Economical Fe-N-C catalysts are considered as promising alternatives to platinum group metal catalysts for proton exchange membrane fuel cells (PEMFCs). Despite exhibiting robust activity on rotating disk electrodes, their performance within membrane electrode assemblies often experiences limitations, such as decreased O₂ diffusion, high H₂O₂ formation, low proton conduction, and a lower electron transfer number. In this study, key factors, including proton transport, electron conduction, and gas diffusion within air-breathing PEMFCs, have been investigated by adjusting cathode catalyst layer (CCL) compositions. From the experimental results, the optimal peak power density was obtained when the loading of Fe-N-C catalyst was 1 mg∙cm^–2 and Nafion content was 0.15 mg∙cm^–2 within CCLs. The addition of polytetrafluoroethylene to enhance hydrophobicity was found to have a negative impact on PEMFC performance. Furthermore, the incorporation of diverse carbon nanotubes into CCLs resulted in a significant increase of over 30% in peak power density, attributed to enhancements in the gas diffusion and proton conductivity. The critical roles of gas transport and proton conductivity within Fe-N-C-based CCLs have been highlighted by this study. These findings contribute to the advancement of rational design principles for economical PEMFCs, offering valuable insights to drive the development of efficient and cost-effective technology in future.

The resolution of conventional optical microscopy is only ~200 nm, which is becoming less and less sufficient for a variety of applications. In order to surpass the diffraction limited resolution, super-resolution microscopy (SRM) has been developed to achieve a high resolution of one to tens of nanometers. The techniques involved in SRM can be assigned into two broad categories, namely "true" super-resolution techniques and "functional" super-resolution techniques. In "functional" super-resolution techniques, stochastic super-resolution microscopy (SSRM) is widely used due to its low expense, simple operation, and high resolution. The principle process in SSRM is to accumulate the coordinates of many diffraction-limited emitters (e.g., single fluorescent molecules) on the object by localizing the centroids of the point spread functions (PSF), and then reconstruct the image of the object using these coordinates. When the diffraction-limited emitters take part in a catalytic reaction, the activity distribution and kinetic information about the catalysis by nanoparticles can be obtained by SSRM. SSRM has been applied and exhibited outstanding advantages in several fields of catalysis, such as metal nanoparticle catalysis, molecular sieve catalysis, and photocatalysis. Since SSRM is able to resolve the catalytic activity within one nanoparticle, it promises to accelerate the development and discovery of new and better catalysts. This review will present a brief introduction to SRM, and a detailed description of SSRM and its applications in nano-catalysis.