Correlation of AFM-Derived Surface Topography with Nitrogen Adsorption Measurements for the Surface Characterization of a Au/γ-Al2O3 Catalyst

The structural and surface characteristics of supported heterogeneous catalysts were investigated using a combination of nitrogen adsorption-desorption porosimetry and Atomic Force Microscopy (AFM). A Au/γ-Al2O3 catalyst was characterized in terms of specific surface area, pore size distribution, surface morphology and roughness. Nitrogen adsorption measurements revealed type IV isotherms, confirming their mesoporous nature. The Au/γ-Al2O3 catalyst exhibited a BET surface area of 266 m2 g−1 and a dominant mesopore size distribution centred approximately within the 9–13 nm range. AFM imaging was employed to investigate the nanoscale surface topography and revealed a heterogeneous surface. Quantitative image analysis was further performed using threshold segmentation and grain analysis to identify nanoscale topographical depressions and surface features. A total of 2539 surface features were detected, corresponding to a planar surface fraction of 6.71%. Characteristic lateral dimensions of surface features derived from the image analysis were found to be in the range of approximately 9–12 nm, indicating that these can be correlated with pores since they bear similar dimensions to the latter (as estimated by porosimetry). Statistical validation using Student’s t-test demonstrated good agreement between local measurements obtained from representative scan lines and the overall image-based analysis. Surface roughness measurements yielded average roughness (Ra) and root mean square roughness (Rq) values of 2.06 ± 1.90 nm and 3.42 ± 1.91 nm, respectively, indicating a moderately rough nanostructured surface. The characteristic dimensions of the AFM-derived surface features were found to be consistent with the mesopore size range determined independently by nitrogen adsorption but they do not directly represent internal pores. AFM probes only the external surface morphology and does not directly characterise the internal pore network. The combined approach demonstrates that AFM image analysis can provide valuable complementary information on catalyst surface morphology, roughness and nanoscale textural features alongside conventional gas adsorption techniques.