Higher-Magnesium-Doping Effects on the Singlet Ground State of the Shastry–Sutherland SrCu<sub>2</sub>(BO<sub>3</sub>)<sub>2</sub>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Doping of quantum antiferromagnets is an established approach to investigate the robustness of their ground state against the competing phases. Predictions of doping effects on the ground state of the Shastry–Sutherland dimer model are here verified experimentally on Mg-doped SrCu 2 (BO 3 ) 2 . A partial incorporation of Mg 2+ on the Cu 2+ site in the SrCu 2 (BO 3 ) 2 structure leads to a subtle but systematic lattice expansion with the increasing Mg-doping concentration, which is accompanied by a slight decrease in the spin gap, the Curie–Weiss temperature, and the peak temperature of the susceptibility. These findings indicate a doping-induced breaking of Cu 2+ spin-1/2 dimers that is also corroborated by X-band EPR spectroscopy that points to a systematic increase in the intensity of free Cu 2+ sites with increasing Mg-doping concentration. Extending the Mg-doping up to nominal x = 0.10 yielding SrCu 1.9 Mg 0.1 (BO 3 ) 2, in the magnetization measurements taken up to 35 T, a suppression of the pseudo-1/8 plateau is found along with a clear presence of an anomaly at an onset critical field μ 0 H’ C0 ≈ 9 T. The latter, absent in pure SrCu 2 (BO 3 ) 2, emerges due to the pairwise coupling of liberated Cu 2+ spin-1/2 entities in the vicinity of Mg-doping induced impurities.
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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) | 0.002 | 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".