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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.
Composites containing 4-n-octyl-4?-cyanobiphenyl (8CB) either confined to nanopores of molecular sieves with very large pores or coating silica nanoparticles of aerosil type at high silica-to-8CB ratios are investigated by IR spectroscopy. Band shape analysis was performed in wavenumber regions in which the peaks due to CN stretching, CH stretching and CH out-of-plane vibrations appear. Some of molecules confined to molecular sieves show spectroscopic features characteristic to a bulk-like 8CB matter located in the centre of the pores or in the inter-grain space. Other features of the IR spectra are due to 8CB molecules located in the surface layer, mostly forming hydrogen bonds between their CN groups and surface OH groups. Another part of the 8CB molecules in the surface layer may also interact by ? electrons of the aromatic rings. Hydrogen bonding is less hindered for the molecules of the surface layers onto aerosil particles than inside pores of the molecular sieves. Comparison is also made with the case of composites based on molecular sieves with small pores.
On the mobility of sodium ions and of confined liquid crystals in molecular sieves of faujasite type
(2002)
Broadband dielectric spectroscopy (10-2109 Hz) was employed to study in a rather large temperature interval the molecular dynamics of the composite systems containing a nematic liquid crystal (LC), 4-n-pentyl-4?-cyanobiphenyl, confined to NaY, a zeolite with faujasite structure. Thermal analysis was also performed in order to get information concerning the loading of the zeolite and the possible phase transitions. No phase transitions and no relaxation processes of the LC molecules inside the pores were observed. But remarkably, in the presence of these molecules the dielectric behavior of the faujasite host changes due to modifications appeared in the mobility of Na+ ions.
Großes duktiles Rißwachstum zweier seitengekerbter Proben, CTsg
und CCTsg, aus StE 460 wird mit der Finite-Elemente-Methode an 2D
EVZ-Modellen numerisch simuliert. Zwei verschiedene Simulationsmodelle des Rißwachstums, Knotenfreisefczung mit und ohne vorausgehende Knotenversetzung, werden untersucht. Die rechnerischen
Analysen liefern detaillierte Aussagen über die Spannungs- und
Verformungszustände in den Proben. Die unterschiedlichen Dehnungsbehinderungen beider Probengeometrien im Ligament (in-plane constraint) begründen die bekannten Geometrieabhängigkeiten ihrer
Rißwiderstandskurven. Eine ergänzende 3D Analyse der CCT-Probe
liefert Aussagen über Dickeneffekte und lokales Rißwiderstandsverhalten.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally 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 realized 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-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a 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.