Engineering Nitrogen-Doped Carbon Quantum Dots: Tailoring Optical and Chemical Properties through Selection of Nitrogen Precursors

The process of N-doping is frequently employed to enhance the properties of carbon quantum dots. However, the precise requirements for nitrogen precursors in producing high-quality N-doped carbon quantum dots (NCQDs) remain undefined. This research systematically examines the influence of various nitrogen dopants on the morphology, optical features, and band structure of NCQDs. The dots are synthesized using an efficient, eco- friendly, and rapid continuous hydrothermal flow technique. This method offers unparalleled control over synthesis and doping, while also eliminating convention-related issues. Citric acid is used as the carbon source, and urea, trizma base, beta-alanine, L-arginine, and EDTA are used as nitrogen sources. Notably, urea and trizma produced NCQDs with excitation-independent fluorescence, high quantum yields (up to 40%), and uniform dots with narrow particle size distributions. Density functional theory (DFT) and time-dependent DFT modelling established that defects and substituents within the graphitic structure have a more significant impact on the NCQDs' electronic structure than nitrogen-containing functional groups. Importantly, for the first time, this work demonstrates that the conventional approach of modelling single-layer structures is insufficient, but two layers suffice for replicating experimental data. This study, therefore, provides essential guidance on the selection of nitrogen precursors for NCQD customization for diverse applications. This manuscript highlights continuous hydrothermal flow synthesis (CHFS) that directly obtains luminescent nitrogen doped carbon quantum dots using a simple precursor, citric acid, whilst varying the N-dopants to systematically engineer carbon dots with varied but controllable optical characteristics. It reports that a common approach of modelling single-layer structures is insufficient, but two layers suffice to reproduce the experimental data. image