Solvent-Directed Assembly of π-Stacked 3D Metal–Organic Frameworks with Tunable Conductivity Enhanced by C<sub>60</sub> Encapsulation
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) with tunable structures and unique host–guest chemistry have emerged as promising candidates for conductive materials. However, the tunability of conductivity and porosity in conductive MOFs, as well as their interrelationship, still lacks a systematic study. Herein, we report the synthesis of a series of 3D copper MOFs (NU-4000 to NU-4003) using a triphenylene-based hexatopic carboxylate linker. By modulating the ratio of mixed solvents, distinct structural topologies and π–π stacking arrangements were achieved, resulting in electrical conductivities ranging from insulators (<10 –6 S/cm) to semiconductors (10 –8 ∼10 2 S/cm). Among them, NU-4003 features continuous π–π stacking and exhibits a conductivity of 1.7 × 10 –6 S/cm. To further enhance the conductivity, we encapsulated C 60, a strong electron acceptor, within the circular channels of NU-4003, resulting in a remarkable conductivity increase to 140 S/cm with approximately 100% pore occupancy. Even at lower C 60 loadings that leave 54% of the pore volume accessible, the conductivity remains exceptionally high at 104 S/cm. This represents an eight-order magnitude enhancement and positions NU-4003-C 60 as one of the most conductive 3D MOFs reported to date. This work integrates two charge transport pathways (through-space and electron donor-acceptor interactions) into a single MOF host–guest material, achieving a significant enhancement in conductivity. This study demonstrates the potential of combining host–guest chemistry and π–π stacking to design conductive MOFs with permanent porosity maintained, providing a blueprint for the development of next-generation materials for electronic and energy-related applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".