Ratio fluorescence and smartphone-assisted colorimetry dual-mode detection of creatinine based on F, B, N-doped of red fluorescent carbon dots.

Background: Creatinine is a small molecule disease biomarker that reflects kidney function, accurate and effective detection of creatinine will play an important role in the prevention and treatment of diseases. Currently, commonly used creatinine detection methods are limited by expensive instruments, complex sample preparation, many interference factors from biological samples, and environmental factors that can affect the accuracy of the measurement. Therefore, developing a fast, simple, inexpensive, sensitive analysis method that can eliminate background interference and provide multi-detection modes has strong attraction and value.

Results: Here, we prepared ratio fluorescence and smartphone-assisted colorimetry dual-mode platform for the detection of creatinine based on F, B, N-doped of red fluorescent carbon dots (HAc-CDs). HAc-CDs were synthesized via hydrothermal method using o-phenylenediamine and tetrafluoroboric acid. Under an excitation wavelength of 360 nm, HAc-CDs exhibit two emission peaks at 599 nm and 652 nm with full width at half-maximums of 43.8 nm and 43.3 nm, respectively. In the presence of creatinine, the fluorescence peak at 599 nm with orange light increases, while the peak at 652 nm with red light decreases. This phenomenon of creatinine-HAc-CDs is inferred to be aggregation-induced emission. The ratio of fluorescence intensity (F/F) is linearly related to the concentration of creatinine range from 3.0 × 10 to 1.0 × 10 M with a detection limit of 0.084 nM. Additionally, a smartphone-assisted recognition creatinine intelligent detection system based on the color change of paper strips was constructed.

Significance: Creatinine in urine can be dual-mode detected with ratio fluorescence and smartphone-assisted colorimetry based on the prepared F, B, N-doped of red fluorescent carbon dots. Dual-mode detection system enables convenient, reliable and visual detection for on-site applications, and it has reference significance for the development of other disease biomarker detection methods.