Molecular Gridization of Organic Semiconducting π Backbones
At a Glance
Section titled “At a Glance”| Metadata | Details |
|---|---|
| Publication Date | 2025-06-13 |
| Journal | Accounts of Chemical Research |
| Authors | Tonglin Yang, Yanwei Tang, Ying Wei, Linghai Xie, Wei Huang |
| Institutions | Ministry of Industry and Information Technology, Nanjing University of Posts and Telecommunications |
| Citations | 2 |
Abstract
Section titled “Abstract”ConspectusOrganic π-conjugated molecules and polymers have emerged as some of the most promising candidates of semiconductors for future information, intelligent technology, and smart manufacturing because of their unique properties such as structural diversity, flexibility, stretchability, ultrathinness, light weight, low-cost and large-area fabrication procedures, and excellent biocompatibility. However, several severe challenges remain, including inferior optical and electronic properties compared to inorganic materials, poor stability and lifespan, low yields in solution processing patterning techniques, inadequate mechanical endurance, and difficulties in multifunctionalization. Particularly, there are still no big breakthroughs in terms of the common and long-term challenges, such as flexible organic light-emitting diodes (OLEDs) with printing procedures that could not be achieved at the calibration of commercialization, electrically pumped lasers that become the open global question, and organic integrated circuits and brain-like computing technologies at the conceptual stage. The nanosization of the molecular π systems is one crucial way to address the dilemmas that stem from the molecular limitation of organic semiconductors. Covalent nanoscale strategies of organic semiconducting π backbones enable not only effective suppression of phonon behavior, thereby significantly improving their charge transport capacity and exciton efficiency, but also facilitate functional integration for intelligent semiconductors.In this Account, the molecular gridization of the π backbone has been proposed to lock conformation and to reduce the entropy for ordering since the first reported discovery of organic nanogridarenes (ONGAs) in 2014. We comprehensively summarize the progress in the structural diversity of ONGAs, gridization rules, gridization effects on electron or exciton properties, and their application in organic devices within organic electronics. To date, we have defined six types of monogrids and synthesized a series of ONGA-based nanoatoms, including ladder-type, A-type, angle-lost-type, windmill-type, diamond-type, and tic-tac-toe (“[Formula: see text]”)-type, as well as their nanomolecules of multigrids. The shape-sensitive gridization rule has been explored with the establishment of Friedel-Crafts, superelectrophile, C-H activation, and other C-C coupling gridizations, enabling precise control over their configuration and stereochemistry while achieving efficient yields. These gridization strategies not only allow for the exploration of ONGAs’ diversity but also endow them with unique properties. Specifically, gridization effectively reduces the reorganization energy (ROE), and multigridization breaks through the lowest ROE of ∼28 meV in organic semiconductors. Moreover, gridization can modulate not only the glassy transition temperature and thermal stability but also the excited-state pathways, hole/electron mobility, dielectric characteristics, and ionic-electronic coupling behaviors. These unique attributes render ONGAs as organic quantum grid-dots with potential applications in flexible OLEDs and organic neuromorphic computing devices. Ultraviolet OLEDs have been achieved with a nanohydrocarbon of triangle ONGA that exhibits an external quantum efficiency (EQE) of ∼4.12%. Organic field-effect transistor (OFET) memory based on ladder-type ONGAs, which serve as single-component charge-memorable materials (CMMs), exhibit long-term retention time and fast writing speeds compared to those in devices based on ungridized counterparts, demonstrating the dramatic gridization effects at the device level. Finally, we discuss their future opportunities and challenges along the direction of organic electronic intelligence and their scaling supercycles based on artificially intelligent and robotic chemists (AiRCs).