A Robotic Platform for the Synthesis of Colloidal Nanocrystals

Haitao Zhao; Wei Chen; Hao Huang; Zhehao Sun; Zijian Chen; Lingjun Wu; Baicheng Zhang; Fuming Lai; Zhuo Wang; Mukhtar Lawan Adam; Cheng Heng Pang; Paul K. Chu; Yang Lu; Tao Wu; Jun Jiang; Zongyou Yin; Xuefeng Yu

doi:10.1038/s44160-023-00250-5

A Robotic Platform for the Synthesis of Colloidal Nanocrystals

Haitao Zhao^* (Co-first Author)
, Wei Chen (Co-first Author)
, Hao Huang (Co-first Author)
, Zhehao Sun (Co-first Author)
, Zijian Chen
, Lingjun Wu
, Baicheng Zhang
, Fuming Lai
, Zhuo Wang
, Mukhtar Lawan Adam
, Cheng Heng Pang
, Paul K. Chu
, Yang Lu
, Tao Wu
, Jun Jiang^*
, Zongyou Yin^*
, Xuefeng Yu^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

143 Citations (Scopus)

39 Downloads (CityUHK Scholars)

Abstract

Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining–synthesis–inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond. © The Author(s) 2023

Original language	English
Pages (from-to)	505–514
Journal	Nature Synthesis
Volume	2
Issue number	6
Online published	2 Mar 2023
DOIs	https://doi.org/10.1038/s44160-023-00250-5
Publication status	Published - Jun 2023

Funding

This work was supported by the National Natural Science Foundation of China (52173234) (H.Z.), the Shenzhen-Hong Kong-Macau Technology Research Program (Type C, SGDX2020110309300301) (H.Z., Y.L.), the Shenzhen Excellent Science and Technology Innovation Talent Training Project—Outstanding Youth Project (RCJC20200714114435061) (X.-F.Y.), the Shenzhen Science and Technology Program (JCYJ20210324102008023 and JSGG20210802153408024) (H.Z.), the Natural Science Foundation of Guangdong Province (2022A1515010554) (H.Z.), the ANU Futures Scheme (Q4601024) (Z.Y.), the CCF-Tencent Open Fund (H.Z.), a City University of Hong Kong Strategic Research Grant (SRG) (7005505) (Y.L.), the Shenzhen Key Laboratory for the Materials Synthetic Biology (H.Z.) and the Guangdong Province “Pearl River Talent Plan” (2021QN02C230) (H.Z.).

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This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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title = "A Robotic Platform for the Synthesis of Colloidal Nanocrystals",

abstract = "Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining–synthesis–inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond. {\textcopyright} The Author(s) 2023",

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AU - Zhao, Haitao

AU - Chen, Wei

AU - Huang, Hao

AU - Sun, Zhehao

AU - Chen, Zijian

AU - Wu, Lingjun

AU - Zhang, Baicheng

AU - Lai, Fuming

AU - Wang, Zhuo

AU - Lawan Adam, Mukhtar

AU - Pang, Cheng Heng

AU - Chu, Paul K.

AU - Lu, Yang

AU - Wu, Tao

AU - Jiang, Jun

AU - Yin, Zongyou

AU - Yu, Xuefeng

PY - 2023/6

Y1 - 2023/6

N2 - Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining–synthesis–inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond. © The Author(s) 2023

AB - Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining–synthesis–inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond. © The Author(s) 2023

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U2 - 10.1038/s44160-023-00250-5

DO - 10.1038/s44160-023-00250-5

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A Robotic Platform for the Synthesis of Colloidal Nanocrystals

Abstract

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