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Population analysis to increase the robustness of molecular computational identification and its extension into the near-infrared for substantial numbers of small objects.
Yao, Chaoyi; Ling, Jue; Chen, Linyihong; de Silva, A Prasanna.
Afiliação
  • Yao C; School of Chemistry and Chemical Engineering , Queen's University , Belfast BT9 5AG , Northern Ireland . Email: a.desilva@qub.ac.uk.
  • Ling J; School of Chemistry and Chemical Engineering , Queen's University , Belfast BT9 5AG , Northern Ireland . Email: a.desilva@qub.ac.uk.
  • Chen L; School of Chemistry and Chemical Engineering , Queen's University , Belfast BT9 5AG , Northern Ireland . Email: a.desilva@qub.ac.uk.
  • de Silva AP; School of Chemistry and Chemical Engineering , Queen's University , Belfast BT9 5AG , Northern Ireland . Email: a.desilva@qub.ac.uk.
Chem Sci ; 10(8): 2272-2279, 2019 Feb 28.
Article em En | MEDLINE | ID: mdl-30881652
The first population analysis is presented for submillimetric polymer beads which are tagged with five multi-valued logic gates, YES, 2YES + PASS 1, YES + PASS 1, YES + 2PASS 1 and PASS 1 with H+ input, 700 nm near-infrared fluorescence output and 615 nm red excitation light as the power supply. The gates carry an azaBODIPY fluorophore and an aliphatic tertiary amine as the H+ receptor where necessary. Each logic tag has essentially identical emission characteristics except for the H+-induced fluorescence enhancement factors which consistently map onto the theoretical predictions, after allowing for bead-to-bead statistical variability for the first time. These enhancement factors are signatures which identify a given bead type within a mixed population when examined with a 'wash and watch' protocol under a fluorescence microscope. This delineates the scope of molecular computational identification (MCID) for encoding objects which are too small for radiofrequency identification (RFID) tagging.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article