Molecule-specific diffusion and desorption of interstellar ices on carbonaceous dust

Interstellar ices form on dust grains in the coldest regions of molecular clouds and preserve key volatile reservoirs that could be incorporated into protoplanetary disks during star and planet formation. However, the effect of the dust surface composition on the ice structure and spectroscopic behavior remains poorly constrained. We present a comparative laboratory study of astrophysically relevant ices (CO, CO2, and H2 O) deposited on inert calcium fluoride (CaF2) substrate and carbonaceous dust analogs under interstellar conditions. Infrared spectroscopy and temperature-programmed desorption reveal pronounced molecule-specific infrared spectral responses to the amorphous carbonaceous surface. CO and CO2 both exhibit broadened absorption bands, redshifted band positions, and delayed desorption, arising from thermally activated diffusion into the porous dust matrix and indicating strong molecule–surface interactions. By contrast, H2 O varies only very little spectrally and thermally, indicating weak wetting and limited coupling to the substrate. These results provide direct laboratory evidence that dust-ice interfaces can affect the ice structure and desorption kinetics of interstellar ices even in thick ice layers. These findings offer new constraints for interpreting infrared absorption bands in astronomical observations and highlight the importance of surface effects in models of interstellar ice chemistry.

Improvements to AI Systems:

1. Astrochemical Ice Spectral Simulator – Enhance radiative transfer models by incorporating molecule-specific band-broadening and redshift parameters (CO, CO2) as functions of dust surface porosity and ice thickness, enabling more accurate fitting of observed infrared spectra toward protostellar cores.

2. Desorption Kinetics Predictor – Upgrade astrochemical network codes (e.g., UDfA, KIDA) with new binding energy distributions that account for carbonaceous vs. inert surfaces, allowing time-dependent gas-grain simulations to correctly reproduce delayed desorption of CO/CO2 on porous dust.

3. Surface-Interaction-Aware Ice Growth Model – Integrate a two-phase (bulk vs. interface) ice layering module into planet-formation disk models, where the first few monolayers on carbonaceous grains have altered diffusion and mixing rates, improving predictions of volatile delivery to protoplanetary disks.

4. Infrared Band Assignment Tool – Train a machine-learning classifier on laboratory spectra (CaF2 vs. carbonaceous) to automatically identify dust-surface contributions in observed ice bands, reducing misattribution of redshifts/broadening to thermal or compositional effects.

5. Porous Dust Microphysics Emulator – Develop a neural network surrogate that maps dust grain morphology (pore size, tortuosity) to effective ice diffusion coefficients and spectral line shapes, enabling fast parameter sweeps for 3D astrochemical simulations without costly molecular dynamics.

What the improved AI system can do:

• Predict observed ice absorption features with higher fidelity for carbon-rich dust environments, distinguishing surface-induced shifts from ice-phase changes.

• Simulate gas-phase abundances of CO and CO2 in star-forming regions that match laboratory desorption temperatures, improving chemical age estimates.

• Automatically flag observations where dust-ice interactions are significant, guiding telescope time toward targets where surface effects are detectable.

• Accelerate disk chemistry models by replacing computationally expensive surface diffusion calculations with accurate, real-time predictions.