Constructing High-Entropy Molecular Networks on Metal Surfaces
High-entropy materials (HEMs) have garnered intense attention due to their unique properties derived from compositional complexity, demonstrating promise in a wide range of applications, from catalysis to energy storage and beyond. Traditionally, HEMs have been primarily concerned with metal alloys. However, expanding the principle to organic systems, specifically high-entropy molecular nanostructures, remains underexplored. In this study, we investigated the construction of high-entropy molecular networks on a metal surface using an on-surface chemistry approach. By employing distinct pyridyl-functionalized ligands with differently shaped aromatic backbones, we constructed 2D molecular networks stabilized through coordination with metal atoms. A high-throughput preparation approach leveraging a mask inspired by a Venn diagram was utilized to create multicomponent sample libraries. Scanning tunneling microscopy provided real-space characterization, revealing both ordered and disordered assemblies and highlighting the critical role of the molecular shape and the number of functional centers in promoting or limiting mixed entropy of these networks. Monte Carlo and Molecular Dynamics simulations were further employed to model the formation mechanisms and examine factors influencing their mixed entropies. Our study provides insights into how molecular species influence disorder and stability in high-entropy molecular networks, paving the way for advancing low-dimensional high-entropy molecular systems.
Authors
- Jiayi Lu (ORCID: https://orcid.org/0000-0001-9680-5716)
- Paweł Szabelski (ORCID: https://orcid.org/0000-0002-3543-9430)
- Damian Nieckarz (ORCID: https://orcid.org/0000-0002-0381-7919)
- Qiang Sun (ORCID: https://orcid.org/0000-0003-4903-4570)
- W. Rżysko (ORCID: https://orcid.org/0000-0001-9806-6056)
- Xinyi Zhang (ORCID: https://orcid.org/0009-0007-3305-5453)
- Cha Li (ORCID: https://orcid.org/0000-0002-8219-7040)
- Yizhang Li (ORCID: https://orcid.org/0000-0002-8085-2742)
- Shaoxuan Yuan
Institutions
- Shanghai University (CN)
- Maria Curie-Skłodowska University (PL)
- Shanghai Jian Qiao University (CN)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1021/jacs.5c08377
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52