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Gas-dynamic virtual nozzles (GDVNs) play a vital role in delivering biomolecular samples during diffraction measurements at X-ray free-electron laser facilities. Recently, submicrometer resolution capabilities of two-photon polymerization 3D printing techniques opened the possibility to quickly fabricate gas-dynamic virtual nozzles with practically any geometry. In our previous work, we exploited this capability to print asymmetric gas-dynamic virtual nozzles that outperformed conventional symmetric designs, which naturally leads to the question of how to identify the optimal gas-dynamic virtual nozzle geometry. In this work, we develop a 3D computational fluid dynamics pipeline to investigate how the characteristics of microjets are affected by gas-dynamic virtual nozzle geometry, which will allow for further geometry optimizations and explorations. We used open-source software (OpenFOAM) and an efficient geometric volume-of-fluid method ( isoAdvector ) to affordably and accurately predict jet properties for different nozzle geometries. Computational resources were minimized by utilizing adaptive mesh refinement. The numerical simulation results showed acceptable agreement with the experimental data, with a relative error of about 10% for our test cases that compared bell- and cone-shaped sheath-gas cavities. In these test cases, we used a relatively low sheath gas flow rate (6 mg/min), but future work including the implementation of compressible flows will enable the investigation of higher flow rates and the study of asymmetric drip-to-jet transitions. 

Abstract The protracted nature of the 2016-2017 central Italy seismic sequence, with multiple damaging earthquakes spaced over months, presented serious challenges for the duty seismologists and emergency managers as they assimilated the growing sequence to advise the local population. Uncertainty concerning where and when it was safe to occupy vulnerable structures highlighted the need for timely delivery of scientifically based understanding of the evolving hazard and risk. Seismic hazard assessment during complex sequences depends critically on up-to-date earthquake catalogues—i.e., data on locations, magnitudes, and activity of earthquakes—to characterize the ongoing seismicity and fuel earthquake forecasting models. Here we document six earthquake catalogues of this sequence that were developed using a variety of methods. The catalogues possess different levels of resolution and completeness resulting from progressive enhancements in the data availability, detection sensitivity, and hypocentral location accuracy. The catalogues range from real-time to advanced machine-learning procedures and highlight both the promises as well as the challenges of implementing advanced workflows in an operational environment. 

A set of six large catalogues documenting the seismic sequence that occurred in central Italy between 2016 and 2017, characterized by a cascade of four MW5.5–6.5 events. The earthquake catalogues possess different levels of resolution and completeness that result from progressive enhancements in both detection sensitivity and hypocentral location determination. These quality differences reflect the subsequent application of advanced methods.