We report a joint experimental and theoretical investigation of the transport gap, optical properties, and electronic structure of two chemically similar, inhomogeneously mixed-valent polyoxovanadate molecule-based magnets. We attribute the substantial gap in [

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We measured the infrared vibrational properties of bulk and nanoparticle WS2 in order to investigate the structure-property relations in these novel materials. In addition to the symmetry-breaking effects of local strain, nanoparticle curvature modifies the local charging environment of the bulk material. Performing a charge analysis on the xy-polarized E1u vibrational mode, we find an approximate 1.5:1 intralayer charge difference between the layered 2H material and inorganic fullerene-like (IF) nanoparticles. This effective charge difference may impact the solid-state lubrication properties of nanoscale metal dichalcogenides.

In hyphenated measurement devices, temporal, spatial, and spectral resolutions continue to increase. While this is advantageous from a chemical sensing perspective, the amount of data grows exponentially; this imposes challenges on Chemometric algorithms resulting in long computation times. In online sensing, however, time resolution is of vital importance and time delays introduced by lengthy computations become unacceptable. Further, in many applications, data need to be documented and a continuous stream of large data sets presents another technical burden regarding archiving space. This work builds on the previous wavelet studies that have already been published. It was found that there is no reason to use the same wavelet for all dimensions of a data set. 'Hybrid wavelets' were introduced which combine different wavelets; this facilitates fine-tuning of the compression. The challenge in using hybrid wavelets lies in the very large number of possible wavelet combinations. A method is presented that automatically determines the optimum and near-optimum wavelet combinations for a given data set. These wavelet combinations are found by evaluating each dimension of the data set separately. This procedure enables an optimization of computation speed, compressed data set size and accuracy of the Chemometric model.Two data cubes acquired from two different experiments are used to show the selection algorithm's capabilities. These examples demonstrate that this algorithm selects hybrid wavelets that are superior to randomly selected wavelet combinations regarding data approximation, compressed data set size, and acceleration of computations.

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