Metal Complexes of Bridging Neutral Radical Ligands: pymDTDA and pymDSDA
Bibliographic record
Abstract
Metal complexes of the 4-(2'-pyrimidyl)-1,2,3,5-dithiadiazolyl (pymDTDA) neutral radical ligand and its selenium analogue (pymDSDA) are presented. The following series of metal ions has been studied using M(hfac)(2) as the coordination fragment of choice (hfac = 1,1,1,5,5,5-hexafluoroacetylacetonato): Mn(II), Co(II), Ni(II), and Zn(II). The binuclear cobalt and nickel complexes of pymDTDA both exhibit ferromagnetic (FM) coupling between the unpaired electrons on the ligand and the metal ion, while the binuclear zinc complex of pymDTDA is presented as a comparative example incorporating a diamagnetic metal ion. The binuclear manganese complex of pymDTDA, reported in a preliminary communication, is compared to the pymDSDA analogue, and new insight into the magnetic behavior reveals that intermolecular magnetic coupling, mediated by chalcogen-oxygen contacts, gives rise to a significant increase in the χT product at low temperature. Surprisingly, the binuclear nickel complex of pymDSDA forms dimers in the solid state, as do the mononuclear complexes of cobalt and nickel with pymDTDA. In addition, mixed mononuclear/binuclear complexes of Mn- and Zn(pymDTDA) have been identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".