Structure and Dynamics of Applied Nanomaterials

Neural Network approach for structural characterization of nanocatalysts

• Supervised Machine Learning Methods for XANES data modeling in nanoparticles
• Neural network-based determination of radial distribution function from EXAFS spectra

In situ/operando Characterization

• X-rays and electrons join forces to map catalytic reactions in real time
• Operando characterization of catalysts through use of a portable microreactor
• Application of operando XAS, XRD and Raman Spectroscopy for phase speciation in WGS reaction catalysts

Physico-chemical properties of nanoparticles

• Intracluster atomic and electronic structural heterogeneities in supported nanoscale metal catalysts
• Illuminating Surface Atoms in Nanoclusters by Differential X-ray Absorption Spectroscopy
• Effects of Adsorbate Coverage and Bond Length Disorder on the d-Band Center in Carbon-Supported Pt Catalysts
• An In¬-Situ Study of Bond Strains in 1nm Pt Catalysts

Mechanisms of electromechanical materials

• Mechanism of electrostriction in Gd-doped Ceria

Doped colloidal semiconductor quantum dots

• From impurity doping to metallic growth in diffusion doping: Ag doped InAs nanocrystals
• Impurity location and binding in heavily doped semiconductor nanocrystals: Cu doped InAs nanocrystals
• Controlling anisotropic growth of colloidal ZnSe nanostructures using magic size clusters

Modeling of nanocatalysts structure, guided by XAS data

• A combined theoretical and experimental EXAFS study of the structure and dynamics of Au147 nanoparticles
• Probing structural relaxation in nanosized catalysts by combining EXAFS and reverse Monte Carlo methods
• Solving local structure around dopants in metal nanoparticles with ab initio modeling of XANES