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A supramolecular aggregation-based constitutional dynamic network for information processing.

Xiao LinShu YangDan HuangChen GuoDie ChenQian-Fan YangFeng Li
Published in: Chemical science (2020)
Concepts and strategies offered by constitutional dynamic chemistry (CDC) hold great promise for designing molecular computing systems adaptive to external environments. Despite demonstrable success in storing and processing chemical information using CDC, further employment of such constitutional dynamic networks (CDNs) for processing more complex digital information has not been realized yet. Herein, we introduced a supramolecular CDN based on the aggregation of cyanine MTC (Agg-CDN), which is composed of four reversibly interconvertible constituents, i.e. monomers, dimers, J-aggregates, and H-aggregates. We demonstrated that the equilibrated Agg-CDN is reconfigurable through constituent exchange in response to well-defined chemical inputs. More importantly, the equilibrated states of the Agg-CDN are spectroscopically distinguishable because of the unique optical properties of MTC. We further tuned the Agg-CDN to at least nine unique states for transforming the chemical inputs into digital outputs, and successfully employed it for encoding and encrypting complex digital information, such as multi-pixel images.
Keyphrases
  • health information
  • deep learning
  • healthcare
  • social media
  • convolutional neural network
  • energy transfer
  • mental health
  • big data
  • mental illness
  • single molecule