Emerging Trends in Conductive Two-Dimensional Covalent Organic Frameworks for Large-Area Electronic Applications
Bibliographic record
Abstract
Two-dimensional covalent organic frameworks (2D COFs) are emerging as promising materials for advanced electronic applications due to their tunable porosity, crystalline order, and π-conjugated structures. These properties enable efficient charge transport and bandgap modulation, making 2D COFs strong candidates for electronic devices such as transistors and memristors. However, the practical application of COFs remains limited by challenges in achieving high-quality thin films with large-area uniformity and improved crystallinity. This review explores recent advancements in the fabrication and application of conductive 2D COFs for electronics. Various synthesis strategies, including direct growth, vapor-assisted conversion, and interfacial methods, are discussed in the context of enhancing film quality and scalability. The integration of COFs into electronic devices is classified based on their operation mechanism─planar and vertical field-effect transistors (FETs), electrochemical transistors (ECTs), and memristors─to highlight their electronic properties and device performance. Looking forward, the challenges of large-scale production, material compatibility, and device integration are outlined, alongside potential solutions through innovative synthesis techniques and collaborative research efforts. By addressing these challenges, 2D COFs are poised to drive breakthroughs in electronic devices by their adoption in next-generation semiconducting technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".