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The chemical composition of surfaces functionalized with self-assembled monolayers (SAMs) is an important parameter that determines their performance in a broad range of applications, from immobilizing molecular machines to initiation and growth control of MOFs (Metal-Organic Frameworks). In this article, a critical Survey of XPS (X-ray photoelectron spectroscopy) and NEXAFS (near edge X-ray absorption fine structure) spectroscopy data for pyridine-functionalized monolayers on gold surfaces is presented to compile correlations that have been indiscernible before. Monolayers with aromatic backbones are compared to monolayers with aliphatic backbones.
Monolayers with pyridine end-groups are compared to mixed monolayers formed terpyridine-functionalized by molecules end-groups and non-functionalized molecules. Thiol-oxidation during ageing of the SAMs in air is addressed. And finally, the addressability of the SAMs for the deposition of metal-ions and organic molecules is investigated and compared. This work consequently delivers a comprehensive set of spectroscopic data of (ter-)pyridine-terminated SAMs and their performance as template for the preparation of functional multilayers of macrocycles exhibiting a preferential orientation.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
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.