Machine Learning‐Assisted Microfluidic Synthesis of Perovskite Quantum Dots
The quality and property control of nanomaterials are center themes to guarantee and promote their applications. Different synthesis methods and reaction parameters are control factors for their properties. However, the vast combination number of the factors with multilevels leads to the obstacle that trying all‐through the data space is nearly impossible. Herein, the combination of microfluidic synthesis method with machine learning (ML) models to address this challenge in case of perovskite quantum dots (PQDs) with tunable photoluminescence (PL) is reported. The ML‐assisted synthesis not only helps to elucidate the nucleation growth‐ripening mechanisms, but also successfully guides to synthesize PQDs with precise wavelength and full width of half maximum (FWHM) of the PL by optimizable conditions to match the time‐saving, energy‐saving, and minimal environmental pressure goals.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Machine Learning‐Assisted Microfluidic Synthesis of Perovskite Quantum Dots ; day:20 ; month:10 ; year:2022 ; extent:9
Advanced photonics research ; (20.10.2022) (gesamt 9)
- Creator
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Chen, Gaoyu
Zhu, Xia
Xing, Chenyu
Wang, Yongkai
Xu, Xiangxing
Bao, Jianchun
Huang, Jinghan
Zhao, Yurong
Wang, Xuan
Zhou, Xiuqing
Du, Xiuli
Wang, Xun
- DOI
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10.1002/adpr.202200230
- URN
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urn:nbn:de:101:1-2022102115121013646157
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:37 AM CEST
Data provider
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Associated
- Chen, Gaoyu
- Zhu, Xia
- Xing, Chenyu
- Wang, Yongkai
- Xu, Xiangxing
- Bao, Jianchun
- Huang, Jinghan
- Zhao, Yurong
- Wang, Xuan
- Zhou, Xiuqing
- Du, Xiuli
- Wang, Xun
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