Correlative Fluorescence and Transmission Electron Microscopy Assisted by 3D Machine Learning Reveals Thin Nanodiamonds Fluoresce Brighter

Nitrogen vacancy (NV) centers influorescent nanodiamonds(FNDs)draw widespread attention as quantum sensors due to their room-temperatureluminescence, exceptional photo- and chemical stability, and biocompatibility.For bioscience applications, NV centers in FNDs offer high-spatial-resolutioncapabilities that are unparalleled by other solid-state nanoparticleemitters. On the other hand, pursuits to further improve the opticalproperties of FNDs have reached a bottleneck, with intense debatein the literature over which of the many factors are most pertinent.Here, we describe how substantial progress can be achieved using acorrelative transmission electron microscopy and photoluminescence(TEMPL) method that we have developed. TEMPL enables a precise correlativeanalysis of the fluorescence brightness, size, and shape of individualFND particles. Augmented with machine learning, TEMPL can be usedto analyze a large, statistically meaningful number of particles.Our results reveal that FND fluorescence is strongly dependent onparticle shape, specifically, that thin, flake-shaped particles areup to several times brighter and that fluorescence increases withdecreasing particle sphericity. Our theoretical analysis shows thatthese observations are attributable to the constructive interferenceof light waves within the FNDs. Our findings have significant implicationsfor state-of-the-art sensing applications, and they offer potentialavenues for improving the sensitivity and resolution of quantum sensingdevices.

Automated summary

Nitrogen vacancy (NV) centers in fluorescent nanodiamonds (FNDs) are important for quantum sensors due to their luminescence, stability, and biocompatibility. The shape of FNDs significantly affects their fluorescence brightness, with thin, flake-shaped particles being several times brighter and decreasing particle sphericity leading to increased fluorescence.