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DOI | 10.1038/s41467-018-03374-x |
Identifying an efficient, thermally robust inorganic phosphor host via machine learning | |
Zhuo, Ya; Tehrani, Aria Mansouri; Oliynyk, Anton O.; Duke, Anna C.; Brgoch, Jakoah | |
2018-10-22 | |
发表期刊 | NATURE COMMUNICATIONS
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ISSN | 2041-1723 |
出版年 | 2018 |
卷号 | 9 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Rare-earth substituted inorganic phosphors are critical for solid state lighting. New phosphors are traditionally identified through chemical intuition or trial and error synthesis, inhibiting the discovery of potential high-performance materials. Here, we merge a support vector machine regression model to predict a phosphor host crystal structure's Debye temperature, which is a proxy for photoluminescent quantum yield, with high-throughput density functional theory calculations to evaluate the band gap. This platform allows the identification of phosphors that may have otherwise been overlooked. Among the compounds with the highest Debye temperature and largest band gap, NaBaB9O15 shows outstanding potential. Following its synthesis and structural characterization, the structural rigidity is confirmed to stem from a unique corner sharing [B3O7](5-) polyanionic backbone. Substituting this material with Eu2+ yields UV excitation bands and a narrow violet emission at 416 nm with a full-width at half-maximum of 34.5 nm. More importantly, NaBaB9O15: Eu2+ possesses a quantum yield of 95% and excellent thermal stability. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000447840500007 |
WOS关键词 | BLUE-EMITTING PHOSPHOR ; STRUCTURAL RIGIDITY ; OPTICAL-PROPERTIES ; DEBYE TEMPERATURE ; CE3+ PHOSPHOR ; LUMINESCENCE ; ENERGY ; STABILITY ; CRYSTAL ; AVERAGE |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/203725 |
专题 | 资源环境科学 |
作者单位 | Univ Houston, Dept Chem, Houston, TX 77204 USA |
推荐引用方式 GB/T 7714 | Zhuo, Ya,Tehrani, Aria Mansouri,Oliynyk, Anton O.,et al. Identifying an efficient, thermally robust inorganic phosphor host via machine learning[J]. NATURE COMMUNICATIONS,2018,9. |
APA | Zhuo, Ya,Tehrani, Aria Mansouri,Oliynyk, Anton O.,Duke, Anna C.,&Brgoch, Jakoah.(2018).Identifying an efficient, thermally robust inorganic phosphor host via machine learning.NATURE COMMUNICATIONS,9. |
MLA | Zhuo, Ya,et al."Identifying an efficient, thermally robust inorganic phosphor host via machine learning".NATURE COMMUNICATIONS 9(2018). |
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