Zr-monosubstituted Lindqvist-type polyoxometalates (Zr-POMs), (Bu 4 N) 2 [W 5 O 18 Zr(H 2 O) 3 ] (1) and (Bu 4 N) 6 [{W 5 O 18 Zr(μ-OH)} 2 ] (2), have been employed as molecular models to unravel the mechanism of hydrogen peroxide activation over Zr(IV) sites. Compounds 1 and 2 are hydrolytically stable and catalyze the epoxidation of CC bonds in unfunctionalized alkenes and α,β-unsaturated ketones, as well as sulfoxidation of thioethers. Monomer 1 is more active than dimer 2. Acid additives greatly accelerate the oxygenation reactions and increase oxidant utilization efficiency up to >99%. Product distributions are indicative of a heterolytic oxygen transfer mechanism that involves electrophilic oxidizing species formed upon the interaction of Zr-POM and H 2 O 2 . The interaction of 1 and 2 with H 2 O 2 and the resulting peroxo derivatives have been investigated by UV−vis, FTIR, Raman spectroscopy, HR-ESI-MS, and combined HPLC-ICPatomic emission spectroscopy techniques. The interaction between an 17 O-enriched dimer, (Bu 4 N) 6 [{W 5 O 18 Zr(μ-OCH 3 )} 2 ] (2′), and H 2 O 2 was also analyzed by 17 O NMR spectroscopy. Combining these experimental studies with DFT calculations suggested the existence of dimeric peroxo species [(μ-η 2 :η 2 -O 2 ){ZrW 5 O 18 } 2 ] 6− as well as monomeric Zr-hydroperoxo [W 5 O 18 Zr(η 2 -OOH)] 3− and Zr-peroxo [HW 5 O 18 Zr(η 2 - O 2 )] 3− species. Reactivity studies revealed that the dimeric peroxo is inert toward alkenes but is able to transfer oxygen atoms to thioethers, while the monomeric peroxo intermediate is capable of epoxidizing CC bonds. DFT analysis of the reaction mechanism identifies the monomeric Zr-hydroperoxo intermediate as the real epoxidizing species and the corresponding α-oxygen transfer to the substrate as the rate-determining step. The calculations also showed that protonation of Zr-POM significantly reduces the free-energy barrier of the key oxygen-transfer step because of the greater electrophilicity of the catalyst and that dimeric species hampers the approach of alkene substrates due to steric repulsions reducing its reactivity. The improved performance of the Zr(IV) catalyst relative to Ti(IV) and Nb(V) catalysts is respectively due to a flexible coordination environment and a low tendency to form energy deep-well and low-reactive Zr-peroxo intermediates.
This letter is devoted to the application of machine learning, namely, convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography–mass spectrometry (LC–MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the present work, we developed an algorithm named peakonly, which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high-resolution LC–MS data for the purposes of metabolomics, but potentially it can be applied with several adaptations in other fields, which utilize high-resolution GC− or LC–MS techniques. Peakonly is freely available on GitHub () under an MIT license.
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