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  5. Fluorescent Imaging and Sorting of High-Lipid-Content Strains of Green Algae by Using an Aggregation-Induced Emission Luminogen

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Article
en
2022

Fluorescent Imaging and Sorting of High-Lipid-Content Strains of Green Algae by Using an Aggregation-Induced Emission Luminogen

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en
2022
Vol 16 (9)
Vol. 16
DOI: 10.1021/acsnano.2c05976

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Ben Zhong Tang
Ben Zhong Tang

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Haixiang Liu
Neng Yan
Tin Yan Wong
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Abstract

Microalgae-based biofuels are receiving attention at the environmental, economic, and social levels because they are clean, renewable, and quickly produced. The green algae Chlorella vulgaris has been extensively studied in research laboratories and the biofuel industry as a model organism to increase lipid production to be cost-effective in commercial production. In this work, we utilized a lipid-droplet-specific luminogen with aggregation-induced emission (AIE) characteristics to increase the lipid production of C. vulgaris by fluorescent imaging and sorting of those algal cells with large and rich lipid droplets for subculturing. The AIE-active TPA-A enabled real-time monitoring of the size and number of lipid droplets in C. vulgaris during their growth period so that we can identify the best time for harvesting. Furthermore, the algae cells with high lipid content were identified and collected for subculturing by the technique of fluorescence-activated cell sorting (FACS). The lipid production in the generation of two successive selections was almost doubled compared to the generation with natural selection. This work demonstrated that the technologies of AIE and FACS could be applied together to improve the production of a third-generation biofuel.

How to cite this publication

Haixiang Liu, Neng Yan, Tin Yan Wong, Henry Lam, Jacky W. Y. Lam, Ryan T. K. Kwok, Jianwei Sun, Ben Zhong Tang (2022). Fluorescent Imaging and Sorting of High-Lipid-Content Strains of Green Algae by Using an Aggregation-Induced Emission Luminogen. , 16(9), DOI: https://doi.org/10.1021/acsnano.2c05976.

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Publication Details

Type

Article

Year

2022

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsnano.2c05976

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