Anomalous metal vaporization from Pt/Pd/Al2O3 under redox conditions

Temperature-driven evolution of ceria-zirconia-supported AuPd and AuRu bimetallic catalysts under different atmospheres: insights from IL-STEM studies

The evolution of the structure and composition of the system of particles in two Ce0.62Zr0.38O2-supported bimetallic catalysts based on Au and a 4d metal (Ru or Pd) under high temperature conditions and different reducing and oxidizing environments has been followed by means of Identical Location Scanning Transmission Electron Microscopy (IL-STEM). As an alternative to in situ microscopy, this technique offers valuable insights into the structural modifications occurring in chemical environments with the characteristics of a macro-scale reactor. By tracking exactly the same areas on a large number of metallic entities, it has been possible to reveal the influence of particle size and the nature of the redox environment on the temperature-driven mobilization of the different metals involved. Thus, oxidizing environments evidenced a much higher capacity to mobilize the three metals, preferentially Au. Moreover, the typical storage conditions (under air) of catalysts during the prolonged exposure time has been proved to induce significant modifications in these bimetallic systems, even at room temperature. Regardless of the type of redox environment, bimetallic systems showed better thermal resistance, which demonstrates a beneficial effect of the second metal. In summary, IL-STEM is an invaluable and complementary methodology for characterizing heterogeneous catalysts under realistic reaction conditions and is within the reach of most laboratories.

Quantifying Competitive Degradation Processes in Supported Nanocatalyst Systems

The stability of supported metal nanoparticles determines the activity and lifetime of heterogeneous catalysts. Catalysts can destabilize through several thermodynamic and kinetic pathways, and the competition between these mechanisms complicates efforts to quantify and predict the overall evolution of supported nanoparticles in reactive environments. Pairing in situ transmission electron microscopy with unsupervised machine learning, we quantify the destabilization of hundreds of supported Au nanoparticles in real-time to develop a model describing the observed particle evolution as a competition between evaporation and surface diffusion. Data mining of particle evolution statistics allows us to determine physically reasonable values for the model parameters, quantify the particle size at which the Gibbs-Thomson pressure accelerates the evaporation process, and explore how individual particle interactions deviate from the mean-field model. This approach can be applied to a wide range of supported nanoparticle systems, allowing quantitative insight into the mechanisms that control their evolution in reactive environments.