The report introduces simple yet highly reactive calcium salt, Ca[Al(OR^F)₄]₂ (R^F=C(CF₃)₃), 1, which effectively catalyses olefin hydrosilylation through an unusual mechanism involving the activation of the alkene molecule. Upon dissolution in o-difluorobenzene (oDFB), 1 forms a highly Lewis acidic [Ca(oDFB)₆]²⁺ complex. Our DFT calculations reveal that fluoride ion affinity is comparable to SbF₅. Reactivity tests show that it effectively catalyses the hydrosilylation of olefins with high regioselectivity, also in reactions involving sterically demanding substrates like (iPr)₃SiH or tetrasubstituted olefins. Experimental and computational results point to the mechanism where the olefin molecule forms a complex with Ca²⁺, which significantly facilitates the attack of H-SiR₃ on the C=C double bond.

Metabolites play crucial roles in cellular processes, yet their diffusion in the densely packed interiors of cells remains poorly understood, compounded by conflicting reports in existing studies. Here, we employ pulsed-gradient stimulated-echo NMR and Brownian/Stokesian dynamics simulations to elucidate the behavior of nano- and subnanometer-sized tracers in crowded environments. Using Ficoll as a crowder, we observe a linear decrease in tracer diffusivity with increasing occupied volume fraction, persisting─somewhat surprisingly─up to volume fractions of 30–40%. While simulations suggest a linear correlation between diffusivity slowdown and particle size, experimental findings hint at a more intricate relationship, possibly influenced by Ficoll’s porosity. Simulations and numerical calculations of tracer diffusivity in the E. coli cytoplasm show a nonlinear yet monotonic diffusion slowdown with particle size. We discuss our results in the context of nanoviscosity and discrepancies with existing studies.

NMR spectroscopy is one of the most potent methods in analytical chemistry. NMR titration experiments are particularly useful since they measure molecular binding affinities and other concentration-dependent effects. These experiments, however, require a long series of measurements. An alternative to these serial measurements has recently been presented, exploiting a pH (or generally – a concentration) gradient along the NMR tube. The proposed experiment, although efficient, was based on the sensitivity- and hardware-demanding chemical shift imaging (CSI) method. Thus, it is practically limited to high-resolution NMR spectrometers. This paper proposes modifying and adapting the approach to the popular and cost-efficient benchtop NMR machines. Instead of CSI, we use a device that shifts the NMR tube vertically to measure the spectra of different sample volumes, which have different pH values due to the established gradient along the tube. We demonstrate the potential of the method on the test samples of L-tyrosine and 2,6-lutidine, and two real samples from the food industry – an infant formula and an energy drink. The proposed method boosts spectral resolution and allows for the sampling of a broader range of pH values when compared to the original approach.

Serial NMR experiments are commonly applied in variable-temperature studies, reaction monitoring, and other tasks. The resonance frequencies often shift linearly over the series, and the shift rates help to characterize the studied system. They can be determined using a classical fitting of peak positions or a more advanced method of Radon transform. However, the optimal procedure for data collection remains to be determined. In this paper, we discuss how to invest experimental time, i.e., whether to measure more scans at the expense of the number of spectra or vice versa. The results indicate that classical fitting provides slightly less error than the Radon transform, although the latter can be the method of choice for a low signal-to-noise ratio. We demonstrate this fact through theoretical consideration, simulations, and an experiment. Finally, we extend our considerations to the linear fitting of peak amplitudes. Interestingly, the optimal setup for measuring peak height changes differs from the one for resonance frequency changes — fewer spectra with more scans provide better results.

Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful tools in analytical chemistry. An important step in the analysis of NMR data is the assignment of resonance frequencies to the corresponding atoms in the molecule being investigated. The traditional approach considers the spectrum’s characteristic parameters: chemical shift values, internuclear couplings, and peak intensities. In this paper, we show how to support the process of assigning a series of spectra of similar organic compounds by using temperature coefficients, that is, the rates of change in chemical shift values associated with given changes in temperature.