Resolving energy transfer dynamics in Eu²⁺-activated multi-site phosphors via metaheuristic optimization and...

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Background:

• Traditional multi-exponential fitting for luminescence decay lacks physical basis, offering only empirical convenience.
• Rigorous nonlinear rate-equation models were previously limited by computational expense.
• Advances in AI and computing now enable tractable, physically grounded analyses of complex relaxation dynamics.

Purpose of the Study:

• To investigate donor-acceptor interactions in a prototypical Eu²⁺-activated phosphor exhibiting wavelength quenching.
• To apply advanced computational methods for quantitative analysis of luminescence decay dynamics.
• To compare metaheuristic-driven simulations and physics-informed neural networks for extracting rate constants.

Main Methods:

• Utilized metaheuristic-driven Runge-Kutta simulations to model luminescence decay.
• Employed physics-informed neural networks (PINNs) as a complementary analytical framework.
• Analyzed donor-acceptor interactions in La_2.544Ca_1.456Si₁₂O_4.456N_16.544:Eu²⁺ phosphor.

Main Results:

• Successfully extracted quantitative radiative and non-radiative rate constants using both simulation and PINN approaches.
• Both methods converged to consistent rate constants, validating the models.
• Established donor-acceptor energy transfer as the dominant relaxation pathway.

Conclusions:

• Advanced computational techniques provide physically meaningful insights into luminescence decay.
• Donor-acceptor transfer significantly influences relaxation dynamics, surpassing radiative and same-species interactions.
• This approach offers a quantitative, physics-based understanding applicable beyond phosphors.