Guo Huazhang, a femalepostdoctoral fellow at the School of Environmental and Chemical Engineering,Shanghai University, has demonstrated preferable research capabilities underthe guidance of Professor Wang Liang, culminating in her obtaining a PhD in2020. Since then, she has continued her academic pursuits as a postdoctoralresearcher at Shanghai University, under the joint mentorship of Academician WuMinghong. Throughout her PhD and postdoctoral studies, she has dedicatedherself to the controlled preparation of graphene quantum dot materials andtheir applications in photocatalysis/electrocatalysis, biomedical imaging, andhigh-performance optoelectronic devices.
Guo’s research has been internationally recognized,with more than 10 of her research papers published in esteemed journals, suchas Nat Commun., ACS Nano, Mater. Today, Carbon, Appl. Mater. Today, and ACSSustain Chem. Remarkably, two of these papers have been highly cited by theESI. To further promote her research, she has been awarded the 16th batch of specialpostdoctoral support funds from the China Postdoctoral Science Foundation in2023.
Graphene quantum dots(GQDs) have versatile applications in luminescence,whereas identifying optimal synthesis conditions has been challenging due tonumerous synthesis parameters and multiple desired outcomes, creating anenormous search space. In anattempt to tackle this problem, we present a novel multi-objective optimizationstrategy, which employs machine learning to meticulously guide the hydrothermalsynthesis of GQDs that exhibit full-color fluorescence and high fluorescencequantum yields. Owing to their exceptional fluorescence performance, GQDs have beenextensively employed in applications such as biological imaging, whitelight-emitting diodes (WLED), and filtering out high-energy short-wave bluelight emitted by WLEDs.
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E-mail: meeting@c-gia.org
Abstract: Minyang Lu
Sponsor: Wenyang Yang
Media: Liping Wang
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