Topological innovation in molecular design: advances in Möbius architectures for functional materials
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Bin Hu
Abstract
Molecular topology fundamentally influences self-assembly, molecular recognition, and dynamic behavior in chemical systems. Among topologically nontrivial architectures, Möbius molecules – defined by a non-orientable, fully conjugated cyclic backbone with an odd linking number (Lk) – represent a unique class of π-conjugated macrocycles distinct from geometrically twisted analogues. Their one-sided surface and topological singularity confer exceptional properties, including intrinsic chirality, delocalized electronic pathways, and unconventional charge transport characteristics, positioning them as promising platforms for applications in optoelectronics, chiral sensing, and molecular electronics. Despite significant synthetic challenges arising from ring strain and conformational instability, recent advances in dynamic covalent chemistry, metal-templated cyclization, and heteroatom incorporation have enabled the construction of stable Möbius nanobelts and heterocyclic macrocycles with tunable redox and photophysical properties. Beyond discrete molecules, Möbius topology has also been extended to supramolecular assemblies and interlocked systems, broadening the functional scope of these structures. This review surveys recent progress in the design, synthesis, and functional exploration of Möbius systems, highlighting the critical role of topology in shaping their structure–property relationships and their emerging impact on supramolecular chemistry and molecular materials science.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 21971022
Award Identifier / Grant number: 92156009
Acknowledgments
H.-Y. G. is grateful to the National Natural Science Foundation of China (21971022 and 92156009), the Fundamental Research Funds for the Central Universities and Beijing Normal University for financial support.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Bin Hu and Dan-Yang Wang contributed equally to this work.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: The National Natural Science Foundation of China (21971022 and 92156009).
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Data availability: Not applicable.
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