Identifying Reactive Trends in Glycerol Electro-Oxidation Using an Automated Screening Approach: 28 Ways to Electrodeposit an Au Electrocatalyst.

Automated, rapid electrocatalyst discovery techniques that comprehensively address the exploration of chemical spaces, characterization of catalyst robustness, reproducibility, and translation of results to (flow) electrolysis operation are needed. Responding to the growing interest in biomass valorization, we studied the glycerol electro-oxidation reaction (GEOR) on gold in alkaline media as a model reaction to demonstrate the efficacy of such methodology introduced here. Our platform combines individually addressable electrode arrays with HardPotato, a Python application programming interface for potentiostat control, to automate electrochemical experiments and data analysis operations.

Values Underpinning Integrated, People-Centred Health Services: Similarities and Differences among Actor Groups Across Europe.

Introduction: In addition to the functional aspects of healthcare integration, an understanding of its normative aspects is needed. This study explores the importance of values underpinning integrated, people-centred health services, and examines similarities and differences among the values prioritised by actors across Europe.

Methods: Explorative cross-sectional design with quantitative analysis.

SEM-EDS and water chemistry characteristics at the early stages of glacier recession reveal biogeochemical coupling between proglacial sediments and meltwater.

Most glaciers worldwide are undergoing climate-forced recession, but the impact of glacier changes on biogeochemical cycles is unclear. This study examines the influence of proglacial sediment weathering on meltwater chemistry at the early stages of glacier recession in the High Arctic of Svalbard. Scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS) in combination with a wide range of geochemical analyses were used in this study.

Accelerated screening of colloidal nanocrystals using artificial neural network-assisted autonomous flow reactor technology.

Colloidal semiconductor nanocrystals with tunable optical and electronic properties are opening up exciting opportunities for high-performance optoelectronics, photovoltaics, and bioimaging applications. Identifying the optimal synthesis conditions and screening of synthesis recipes in search of efficient synthesis pathways to obtain nanocrystals with desired optoelectronic properties, however, remains one of the major bottlenecks for accelerated discovery of colloidal nanocrystals. Conventional strategies, often guided by limited understanding of the underlying mechanisms remain expensive in both time and resources, thus significantly impeding the overall discovery process.