Setting the basis for the interpretation of temperature first order reversal curve (TFORC) distributions of magnetocaloric materials
First Order Reversal Curve (FORC) distributions of magnetic materials are a well-known tool to extract information about hysteresis sources and magnetic interactions, or to fingerprint them. Recently, a temperature variant of this analysis technique (Temperature-FORC, TFORC) has been used for the an...
| Authors: | ; |
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| Format: | Article |
| Status: | Published version |
| Publication date: | 2020 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/100994 |
| Online Access: | https://hdl.handle.net/11441/100994 https://doi.org/10.3390/met10081039 |
| Access Level: | Open access |
| Keyword: | Magnetocaloric materials TFORC Thermal hysteresis |
| Summary: | First Order Reversal Curve (FORC) distributions of magnetic materials are a well-known tool to extract information about hysteresis sources and magnetic interactions, or to fingerprint them. Recently, a temperature variant of this analysis technique (Temperature-FORC, TFORC) has been used for the analysis of the thermal hysteresis associated with first-order magnetocaloric materials. However, the theory supporting the interpretation of the diagrams is still lacking, limiting TFORC to a fingerprinting technique so far. This work is a first approach to correlate the modeling of first-order phase transitions, using the Bean–Rodbell model combined with a phenomenological transformation mechanism, with the features observed in experimental TFORC distributions of magnetocaloric materials. The different characteristics of the transformations, e.g., transition temperatures, symmetry, temperature range, etc., are correlated to distinct features of the distributions. We show a catalogue of characteristic TFORC distributions for magnetocaloric materials that exhibit some of the features observed experimentally. |
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