Quantification of Emission Efficiency in Persistent Luminescent Materials [Dataset]

Accurate quantification of efficiency enables rigorous comparison between different photoluminescent materials, providing an optimization path critical to the development of next-generation light sources. Persistent luminescent materials exhibit delayed and long-lasting luminescence due to the temporary storage of optical energy in engineered structural defects. Although these materials have recently gained attention for their potential in a wide range of applications, from smart lighting to in vivo imaging, standard characterization methods do not provide a universal comparison of phosphor performance, making it difficult to assess the efficiency of the different processes involved in afterglow. In this work we establish a protocol to obtain the emission quantum yield of persistent phosphors. We determine the persistent and total luminescence quantum yields by considering the ratio of photons emitted in the afterglow and during charging to those absorbed. The method is first applied to transparent single crystals of the most common persistent phosphors, such as SrAl2O4:Eu2+,Dy3+ and Y3Al2Ga3O12:Ce3+,Cr3+. The versatility of our methodology is then demonstrated by quantifying the quantum yield of a thin film based on ZnGa2O4:Cr3+ persistent luminescent nanoparticles, which are commonly used for in vivo imaging. We confirm the high efficiency of strontium aluminate and reveal a strong dependence of the obtained values on the illumination conditions, highlighting a trade-off between efficiency and brightness, which opens the door to precise optimization of the charging conditions for each material and application. Our results contribute to the development of standard characterization protocols for the analysis of the mechanisms governing afterglow, as well as the assessment of the overall efficiency of the process. Such achievements enable a rigorous comparison of the performance of different persistent materials, allowing for optimization routes beyond the usual trial-and-error approach.