Ligand directed self-assembly vs. metal ion coordination algorithm—when does the ligand or the metal take control?
Bibliographic record
Abstract
Polyfunctional hydrazone ligands with multidentate terminal donor groups offer metal ions many donor choices, and the coordination outcome depends mainly on the identity of the metal ion. Co(ii) and Ni(ii) prefer to adopt largely undistorted, six-coordinate geometries, while Cu(ii) can easily adapt to a variety of coordination situations (e.g. CN 4-6), and will optimize its coordination number and stereochemistry based on all the available donors. Ni(ii) and Co(ii) form simple [2 x 2] [M(4)-(micro(2)-O)(4)] square grids with such ditopic hydrazone ligands, and ignore other coordination options, while Cu(ii) tries to bind to all the available donors, and forms extended and 2D structures based on linked Cu(ii) triads rather than grids. Ni(ii) is also reluctant to compromise its desire to maximize its crystal field stabilization energy (CFSE) by binding to 'weak' ligands, and with a tetratopic pyrazole bis-hydrazone ligand it ignores the oxygen donors in favour of nitrogen, forming a novel trinuclear, triangular cluster. Also, reaction of a linear Ni(ii)(3) complex of a tetratopic pyridazine bis-hydrazone ligand with NiN(6) coordination spheres with Cu(ii), leads exclusively to a square Cu(12) grid based complex, and complete displacement of nickel. Structural and magnetic properties are highlighted, and metal-ligand interactions are discussed in detail.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".