Data on Machine Learning regenerated Lithium-ion battery impedance.

This paper describes and provides the data on the regenerated-impedance spectra that is computed from experimental results of electrochemical impedance spectroscopy measurements taken from a commercial Li-ion battery. The empirical impedance data of secondary coin type Li-ion batteries were collected in different states of charge ranging from empty to full state of charge configurations. This approach utilizes only a small seed (ex grano) experimental data set to first build an ensemble of weighted disparate models selected based on performance and non-correlative criteria ("co-modelling") then second to generate what would be the remaining experimental data synthetically. The "Cooperative Model Framework" demonstrates the efficacy of this approach by assessing the synthetically generated data.

Phase Engineering of 2D Tin Sulfides.

Tin sulfides can exist in a variety of phases and polytypes due to the different oxidation states of Sn. A subset of these phases and polytypes take the form of layered 2D structures that give rise to a wide host of electronic and optical properties. Hence, achieving control over the phase, polytype, and thickness of tin sulfides is necessary to utilize this wide range of properties exhibited by the compound. This study reports on phase-selective growth of both hexagonal tin (IV) sulfide SnS2 and orthorhombic tin (II) sulfide SnS crystals with diameters of over tens of microns on SiO2 substrates through atmospheric pressure vapor-phase method in a conventional horizontal quartz tube furnace with SnO2 and S powders as the source materials. Detailed characterization of each phase of tin sulfide crystals is performed using various microscopy and spectroscopy methods, and the results are corroborated by ab initio density functional theory calculations.