Dataset Information

Supramolecular Weaving by Halogen-Bonding in Functionality-Rich Hexasubstituted Aromatic Synthons.

ABSTRACT: Hexasubstituted benzenes are interesting platforms for the generation of functional materials, whose applications span from supramolecular recognition to organic electronics. Their synthesis is difficult to achieve by controlling multiple substitution steps of all hydrogen atoms on the aromatic benzene skeleton, so, often, cycloaddition reactions from disubsituted alkynes are used. In this work, we report a novel, straightforward route to C₃-symmetrical hexasubstituted aromatic synthons with a diverse and rich pattern of functionalities, and we report about their packing mode in the crystals, in which, unprecedentedly, directional, strong halogen bonding interactions are capable of forming bidimensional supramolecular weaving.

SUBMITTER: Catenazzi M

PROVIDER: S-EPMC9967865 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Supramolecular Weaving by Halogen-Bonding in Functionality-Rich Hexasubstituted Aromatic Synthons.

Catenazzi Matteo M Nitti Andrea A Boiocchi Massimo M Bianchi Gabriele G Po Riccardo R Pasini Dario D

Materials (Basel, Switzerland) 20230217 4

Hexasubstituted benzenes are interesting platforms for the generation of functional materials, whose applications span from supramolecular recognition to organic electronics. Their synthesis is difficult to achieve by controlling multiple substitution steps of all hydrogen atoms on the aromatic benzene skeleton, so, often, cycloaddition reactions from disubsituted alkynes are used. In this work, we report a novel, straightforward route to <i>C</i><sub>3</sub>-symmetrical hexasubstituted aromatic ...[more]

PMID: 36837309

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Project description:Carbon, although the central element in organic chemistry, has been traditionally neglected as a target for directional supramolecular interactions. The design of supramolecular structures involving carbon-rich molecules, such as arene hydrocarbons, has been limited almost exclusively to non-directional π-stacking, or derivatisation with heteroatoms to introduce molecular assembly recognition sites. As a result, the predictable assembly of non-derivatised, carbon-only π-systems using directional non-covalent interactions remains an unsolved fundamental challenge of solid-state supramolecular chemistry. Here, we propose and validate a different paradigm for the reliable assembly of carbon-only aromatic systems into predictable supramolecular architectures: not through non-directional π-stacking, but via specific and directional halogen bonding. We present a systematic experimental, theoretical and database study of halogen bonds to carbon-only π-systems (C-I⋯πC bonds), focusing on the synthesis and structural analysis of cocrystals with diversely-sized and -shaped non-derivatised arenes, from one-ring (benzene) to 15-ring (dicoronylene) polycyclic atomatic hydrocarbons (PAHs), and fullerene C60, along with theoretical calculations and a systematic analysis of the Cambridge Structural Database. This study establishes C-I⋯πC bonds as directional interactions to arrange planar and curved carbon-only aromatic systems into predictable supramolecular motifs. In >90% of herein presented structures, the C-I⋯πC bonds to PAHs lead to a general ladder motif, in which the arenes act as the rungs and halogen bond donors as the rails, establishing a unique example of a supramolecular synthon based on carbon-only molecules. Besides fundamental importance in the solid-state and supramolecular chemistry of arenes, this synthon enables access to materials with exciting properties based on simple, non-derivatised aromatic systems, as seen from large red and blue shifts in solid-state luminescence and room-temperature phosphorescence upon cocrystallisation.

Dataset Information

Supramolecular Weaving by Halogen-Bonding in Functionality-Rich Hexasubstituted Aromatic Synthons.

Publications

Supramolecular Weaving by Halogen-Bonding in Functionality-Rich Hexasubstituted Aromatic Synthons.

Similar Datasets

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