Machine-Learning-Driven Simulations of Hyperthermal Atomic Oxygen Impacts on (0001) Al2O3 for Low-Altitude Satellite Design
- DOI
- 10.1021/acsami.6c14110
- Published
- 2026-10-08
- Container
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acsami.6c14110,
title = {Machine-Learning-Driven Simulations of Hyperthermal Atomic Oxygen Impacts on (0001) Al2O3 for Low-Altitude Satellite Design},
author = {Nico Segreto and Jovan Boskovic and Andrea Beck and Johannes Kästner},
year = {2026},
journal = {ACS Applied Materials \& Interfaces},
doi = {10.1021/acsami.6c14110},
url = {https://doi.org/10.1021/acsami.6c14110}
}RIS
TY - JOUR TI - Machine-Learning-Driven Simulations of Hyperthermal Atomic Oxygen Impacts on (0001) Al2O3 for Low-Altitude Satellite Design AU - Nico Segreto AU - Jovan Boskovic AU - Andrea Beck AU - Johannes Kästner PY - 2026 JO - ACS Applied Materials & Interfaces DO - 10.1021/acsami.6c14110 UR - https://doi.org/10.1021/acsami.6c14110 ER -
APA
Segreto, N., Boskovic, J., Beck, A., & Kästner, J. (2026). Machine-Learning-Driven Simulations of Hyperthermal Atomic Oxygen Impacts on (0001) Al2O3 for Low-Altitude Satellite Design. ACS Applied Materials & Interfaces. https://doi.org/10.1021/acsami.6c14110
Source records
- crossref · retrieved 2026-11-10T09:59:41.742Z