Benzene-1,3,5-tricarboxamide Metal Complexes Self-Assembled in Nanofibers: Implications for Bimetallic Catalytic Nanomaterials
Bibliographic record
Abstract
Multicomponent supramolecular self-assembled systems can potentially harness the properties of multiple systems simultaneously. However, creating multicomponent supramolecular nanostructures with narrow size distributions is challenging due to the dynamic nature of noncovalent interactions. In this article, we report the coassembly of a tris-Ni(II)-salphen and a tris-Cu(II)-salphen complex. Co-assembly of the complexes afforded nanofibers with low dispersity, with the metal complexes homogeneously distributed throughout the nanofibers. The length of the nanofibers could also be tuned by varying the ratio of the metal complexes. Density functional theory (DFT) calculations indicate that the dimerization of the copper(II) complex is unfavorable, unlike the dimerization of the nickel(II) complex. Co-assembly with the copper(II) complex inhibits the self-assembly of the nickel(II) complex, enabling length control of the bimetallic nanofibers. These results could pave the way for designing multicomponent supramolecular systems with applications in catalysis and magnetic devices.
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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) | 0.001 | 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".