Static magnetic order with strong quantum fluctuations in spin-1/2 honeycomb magnet Na2Co2TeO6
Bibliographic record
Abstract
Kitaev interactions, arising from the interplay of frustration and bond anisotropy, can lead to strong quantum fluctuations and, in an ideal case, to a quantum-spin-liquid state. However, in many nonideal materials, spurious non-Kitaev interactions typically promote a zigzag antiferromagnetic order in the d-orbital transition-metal compounds. Here, by combining neutron scattering with muon-spin rotation and relaxation techniques, we provide mechanism insights into the exotic properties of Na2Co2TeO6, a candidate material of the Kitaev model. Below TN, the zero-field muon-spin relaxation rate becomes almost constant (~0.45 μs−1). We attribute this temperature-independent relaxation rate to the strong quantum fluctuations, as well as to the frustrated Kitaev interactions. As the magnetic field increases, neutron scattering data indicate a broader spin-wave excitation at the K-point. Therefore, quantum fluctuations seem not only robust but are even enhanced by the applied magnetic field. Our findings provide valuable hints for understanding the onset of the quantum-spin-liquid state in Kitaev materials. Na2Co2TeO6 is a candidate Kitaev spin-liquid, but the nature of its ground state remains controversial, as spurious non-Kitaev exchange interactions promote magnetic ordering. Here, neutron scattering and muon-spin rotation experiments indicate the presence of strong quantum fluctuations in the ground state, giving rise to a multi-domain zigzag antiferromagnetic order.
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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".