Massively Parallel Selection of NanoCluster Beacons (Adv. Mater. 41/2022)

Massively Parallel Selection of NanoCluster Beacons (Adv. Mater. 41/2022)

Fluorescent Nanomaterials By repurposing next‐generation sequencing chips, millions of fluorescent NanoCluster Beacons (NCBs) can be screened in a single experiment. Combining this high‐throughput screening platform with machine‐learning algorithms, in article number 2204957, Hsin‐Chih Yeh and co‐wo...

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Veröffentlicht in:Advanced materials (Weinheim) 2022-10, Vol.34 (41), p.n/a
Hauptverfasser: Kuo, Yu‐An, Jung, Cheulhee, Chen, Yu‐An, Kuo, Hung‐Che, Zhao, Oliver S., Nguyen, Trung D., Rybarski, James R., Hong, Soonwoo, Chen, Yuan‐I, Wylie, Dennis C., Hawkins, John A., Walker, Jada N., Shields, Samuel W. J., Brodbelt, Jennifer S., Petty, Jeffrey T., Finkelstein, Ilya J., Yeh, Hsin‐Chih
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Beacons
Fluorescence
fluorescent nanomaterials
high‐throughput screening
Machine learning
Materials science
NanoCluster Beacons
Nanoclusters
Nanomaterials
next‐generation sequencing
Pipeline design
silver nanoclusters
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Zusammenfassung:Fluorescent Nanomaterials By repurposing next‐generation sequencing chips, millions of fluorescent NanoCluster Beacons (NCBs) can be screened in a single experiment. Combining this high‐throughput screening platform with machine‐learning algorithms, in article number 2204957, Hsin‐Chih Yeh and co‐workers establish a pipeline to design bright and multicolor NCBs in silico.
ISSN:0935-9648
1521-4095
DOI:10.1002/adma.202270286