Published October 6, 2025 | Version v1
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Multiparametric Optical Pressure Sensing in YAG:Ce3+ Enabled by Multiple Linear Regression: A New Paradigm for Superior Sensor Performance

  • 1. ROR icon Adam Mickiewicz University in Poznań
  • 2. EDMO icon La Laguna University
  • 3. ROR icon Tohoku University
  • 4. ROR icon University of Gdańsk
  • 5. ROR icon Universidad de La Laguna

Description

Existing luminescent pressure sensors, including multi-modal ones, rely on a single spectroscopic parameter at a time for optical readout, which limits their sensing accuracy and precision. This work presents the first example of a truly multiparametric luminescent manometer, simultaneously utilizing several parameters for optical readout - based on the photophysical analysis of Ce³⁺ in a single-crystal yttrium aluminum garnet (YAG) host. The band energies, intensities, bandwidths and excited-state lifetime of the Ce³⁺-doped YAG were studied under extreme conditions (>14 GPa), to assess its potential as a new-generation optical pressure gauge. Both conventional single-parameter and the proposed multiparametric approaches were applied to the same dataset to evaluate its sensing performance. This new, multiparametric approach, based on multiple linear regression (MLR), led to a 50-fold increase in relative sensitivity and a 4-fold reduction in pressure uncertainty compared to the best single-parameter analysis. These results demonstrate that multiparametric analysis provides significantly enhanced accuracy and reliability in pressure sensing, without requiring changes to the material or experimental setup. This methodology is readily transferable and could be applied to a wide range of luminescent materials, laying the foundation for a new generation of high-performance optical manometers for use in high-pressure physics, materials science, and beyond.

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Additional details

Funding

National Science Centre
2023/51/D/ST5/00579
Ministerio de Ciencia, Innovación y Universidades
BG22/00061
Gobierno de Canarias
Programa FEDER Canarias 2021-2027 ProID2024010034