RESUMEN
Recently, we presented a new approach for simultaneous phase and baseline correction of nuclear magnetic resonance (NMR) signals (SINC) that is based on multiobjective optimization. The algorithm can automatically correct large sets of NMR spectra, which are commonly acquired when reactions and processes are monitored with NMR spectroscopy. The aim of the algorithm is to provide spectra that can be evaluated quantitatively, for example, to calculate the composition of a mixture or the extent of reaction. In this work, the SINC algorithm is tested in three different studies. In an in-house comparison study, spectra of different mixtures were corrected both with the SINC method and manually by different experienced users. The study shows that the results of the different users vary significantly and that their average uncertainty of the composition measurement is larger than the uncertainty obtained when the spectra are corrected with the SINC method. By means of a dilution study, we demonstrate that the SINC method is also applicable for the correction of spectra with low signal-to-noise ratio. Furthermore, a large set of NMR spectra that was acquired to follow a reaction was corrected with the SINC method. Even in this system, where the areas of the peaks and their chemical shifts changed during the course of reaction, the SINC method corrected the spectra robustly. The results show that this method is especially suited to correct large sets of NMR spectra and it is thus an important contribution for the automation of the evaluation of NMR spectra.
RESUMEN
Spectral data preprocessing is an integral and sometimes inevitable part of chemometric analyses. For Nuclear Magnetic Resonance (NMR) spectra a possible first preprocessing step is a phase correction which is applied to the Fourier transformed free induction decay (FID) signal. This preprocessing step can be followed by a separate baseline correction step. Especially if series of high-resolution spectra are considered, then automated and computationally fast preprocessing routines are desirable. A new method is suggested that applies the phase and the baseline corrections simultaneously in an automated form without manual input, which distinguishes this work from other approaches. The underlying multi-objective optimization or Pareto optimization provides improved results compared to consecutively applied correction steps. The optimization process uses an objective function which applies strong penalty constraints and weaker regularization conditions. The new method includes an approach for the detection of zero baseline regions. The baseline correction uses a modified Whittaker smoother. The functionality of the new method is demonstrated for experimental NMR spectra. The results are verified against gravimetric data. The method is compared to alternative preprocessing tools. Additionally, the simultaneous correction method is compared to a consecutive application of the two correction steps.
RESUMEN
Metal-organic frameworks (MOFs) offer an attractive alternative to traditional hard and soft templates for nanocluster synthesis because their ordered crystalline lattice provides a highly controlled and inherently understandable environment. We demonstrate that MOFs are stable hosts for metal hydrides proposed for hydrogen storage and their reactive precursors, providing platform to test recent theoretical predictions that some of these materials can be destabilized with respect to hydrogen desorption by reducing their critical dimension to the nanoscale. With the MOF HKUST-1 as template, we show that NaAlH(4) nanoclusters as small as eight formula units can be synthesized. The confinement of these clusters within the MOF pores dramatically accelerates the desorption kinetics, causing decomposition to occur at approximately 100 degrees C lower than bulk NaAlH(4). However, using simultaneous thermogravimetric modulated beam mass spectrometry, we also show that the thermal decomposition mechanism of NaAlH(4) is complex and may involve processes such as nucleation and growth in addition to the normally assumed two-step chemical decomposition reactions.
RESUMEN
Through the use of simultaneous thermogravimetry modulated beam mass spectrometry, optical microscopy, hot-stage time-lapsed microscopy, and scanning electron microscopy measurements, the physical and chemical processes that control the thermal decomposition of 1,3,5-trinitrohexahydro-s-triazine (RDX) below its melting point (160-189 degrees C) have been identified. Two gas-phase reactions of RDX are predominant during the early stages of an experiment. One involves the loss of HONO and HNO and leads to the formation of H2O, NO, NO2, and oxy-s-triazine (OST) or s-triazine. The other involves the reaction of NO with RDX to form NO2 and 1-nitroso-3,5-dinitrohexahydro-s-triazine (ONDNTA), which subsequently decomposes to form a set of products of which CH2O and N2O are the most abundant. Products from the gas-phase RDX decomposition reactions, such as ONDNTA, deposit on the surface of the RDX particles and lead to the development of a new set of reaction pathways that occur on the surface of the RDX particles. The initial surface reactions occur on surfaces of those RDX particles in the sample that can accumulate the greatest amount of products from the gas-phase reactions. Initial surface reactions are characterized by the formation of islands of reactivity on the RDX surface and lead to the development of an orange-colored nonvolatile residue (NVR) film on the surface of the RDX particles. The NVR film is most likely formed via the decomposition of ONDNTA on the surface of the RDX particles. The NVR film is a nonstoichiometric and dynamic material, which reacts directly with RDX and ONDNTA, and is composed of remnants from RDX and ONDNTA molecules that have reacted with the NVR. Reactions involving the NVR become dominant during the later stage of the decomposition process. The NVR reacts with RDX to form ONDNTA via abstraction of an oxygen atom from an NO2 group. ONDNTA may undergo rapid loss of N2 and NO2 with the remaining portion of the molecule being incorporated into the dynamic NVR. The dynamic NVR also decomposes and leads to the formation of H2O, CH2O, N2O, NH2CHO, (CH3)2NCHO, (CH3)2NNO, C2H2N2O, and (CH3)3N or CH3NCH2CH3. The competition between reaction of the dynamic NVR with RDX and its own thermal decomposition manifests itself in a rapid increase in the rate of evolution of the NVR decomposition products as the amount of RDX remaining in the sample nears depletion. The reactions between the NVR film and RDX on the surface of the RDX particles leads to a localized environment that creates a layer of molten RDX on the surface of the particles where reactions associated with the liquid-phase decomposition of RDX may occur. The combination of these reaction processes leads to an acceleration of the reaction rate in the later stage of the decomposition process and creates an apparent reaction rate behavior that has been referred to as autocatalytic in many previous studies of RDX decomposition. A reaction scheme summarizing the reaction pathways that contribute to the decomposition of RDX below its melting point is presented.