Ni(II) and<i>hs</i>-Fe(II) Complexes of a Paramagnetic Thiazyl Ligand, and Decomposition Products of the Iron Complex, Including an Fe(III) Tetramer
Bibliographic record
Abstract
Synthesis and structural, magnetic and electrochemical characterization of the Ni(hfac) 2(pyDTDA) and the Fe(hfac) 2(pyDTDA) complexes are reported (hfac = 1,1,1,5,5,5-hexafluoroacetylacetonato-; pyDTDA = 4-(2'-pyridyl)-1,2,3,5-dithiadiazolyl). Unlike the previously reported Mn(II) and Cu(II) complexes, but similar to the Co(II) complex, the Ni(II) and Fe(II) complexes are not dimerized in the solid state, allowing for magnetic coupling between the metal ion and paramagnetic ligand to be readily obtained from solid state magnetic measurements: Ni complex, J/k B = +132(1) K, using H = -2 J{ S Ni. S Rad} and g Ni = 2.04(2) and g Rad = 1.99(2); Fe complex, J/k B = -60.3(3) K, using H = -2 J{ S Fe. S Rad} and g av = 2.11(2). The iron complex is unusually unstable. A thermal decomposition product is isolated wherein the coordinated pyDTDA ligand appears to have been transformed into a coordinated 2-(2'-pyridyl)-4,6-bis(trifluoromethyl)pyrimidine. The iron complex also yields a solution decomposition product in the presence of air that is best described as an oxygen bridged iron(III) tetramer with two hfac ligands on each of three iron atoms and two oxidized pyDTDA ligands chelated on the fourth.
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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".