Pressure-tuned spin chains in brochantite, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Cu</mml:mi> <mml:mn>4</mml:mn> </mml:msub> <mml:msub> <mml:mi>SO</mml:mi> <mml:mn>4</mml:mn> </mml:msub> <mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>OH</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>6</mml:mn> </mml:msub> </mml:mrow> </mml:math>
Bibliographic record
Abstract
Using high-pressure single-crystal x-ray diffraction combined with thermodynamic measurements and density-functional calculations, we uncover the microscopic magnetic model of the mineral brochantite, Cu 4 SO 4 ( OH ) 6 , and its evolution upon compression. The formation of antiferromagnetic spin chains with the effective intrachain coupling of J ≃ 100 K is attributed to the occurrence of longer Cu–Cu distances and larger Cu–O–Cu bond angles between the structural chains within the layers of the brochantite structure. These zigzag spin chains are additionally stabilized by ferromagnetic couplings J 2 between second neighbors and moderately frustrated by several antiferromagnetic couplings that manifest themselves in the reduced Néel temperature of the material. Pressure tuning of the brochantite structure keeps its monoclinic symmetry unchanged and leads to the growth of antiferromagnetic J with the rate of 3.2 K/GPa, although this trend is primarily caused by the enhanced ferromagnetic couplings J 2 . Our results show that the nature of magnetic couplings in brochantite and in other layered Cu 2 + minerals is controlled by the size of the lattice translation along their structural chains and by the extent of the layer buckling.
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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 | 0.001 | 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) | 0.003 | 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".