About

SLP Calculator is a web-based application developed in Python to calculate the Specific Loss Power (SLP) - sometimes also called Specific Absorption rate (SAR) - of magnetic nanoparticles* from experimental time–temperature data. The platform provides an interactive workflow that guides users through the entire analysis process (from data import to final SLP calculation) ensuring both accuracy and reproducibility.

The tool supports several input formats (CSV, TXT, Excel) and automatically detects the sampling interval (Δt). Imported datasets are displayed for verification, with the option to export the corresponding plots. Before analysis, the user is prompted to enter the physical parameters of the system (including the density, specific heat, and concentration of both the nanoparticles and the carrier fluid) which are used for SLP determination.

SLP Calculator integrates two complementary computational approaches: the Peak Analysis Method (PAM) and the Initial Slope Method (ISM). Both are implemented with rigorous statistical validation using the F-test to identify the most reliable fitting regions. The output includes clear graphical visualizations with confidence bands and indicators of the optimal fitting windows.

Designed with a modular and user-friendly interface, SLP Calculator automates repetitive tasks, guarantees consistent methodology, and makes SLP analysis accessible even to users without programming experience — providing a robust, transparent, and reproducible tool for the study of magnetic nanoparticle heating efficiency.

Authors

Iago López Vázquez¹˒², Yilian Fernández-Afonso³˒⁴, Sergiu Ruta⁵, Alfredo Amigo¹, María del Puerto Morales⁴, Roy W. Chantrell⁶, Lucía Gutiérrez³, and David Serantes¹˒²

¹ Departamento de Física Aplicada, Universidade de Santiago de Compostela, Spain

² Instituto de Materiales iMATUS, Universidade de Santiago de Compostela, Spain

³ Instituto de Nanociencia y Materiales de Aragón (INMA, UNIZAR-CSIC), Spain

⁴ Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), Spain

⁵ College of Business, Technology and Engineering, Sheffield Hallam University, UK

⁶ School of Physics, Electronics and Technology, The University of York, York, UK

Acknowledgments

The authors would like to acknowledge financial support from the following projects: Projects PID2019-109514RJ-100 and PID2020-13480RB-I0 funded by MICIU/AEI/10.13039/501100011033, and project CNS2023-144321 funded by MICIU/AEI/10.13039/501100011033 and NextGenerationEU/PRTR. Xunta de Galicia is acknowledged for project ED431F 2022/005 (to D.S.). AEI is also acknowledged for the Ramón y Cajal grant RYC2020-029822-I to D.S.