Dual fluorescent hollow silica nanofibers for in situ pH monitoring using an optical fiber.
Junhu ZhouYundong RenYuan NieCongran JinJiyoon ParkJohn X J ZhangPublished in: Nanoscale advances (2023)
This study reports a sensitive and robust pH sensor based on dual fluorescent doped hollow silica nanofibers (hSNFs) for in situ and real-time pH monitoring. Fluorescein isothiocyanate (FITC) and tris(2,2'-bipyridyl)dichlororuthenium(ii) hexahydrate (Ru(BPY) 3 ) were chosen as a pH sensitive dye and reference dye, respectively. hSNFs were synthesized using a two-step method in a reverse micelle system and were shown to have an average length of 6.20 μm and average diameter of 410 nm. The peak intensity ratio of FITC/Ru(BPY) 3 was used to calibrate to solution pH changes. An optical-fiber-based fluorescence detection system was developed that enabled feasible and highly efficient near-field fluorescence detection. The developed system enables fully automated fluorescence detection, where components including the light source, detector, and data acquisition unit are all controlled by a computer. The results show that the developed pH sensor works in a linear range of pH 4.0-9.0 with a fast response time of less than 10 s and minimal sample volume of 50 μL, and can be stored under dark conditions for one month without failure. In addition, the as-prepared hSNF-based pH sensors also have excellent long-term durability. Experimental results from ratiometric sensing confirm the high feasibility, accuracy, stability and simplicity of the dual fluorescent hSNF sensors for the detection of pH in real samples.
Keyphrases
- highly efficient
- quantum dots
- label free
- energy transfer
- loop mediated isothermal amplification
- living cells
- real time pcr
- deep learning
- high resolution
- computed tomography
- machine learning
- single molecule
- emergency department
- hydrogen peroxide
- magnetic resonance imaging
- artificial intelligence
- sensitive detection
- electronic health record
- magnetic resonance
- high intensity
- big data
- tandem mass spectrometry
- ionic liquid
- metal organic framework
- molecularly imprinted
- image quality