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dc.contributor.authorHan, Yuen_US
dc.contributor.authorTang, Bijunen_US
dc.contributor.authorWang, Liangen_US
dc.contributor.authorBao, Hongen_US
dc.contributor.authorLu, Yuhaoen_US
dc.contributor.authorGuan, Cuntaien_US
dc.contributor.authorZhang, Liangen_US
dc.contributor.authorLe, Mengyingen_US
dc.contributor.authorLiu, Zhengen_US
dc.contributor.authorWu, Minghongen_US
dc.identifier.citationHan, Y., Tang, B., Wang, L., Bao, H., Lu, Y., Guan, C., Zhang, L., Le, M., Liu, Z. & Wu, M. (2020). Machine-learning-driven synthesis of carbon dots with enhanced quantum yields. ACS Nano, 14(11), 14761-14768.
dc.description.abstractKnowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs' synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe3+ ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe3+ ion with a wide concentration range (0-150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.relation.ispartofACS Nanoen_US
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Nano, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see
dc.titleMachine-learning-driven synthesis of carbon dots with enhanced quantum yieldsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Materials Science and Engineeringen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsCarbon Dotsen_US
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