Dipole-Directed Nanoporous Networks on Surfaces

15 October 2025, Version 1
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

Porous materials have evolved from rigid, strongly bonded inorganic materials, such as zeolites, to modular and adaptable metal-organic and covalent organic frameworks (MOFs and COFs), based on reversible intermolecular interactions. The shift towards weak and reversible interactions continues, where recent research has explored porous materials based on even weaker interactions, such as hydrogen bonding and electrostatic forces. Building on this trend, here we report the formation of two-dimensional (2D) honeycomb porous supramolecular networks driven by weak, yet directional dipole-dipole interactions formed at the solution-solid interface. Scanning tunneling microscopy (STM) and molecular modeling reveal how these interactions guide self-assembly and structural stability. A comparative investigation of a structurally similar building block highlights the role of molecular design and interfacial forces in tuning these interactions. Molecular mechanics and molecular dynamics (MM and MD) simulations were conducted to understand the stabilization of experimentally observed supramolecular networks, considering the roles of molecule-substrate and molecule-solvent interactions. We further report on electric field-mediated switching behavior within these networks and their ability to host suitable guest molecules. This study highlights the increasing importance of weak intermolecular forces in the design of porous materials, paving the way for dynamic and reconfigurable nanomaterials.

Keywords

Self-assembly
Scanning Tunneling Microscopy (STM)
Supramolecular chemistry

Supplementary materials

Title
Description
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Title
Supporting information with additional and supporting data
Description
Experimental details (STM sample preparation, STM imaging), details of computational analysis, additional and supporting STM data, MM optimized molecular models
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