Kinetically limited growth of dendritic tin oxide thin films : a machine learning study beyond the structure zone diagram
(2025) In Advanced Science 12.- Abstract (Swedish)
- Even after fifty years since its introduction, the empirical Thornton’s structure zone diagram remains a valuable tool for predicting thin film microstructure. This diagram is essential for understanding the correlation between synthesis, composition, structure, and physical properties in emerging applications. In this work, we critically appraise this diagram by examining Sn─O thin films grown at room temperature using reactive magnetron sputtering. Based on transmission electron microscopy, Sn0.6 O0.4 thin films form dendrites featuring nanosized Sn and SnO grains, rather than columns, which are not captured by the structure zone diagram. Using density functional theory and machine learning, we constructed a model to explain this unusual... (More)
- Even after fifty years since its introduction, the empirical Thornton’s structure zone diagram remains a valuable tool for predicting thin film microstructure. This diagram is essential for understanding the correlation between synthesis, composition, structure, and physical properties in emerging applications. In this work, we critically appraise this diagram by examining Sn─O thin films grown at room temperature using reactive magnetron sputtering. Based on transmission electron microscopy, Sn0.6 O0.4 thin films form dendrites featuring nanosized Sn and SnO grains, rather than columns, which are not captured by the structure zone diagram. Using density functional theory and machine learning, we constructed a model to explain this unusual microstructure on the atomic scale. Kinetically limited surface diffusion yields
SnO islands on Sn(001), which constitute the initial stage of dendrite formation. This study provides the potential to devise models for thin film microstructure evolution, enhancing performance in advanced applications, such as green energy generation and storage. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8bc94ae9-653f-488b-83c6-2a76242636d9
- author
- Music, Denis ; Xiao, Xuelian ; Naser, Rami ; Chang, Keke ; Sadowski, Grzegorz and Olsson, Pär A.T. LU
- organization
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Advanced Science
- volume
- 12
- article number
- e04627
- pages
- 9 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:40439599
- scopus:105007009243
- ISSN
- 2198-3844
- DOI
- 10.1002/advs.202504627
- language
- English
- LU publication?
- yes
- id
- 8bc94ae9-653f-488b-83c6-2a76242636d9
- date added to LUP
- 2025-09-14 20:00:38
- date last changed
- 2025-10-14 13:29:53
@article{8bc94ae9-653f-488b-83c6-2a76242636d9,
abstract = {{Even after fifty years since its introduction, the empirical Thornton’s structure zone diagram remains a valuable tool for predicting thin film microstructure. This diagram is essential for understanding the correlation between synthesis, composition, structure, and physical properties in emerging applications. In this work, we critically appraise this diagram by examining Sn─O thin films grown at room temperature using reactive magnetron sputtering. Based on transmission electron microscopy, Sn0.6 O0.4 thin films form dendrites featuring nanosized Sn and SnO grains, rather than columns, which are not captured by the structure zone diagram. Using density functional theory and machine learning, we constructed a model to explain this unusual microstructure on the atomic scale. Kinetically limited surface diffusion yields<br/>SnO islands on Sn(001), which constitute the initial stage of dendrite formation. This study provides the potential to devise models for thin film microstructure evolution, enhancing performance in advanced applications, such as green energy generation and storage.}},
author = {{Music, Denis and Xiao, Xuelian and Naser, Rami and Chang, Keke and Sadowski, Grzegorz and Olsson, Pär A.T.}},
issn = {{2198-3844}},
language = {{eng}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Advanced Science}},
title = {{Kinetically limited growth of dendritic tin oxide thin films : a machine learning study beyond the structure zone diagram}},
url = {{https://lup.lub.lu.se/search/files/227718644/Advanced_Science_-_2025_-_Music_-_Kinetically_Limited_Growth_of_Dendritic_Tin_Oxide_Thin_Films_a_Machine_Learning_Study.pdf}},
doi = {{10.1002/advs.202504627}},
volume = {{12}},
year = {{2025}},
}