通过机器学习设计的稳定和高发射红外掺镱包晶量子切割器（Adv. Mater.）

IF 26.8 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Materials Pub Date : 2024-11-01 DOI:10.1002/adma.202470352

Yao Jing, Andre K. Y. Low, Yun Liu, Minjun Feng, Jia Wei Melvin Lim, Siow Mean Loh, Quadeer Rehman, Steven A. Blundel, Nripan Mathews, Kedar Hippalgaonkar, Tze Chien Sum, Annalisa Bruno, Subodh G. Mhaisalkar

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引用次数: 0

摘要

量子切割量子切割（QC）可将高能光子转换为低能光子，在红外通信领域具有巨大潜力。在编号为 20245973 的文章中，Tze Chien Sum、Annalisa Bruno、Subodh G. Mhaisalkar 及合作者报告了通过机器学习设计的稳定且高发射率的掺镱包晶量子切割器。他们通过新的氧化膦合成路线开发出一种配体工程方法，推动了基础物理现象和近红外量子通信的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Stable and Highly Emissive Infrared Yb-Doped Perovskite Quantum Cutters Engineered by Machine Learning (Adv. Mater. 44/2024)

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Stable and Highly Emissive Infrared Yb-Doped Perovskite Quantum Cutters Engineered by Machine Learning (Adv. Mater. 44/2024)

Quantum Cutting

Quantum cutting (QC) allows the conversion of high-energy photons into lower-energy photons, exhibiting great potential for infrared communications. In article number 20245973, Tze Chien Sum, Annalisa Bruno, Subodh G. Mhaisalkar, and co-workers report stable and highly emissive Yb-doped perovskite quantum cutters engineered by machine learning. They develop a ligands engineering method through a newly phosphine oxide synthesis route, driving the study of fundamental physics phenomena and near-infrared quantum communications.

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来源期刊

Advanced Materials 工程技术-材料科学：综合

CiteScore

43.00

自引率

4.10%

发文量

2182

审稿时长

2 months

期刊介绍： Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.