Asymmetrical Substitution Manipulates Stacking Modes in 2D Conductive MOF Crystals
Bibliographic record
Abstract
The stacking modes in two-dimensional conductive metal-organic frameworks (2D c-MOFs) serve as a pivotal design parameter for precisely controlling charge transport pathways, thereby directly regulating carrier mobility and anisotropic transport. Precise control over the stacking modes of atomically precise single-crystal structures of 2D c-MOFs through the bottom-up synthesis remains a major challenge. The reason lies in the dominant role of coordination bonds during 2D c-MOF synthesis, where the weak van der Waals interactions between ligands are often overridden and fail to be expressed in the final MOF architecture. Moreover, most 2D c-MOFs can only be obtained as nanocrystalline powders, making it difficult to obtain precise structural information. Here, we report a new strategy for achieving controllable stacking and enhanced crystallinity in 2D c-MOFs through the asymmetrical electrostatic potential modulation of ligands. By strategically substituting fluorine atoms into the hexahydroxytriphenylene (HHTP) ligands, we modulated the intrinsic charge distribution, enabling the synthesis of two HHTP derivatives with different packing modes. The c-MOF crystals synthesized through this approach exhibit distinct stacking modes and tunable electrical properties. Through the systematic modulation of ligand stacking configurations, this work elucidates fundamental structure-property correlations in 2D c-MOFs, providing a rational design strategy for tailoring 2D c-MOF materials with optimized performance characteristics.
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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".