通过多目标优化快速发现高效长波长发射石榴石：Cr近红外荧光粉

Rapid Discovery of Efficient Long-Wavelength Emission Garnet:Cr NIR Phosphors via Multi-Objective Optimization.

作者信息

Jiang Lipeng, Jiang Xue, Wang Changxin, Liu Pei, Zhang Yan, Lv Guocai, Lookman Turab, Su Yanjing

机构信息

Beijing Advanced Innovation Center for Materials Genome Engineering, Corrosion and Protection Center, University of Science and Technology Beijing, Beijing100083, China.

Basic Experimental Center of Natural Science, University of Science and Technology Beijing, Beijing100083, China.

出版信息

ACS Appl Mater Interfaces. 2022 Nov 23;14(46):52124-52133. doi: 10.1021/acsami.2c12923. Epub 2022 Nov 9.

DOI:10.1021/acsami.2c12923

PMID:36350624

Abstract

High-efficiency long-wavelength emission near-infrared (NIR) phosphors are the key to next-generation LED light sources. However, high-efficiency phosphors usually exhibit narrow-band emission at shorter wavelengths due to the crystal field intensity. In this paper, we utilize multi-objective optimization to discover the NIR phosphor GdMgAlGaGeO:0.04Cr. It exhibits a broadband NIR emission from 650 to 1100 nm peaking at 763 nm, with a full width at half maximum (FWHM) of 150 nm, an internal quantum efficiency (IQE)/external quantum efficiency (EQE) of 90%/53.1%, and good thermal stability (85.3% @ 150 °C). The packaging results show that ∼53.2 mW of output power is achieved at 915 mW input power, which suggests promising applications for NIR pc-LED. Our approach is based on the data of emission wavelength (WL) and IQE for garnet:Cr NIR phosphors to construct machine learning models. An active learning strategy is used to make tradeoffs between WL and IQE, and we are able to find the targeted phosphor after only four iterations of synthesis and characterization.

摘要

高效长波长发射近红外（NIR）磷光体是下一代LED光源的关键。然而，由于晶体场强度，高效磷光体通常在较短波长处表现出窄带发射。在本文中，我们利用多目标优化发现了近红外磷光体GdMgAlGaGeO:0.04Cr。它在650至1100 nm范围内呈现宽带近红外发射，峰值位于763 nm，半高宽（FWHM）为150 nm，内量子效率（IQE）/外量子效率（EQE）为90%/53.1%，并且具有良好的热稳定性（150°C时为85.3%）。封装结果表明，在915 mW输入功率下可实现约53.2 mW的输出功率，这表明近红外功率型发光二极管（pc-LED）具有广阔的应用前景。我们的方法基于石榴石:Cr近红外磷光体的发射波长（WL）和IQE数据来构建机器学习模型。采用主动学习策略在WL和IQE之间进行权衡，仅经过四次合成和表征迭代，我们就能找到目标磷光体。