H5N1亚型流感病毒                        
                
                                
                        
                            生物传感器                        
                
                                
                        
                            电容感应                        
                
                                
                        
                            病毒学                        
                
                                
                        
                            化学                        
                
                                
                        
                            纳米技术                        
                
                                
                        
                            材料科学                        
                
                                
                        
                            生物                        
                
                                
                        
                            病毒                        
                
                                
                        
                            工程类                        
                
                                
                        
                            电气工程                        
                
                        
                    
            作者
            
                Joshin Kumar,Xu Meng,Y. Li,Shu-Wen You,Brookelyn M. Doherty,Woodrow D. Gardiner,John R. Cirrito,Carla M. Yuede,Ananya Benegal,Michael D. Vahey,Astha Joshi,Kuljeet Seehra,Adrianus C. M. Boon,Yin-Yuan Huang,Joseph V. Puthussery,Rajan K. Chakrabarty            
         
                    
            出处
            
                                    期刊:ACS Sensors
                                                         [American Chemical Society]
                                                        日期:2025-02-21
                                                                        被引量:6
                                 
         
        
    
            
            标识
            
                                    DOI:10.1021/acssensors.4c03087
                                    
                                
                                 
         
        
                
            摘要
            
            Airborne transmission via aerosols is a dominant route for the transmission of respiratory pathogens, including avian H5N1 influenza A virus and E. coli bacteria. Rapid and direct detection of respiratory pathogen aerosols has been a long-standing technical challenge. Herein, we develop a novel label-free capacitive biosensor using an interlocked Prussian blue (PB)/graphene oxide (GO) network on a screen-printed carbon electrode (SPCE) for direct detection of avian H5N1 and E. coli. A single-step electro-co-deposition process grows GO branches on the SPCE surface, while the PB nanocrystals simultaneously decorate around the GO branches, resulting in an ultrasensitive capacitive response at nanofarad levels. We tested the biosensor for H5N1 concentrations from 2.0 viral RNA copies/mL to 1.6 × 105 viral RNA copies/mL, with a limit of detection (LoD) of 56 viral RNA copies/mL. We tested it on E. coli for concentrations ranging from 2.0 bacterial cells/mL to 1.8 × 104 bacterial cells/mL, with a LoD of 5 bacterial cells/mL. The detection times for both pathogens were under 5 min. When integrated with a custom-built wet cyclone bioaerosol sampler, our biosensor could detect and quasi-quantitatively estimate H5N1 and E. coli concentrations in air with spatial resolutions of 93 viral RNA copies/m3 and 8 bacterial cells/m3, respectively. The quasi-quantification method, based on dilution and binary detection (positive/negative), achieved an overall accuracy of >90% for pathogen-laden aerosol samples. This biosensor is adaptable for multiplexed detection of other respiratory pathogens, making it a versatile tool for real-time airborne pathogen monitoring and risk assessment.
         
            
 
                 
                
                    
                    科研通智能强力驱动
Strongly Powered by AbleSci AI