Controlled beams of shockfrozen, isolated, biological and artificial nanoparticles

CMInject: Python framework for the numerical simulation of nanoparticle injection pipelines

“…Creating high-quality nanoparticle beams poses diverse technical and scientific challenges [14,15,23,27]. The development of improved or sample-adjusted injection pipelines [15] needs to be supported by a flexible and extensible simulation package, which enables quantitative predictions of arbitrary nanoparticle injection pipelines.…”

“…Further points of interest for nanoparticle injection are the separation of species, e. g., by quantum state, conformation, or enantiomer [16,28], the alignment or orientation in space [29][30][31][32][33], or the preservation of native biological structures [14,34,35]. These are not yet implemented in CMInject and will not be discussed further in this paper, but we designed our framework foreseeing corresponding as well as unforeseen extensions.…”

“…Thus, it is important to understand how the variation in particles' sizes/shapes and structural conformations will affect their trajectories in the injection system. These trajectories are also dependent on several experimental parameters, e. g., the geometry of the injection system, the temperature and pressure of the guiding aerosols, and the initial phase space distribution of the injected particles [14,15]. Accordingly, selecting specific particle species, e. g., through the use of inhomogeneous electric fields [16], are an advanced topic for creating a highquality particle beam.…”

“…Simulations are repeatedly used as a basis, supplementary, guiding, or verification method in SPI research. Examples for this are (1) optimization of experimentally verified aerodynamical injector designs for a variety of specific particle sizes and materials [15,[17][18][19][20][21], (2) exploration of the effects of experimental injection parameters [22] and types of injectors [23] on diffraction patterns, and (3) control of shock frozen isolated particles of both biological and artificial origin [14]. Progress in all of these areas was the foundation of recent significant improvements of the amount of data that can be collected in a given timeframe in SPI experiments [11].…”

“…Figure 1 depicts examples of simulation results, indicating that recent developments, as well as future ideas, are supported by our framework. 3D trajectories of a focusing experiment [14], where a cooled buffer gas cell (BGC) focuses d = 490 nm polystyrene nanoparticles by a flowing carrier gas at a cryogenic temperature (4 K). The used force model is a new theoretical development for particles moving in the molecular flow regime and at low temperatures, see Section 3.3.4 [24].…”