Nonemployed Simple Carboxylate Ions in Well-Investigated Areas of Heterometallic Carboxylate Cluster Chemistry: A New Family of {Cu<sup>II</sup><sub>4</sub>Ln<sup>III</sup><sub>8</sub>} Complexes Bearing <i>tert</i>-Butylacetate Bridging Ligands
Bibliographic record
Abstract
The first use of tert-butylacetate as bridging ligand in 3d/4f-metal cluster chemistry, in conjunction with the versatile chelate ligand pyridine-2,6-dimethanol, has afforded a new family of [Cu4Ln8(OH)6(NO3)2(O2CCH2Bu(t))16(pdm)4] clusters with unprecedented structures and slow magnetization relaxation for the {Cu(II)4Dy(III)8} member. The molecular structure of representative complex 1 consists of a {Cu(II)4Gd(III)8} cage-like cluster built from two {CuGd3} cubanes which are linked to each other and to two {CuGd} subunits on opposite sides through two η(2):η(2):η(2):μ5 NO3(-) ions. The metal ions are additionally bridged by μ3-OH(-), μ3-OR(-), and μ-OR(-) groups to give an overall [Cu4Gd8(μ5-NO3)2(μ3-OH)6(μ3-OR)2(μ-OR)8](14+) core. Peripheral ligation about the core is provided by the N,O,O-chelating part of the pdm(2-) groups and, more impressively, by the oxygen atoms of 16 bridging Bu(t)CH2CO2(-) ligands; the latter are arranged into five classes, adopting a total of six different binding modes with the metal centers. The combined work demonstrates the ligating flexibility of tert-butylacetate ion and its usefulness in the synthesis of new 3d/4f-metal cluster compounds.
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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".