Precursor Symmetry Triggered Modulation of Fluorescence Quantum Yield in Graphene Quantum Dots

Although various effective machine-learning attempts have been made to investigate the photoluminescence properties of graphene quantum dots (GQDs) or carbon dots, the physical correlation behind their mathematical models has not been reasonably elucidated. In this work, the correlation mechanism between the precursor structure and quantum yield of GQDs prepared by a "bottom-up" method is sufficiently studied. Three decisive factors affecting the quantum yield of GQDs during the two-component reaction system preparation are revealed, namely structure factor (F1), temperature factor (F2), and concentration factor (F3). The symmetry of precursors in the formation of sp2-sp3 hybrid carbon nanostructures is considered the key factor in the modulation of fluorescence quantum yield in GQDs. Notably, in contrast to previous work, it is first demonstrated that the normal modes of molecular vibration are the core mechanism by which the structural properties of the precursors act on the fluorescence quantum yield of GQDs. The conclusion further proved conducive in obtaining GQDs with a higher absolute quantum yield up to 83.33%. From Precursor to Carbon Nanostructure: New Perspectives of Machine Learning (ML)-Assisted Preparation. Leveraging the advantages of group theory together with ML, the core mechanism between the precursor structure and fluorescence quantum yield (QY) of GQDs is revealed for the first time. Practical modulation of QY (83.33%) in GQDs is achieved in two-component multi-variable reaction systems. image