Amorphous-Crystalline Synergy in CoSe<sub>2</sub>/CoS<sub>2</sub> Heterostructures: High-Performance SERS Substrates for Esophageal Tumor Cell Discrimination.
Authors
Affiliations (5)
Affiliations (5)
- Department of Gastroenterology, The First Medical Center of Chinese, PLA General Hospital, Beijing, 100853, China.
- School of Chemistry, Beihang University, Beijing, 100191, China.
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
- School of Basic Medical Sciences, Hebei University, Baoding, 071002, China.
- Key Laboratory of Jiangxi Province for Persistent Pollutants Control and Resources Recycle, Nanchang Hangkong University, Nanchang, 330063, China.
Abstract
Although surface-enhanced Raman scattering (SERS) spectroscopy is applied in biomedicine deeply, the design of new substrates for wider detection is still in demand. Crystalline-amorphous CoSe<sub>2</sub>/CoS<sub>2</sub> heterojunction is synthesized, with high SERS performance and stability, composed of orthorhombic (o-CoSe<sub>2</sub>) and amorphous CoS<sub>2</sub> (a-CoS<sub>2</sub>). By adjusting feed ratio, the proportion of a-CoS<sub>2</sub> to o-CoSe<sub>2</sub> is regulated, where CoSe<sub>2</sub>/CoS<sub>2</sub>-S50 with a 1:1 ratio demonstrates the best SERS performance due to the balance of two components. It is confirmed through experimental and simulation methods that o-CoSe<sub>2</sub> and a-CoS<sub>2</sub> have unique contribution, respectively: a-CoS<sub>2</sub> has rich vacancies and a higher density of active sites, while o-CoSe<sub>2</sub> further enriches vacancies, enhances electron delocalization and charge transfer (CT) capabilities, and reduces bandgap. Besides, CoSe<sub>2</sub>/CoS<sub>2</sub>-S50 achieves not only SERS detection of two common esophageal tumor cells (KYSE and TE) and healthy oral epithelial cells (het-1A), but also the discrimination with high sensitivity, specificity, and accuracy via machine learning (ML) analysis.