Interference-free SERS nanoprobes for labeling and imaging of MT1-MMP in breast cancer cells

Triple-Bond Vibrations: Emerging Applications in Energy and Biological Sciences

Triple bonds, such as that formed between two carbon atoms (i.e., C≡C) or that formed between one carbon atom and one nitrogen atom (i.e., C≡N), afford unique chemical bonding and hence vibrational characteristics. As such, they are not only frequently used to construct molecules with tailored chemical and/or physical properties but also employed as vibrational probes to provide site-specific chemical and/or physical information at the molecular level. Herein, we offer our perspective on the emerging applications of various triple-bond vibrations in energy and biological sciences with a focus on C≡C and C≡N triple bonds.

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.