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Predicting spacecraft surface degradation under atomic oxygen in Low Earth Orbit

This paper presents a first-principles multiscale framework that successfully predicts both the erosion yields and evolving surface microstructures of spacecraft materials exposed to atomic oxygen in Low Earth Orbit by linking atomic-scale reactive molecular dynamics simulations with a macro-scale statistical surface model.

Original authors: Sabin-Viorel Anton, Christian Siemes, Othonas A. Moultos, Jose van den IJssel, Pieter N.A.M. Visser

Published 2026-07-06

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CC BY 4.0

Original authors: Sabin-Viorel Anton, Christian Siemes, Othonas A. Moultos, Jose van den IJssel, Pieter N.A.M. Visser

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a satellite orbiting Earth is like a hiker walking through a dense, invisible forest. But instead of trees, the forest is filled with tiny, super-fast, single oxygen atoms. In the low orbit where most satellites live, these atoms are the dominant species. Because the satellite is moving so fast (about 17,500 mph), these atoms hit the satellite with the force of a bullet, even though they are just single atoms.

This paper is about figuring out exactly how these "atomic bullets" chip away at the satellite's skin over time, and how that damage changes the satellite's shape.

Here is the breakdown of their work using simple analogies:

1. The Problem: The "Sandpaper" Effect

For a long time, scientists knew that these oxygen atoms erode satellite materials (like the plastic coatings on solar panels), but they didn't have a good way to predict exactly how fast it happens or what the surface would look like after years of exposure.

Think of it like sandblasting a piece of wood. You know the sand will wear it down, but if you want to predict the final shape, you need to know:

How hard the sand hits.
How soft the wood is.
How the wood changes shape as it wears down (does it get smooth, or do deep grooves form?).

Previous computer models were like guessing the result by just looking at the wood type. They often got the speed of erosion wrong, sometimes predicting the wood would vanish ten times faster than it actually does in space.

2. The Solution: A Two-Step "Microscope and Map" Approach

The authors built a new computer framework that acts like a two-step process to solve this puzzle:

Step A: The Microscope (Molecular Dynamics)
First, they zoomed in to the atomic level. They used powerful computers to simulate a single oxygen atom hitting a tiny patch of material (like Kapton or Teflon).

The Challenge: In real space, atoms hit slowly (one every few seconds). In the computer, they had to speed this up to make the simulation run in a reasonable time. But if you just speed it up, the material gets super hot and melts, which ruins the test.
The Fix: They used a "digital thermostat" (a special cooling system in the code) that kept the material at a steady temperature, mimicking the real space environment where the satellite radiates heat away between hits.
The Result: They learned exactly how different materials react. Some materials (like Kapton) are like soft clay that gets eaten away easily. Others (like Teflon) are like hard plastic that resists the hits better, but only if the hit comes straight on. If the hit comes from the side, the behavior changes.

Step B: The Map (Statistical Surface Model)
Next, they zoomed out. They realized that as the material erodes, it doesn't stay flat. It develops tiny, cone-shaped bumps and valleys (like a miniature mountain range).

The Analogy: Imagine a flat sheet of paper. As you sand it, the soft spots wear down faster, leaving the harder spots sticking up. Eventually, you have a landscape of tiny peaks and valleys.
The Innovation: They created a mathematical "map" (a poly-Gaussian model) that predicts how these tiny mountains and valleys grow over years. They didn't just guess; they used the data from their "microscope" simulations to drive the growth of this digital landscape.

3. The "Ash" Factor

When these materials erode, they don't just disappear; they leave behind a residue, like ash from a fire.

In some materials, this ash acts like a shield, protecting the material underneath and slowing down the erosion.
In others, the ash doesn't stick, and the material keeps wearing away.
The new model accounts for this "ash" by scattering tiny digital particles on the surface, which then influence how the oxygen atoms bounce off and hit the surface again.

4. The Results: Matching Reality

The team tested their model against real data from the International Space Station (ISS), where samples of these materials were left in space for years (the MISSE experiments).

Accuracy: Their predictions for how fast the materials wore away were very close to reality (within about 19% error), which is much better than previous models.
Shape: When they looked at the digital "mountains" their model created, they looked just like the real microscopic cones seen in photos of the space-exposed samples.
The Connection: They discovered a strong link: materials that erode faster tend to develop rougher, more jagged surfaces. Rougher surfaces change how the satellite interacts with the thin air around it, which is crucial for calculating how much drag (air resistance) the satellite feels.

Summary

In short, this paper created a new "digital twin" for satellite surfaces. It combines a microscopic view of how atoms react with a macroscopic view of how the surface shape evolves. By doing this, they can now predict not just how fast a satellite's skin will wear away, but exactly what the worn surface will look like, helping engineers design better satellites that can survive the harsh environment of Low Earth Orbit.

Technical Summary: Predicting Spacecraft Surface Degradation under Atomic Oxygen in Low Earth Orbit

Problem Statement
Accurate aerodynamic modeling for thermospheric research and satellite operations in Low Earth Orbit (LEO) is hindered by poor knowledge of surface degradation caused by atomic oxygen (O1). While in-situ experiments like the Materials International Space Station Experiments (MISSE) 2–8 have documented the erosion yields and microscopic conical roughening of various polymers, a first-principles theoretical framework linking chemical composition to these macro-scale morphological changes has been lacking. Existing empirical models fit in-situ data but cannot predict behavior for untested materials or explain texture formation. Conversely, standard reactive Molecular Dynamics (MD) simulations often fail to match orbital measurements due to timescale limitations, overheating artifacts, and the neglect of evolving surface topography which alters local incidence angles.

Methodology
The authors propose a first-principles multiscale framework that bridges atomic-scale chemistry with macro-scale surface evolution. The approach consists of two coupled components:

Reactive Molecular Dynamics (MD) Simulations:
- Setup: Extensive simulations were performed on four polymers (Kapton H, Teflon ETFE, Teflon FEP, and Teflon PTFE) using the ReaxFF reactive force field. Simulations covered local incidence angles ( $0^\circ$ – $75^\circ$ ), impact speeds (3–10 km/s), and surface temperatures (200–400 K).
- Thermostating: To address the overheating issues common in short-timescale MD, a Nosé-Hoover chain thermostat was applied during surface atom integration. This maintained a constant surface temperature (mimicking the slow thermal re-equilibration of real satellites) without perturbing the hyperthermal gas-surface collision dynamics.
- Transient Filtering: Simulations identified a transient regime where loosely bound fragments are ejected. Data from this initial phase were excluded to isolate the steady-state oxidative erosion regime.
- Outputs: The MD simulations yielded three local response functions as inputs for the macro-model: local erosion yield ( $E_y$ ), reaction fraction ( $\Psi$ ), and energy accommodation coefficient ( $\alpha_E$ ).
Statistical Macro-Scale Erosion Model:
- Framework: The local response functions were integrated into a statistical erosion model based on the poly-Gaussian surface description and the Level-Set Method (LSM).
- Mechanics: The model treats surface evolution on two scales: atomic-scale interactions and macro-scale roughness driven by "ash" particles (erosion-resistant residues). It accounts for both first-reflection and multi-reflection particle fluxes, updating the surface geometry over time to simulate prolonged exposure (approx. 4 years of MISSE 2 conditions).
- Validation: The model was validated against MISSE 2 flight data, including Field Emission Scanning Electron Microscopy (FESEM) images and measured erosion yields.

Key Contributions

First-Principles Prediction: The study establishes a direct link between a material's chemical composition and its predicted erosion yield and surface morphology without relying on empirical fitting to in-situ data.
Thermostated MD Protocol: The introduction of a specific thermostating protocol allows for longer bombardment durations in MD, preventing artificial overheating and enabling more realistic erosion statistics.
Morphology Prediction: The framework successfully predicts the development of conical surface features and their aspect ratios, capturing the transition from smooth to rough surfaces.
Roughness-Erosion Correlation: The authors identify and quantify a strong correlation between the global erosion yield and the steady-state surface roughness (specifically the standard deviation of surface slopes).

Results

Erosion Yield Accuracy: The framework reproduced the measured global erosion yields for Kapton H, Teflon ETFE, FEP, and PTFE with a maximum relative error of 19% (average errors ranging from 4% to 19%). This performance significantly exceeds previous empirical models (which had errors of 29–36% for the same materials).
Morphological Agreement: The simulated surface morphologies qualitatively matched FESEM images from MISSE 2, correctly reproducing the conical features, aspect ratios, and specific textures (e.g., the "melted" look of ETFE and the intrinsic texture of PTFE).
Transient Regimes: The study identified two distinct erosion regimes:
1. High-Crystallinity Materials (e.g., PTFE, FEP): Exhibit an initial high erosion yield that decreases as roughness develops and shields the surface, eventually reaching a steady state.
2. Low-Crystallinity/Reactive Materials (e.g., Kapton H): Show different dependencies on incidence angle, leading to different roughness evolution patterns.
Roughness Prediction: Using the derived correlation between erosion yield and roughness, the authors estimated the roughness parameters for all 41 materials tested in MISSE 2. They predict that even materials with lower erosion yields develop significant roughness ( $\sigma_s \geq 1.5$ ), suggesting that most LEO surfaces will promote predominantly diffuse gas reflection.

Significance
The paper claims that this framework provides a robust, physics-based tool for predicting the long-term degradation of spacecraft surfaces. By accurately capturing both the erosion yield and the evolving geometric roughness, the model addresses a critical source of uncertainty in thermospheric research: gas-surface interaction modeling. The authors assert that the predicted roughness implies that aerodynamic drag coefficients for eroded surfaces should be modeled using diffuse reflection assumptions (DRIA model) with limited variation between different polymer coatings. This work offers a pathway to improve satellite orbit prediction and material selection for Very Low Earth Orbit (VLEO) missions without relying solely on costly and time-consuming in-situ exposure experiments.

1. The Problem: The "Sandpaper" Effect

2. The Solution: A Two-Step "Microscope and Map" Approach

3. The "Ash" Factor

4. The Results: Matching Reality

Summary

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