Expected and unexpected products of reactions of 2-hydrazinylbenzothiazole with 3-nitrobenzenesulfonyl chloride in different solvents

Acknowledgements We thank the EPSRC National Crystallography Service (University of Southampton) for the X-ray data collections. Funding information MVNdS and JLW thank CNPq (Brazil) for financial support.Peer reviewedPublisher PD

Control of Ionic Conductivity by Lithium Distribution in Cubic Oxide Argyrodites Li6+XP1-XSiXO5Cl

Argyrodite is a key structure type for ion-transporting materials. Oxide argyrodites are largely unexplored despite sulfide argyrodites being a leading family of solid-state lithium-ion conductors, in which the control of lithium distribution over a wide range of available sites strongly influences the conductivity. We present a new cubic Li-rich (>6 Li+ per formula unit) oxide argyrodite Li7SiO5Cl that crystallizes with an ordered cubic (P213) structure at room temperature, undergoing a transition at 473 K to a Li+ site disordered F4̅3m structure, consistent with the symmetry adopted by superionic sulfide argyrodites. Four different Li+ sites are occupied in Li7SiO5Cl (T5, T5a, T3, and T4), the combination of which is previously unreported for Li-containing argyrodites. The disordered F4̅3m structure is stabilized to room temperature via substitution of Si4+ with P5+ in Li6+xP1-xSixO5Cl (0.3 x + sites leads to a maximum ionic conductivity of 1.82(1) × 10-6 S cm-1 at x = 0.75, which is 3 orders of magnitude higher than the conductivities reported previously for oxide argyrodites. The variation of ionic conductivity with composition in Li6+xP1-xSixO5Cl is directly connected to structural changes occurring within the Li+ sublattice. These materials present superior atmospheric stability over analogous sulfide argyrodites and are stable against Li metal. The ability to control the ionic conductivity through structure and composition emphasizes the advances that can be made with further research in the open field of oxide argyrodites

Low thermal conductivity in Bi8CsO8SeX7 (X = Cl, Br) by combining different structural motifs

By combining different structural features to scatter phonons Bi8CsO8SeX7 (X = Cl, Br) exhibits ultra-low thermal conductivities of ∼0.22 W m−1 K−1 at room temperature.</jats:p

Control of Ionic Conductivity by Lithium Distribution in Cubic Oxide Argyrodites Li_6+<i>x</i>P_1–<i>x</i>Si_<i>x</i>O₅Cl

Argyrodite is a key structure type for ion-transporting materials. Oxide argyrodites are largely unexplored despite sulfide argyrodites being a leading family of solid-state lithium-ion conductors, in which the control of lithium distribution over a wide range of available sites strongly influences the conductivity. We present a new cubic Li-rich (>6 Li+ per formula unit) oxide argyrodite Li7SiO5Cl that crystallizes with an ordered cubic (P213) structure at room temperature, undergoing a transition at 473 K to a Li+ site disordered F4̅3m structure, consistent with the symmetry adopted by superionic sulfide argyrodites. Four different Li+ sites are occupied in Li7SiO5Cl (T5, T5a, T3, and T4), the combination of which is previously unreported for Li-containing argyrodites. The disordered F4̅3m structure is stabilized to room temperature via substitution of Si4+ with P5+ in Li6+xP1–xSixO5Cl (0.3 x < 0.85) solid solution. The resulting delocalization of Li+ sites leads to a maximum ionic conductivity of 1.82(1) × 10–6 S cm–1 at x = 0.75, which is 3 orders of magnitude higher than the conductivities reported previously for oxide argyrodites. The variation of ionic conductivity with composition in Li6+xP1–xSixO5Cl is directly connected to structural changes occurring within the Li+ sublattice. These materials present superior atmospheric stability over analogous sulfide argyrodites and are stable against Li metal. The ability to control the ionic conductivity through structure and composition emphasizes the advances that can be made with further research in the open field of oxide argyrodites

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

AbstractThe application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.</jats:p