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 |
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| creator | 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 |
| description | 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. |
| doi_str_mv | 10.1002/adma.202270286 |
| format | Article |
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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.</description><subject>Algorithms</subject><subject>Beacons</subject><subject>Fluorescence</subject><subject>fluorescent nanomaterials</subject><subject>high‐throughput screening</subject><subject>Machine learning</subject><subject>Materials science</subject><subject>NanoCluster Beacons</subject><subject>Nanoclusters</subject><subject>Nanomaterials</subject><subject>next‐generation sequencing</subject><subject>Pipeline design</subject><subject>silver nanoclusters</subject><issn>0935-9648</issn><issn>1521-4095</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LAzEQhoMoWKtXzwEvetjtJLubbI5r_Sq0KqjnMJtmYUu6qUlb6b93S0WPngZmnndeeAi5ZJAyAD7C-RJTDpxL4KU4IgNWcJbkoIpjMgCVFYkSeXlKzmJcAIASIAZkMsMY2611O_qKAZ2zjr5ZZ8269R31DX3Gzo_dJq5toLcWje8iva7m25TOsN-lNGejfenNOTlp0EV78TOH5OPh_n38lExfHifjapoYxqRICpCZMY2VQko5zxjHwoj-UoumyaBmOeZlyZEDUwwMZlZAXdaSC-hZxotsSK4Of1fBf25sXOuF34Sur9Rc8jxTQpWqp9IDZYKPMdhGr0K7xLDTDPRel97r0r-6-oA6BL5aZ3f_0Lq6m1V_2W8vtWo2</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Kuo, Yu‐An</creator><creator>Jung, Cheulhee</creator><creator>Chen, Yu‐An</creator><creator>Kuo, Hung‐Che</creator><creator>Zhao, Oliver S.</creator><creator>Nguyen, Trung D.</creator><creator>Rybarski, James R.</creator><creator>Hong, Soonwoo</creator><creator>Chen, Yuan‐I</creator><creator>Wylie, Dennis C.</creator><creator>Hawkins, John A.</creator><creator>Walker, Jada N.</creator><creator>Shields, Samuel W. 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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.</abstract><cop>Weinheim</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/adma.202270286</doi><tpages>1</tpages></addata></record> |
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| subjects | Algorithms Beacons Fluorescence fluorescent nanomaterials high‐throughput screening Machine learning Materials science NanoCluster Beacons Nanoclusters Nanomaterials next‐generation sequencing Pipeline design silver nanoclusters |
| title | Massively Parallel Selection of NanoCluster Beacons (Adv. Mater. 41/2022) |
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