer. In this study, the raw spectral data are processed by baseline correction, polynomial smoothing, and normalization. Then, the relevant feature information of SERS and RS is extracted by principal component analysis (PCA), and five classification models are established to compare the diagnostic performance of SERS and RS models respectively. The experimental results show that the breast cancer diagnosis model based on the improved SERS substrate combined with the machine learning algorithm can be used to distinguish breast cancer patients from controls. The accuracy, sensitivity, specificity and AUC values of the SVM model are 100%, 100%, 100% and 100%, respectively, as well as the training time of 4ms. The above experimental results show that the SERS technology based on AgNPs/PSB composite substrate, combined with machine learning methods, has great potential in the rapid and accurate identification of breast cancer patients.