Carbon-supported nanoparticles have been used widely as efficient catalysts due to their enhanced surface-to-volume ratio. To investigate their structure–property relationships, acquiring 3D elemental distribution is required. Here, carbon-supported Pt, PtMn alloy, and ordered Pt3Mn nanoparticles are synthesized and analyzed with atom probe tomography as model systems. A significant difference of Mn distribution after the heat-treatment was found. Finally, the field evaporation behavior of the carbon support was discussed and each acquired reconstruction was compared with computational results from an evaporation simulation. This paper provides a guideline for studies using atom probe tomography on the heterogeneous carbon-supported nanoparticle system that leads to insights toward a wide variety of applications.

Atomic mapping of nanomaterials, in particular nanoparticles, using atom probe tomography (APT) is of great interest, as their properties strongly depend on shape, size, and composition. However, APT analyses of nanoparticles are extremely challenging, and there is an urgent need for developing robust and universally applicable sample preparation methods. Herein, we explored a method based on pulse electrodeposition to embed Ag nanoparticles in a Ni matrix and prepare APT specimens from the resulting composite film. By systematically varying the duty cycle during pulse electrodeposition, the dispersion and number density of the nanoparticles within the matrix was significantly enhanced as compared to DC electrodeposition. Several Ag nanoparticles were analyzed with APT from such samples. Shape distortions and biased compositions were observed for the Ag nanoparticles after applying a standard data reconstruction protocol. Numerical simulations of the field evaporation process showed that such artifacts were caused by a difference in the evaporation field of Ni and Ag and a local magnification effect. We expect such detrimental effects to be mitigated by a careful selection of the matrix material, matching the evaporation field of the nanoparticles. Furthermore, we anticipate that the method presented herein can be extended to a wider range of nanomaterials.

Atom-probe tomography is a materials characterization method ideally suited for the investigation of clustering and precipitation phenomena. To distinguish the clusters from the surrounding matrix, the maximum separation algorithm is widely employed. However, the results of the cluster analysis strongly depend on the parameters used in the algorithm and hence, a wrong choice of parameters leads to erroneous results, e.g., for the cluster number density, concentration, and size. Here, a new method to determine the optimum value of the parameter dmax is proposed, which relies only on information contained in the measured atom-probe data set. Atom-probe simulations are employed to verify the method and to determine the sensitivity of the maximum separation algorithm to other input parameters. In addition, simulations are used to assess the accuracy of cluster analysis in the presence of trajectory aberrations caused by the local magnification effect. In the case of Cu-rich precipitates (Cu concentration 40–60 at% and radius 0.25–1.0 nm) in a bcc Fe–Si–Cu matrix, it is shown that the error in concentration is below 10 at% and the error in radius is <0.15 nm for all simulated conditions, provided that the correct value for dmax, as determined with the newly proposed method, is employed.