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Seamless Integration of Rapid Separation and Ultra-Sensitive Detection for Complex Biological Samples using Multistage Annular Functionalized Carbon Nanotube Arrays.

Lihuang LiJialing ZhangZhengqi JiaoXi ZhouLei RenMiao Wang
Published in: Advanced materials (Deerfield Beach, Fla.) (2024)
Efficient separation, enrichment, and detection of bacteria in diverse media are pivotal for identifying bacterial diseases and their transmission pathways. However, conventional bacterial detection methods that split the separation and detection steps are plagued by prolonged processing times, high costs, and intricate procedures that hinder the advancement of pathogen detection techniques. Herein, we introduce a multistage annular-functionalized carbon nanotube array device designed for the seamless integration of complex biological sample separation and multi-marker detection. This device resorted to the super-smooth fluidity of the liquid sample in the carbon nanotubes interstice through rotation assistance, achieving the ability to quickly separate impurities and capture biomarkers (1 mL sample cost time of 2.5 s). Fluid dynamics simulations showed that the reduction of near-surface hydrodynamic resistance drives the capture of bacteria and related biomarkers on the functionalized surface of carbon nanotube in sufficient time. When further assembled as an even detection device, it exhibited fast detection (<30 min), robust linear correlation (10 1 -10 7 colony-forming units [CFU]/mL, R 2 = 0.997), ultrasensitivity (limit of detection = 1.7 CFU/mL), and multi-target detection (Staphylococcus aureus, extracellular vesicles, and enterotoxin proteins). Collectively, our material and system offer an expanded platform for real-time diagnostics, enabling integrated rapid separation and detection of various disease biomarkers. The device has significant potential applications in fields such as emergent infectious disease diagnosis and treatment, infection source tracing, and environmental monitoring. This article is protected by copyright. All rights reserved.
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