Evaluating Thermal Quenching Temperature in Eu³⁺-Substituted Oxide Phosphors via Machine Learning

Abstract

One of society’s grand challenges is to reduce energy usage in ways that are cost-effective, sustainable, and environmentally benign. Replacing incandescent and compact fluorescent light bulbs with energy-efficient, solid-state white lighting is one of the easiest and most promising solutions. Eu3+-substituted inorganic oxide phosphors are one class of materials that can serve as the red component in these new light bulbs, allowing the creation of warm white light. Unfortunately, the emission intensity in most of these materials cannot be reliably maintained at elevated temperatures. There is therefore a need to discover entirely novel phosphor materials that are thermally robust; however, this is generally a prolonged and expensive process requiring extensive synthetic effort. In this work, we develop a machine-learning regression algorithm based on 134 experimentally measured temperature-dependent Eu3+ emission data points to rapidly estimate the thermal quenching temperature (T 50), which is defined as the temperature when the emission intensity is half of the initial value. The T 50 was then predicted for more than 1000 potential oxide Eu3+ phosphor hosts using this model. Five compounds with predicted thermal quenching temperatures >423 K were subsequently selected and synthesized for validation of this approach. The phosphors, Sr2ScO3F, Cs2MgSi5O12, Ba2P2O7, LiBaB9O15, and Y3Al5O12, all exhibit good thermal stability when substituted with Eu3+, suggesting the success of our methodology.

Data-Driven Photoluminescence Tuning in Eu2+-Doped Phosphors

“…9 Driven by the rapid development of computers , the advancement of algorithms, and the explosion of experimental material databases, machine learning techniques have become powerful tools for materials discovery. 13,14 Recently, datadriven methodologies have been used in computational design and experimental identification for novel phosphors. Brgoch's group established a thermally robust phosphor NaBaB9O15:Eu 2+ with the assistance of machine learning and high-throughput density functional theory (DFT) calculations.…”

Ca[B8O11(OH)4] : Eu2+ – A Highly Efficient Deep Blue‐Emitting Phosphor Prepared by Low‐Temperature Self‐reduction

“…It can be observed from Figure 6a that the excitation spectra are distributed ranging from 220 to 400 nm, with peaks at 277 nm and 327 nm. At the excitation wavelength of 277 nm, the emission spectra of CBH : xEu 2+ are narrowly distributed from 400 nm to 500 nm with the peak at 428 nm, which is attributed to the electronic transitions of 5d orbitals to the 4 f orbital of Eu 2+ [4,5] . The PL intensity of CBH : xEu 2+ (0.5 %≤x≤6 %) increases first and then decreases with the increase of x, reaching the maximum value at x=2 % (Figure 6b).…”

“…Phosphors commonly consist of an appropriate matrix and activator [3] . Usually, Eu 2+ and Ce 3+ are used as the activator due to the spin‐allowed 5d→4 f transition [4,5] . Crystals with rigid structure, wide bandgap, and high symmetry [4,6] are generally chosen as the matrix, such as the garnet‐type model (Y 3 Al 5 O 12 ), [7] Si 3 N 4 ‐type model (β‐SiAlON), [8] apatite‐type model (Sr 5 (PO 4 ) 3 Cl), [9] melilite‐type model (Ca 2 Al 2 SiO 7 ), [10] whitlockite‐type model (β‐Ca 3 (PO 4 ) 2 ), [11] and UCr 4 C 4 ‐type model (RbLi(Li 3 SiO 4 ) 2 , CsKNa 2−y Li y (Li 3 SiO 4 ) 4 ) [12,13] .…”

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

“…Rare-earth doped inorganic phosphors are the key to solid-state lighting [113][114][115]. Traditionally, new phosphor was prepared by solid state reaction [115].…”