Improving the High-Pressure Sensing Characteristics of Y2MoO6:Eu3+ Using a Machine Learning Approach

In this study, we explore the potential of applying machine learning (ML) to enhance high-pressure luminescence sensing. We investigate the luminescence behavior of Y2MoO6:Eu3+, synthesized via a self-initiated, self-sustained reaction. Emission spectra were collected under varying pressures using a 405 nm laser diode and an AVANTES AvaSpec 2048TEC USB2 spectrometer. An analysis of the pressure-dependent curve, based on the intensities of two key peaks, indicates a possible crystal phase transition or another underlying physical phenomenon. Moreover, the non-unique relationship between pressure and peak intensity limits its effectiveness for precise sensing. To overcome this challenge, we employ an ML-based approach, combining Uniform Manifold Approximation and Projection (UMAP) for data visualization with a deep neural network to estimate pressure directly from the full luminescence spectrum. This strategy significantly extends the usable pressure range of Y2MoO6:Eu3+ up to 12 GPa, representing a marked improvement over conventional methods.