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Oxidative coupling reactions enable biomass-derived oxygenates to serve as sustainable platform molecules for a wide range of high-value chemicals. These catalytic reactions can be selectively triggered over alloys wherein a highly active dopant metal such as Pd is diluted into a sea of highly selective host metal atoms such as Au. Here, a range of supported Pd1Aux (x = 5–200) alloy nanoparticles were synthesized using a sequential reduction method with colloidal Au to achieve a high degree of compositional control and particle size uniformity. The promotional role of Pd was examined in the oxidation of ethanol to yield acetaldehyde and the coupling product ethyl acetate. Reactivity trends indicate that both the overall rate of ethanol oxidation and the selectivity toward coupling increase with Pd doping. Rate order and activation energy trends further suggest that the promotional role of Pd does not likely originate from simple O2 dissociation and spillover but rather from the stabilization of alkoxides at Pd-Au interfaces, disproportionately increasing coupling vs simple oxidation. Infrared spectroscopy and density functional theory calculations offer further insights into Pd microstructures in the presence of various key adsorbates, suggesting that Pd can lend this promotion in an isolated state. While this state is generally unstable in the surface due to preferences for segregation into the bulk, oxygen and pathway intermediates may aid in stabilizing surface structures. These findings lay groundwork to explain selectivity and activity control in a much wider range of oxidative functionalizations and to guide further catalyst development. 

This work shows the complete, solvent-free conversion of lignin-derivedcis–cis-muconic acid, a platform biochemical, into levulinic acid. 

The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world scenarios. To address the limitation, we introduce M3Act, a synthetic data generator for multi-view multi-group multi-person human atomic actions and group activities. Powered by Unity Engine, M3Act features multiple semantic groups, highly diverse and photorealistic images, and a comprehensive set of annotations, which facilitates the learning of human-centered tasks across singleperson, multi-person, and multi-group conditions. We demonstrate the advantages of M3Act across three core experiments. The results suggest our synthetic dataset can significantly improve the performance of several downstream methods and replace real-world datasets to reduce cost. Notably, M3Act improves the state-of-the-art MOTRv2 on DanceTrack dataset, leading to a hop on the leaderboard from 10th to 2nd place. Moreover, M3Act opens new research for controllable 3D group activity generation. We define multiple metrics and propose a competitive baseline for the novel task. Our code and data are available at our project page: http://cjerry1243.github.io/M3Act.