The formation and reaction mechanism of polynuclear copper hydrides remain unstudied since their first synthesis in the late 80s. This is due to the myriad of potential active species that can arise from aggregation and fragmentation of Cu. Carrying out mechanistic studies requires structural information on the intermediate complexes, clusters and particles. EXAFS is a suitable method to resolve complex reaction mixtures, and spectroscopic simulation on realistic models can assist the interpretation of the data.We will identify the key reaction intermediates in the formation and reactivity of copper-hydride cluster-catalyzed reductions using multivariate-resolved HERFD-EXAFS assisted by spectroscopic simulation on a library of potential intermediates sampled computationally through DFT-MD.

Traditionally, Ni-carbene catalysts are regarded as either heterogeneous (functionalized nanoparticles) or homogeneous (complexes). However, in the past decade, seminal work by Ananikov and co-workers has established that Pd- and Pt-based carbene-functionalized nanoparticles, clusters and complexes co-exist in a dynamic equilibrium. High-resolution X-ray absorption measurements (HERFD-XAS) have proven useful for the resolution of complex reaction mixtures. The interpretation of XAS is facilitated using cross-reading based on computational models. Since solvent effects are crucial for these systems, molecular dynamics simulations at the DFT level (DFT-MD) of fully solvated Ni nanoparticles in a solvent cell are expected to provide an appropriate model for these complex systems. We will use a combined HERFD-XAS and DFT-MD approach to probe ligand effects on the behavior of carbene-ligated Ni nanoparticles, focusing on the leaching to clusters and molecular complexes in solution.

Motion-based control is gaining popularity, and motion gestures form a complementary modality in human-computer interactions. To achieve more robust user independent motion gesture recognition in a manner analogous to automatic speech recognition, we need a deeper understanding of the motions in gesture, which arouses the need for a 6D motion gesture database. In this work, we present a database that contains comprehensive motion data, including the position, orientation, acceleration, and angular speed, for a set of common motion gestures performed by different users. We hope this motion gesture database can be a useful platform for researchers and developers to build their recognition algorithms as well as a common test bench for performance comparisons. Associated codes with the dataset along with instructions may be found on our GitHub page at https://github.com/olivesgatech/6DMG.

Motion-based control is gaining popularity, and motion gestures form a complementary modality in human-computer interactions. To achieve more robust user independent motion gesture recognition in a manner analogous to automatic speech recognition, we need a deeper understanding of the motions in gesture, which arouses the need for a 6D motion gesture database. In this work, we present a database that contains comprehensive motion data, including the position, orientation, acceleration, and angular speed, for a set of common motion gestures performed by different users. We hope this motion gesture database can be a useful platform for researchers and developers to build their recognition algorithms as well as a common test bench for performance comparisons. Associated codes with the dataset along with instructions may be found on our GitHub page at https://github.com/olivesgatech/6DMG.