Solid-liquid Equilibrium in the System 2-keto-L-gulonic Acid + Sodium-2-keto-L-gulonate + Hydrochloric Acid + Sodium Chloride + Water

The reactive solid-liquid equilibrium (SLE) in the quinary system 2-keto-l-gulonic acid (HKGA) + sodium-2-keto-l-gulonate (NaKGA) + hydrochloric acid (HCl) + sodium chloride (NaCl) + water was studied experimentally at 298 K and ambient pressure. The precipitation of three solid species was observed: HKGA monohydrate (HKGA⋅H2O), NaKGA monohydrate (NaKGA⋅H2O), and NaCl. A thermodynamic model based on the Pitzer model to calculate the activity coefficients in the liquid phase for the SLE was developed and unreported Pitzer parameters were fitted to the experimental data of this work. The equilibrium constant for the dissociation of HKGA and the solubility products of HKGA⋅H2O and NaKGA⋅H2O at 298 K were calculated from experimental data, whereas the equilibrium constant for the autoprotolysis of water and the solubility product of NaCl were adopted from literature data. The agreement between the experimental data and the results from the model is excellent, both regarding the liquid phase composition and the solid species in solid-liquid equilibrium

Accurate measurements of self-diffusion coefficients with benchtop NMR using a QM model-based approach

The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model-based approach for the estimation of self-diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model-based approach, the self-diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self-diffusion coefficients estimated from high-field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self-diffusion coefficients, even in complex mixtures

A Comparison of Non‑uniform Sampling and Model-based Analysis of NMR Spectra for Reaction Monitoring

Nuclear magnetic resonance (NMR) spectroscopy is widely used for applications in the field of reaction and process monitoring. When complex reaction mixtures are studied, NMR spectra often suffer from low resolution and overlapping peaks, which places high demands on the method used to acquire or to analyse the NMR spectra. This work presents two NMR methods that help overcome these challenges: 2D non-uniform sampling (NUS) and a recently proposed model-based fitting approach for the analysis of 1D NMR spectra. We use the reaction of glycerol with acetic acid as it produces five reaction products that are all chemically similar and, hence, challenging to distinguish. The reaction was measured on a high-field 400 MHz NMR spectrometer with a 2D NUS-heteronuclear single quantum coherence (HSQC) and a conventional 1D 1H NMR sequence. We show that comparable results can be obtained using both 2D and 1D methods, if the 2D volume integrals of the 2D NUS-HSQC NMR spectra are calibrated. Further, we monitor the same reaction on a low-field 43 MHz benchtop NMR spectrometer and analyse the acquired 1D 1H NMR spectra with the model-based approach and with partial least-squares regression (PLS-R), both trained using a single, calibrated data set. Both methods achieve results that are in good quantitative agreement with the high-field data. However, the model-based method was found to be less sensitive to the training data set used than PLS-R and, hence, was more robust when the reaction conditions differed from that of the training data