Super-sensitive Detection On Gap-enhanced Raman Tags-based Lateral Flow Systems

Traditional lateral flow system is widely used in(bio)molecular detections,but its low sensitivity hampers the further clinical applications.It is vital to develop new detection method with a higher sensitivity in the lateral flow system.Recently,surface-enhanced Raman spectroscopy(SERS)technique has been widely used because of high sensitivity and high specificity.However,SERS technique suffers the poor reproducibility issue and the traditional peak integral method fails when Raman bands overlap,limiting the choice of Raman reporters.In this thesis,we firstly synthesized three kinds of novel SERS tags termed as gap-enhanced Raman tags(GERTs),which are core-shell nanoparticles with an interior nanogap and the embedded Raman reporters.GERTs greatly improve the reproducibility and photostability of Raman signals,and as well as dramatically increase the Raman enhancement factor.Next,we introduced two spectral-fitting methods,called classical least square and non-negative matrix factorization,to demultiplex the mixed Raman spectrum into pure components.These two methods provided a solution to overcome the overlapping issue of Raman bands.Finally,we designed a GERTs-based lateral flow system with dual-particle calibration and spectral-fitting methods.This system greatly decreased the influence of non-specific binding,which is a serious issue in common immunoassay,on the limit of detection(LOD)and thus improved the LOD by two orders of magnitude compared to the traditional lateral flow system.We applied this system for the detection of Human chorionic gonadotropin(HCG),cardiac troponin I(c Tn I)and brain natriuretic peptide(BNP)and realized LODs of 0.99,0.69,and 0.18 pg/m L correspondingly,which are far beyond the clinical requirements.As a result,this novel lateral flow system has great potential in fast and easy diagnose of acute myocardial infarction.