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Parallel Chirality Inductions in Möbius Zn(II) Hexaphyrin Transformation Networks
Thomas Nédellec1, Bernard Boitrel1, Stéphane Le Gac1
1Univ Rennes, CNRS, ISCR (Institut des Sciences Chimiques de Rennes)-UMR 6226, 35000 Rennes, France.
Researchers designed chiral Möbius [28]hexaphyrin ligands for dynamic chirality induction. Self-sorting and compartmentalized switching in mixtures of these ligands demonstrate controllable chiroptical properties for new molecular networks.
Area of Science:
- Supramolecular Chemistry
- Organic Chemistry
- Materials Science
Background:
- Biological systems utilize networked chemical transformations for complex functions.
- Chirality induction in dynamic Möbius π-systems is of significant interest.
- Investigating molecular mixtures rather than isolated molecules offers new insights.
Purpose of the Study:
- To design and synthesize Möbius [28]hexaphyrin ligands for chirality induction studies.
- To explore self-sorting and dynamic transformations in mixtures of these ligands.
- To develop controllable chiroptical properties in molecular ensembles.
Main Methods:
- Synthesis and optimization of hexaphyrin ligands with chiral arms.
- Coordination with ZnOAc to induce chirality.
- Social self-sorting triggered by achiral effectors (acetate and butylamine).
- Compartmentalized switching via selective effector protection and restoration.
Main Results:
- Quasi-quantitative chirality induction over the Möbius π-system was achieved.
- Social self-sorting led to well-defined 1:1 mixtures of Möbius complexes with parallel chirality inductions.
- Compartmentalized switching demonstrated reversible transformations over five cycles.
- Tunable chiroptical properties were achieved by altering complex composition or twist configuration.
Conclusions:
- The designed Möbius [28]hexaphyrins enable dynamic chirality induction and self-sorting.
- Compartmentalized switching offers a method for controlling molecular transformations in mixtures.
- This work lays the foundation for Möbius-type stereoselective transformation networks with novel functions.
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