An eco-friendly near infrared fluorescence molecularly imprinted sensor based on zeolite imidazolate framework-8 for rapid determination of trace trypsin

This report depicts an environment-friendly molecularly imprinted fluorescence sensor for rapid determination of trypsin based on new near infrared (NIR) fluorescence carbon dots and metal-organic frameworks. The novel NIR fluorescence carbon dots were facilely synthesized from green plants by one-step solvothermal treatment, and the imprinted sensor was prepared by sol-gel under mild condition. The imprinted NIR fluorescence sensor was characterized by Fourier-transform infrared spectrometry, energy disperse spectroscopy, scanning electron microscopy, X-ray diffraction and transmission electron microscopy. The results showed the introduction of zeolite imidazolate framework-8 can improve the fluorescence response sensitivity and response speed. Under optimized conditions, the fluorescence quenching of the imprinted NIR fluorescence sensor was linear relationship with the increasing trypsin concentration in the range of 0.05-9.0 ng/L with a detection limit of 8.8 pg/L. The developed imprinted fluorescence sensor can selectively recognize trypsin in 1.5 min, which was applied to rapidly and sensitively detect trypsin in serum and urine samples successfully with the recoveries of 95.3-104.1% and 97.7-105.2%, respectively. The as-prepared imprinted fluorescence sensor provides a viable and eco-friendly method for rapidly sensitive detection of trace bio-macromolecule in complex samples.

Automated summary

An eco-friendly molecularly imprinted fluorescence sensor utilizing novel NIR fluorescence carbon dots and metal-organic frameworks was developed for the rapid determination of trypsin. The sensor showed improved sensitivity and response speed with the introduction of zeolite imidazolate framework-8. The sensor exhibited a linear relationship between fluorescence quenching and trypsin concentration with a detection limit of 8.8 pg/L, providing a viable method for sensitive detection in complex samples.