Strain modulated magnetocaloric effect in (111) oriented La<sub>0.7</sub>Sr<sub>0.3</sub>MnO<sub>3</sub>–SrRuO<sub>3</sub> superlattices
Bibliographic record
Abstract
Magnetocaloric effect in (111)-oriented La0.7Sr0.3MnO3-SrRuO3 (LSMO-SRO) superlattices grown with both the stacking orders by reversing the individual layer thickness on (111)-oriented SrTiO3(STO) substrates using the pulsed laser deposition technique has been studied. Pseudomorphic growth with 0.64% in-plane tensile strain in [11 unit cell (u.c.)SRO/3u.c.LSMO]×15 superlattice is favourable for a larger change in entropy (ΔSM) as compared to relaxed growth with in-plane compressive strain in [11u.c.LSMO/3u.c.SRO]×15 superlattice. The reduction of ΔSM in [11u.c.LSMO/3u.c.SRO]×15 could be due to the orientation-dependent in-phase and out-of-phase tilt of the unit cell between ±1° along the 103pc of the {103}pc, which softens the exchange coupling and leads to the faster alignment of the magnetization near the Curie temperature (TC). Stabilization of the orthorhombic phase of LSMO in the superlattices with both stacking orders is evidenced from the existence of anomaly around the TC of LSMO and SRO in the temperature-dependent phonon frequency shifts. Reduction in symmetry of LSMO from the rhombohedral to orthorhombic structure modulates the Mn-O-Mn bond length and angles, which induces the spin reorientations and hence, modifies the electronic and magnetic properties in these LSMO-SRO superlattices. The ΔSM of these superlattices suggest that the strain, magnitude of the magnetic field, volume and magnetization of the ferromagnet can control the magnetocaloric effect. These results will be useful for designing the magnetic entropy based devices to improve renewable energy systems.
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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".