Stable and Highly Emissive Infrared Yb‐Doped Perovskite Quantum Cutters Engineered by Machine Learning

Stable and Highly Emissive Infrared Yb‐Doped Perovskite Quantum Cutters Engineered by Machine Learning

Advanced Materials

10.1002/adma.202405973

https://doi.org/10.1002/adma.202405973

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

General Materials Science

31 July 2024

Jing, Y., Low, A. K. Y., Liu, Y., Feng, M., Lim, J. W. M., Loh, S. M., Rehman, Q., Blundel, S. A., Mathews, N., Hippalgaonkar, K., Sum, T. C., Bruno, A., & Mhaisalkar, S. G. (2024). Stable and Highly Emissive Infrared Yb‐Doped Perovskite Quantum Cutters Engineered by Machine Learning. Advanced Materials, 36(44). Portico. https://doi.org/10.1002/adma.202405973

AbstractQuantum cutting (QC) allows the conversion of high‐energy photons into lower‐energy photons, exhibiting great potential for infrared communications. Yb‐doped perovskite nanocrystals can achieve an efficient QC process with extremely high photoluminescence quantum yield (PLQY) thanks to the favorable Yb3+ incorporation in the perovskite structure. However, conventionally used oleic acid–oleylamine‐based ligand pairs cause instability issues due to highly dynamic binding to surface states that have curbed their potential applications. Herein, zwitterionic type C3‐sulfobetaine 3‐(N,N‐Dimethylpalmitylammonio)propanesulfonate molecule is utilized to build a strong binding state on the nanocrystals’ surface through a new phosphine oxide synthesis route. Leveraging machine learning and Bayesian Optimization workflow to determine optimal synthesis conditions, near‐infrared PLQY above 190% is achieved. The high PLQY is well maintained after over three months of aging, under high‐flux continuous UV irradiation, and long continuous annealing. This is the first report of highly efficient and stable perovskite quantum cutters, which will drive the study of fundamental physics phenomena and near‐infrared quantum communications.

Publisher Copyright

This research / project is supported by the A*STAR - Career Development Fund
Grant Reference no. : C233312001

This research / project is supported by the National Research Foundation - Competitive Research Programme
Grant Reference no. : NRF-CRP25-2020-0004

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : MOE-T2EP50221-0035

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : MOE- T2EP50123-0001

This research / project is supported by the A*STAR - Young Achiever Award
Grant Reference no. : NA

This work was also supported by the A*STAR Computational Resource Center (ACRC) and the National Supercomputing Centre (NSCC) through the use of their high-performance computing facilities. S.A.B. is grateful to the CEA’s PTC-MP program and GENCI project AD010914188 for support. A.N. acknowledges the NTU SUG Grant.

This is the peer reviewed version of the following article: Jing, Y., Low, A. K. Y., Liu, Y., Feng, M., Lim, J. W. M., Loh, S. M., Rehman, Q., Blundel, S. A., Mathews, N., Hippalgaonkar, K., Sum, T. C., Bruno, A., & Mhaisalkar, S. G. (2024). Stable and Highly Emissive Infrared Yb‐Doped Perovskite Quantum Cutters Engineered by Machine Learning. Advanced Materials, 36(44). Portico. https://doi.org/10.1002/adma.202405973 , which has been published in final form at https://doi.org/10.1002/adma.202405973. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

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