Saturn’s largest moon, Titan, is even larger than Mercury and only dwarfed by Jupiter’s moon Ganymede (Figure 1). Like Ganymede, Titan is an icy moon with a water ice crust. What sets Titan apart from the other icy moons is its thick atmosphere composed primarily of nitrogen with small amounts of methane. As cosmic radiation interacts with the atmosphere, particles become ionized and dissociated. The spare N, C and H atoms recombine to form larger organic molecules (Figure 2).
Signature among these are the tholins first proposed by Carl Sagan as the stuff of life. These tholins have the potential to form prebiotic chemistry and have been shown to form amino acids when oxygenated (Figure 3; Neish et al. 2010).
The easiest way to oxygenate on Titan is by the introduction of liquid water, but with a surface at 94 K, liquid water is rare. There are cases where it can be found (Figure 4). Cryovolcanism offers a potential pathway (Neish et al., 2006), but more ideal is the melt produced by impact cratering (Neish et al., 2018). Cryovolcanic flows are thinner and colder making them shorter lived. Impact crater melt has the potential to last for 1000s of years (O’Brien et al., 2005). These melts are natural laboratories of chemical reactions occurring on time scales that are significantly longer than can be done in lab experiments. This is the motivation behind the proposed New Frontiers mission to Titan.
Dragonfly is a proposed quadcopter lander that would be sent to Titan to investigate its prebiotic chemistry and dynamic geology (Figure 5). It is in the second phase of the New Frontiers selection process with a final decision expected later this summer. The quadcopter will have unprecedented access to various terrains and able to travel kilometers in a day. The primary objective is to investigate impact crater melts for potential biomolecules frozen in the ice. The relevance of this mission to this project relates to the planning and approach of the lander. Neish et al. (2018) presented a detailed outline of how to identify and retrieve potential samples (Figure 6). In the case of an impact crater melt-pond, freezing is expected to occur from the top and bottom inward effectively forcing the impurities into a thin lens in the middle. Titan’s craters are known to undergo significant fluvial erosion by methane rain (Neish et al., 2016), and Neish et al. (2018) proposes using this natural incising into the crater melt to sample the molecules in the middle lens. This is expected to be the best way of retrieving samples from the melt-pond, but the freezing of the melt-pond is likely more complicated than suspected.
The Subtleties of Freezing Ice
When ice freezes it primarly rejects dissolved impurities from ice into the liquid (Rempel and Worster 1999). The impurities begin to concentrate in the liquid solution; however, some of it becomes trapped within the ice layer. Early experimental work characterized this process, formally known as doping (Hobbs, 1974). Most of the research in this area has focused on ions (inorganic matter). Baker (1967) was one of the few investigations into organic doping showing it does occur at significant concentrations. There is evidence of extremophiles living in ice (Figure 7; Thomas and Dieckmann 2002).
However, extremophiles have evolved to survive in these types of environments, and that may entail adapting ways of imbedding themselves into the ice the biotic means. Recent evidence suggests that nonliving organic mater become included into ice as effectively as living bacteria and both at higher rates than inorganic matter (Figure 8; Santibáñez et al., 2019). Therefore, the doping of ice with organic matter can be though as analogous to the doping of inorganic matter. Most of the material is destined to be rejected. That which is not rejected is incorporated into the ice lattice or trapped between ice boundaries (Petrenko & Whitworth, 1999). It then becomes a matter of constraining what fraction of the impurities is rejected.
The results of Santibáñez et al. (2019) suggest organic matter incorporates into ice at higher concentrations than inorganic matter. This is true for the bacteria and the total organic carbon (TOC) concentrations, but the TOC K_eff (Figure 8) was less than the bacteria. It is possible that the bacteria biases the TOC measurements. The control suggests it is not entirely a biotic bias because the segregation of dead bacteria is still on par with the living bacteria.
Santibáñez et al. (2019) is one of the first studies to measure the concentrations of organic matter in ice cores, but similar work has been done on sea ice only looking at inorganic concentrations (Feltham et al., 2006; Wettlauger et al., 1997). Extensive theoretical modeling has been done on the subject (Buffo et al., 2018; Turner and Hunke, 2015; Hunke et al., 2011). Naturally, these works use terrestrial examples (e.g. lakes, Antarctica), but the theory is applicable to any type of ice-water environment. Buffo et al. (2019) modified the model created by Buffo et al. (2018) of Antarctic ice, who used ground truthing to confirm the accuracy of the model, to model the Europa environment (e.g. Figure 9). The same laws that govern ice on Earth and Europa apply to Titan, so the same model can be adapted to the Titan environment.
The goal of this project is to study how the organic molecules on Titan will interact with the freezing melt-pond. This is a necessary step in optimizing a mission’s success by understanding the environment they intend to target. Given the nature of ice formation, this leads us to three main objectives.
First, identify the difference between organic and inorganic doping. Previous models have tracked inorganic matter in ice. Baker (1967) showed that organics are capable of the same process, but it is unclear how significant this process will be, particularly for Titan like concentrations. Santibáñez et al. (2019) suggests the doping of organics is more extreme than inorganic matter, but this needs to be demonstrated theoretically to ensure the higher organic concentrations were abiotically driven and not biotically driven.
Santibáñez et al. (2019) was also in a very different environment. The lakes studied were on the order of meters thick; the crater’s Dragonfly is likely to study are thought to have melt-ponds 100s of meters thick. One of the findings of Buffo et al. (2019) was that inclusions of salt were highest for the highest thermal gradient. Naturally, as ice freezes, it becomes a good insulator, so the temperature gradient drops followed by a drop in the salinity in the ice. Even if organics are observed in the ice, the concentration may drop off at too high a rate. Sampling regions that exhibited the highest temperature gradient may, in turn, mean sampling ice that froze too quickly. Therefore, the next objective must be to relate the evolution of the thermal environment to the amount of intrusions in the ice.
Finally, a complete distribution of the level of doping in the ice can be ascertained. This information will provide a first order guide to finding the best depth at which to sample. This step necessarily follows objective two because it will require significant processing power and time. The thermal environment can be analyzed by looking at the upper boundary, and that will decide whether it is worth modeling for the entire system as the change in the thermal gradient will be near the surface.
The evolution of the freezing crater melt-ponds with organic impurities is performed using the one-dimensional, two-phase, reactive transport model that Buffo et al. (2019) adapted from their work tracking sea ice (Buffo et al., 2018). This model is chosen because it has been verified by direct sea ice measurements. It conserves the mass of the water and ice, energy, and salute concentration using the governing principles in the mushy layer theory of sea ice and incorporating enthalpy. I won’t go into any more detail here as the paper is pending publication.
The application to Titan is simple; the environment and brine parameters (density, diffusivity) are adjusted to the new solute. The range of organics are known on Titan. A simple organic (e.g. HCN) is chosen and modeled for a depth of ~10m (Neish et al., 2018; Ferris et al., 1978). More complex chemistry is incorporated ultimately testing synthesized amino-acids (e.g. Glycine). For each, a range of concentrations are used that are based on the estimated supply of organics from the existing remote sensing data. Several small-scale analyses will provide answers to the first science objective.
Next, the process is repeated for deeper deaths. There will be a critical point where the temperature gradient drops that may lead to lower doping. Buffo et al. (2019) found at ~50m of ice was where the thermal gradient loses effect. Therefore, the models are run to depths of ~75m. This should be deep enough to relate the temperature gradient to the level of doping and identify where the effect fades.
The third science objective is the most computationally intense. To model the entire profile the model will extend for hundreds of meters (Figure 10; O’Brien et al., 2005). The feasibility and speed will depend significantly on the computer power available. There is the opportunity to request time with the NASA super computer if necessary. Analysis of the upper boundary may reveal it is not necessary to model the entire depth of the melt-pond to assess whether intermediate inclusions would make viable samples, but even in the event of insufficient doping beyond the critical thermal gradient (~50m), a full-scale model would improve the understanding of large-scale ice formation and the interactions of impact melts with surrounding materials.
It is important that we have a good grasp on the distribution of molecules in the ice of Titan’s crater melt-pond if Dragonfly intends to sample the ice. An important variable in identifying the best target is the distribution of the biomolecules in the ice. The propensity of ice to reject impurities is well understood, but the fraction of inclusions may be significant enough for analysis. Therefore, the mission may need to alter its approach to broaden the range of depths it can considers sampling. Our ability to sample is largely controlled by the level of incision by fluvial erosion. Molecules may exist closer to the surface of the crater melt-pond. However, Dragonfly is targeting Titan because of its potential for a long-term biochemical environment. It would defeat the purpose of the mission to sample ice that froze on short time scales. There likely exists a critical point where the ice can be said to have been liquid for a significant amount of time, and there we can assess the density of molecules expected. Our work will constrain the level of incision Dragonfly should aim for by relating the depth of material in the ice to the timescales over which it froze.
Ice Science and Astrobiology
The interaction between organics and ice is not well understood. This work will use existing models substituting organic solute properties to better understand how they will react during ice formation. Furthermore, understanding how organics interact with ice will improve our ability to search for life with missions beyond Dragonfly. Icy moons are likely our best shot at finding extraterrestrial life, but that requires we know where to look for it.
The scientific objective is to investigate interactions between water and the organics within it. However, these are still impact cratering processes. The physics of water and magma are very different, but at the core, these are intrusions in melt. We cannot perform the same level of analysis on these ice-rocks as on Earth rock. However, we have a chance to understand the nature of these intrusions and how they are emplaced. This work tethers the evolution of crater melt to the differentiation of the particles within it. It is a type of analogue for processes on Earth. We may find that the differentiation of intrusions (and vesicles) within crater melt on Earth can be related to the thermal evolution of the melt itself, and it would an opportunity to extend this work to terrestrial examples.
The timeline of the project is detailed in Table 1. The project is currently in the preliminary stages. I have begun reviewing the relevant material including logging the chemical information of the organics to be modeled. In May 2019, I will begin an international internship in Atlanta at the Georgia Institute of Technology. I am spending the summer months working with the lead authors and code creator of the Buffo et al. (2019, 2018) model. After a brief period of familiarizing myself with the code, the modeling will begin. The first two objectives will be completed by the end of the internship at the end of August. The final objective will be completed by the end November. The work will then be formally submitted for publication with a goal of a January 2020 deadline. A summary of the work will also be presented at the next Lunar and Planetary Science Conference. Then the next project will be concluded with possibility for future work.
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