Astronomy & Astrophysics (A&A) is an international journal which publishes papers on all aspects of astronomy and astrophysics

Quantifying the efficiency of dust destruction in the interstellar medium (ISM) due to supernovae (SNe) is crucial for the understanding of galactic dust evolution. We present 3D hydrodynamic simulations of an SN blast wave propagating through the ISM. The interaction between the forward shock of the remnant and the surrounding ISM leads to destruction of ISM dust by the shock-heated gas. We consider the dust processing due to ion sputtering, accretion of atoms/molecules, and grain–grain collisions. Using 2D slices from the simulation time series, we apply post-processing calculations using the paperboats code. We find that efficiency of dust destruction depends strongly on the rate of grain shattering due to grain–grain collisions. The effective dust destruction is similar to previous theoretical estimates when grain–grain collisions are omitted, but with grain shattering included, the net destruction efficiency is roughly one order of magnitude higher. This result indicates that the dust-destruction rate in the ISM may have been severely underestimated in previous work, which only exacerbates the dust-budget crises seen in galaxies at high redshifts.

Metals in the interstellar medium (ISM) of essentially all types of galaxies are observed to be depleted compared to the expected values. The depletion is most likely due to dust condensation in, for example, cold molecular clouds and various circumstellar and interstellar environments. The relative observed metal abundances should thereby reflect the composition of the ISM dust components. We aim at identifying the most dominant dust species or types, including silicate and iron oxide grains present in the ISM, using recent observations of dust depletion of galaxies at various evolutionary stages. We use the observed elemental abundances in dust of several metals (O, S, Si, Mg, and Fe) in different environments, considering systems with different metallicities and dust content, namely damped Lyman-α absorbers (DLAs) towards quasars and the Galaxy. We derive a possible dust composition by computationally finding the statistically expected elemental abundances in dust assuming a set of key dust species with the iron content as a free parameter. Carbonaceous dust is not considered in the present study. Metallic iron (likely in the form of inclusions in silicate grains) and iron oxides are important components of the mass composition of carbon-free dust. The latter make up a significant mass fraction (~1∕4 in some cases) of the oxygen-bearing dust and there are good reasons to believe that metallic iron constitutes a similar mass fraction of dust. Wüstite (FeO) could be a simple explanation for the depletion of iron and oxygen because it is easily formed. There appears to be no silicate species clearly dominating the silicate mass, but rather a mix of iron-poor as well as iron-rich olivine and pyroxene. To what extent sulphur depletion is due to sulfides remains unclear. In general, there seems to be little evolution of the dust composition (not considering carbonaceous dust) from low-metallicity systems to the Galaxy.

Turbulence can significantly accelerate the growth of dust grains by accretion of molecules. For dust dynamically coupled to the gas, the growth rate scales with the square of the Mach number, which means that the growth time-scale can easily be reduced by more than an order of magnitude. The limiting time-scale is therefore rather the rate of molecular cloud formation, which means that dust production in the interstellar medium can rapidly reach the levels needed to explain the dust masses observed at high redshifts. Thus, turbulence may be the solution to the replenishment problem in models of dust evolution in high-redshift galaxies and explain the dust masses seen at $z$ = 7–8. A simple analytic galactic dust-evolution model is presented, where grain growth nicely compensates for the expected higher rate of dust destruction by supernova shocks. This model is simpler, relies on fewer assumptions and seems to yields a better fit to data derived from observations, compared to previous models of the same type.