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Laser pump power density as a factor controlling up-conversion luminescence quantum yield of lanthanide nanocomplexes

O.E. Sarmanova, S.A. Burikov, T.A. Dolenko

Moscow State University, Department of Physics, Moscow, Russia

Abstract

Up-conversion nanoparticles (UCNP) are a class of promising nanomaterials that convert low-frequency light into high-frequency radiation. This unique physical property opens broad prospects for the application of up-conversion nanoparticles (UCNP) in life sciences due to the several advantages of UCNPs over down-conversion NPs. These include greater penetration depth, higher spatial image resolution, as well as the fact that infrared radiation required for up-conversion luminescence (UCL) excitation does not excite autofluorescence of biological tissues.
Due to their unique energy levels structure, enabling up-conversion process, UCNP show non-linear dependence of UCL intensity on the power density of excitation radiation. Such specific behaviour of UCL intensity may be explained in terms of two competing processes: linear decay of metastable intermediate energy level and process of energy transfer upconversion. The observed non-linear UCL dependence means similar behaviour of quantum yield (QY) under pump power density changes. To estimate the applicability of any UCNP in specific biomedical fields one has to evaluate their QY in a wide range of pump power density.
Aqueous suspensions of NaYF4 nanoscale crystalline matrix doped with Yb3+ and Tm3+ ions differing in ions ratio Yb3+/ Tm3+ in the complexes (10:4 and 14:4) were the objects of the study. UCL spectra of the nanocomplexes excited by 980 nm laser radiation corresponding to pump power density ranging from 1*1010 W*cm-2 to 1*1012 W*cm-2 were registered and analysed. The obtained spectral material was processed by machine learning methods. The authors discuss the mechanisms of UCL saturation, as well as the influence of the lanthanoid ions ratio.
This study was supported by Russian Foundation for Basic Research (Project №18-02-01023). The contribution of O.E.Sarmanova (programming and training of neural networks) was supported by the Foundation for the Advancement of Theoretical Physics and Mathematics “BASIS” (Project No. 19-2-6- 6-1).

Speaker

Olga Sarmanova
Lomonosov Moscow State University, Department of Physics, Moscow, Russia
Russia

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