Convolutional neural network framework for the automated analysis of transition 
metal X-ray photoelectron spectra

Pielsticker, Lukas; Nicholls, Rachel L.; DeBeer, Serena; Greiner, Mark

doi:10.1016/j.aca.2023.341433

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Convolutional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra

Pielsticker, L., Nicholls, R. L., DeBeer, S., & Greiner, M. (2023). Convolutional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra. Analytica Chimica Acta, (1271): 341433. doi:10.1016/j.aca.2023.341433.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-D921-9 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-D922-8

Genre: Journal Article

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Creators:
Pielsticker, Lukas¹, Author
Nicholls, Rachel L.¹, Author
DeBeer, Serena², Author
Greiner, Mark¹, Author

Affiliations:
1Research Department Schlögl, Max Planck Institute for Chemical Energy Conversion, Max Planck Society, ou_3023874
2Research Department DeBeer, Max Planck Institute for Chemical Energy Conversion, Max Planck Society, ou_3023871

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Language(s): eng - English

Dates: Published Online: 2023

Publication Status: Published online

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Rev. Type: Peer

Identifiers: ISI: 001011574700001
DOI: 10.1016/j.aca.2023.341433

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Title: Analytica Chimica Acta

Abbreviation : Anal. Chim. Acta

Source Genre: Journal

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Publ. Info: Amsterdam : Elsevier

Pages: - Volume / Issue: (1271) Sequence Number: 341433 Start / End Page: - Identifier: ISSN: 0003-2670
CoNE: https://pure.mpg.de/cone/journals/resource/954925379952