Temperature effects on luminescent properties of Sr2CeO4:Eu3+ nanophosphor: A machine learning approach

Abstract

In this paper we analyze possibilities of application of Sr2CeO4:Eu3+ nanopowder for temperature sensing using machine learning. The material was prepared by simple solution combustion synthesis. Photoluminescence technique has been used to measure the optical emission temperature dependence of the prepared material. Principal Component Analysis, the basic machine learning algorithm, provided insight into temperature dependent spectral data from another point of view than usual approach.