Dokument-ID Dokumenttyp Autoren/innen Persönliche Herausgeber/innen Haupttitel Abstract Auflage Verlagsort Verlag Herausgeber (Institution) Erscheinungsjahr Titel des übergeordneten Werkes Jahrgang/Band ISBN Veranstaltung Veranstaltungsort Beginndatum der Veranstaltung Enddatum der Veranstaltung Ausgabe/Heft Erste Seite Letzte Seite URN DOI Lizenz Datum der Freischaltung OPUS4-54481 Forschungsdatensatz Liehr, Sascha ANNforPAT - Artificial Neural Networks for Process Analytical Technology This code accompanies the paper "Artificial neural networks for quantitative online NMR spectroscopy" published in Analytical and Bioanalytical Chemistry (2020). San Francisco GitHub 2020 https://creativecommons.org/licenses/by/4.0/deed.de 2022-03-16 OPUS4-50456 Forschungsdatensatz Kern, Simon; Liehr, Sascha; Wander, Lukas; Bornemann-Pfeiffer, Martin; Müller, S.; Maiwald, Michael; Kowarik, Stefan Training data of quantitative online NMR spectroscopy for artificial neural networks Data set of low-field NMR spectra of continuous synthesis of nitro-4'-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans. Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model. Synthetic low-field NMR spectra First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum. Xi ("pure component spectra dataset") Xii ("spectral model dataset") Experimental low-field NMR spectra from MNDPA-Synthesis This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4'-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included. Geneva Zenodo 2020 10.5281/zenodo.3677139 https://creativecommons.org/licenses/by/4.0/deed.de 2020-02-26 OPUS4-51888 Forschungsdatensatz Kowarik, Stefan; Pithan, L. kowarik-labs/AI-reflectivity: v0.1 AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication: Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst. For an online live demonstration using a pre-trained network have a look at github. Geneva Zenodo Bundesanstalt für Materialforschung und -prüfung (BAM) 2019 10.5281/zenodo.3477583 https://opensource.org/licenses/GPL-3.0 2020-12-21