Abstract.SignificanceCarbon dots (CDs) have attracted a host of research interest in recent years mainly due to their unique photoluminescence (PL) properties that make them applicable in various biomedical areas, such as imaging and image-guided therapy. However, the real mechanism underneath the PL is a subject of wide controversy and can be investigated from various angles.AimOur work investigates the effect of the isomeric nitrogen position as the precursor in the synthesis of CDs by shedding light on their photophysical properties on the single particles and ensemble level.ApproachTo this end, we adopted five isomers of diaminopyridine (DAP) and urea as the precursors and obtained CDs during a hydrothermal process. The various photophysical properties were further investigated in depth by mass spectroscopy. CD molecular frontier orbital analyses aided us in justifying the fluorescence emission profile on the bulk level as well as the charge transfer processes. As a result of the varying fluorescent responses, we indicate that these particles can be utilized for machine learning (ML)-driven sensitive detection of oral microbiota. The sensing results were further supported by density functional theoretical calculations and docking studies.ResultsThe generating isomers have a significant effect on the overall photophysical properties at the bulk/ensembled level. On the single-particle level, although some of the photophysical properties such as average intensity remained the same, the overall differences in brightness, photo-blinking frequency, and bleaching time between the five samples were conceived. The various photophysical properties could be explained based on the different chromophores formed during the synthesis. Overall, an array of CDs was demonstrated herein to achieve∼100%separation efficacy in segregating a mixed oral microbiome culture in a rapid (<0.5h), high-throughput manner with superior accuracy.ConclusionsWe have indicated that the PL properties of CDs can be regulated by the precursors’ isomeric position of nitrogen. We emancipated this difference in a rapid method relying on ML algorithms to segregate the dental bacterial species as biosensors.