Visualizing the gas-sensitive structure of the CuZn surface in methanol synthesis catalysis

Methanol formation over Cu/ZnO catalysts is linked with a catalytically active phase created by contact between Cu nanoparticles and Zn species whose chemical and structural state depends on reaction conditions. Herein, we use variable-temperature scanning tunneling microscopy at elevated pressure conditions combined with X-ray photoelectron spectroscopy measurements to investigate the surface structures and chemical states that evolve when a CuZn/Cu(111) surface alloy is exposed to reaction gas mixtures. In CO2 hydrogenation conditions, Zn stays embedded in the CuZn surface, but once CO gas is added to the mixture, the Zn segregates onto the Cu surface. The Zn segregation is CO-induced, and establishes a new dynamic state of the catalyst surface where Zn is continually exchanged at the Cu surface. Candidates for the migrating few-atom Zn clusters are further identified in time-resolved imaging series. The findings point to a significant role of CO affecting the distribution of Zn in the multiphasic ZnO/CuZn/Cu catalysts.

cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.