The development of dendritic metal ion carrier systems for use in a biological environment is a challenging task as the carrier system must possess multiple features (e.g. a protective shell for metal decomplexation, targeting functions, metal-intradendrimer complexes, etc.) to substitute for the function of metal proteins in processes such as copper metabolism. Thus, Cu(II) complexation by a series of poly(propyleneimine) glycodendrimers ranging up to the fifth generation that have either a dense maltose or maltotriose shell was investigated by UV/Vis spectroscopy and electron paramagnetic resonance (EPR). As a necessary step towards potential biological application, we elucidated the complexation capacity, location of the Cu(II)-dendrimer complexes and the Cu(II) coordination sphere in the dendritic environment. A generation-dependent Cu(II) complexation was found. Furthermore, analysis of the EPR spectra revealed that internal and external Cu(II) coordination and the symmetry (axial and rhombic) of the generated complexes depend on the oligosaccharide shell, dendrimer generation and the relative concentrations of Cu(II) and the dendrimers. Both axial and rhombic symmetries are generation dependent, but also distort with increasing generation number. External coordination of Cu(II) is supported by sugar groups and water molecules. Finally, a third-generation dendrimer with a maltose shell was used to explore the general complexation behaviour of the dendritic poly(propyleneimine) scaffold towards different metal ions [Cu(II), Ag(I), VO(IV), Ni(II), Eu(III) and UO2(VI)].
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.