Experimentally Calibrated Computational Prediction Enables Accurate Fine-Tuning of Near-Infrared Rhodamines for Multiplexing.

ABSTRACT: A significant barrier inhibiting multiplexed imaging in the near-infrared (NIR) is the extensive trial and error associated with fine-tuning NIR dyes. In particular, the need to synthesize and experimentally evaluate dye derivatives in order to empirically identify those that can be used in multiplexing applications, requires a large investment of time. While coarse-tuning efforts benefit from computational prediction that can be used to identify target dye structures for synthetic campaigns, errors in computational prediction remain too large to accurately parse modifications aimed at fine-tuning changes in dye absorbance and emission. To address this issue, we screened different levels of theory and identified a time-dependent density functional theory (TD-DFT) approach that can rapidly,

SUBMITTER: DiMeglio D 

PROVIDER: S-EPMC9898109 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature