ReviewPrecision chemistry2025
Constructing Two-Dimensional, Ordered Networks of Carbon-Carbon Bonds with Precision.
Review in Precision chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.