Packing Order Control in Conductive Metal–Organic Frameworks by Tuning Ligand Oxidation State
Bibliographic record
Abstract
Abstract Conductive metal–organic frameworks (c‐MOFs), composed of metal nodes and redox‐active ligands, have attracted growing interest due to the coexistence of porosity and charge transport. Notably, their electrical performance is closely related to the packing and ligand oxidation state within the framework, which has rarely been explored. Typical divalent metal nodes favor saturated intralayer square‐planar coordination to ligands in a single oxidation state, thereby predetermining the framework topology. Here, we report a packing and topology control strategy, achieved by tuning the ligand oxidation state and grounded in lanthanides (e.g., Gd) versatile coordination chemistry. Diffuse reflectance spectroscopy and single‐crystal transport measurements reveal that, at low temperature, coordination of Gd 3+ with 2,3,6,7,10,11‐hexahydroxytriphenylene (HHTP) in a lower mixed oxidation state (−4 and −5) yields a more ordered porous packing (Gd 1.5 HHTP) with superior electronic transport performance. In contrast, at elevated temperature, the ligand adopts a higher oxidation state (−3), and coordination with Gd 3+ yields a densely packed structure with local coordination disorder (GdHHTP), resulting in a markedly reduced electrical conductivity. This study demonstrates ligand‐oxidation‐state tuning provides an effective strategy for the precise control of structural order and charge transport in c‐MOFs, laying a theoretical foundation for the rational design of materials with tunable electronic properties.
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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".