Abstract
Organic semiconducting nanomembranes (OSNMs), particularly carbon-based ones, are at the forefront of next-generation two-dimensional (2D) semiconductor research. These materials offer remarkable promise due to their diverse chemical properties and unique functionalities, paving the way for innovative applications across advanced semiconductor material sectors. Graphene stands out for its extraordinary mechanical strength, thermal conductivity, and superior charge transport capabilities, inspiring extensive research into other 2D carbon allotropes like graphyne and graphdiyne. With its high electron mobility and tunable bandgap, graphdiyne is particularly attractive for power-efficient electronic devices. However, synthesizing graphdiyne presents significant challenges, primarily due to the difficulty in achieving precise and deterministic control over the coupling of its monomers. This precision is crucial for determining the material's porosity, periodicity, and overall functionality. Innovative approaches have been developed to address these challenges, such as the strategic assembly of molecular building blocks at heterogeneous interfaces. Furthermore, data-driven techniques, such as machine learning and artificial intelligence (AI), are proving invaluable in this field, assisting in screening precursors, optimizing structural configurations, and predicting novel properties of these materials. These advancements are essential for producing durable monolayer sheets that can be integrated into existing electronic components. Despite these advancements, the integration of graphdiyne into semiconductor technology remains complex. Achieving long-range coherence in bonding configurations and enhancing charge transport characteristics are significant hurdles. Continued research into robust and controllable synthesis techniques is essential for unlocking the full potential of graphdiyne and other 2D materials, leading to more efficient, faster, and mechanically robust electronics.