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Abstract Microswimmers are self‐propelled particles that navigate fluid environments, offering significant potential for applications in environmental pollutant decomposition, biosensing, and targeted drug delivery. Their performance relies on engineered catalytic surfaces. Gold nanoclusters (AuNCs), with atomically precise structures, tunable optical properties, and high surface area‐to‐volume ratio, provide a new optimal catalyst for enhancing microswimmer propulsion. Unlike bulk gold or nanoparticles, AuNCs may deliver tunable photocatalytic activity and increased catalytic specificity, making them ideal co‐catalysts for hybrid microswimmers. For the first time, this study combines AuNCs with TiO₂/Cr₂O₃Janus microswimmers, combining the unique properties of both materials. This hybrid system capitalizes on the tuned optical properties of AuNCs and their role as co‐catalysts with TiO₂, driving enhanced photocatalytic performance under ultraviolet (UV) excitation. Using motion analysis, it is shown that the AuNC‐microswimmers exhibit significantly greater propulsion and mean squared displacement (MSD) as compared to controls. These findings suggest that the integration of nanoclusters with semiconductor materials enables state of the art, light‐switchable microswimmers. These AuNC‐microswimmer systems may thus offer new opportunities for environmental catalysis and other applications, providing precise control over catalytic and motile behaviors at the microscale. 

Abstract Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.