Deep Learning to Analyze Sliding Drops

Shumaly, Sajjad; Darvish, Fahimeh; Li, Xiaomei; Saal, Alexander; Hinduja, Chirag; Steffen, Werner; Kukharenko, Oleksandra; Butt, Hans-Jürgen; Berger, Rüdiger

doi:10.1021/acs.langmuir.2c02847

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Deep Learning to Analyze Sliding Drops

Shumaly, S., Darvish, F., Li, X., Saal, A., Hinduja, C., Steffen, W., et al. (2023). Deep Learning to Analyze Sliding Drops. Langmuir, 39(3), 1111-1122. doi:10.1021/acs.langmuir.2c02847.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-8AEF-C Version Permalink: https://hdl.handle.net/21.11116/0000-000C-8AF0-9

Genre: Journal Article

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Creators:
Shumaly, Sajjad¹, Author
Darvish, Fahimeh¹, Author
Li, Xiaomei¹, Author
Saal, Alexander¹, Author
Hinduja, Chirag¹, Author
Steffen, Werner¹, Author
Kukharenko, Oleksandra², Author
Butt, Hans-Jürgen¹, Author
Berger, Rüdiger¹, Author

Affiliations:
1Dept. Butt: Physics at Interfaces, MPI for Polymer Research, Max Planck Society, ou_1800286
2Dept. Kremer: Polymer Theory, MPI for Polymer Research, Max Planck Society, ou_1800287

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Language(s): eng - English

Dates: Published Online: 2023-01-12Date issued: 2023-01-24

Publication Status: Issued

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Identifiers: DOI: 10.1021/acs.langmuir.2c02847

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Project name : DynaMo

Grant ID : 883631

Funding program : ERC-2019-ADG (H2020)

Funding organization : European Commission (EC)

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Title: Langmuir

Abbreviation : Langmuir

Source Genre: Journal

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Publ. Info: Columbus, OH : American Chemical Society

Pages: - Volume / Issue: 39 (3) Sequence Number: - Start / End Page: 1111 - 1122 Identifier: ISSN: 0743-7463
CoNE: https://pure.mpg.de/cone/journals/resource/954925541194