# Binding energy distributions of alcohols, thiols, and their precursors on interstellar water ice surfaces

## TL;DR

This paper calculates and incorporates binding energy distributions for alcohols, thiols, and their precursors on interstellar water ice, improving astrochemical models by accounting for realistic adsorption site variations.

## Contribution

It introduces a method to include binding energy distributions from quantum calculations into astrochemical models, enhancing the accuracy of molecular retention and reaction predictions.

## Key findings

- Oxygen-bearing species have higher BEs than sulfur analogues.
- BE distributions significantly affect predicted molecular abundances.
- Incorporating distributions improves astrochemical model accuracy.

## Abstract

Binding energy (BE) is a critical parameter in astrochemical modeling, governing the retention of species on interstellar dust grains and their subsequent chemical evolution. However, conventional models often rely on single-valued BEs, overlooking the intrinsic distribution arising from diverse adsorption sites. In this study, we present BEs for monohydric alcohols, thiols, and their plausible precursors, including aldehydes and thioaldehydes. We incorporate a distribution of BEs to capture the realistic variation in adsorption strengths. The quantum chemical calculations provide a range of BE values rather than a single estimate, ensuring a more precise description of molecular diffusion and surface chemistry. The BE trend of analogous species provides qualitative insight into the dominant reaction pathways and key precursors that drive the formation of larger molecules under interstellar conditions. Oxygen-bearing species generally exhibit higher BEs than their sulfur analogues, primarily due to stronger interactions, further influencing molecular adsorption and reactivity. We implemented BE distributions in astrochemical models, revealing significant effects on predicted abundances and establishing a more accurate framework for future astrochemical modeling.