Advanced materials for magnetic cooling: Fundamentals and practical aspects
Bibliographic record
Abstract
Over the last two decades, the research activities on magnetocalorics have been exponentially increased, leading to the discovery of a wide category of materials including intermetallics and oxides. Even though the reported materials were found to show excellent magnetocaloric properties on a laboratory scale, only a restricted family among them could be upscaled toward industrial levels and implemented as refrigerants in magnetic cooling devices. On the other hand, in the most of the reported reviews, the magnetocaloric materials are usually discussed in terms of their adiabatic temperature and entropy changes (ΔTad and ΔS), which is not enough to get more insight about their large scale applicability. In this review, not only the fundamental properties of the recently reported magnetocaloric materials but also their thermodynamic performance in functional devices are discussed. The reviewed families particularly include Gd1-xRx alloys, LaFe13-xSix, MnFeP1-xAsx, and R1-xAxMnO3 (R = lanthanide and A = divalent alkaline earth)–based compounds. Other relevant practical aspects such as mechanical stability, synthesis, and corrosion issues are discussed. In addition, the intrinsic and extrinsic parameters that play a crucial role in the control of magnetic and magnetocaloric properties are regarded. In order to reproduce the needed magnetocaloric parameters, some practical models are proposed. Finally, the concepts of the rotating magnetocaloric effect and multilayered magnetocalorics are introduced.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".