Strengthening the Magnetic Interactions in Pseudobinary First-Row Transition Metal Thiocyanates, M(NCS)<sub>2</sub>
Bibliographic record
Abstract
Understanding the effect of chemical composition on the strength of magnetic interactions is key to the design of magnets with high operating temperatures. The magnetic divalent first-row transition metal (TM) thiocyanates are a class of chemically simple layered molecular frameworks. Here, we report two new members of the family, manganese(II) thiocyanate, Mn(NCS) 2, and iron(II) thiocyanate, Fe(NCS) 2 . Using magnetic susceptibility measurements on these materials and on cobalt(II) thiocyanate and nickel(II) thiocyanate, Co(NCS) 2 and Ni(NCS) 2, respectively, we identify significantly stronger net antiferromagnetic interactions between the earlier TM ions—a decrease in the Weiss constant, θ, from 29 K for Ni(NCS) 2 to −115 K for Mn(NCS) 2 —a consequence of more diffuse 3d orbitals, increased orbital overlap, and increasing numbers of unpaired t 2g electrons. We elucidate the magnetic structures of these materials: Mn(NCS) 2, Fe(NCS) 2, and Co(NCS) 2 order into the same antiferromagnetic commensurate ground state, while Ni(NCS) 2 adopts a ground state structure consisting of ferromagnetically ordered layers stacked antiferromagnetically. We show that significantly stronger exchange interactions can be realized in these thiocyanate frameworks by using earlier TMs.
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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".