The phase diagram of the magnetocaloric Mn_xFe_2−xP_1−ySi_y quaternary compounds was established by characterising the structure, thermal and magnetic properties in a wide range of compositions (for a Mn fraction of 0.3 ≤ x < 2.0 and a Si fraction of 0.33 ≤ y ≤ 0.60). The highest ferromagnetic transition temperature (Mn_0.3Fe_1.7P_0.6Si_0.4, T_C = 470 K) is found for low Mn and high Si contents, while the lowest is found for low Fe and Si contents (Mn_1.7Fe_0.3P_0.6Si_0.4, T_C = 65 K) in the Mn_xFe_2−xP_1−ySi_y phase diagram. The largest hysteresis (91 K) was observed for a metal ratio close to Fe:Mn = 1:1 (corresponding to x = 0.9, y = 0.33). Both Mn-rich with high Si and Fe-rich samples with low Si concentration were found to show low hysteresis (≤2 K). These compositions with a low hysteresis form promising candidate materials for thermomagnetic applications.

Improving mass transfer in gas diffusion layers is critical to achieving high-performance proton-exchange membrane fuel cells (PEMFCs). Leaks through the interface between the gas and the membrane electrode assembly frame have been widely investigated, and the controllability of the cathode gas diffusion has not been achieved in most studies. In this study, we develop a structural parameter to investigate the controllability of the gas diffusion mechanism in the cathode in order to improve upon the design and performance of PEMFCs. This parameter accounts for the cathode gas diffusion layer porosity and carbon loading inside the catalyst layer. It is comprehensively calculated to relax the two segments’ distribution along three directions of the coordinate axis. The experimental and simulation results show that the obtained values of the parameter vary and change during voltage stabilization. According to the results, regardless of the materials in the cathode gas diffusion layer, the same steady-state voltage is obtained when the parameter is fixed. The cell could be controllably operated for a wide range of diffusion layer thicknesses by selecting the optimal parameter.