Related Experiment Video
Updated: Feb 1, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
Predicting the afterglow duration in persistent phosphors: a validated approach to derive trap depth distributions
Olivier Q De Clercq1, Jiaren Du, Philippe F Smet
1LumiLab, Department of Solid State Sciences, Ghent University, Ghent, Belgium. dirk.poelman@ugent.be.
Researchers studied persistent phosphors for applications like imaging. They developed a method to analyze trapping mechanisms in LiGa5O8:Cr3+ (LGO:Cr) phosphors, enabling accurate prediction of their storage properties.
Area of Science:
- Materials Science
- Solid State Physics
- Luminescence
Background:
- Persistent phosphors store and release energy, acting as optical batteries for applications in signage, dosimetry, and imaging.
- Understanding defect sites and trapping mechanisms is crucial for optimizing persistent phosphor properties.
- LiGa5O8:Cr3+ (LGO:Cr) is a near-infrared (NIR) emitting persistent phosphor with potential applications.
Purpose of the Study:
- To investigate the thermoluminescence and afterglow properties of LGO:Cr.
- To present a generalizable method for deriving trap depth distributions in persistent luminescent materials.
- To establish a rigorous approach for determining trapping parameters in storage phosphors.
Main Methods:
- Utilized the Tstop-Tmax method combined with initial rise analysis to identify trap distributions.
- Employed computerized glow curve fitting for simultaneous analysis of experimental data.
- Applied a model system (LGO:Cr) to demonstrate the methodology.
Main Results:
- Identified a broad distribution of trapping states within the LGO:Cr phosphor.
- Successfully extracted a consistent set of trapping parameters using the developed methods.
- Demonstrated that the model parameters accurately describe both thermoluminescence and afterglow data.
Conclusions:
- The developed methodology reliably determines trap structure and parameters in persistent phosphors.
- The approach is applicable to various persistent and storage phosphors.
- Accurate trapping parameters enable prediction of afterglow and storage behavior under different conditions.
More Related Videos
07:12Author Spotlight: Advancing Bioimaging and Therapy with Functional Nanomaterials
Published on: September 13, 2024
13:18Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
Published on: March 3, 2023
Related Concept Videos
Reliability and Validity
Social Traps
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Predicting Molecular Geometry
Uniform Depth Channel Flow
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.