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 t2g 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".