Recoverable Identifier
advisory
doi_compliance
recoverable_identifier
DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1063/1.3092928/13871093/124308_1_online.pdf.32S.Li) was visible in the surrounding text but could not be confirmed against doi.org as printed.
Paper page Integrity report arXiv Try DOI
Evidence text
publication Title: ESCA applied to free molecules. 3R. Seidel, B. Winter, and S. E. Bradforth, “Valence Electronic Structure of Aqueous Solutions: Insights from Photoelectron Spectroscopy,” Annual Review of Physical Chemistry67, 283–305 (2016). 4B. B. Hurisso, K. R. J. Lovelock, and P. Licence, “Amino acid-based ionic liquids: using XPS to probe the electronic environment via binding energies,” Physical Chemistry Chemical Physics13, 17737 (2011). 5E. J. Dick, A. E. A. Fouda, N. A. Besley, and P. Licence, “Probing the electronic structure of ether functionalised ionic liquids using X-ray photo- electron spectroscopy,” Phys. Chem. Chem. Phys.22, 1624–1631 (2020). 6B. D. Ratner, “Surface characterization of biomaterials by electron spec- troscopy for chemical analysis,” Annals of Biomedical Engineering11, 313–336 (1983). 7P. S. Bagus, F. Illas, G. Pacchioni, and F. Parmigiani, “Mechanisms re- sponsible for chemical shifts of core-level binding energies and their rela- tionship to chemical bonding,” Journal of Electron Spectroscopy and Re- lated Phenomena100, 215–236 (1999). 8L. Pielsticker, R. L. Nicholls, S. DeBeer, and M. Greiner, “Convolu- tional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra,” Analytica Chimica Acta1271, 341433 (2023). 9G. Drera, C. M. Kropf, and L. Sangaletti, “Deep neural network for x- ray photoelectron spectroscopy data analysis,” Machine Learning: Science and Technology1, 015008 (2020). 10A. A. Vakhru
Evidence payload
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"reconstructed_doi": "10.1063/1.3092928/13871093/124308_1_online.pdf.32S.Li",
"ref_index": 2,
"resolved_title": null,
"verdict_class": "incontrovertible"
}