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Nanocrystalline hydroxyapatite (HAp) prepared by a precipitation route was investigated. The X-ray diffraction (XRD) powder patterns of the elongated nanocrystals with a typical diameter of about 10 nm and length of 30-50 nm (by transmission electron microscopy (TEM)) revealed the presence of HAp with significantly broadened XRD reflections. However, Ca deficiency was found, as the Ca/P ratio was 1.5 only (so-called calcium-deficient hydroxyapatite (CDHA)), and not 1.67. This Ca deficiency of nanocrystalline HAp is explained using NMR. It is shown unambiguously that (i) the nanocrystals consist of a crystalline core and a (disordered) surface region with a relative phosphate content of about 1:1, (ii) the crystalline core is HAp, and (iii) the surface region is dominated by hydrogen phosphate anions (with no hydroxyapatite-like structural motif) and structural water (hydrate). From the relative phosphate content and taking into account the crystal shape, the thickness of the surface layer along the main crystal axis could be estimated to be about 1 nm, and the average chemical composition of the surface layer has been determined. Finally, a Ca/P ratio of 1.52 was estimated from the NMR data that compares well with the value of 1.51 from chemical analysis. The important consequences are that the surface of nanocrystalline HAp has nothing in common with the bulk composition and that the chemistry of such materials (e.g. the binding of protein molecules to phosphate surfaces) must be reconsidered.
The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.
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.
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.
Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
Near-ambient pressure x-ray photoelectron spectroscopy (NAP-XPS) is a less traditional form of XPS that allows samples to be analyzed at relatively high pressures, i.e., at greater than 2500 Pa. With NAP-XPS, XPS can probe moderately volatile liquids, biological samples, porous materials, and/or polymeric materials that outgas significantly. In this submission, we show survey, C 1s, O 1s, and N
1s narrow scans from an aqueous solution of a common protein, bovine serum albumin. The C 1s peak envelope is well fit to four symmetric peaks of equal width that correspond to carbon bonded to carbon and hydrogen (C-1), carbon singly bonded to oxygen (C-2), carbonyl and/or amide carbon (C-3), and carboxyl carbon (C-4). Two possible peak fits are considered for the N 1s and O 1s peak envelopes. The N 1s signal is fit to four peaks that correspond to amine (—NH2), Amide (OvCZNH2), ammonium (—NH3 +), and N2(g) nitrogen, and alternatively to three peaks that correspond to amine, amide, and N2(g) nitrogen. The O 1s peak envelope is similarly fit to three and four components.