Bifunctional nanoprobe for dual-mode detection based on blue emissive iron and nitrogen co-doped carbon dots as a peroxidase-mimic platform

In this study, iron and nitrogen co-doped carbon dots (Fe/N@C-dots) were fabricated via one pot hydrothermal method. The as-synthesized Fe/N@C-dots exhibited unique properties such as high catalytic activity and fluorescence quantum yield (33.8%). The peroxidase-like activity of Fe/N@C-dots was used to detect H 2 O 2 in the presence of phenol and 4-aminoantipyrine (4-AMAP) to yield a pink colored product (quinoneimine chromogen), which was measured at λ max = 505 nm (colorimetric method). Moreover, the pink colored product quenched the fluorescence of Fe/N@C-dots via inner-filter effect and static quenching (fluorometric method). The enzyme like activity of Fe/N@C-dots was cascaded with the enzymatic activity of glucose oxidase (GOx) to evaluate the practical application of Fe/N@C-dots to develop dual-channel sensor (fluorometric and colorimetric) for high selective and sensitive detection of glucose. It was found that the absorption intensities were increased linearly with glucose concentration in the range of 2.13–130 μM with LOD (S/N = 3) of 0.58 μM, while fluorescence intensities were decreased linearly after addition of glucose in the range of 1.33–140 μM with LOD (S/N = 3) of 0.36 μM. The Fe/N@C-dots/4-AMAP/phenol/GOx system along with the fluorometric method were applied efficiently to determine glucose in many samples including human serum, urine, and saliva with acceptable recoveries % in the range of 93.8–106.1% and RSD % not more than 3.7%, suggesting the reliability of the as-fabricated biosensor. • New iron and nitrogen co-doped carbon dots (Fe/N@C-dots) were synthesized. • The Fe/N@C-dots platform was utilized as a peroxidase-mimetic platform for dual-mode detection. • The Fe/N@C-dots platform was used to detect H 2 O 2 and glucose. • It was utilized to detect glucose in human serum, urine, and saliva samples. • It exhibits many advantages such as low-cost, good selectivity, reliability, and satisfactory sensitivity.