AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability

• Main Authors: Xu, Xiaoyan, Feng, Haowen, Zhao, Ying, Shi, Yunzhu, Feng, Wei, Loh, Xian Jun, Vancso, Gyula Julius, Guo, Shifeng
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Atomic force microscopy; Bacterial viability
• Online Access: https://hdl.handle.net/10356/179817
• Institution: Nanyang Technological University

Detecting bacterial viability remains a critical necessity across the pharmaceutical, medical, and food sectors. Yet, a rapid, non-destructive approach for distinguishing between intact live and dead bacteria remains elusive. Here, this work introduces a robust and accessible methodology that integr...