Bayesian Inference for Model Analyses of Supramolecular Complexes: A Case Study with Nanocarbon Hosts

Area of Science:

• Supramolecular Chemistry
• Nanotechnology
• Computational Chemistry

Background:

• Development of novel nanocarbon architectures is crucial for advanced materials.
• Understanding host-guest interactions in nanoscale systems is key to designing functional molecular assemblies.
• Accurate determination of stoichiometry in host-guest complexes remains a challenge.

Purpose of the Study:

• To synthesize a novel nanobowl molecule with encapsulation capabilities.
• To investigate the host-guest stoichiometry of the synthesized nanobowl with C70 fullerenes.
• To develop and validate a robust computational method for analyzing host-guest equilibria.

Main Methods:

• Multi-step organic synthesis involving polygon cyclization and Ni-mediated macrocyclization.
• X-ray crystallography for determining the solid-state structure of the host-guest complex.
• Isothermal titration calorimetry (ITC) for studying host-guest binding thermodynamics in solution.
• Bayesian inference for quantitative stoichiometric analysis of binding equilibria.

Main Results:

• Successful synthesis of phenine tridehydrosumanene, a 126π-electron nanobowl.
• Demonstration of C70 fullerene encapsulation within the nanobowl, confirmed by X-ray crystallography (ball-in-bowl structure).
• ITC experiments provided binding data, and Bayesian inference successfully determined the host-guest stoichiometry, acting as a quantitative Occam's razor.
• The Bayesian method showed versatility across five different nanocarbon host-guest systems.

Conclusions:

• Phenine tridehydrosumanene represents a new class of nanobowl hosts for fullerene guests.
• Bayesian inference offers a powerful and versatile tool for accurate stoichiometric analysis in supramolecular chemistry.
• This work advances the design and understanding of complex molecular recognition systems.