Time-Resolved In Situ Neutron Diffraction Study of Cu₂₂Fe₈Ge₄S₃₂ Germanite: A Guide for the Synthesis of Complex Chalcogenides

Abstract

Solid-state synthesis in sealed silica tube is one of the most popular methods for the production of inorganic materials. Nevertheless, the determination of the best synthesis conditions is a trial and error process that generally requires many experiments, varying temperature and duration time. In the present work, the synthesis of a polycrystalline sulfide by solid-liquid-gas reaction in sealed silica tube is, for the first time, monitored in situ by time-resolved neutron powder diffraction. All successive crystallization/decomposition sequences and intermediate products were analyzed in detail. These results were combined with ex situ X-ray powder diffraction and thermal analyses on crystallized samples. Our results revealed that sealed tube synthesis is highly dynamic with reaction times shorter than expected. Importantly, the experiments revealed the primordial role of the cooling conditions on the secondary phase formation and stability. Beyond new data on the complex crystal chemistry of germanite Cu 22 Fe 8 Ge 4 S 32 , our study demonstrates the compelling benefits of time-resolved in situ neutron powder diffraction in the optimization of the synthesis process for a wide range of complex chalcogenide materials.

Resolution of the Cationic Distribution in Synthetic Germanite Cu22Fe8Ge4S32 by an Experimental Combinatorial Approach Based on Synchrotron Resonant Powder Diffraction Data: A Case Study and Guidelines for Analogous Compounds

“…A sample of synthetic germanite Cu 22 Fe 8 Ge 4 S 32 was produced by a sealed tube following the conditions reported by Paradis-Fortin et al Cu (99 at. %, Alfa Aesar), Fe (99.5 at.…”

“…This approach developed in this case study can be used as a guideline for the crystal structure resolution of analogous compounds. was produced by a sealed tube following the conditions reported by Paradis-Fortin et al 66 Cu (99 at. %, Alfa Aesar), Fe (99.5 at.…”

Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions

“…While solid-state synthesis is the prevailing approach for making inorganic solids, the determination of synthesis conditions for new solids is mostly based on heuristics and human-acquired experiences, with no analytical predictive approaches. , Recent work has focused on rationalizing solid-state reaction pathways observed in in situ experiments − by decomposing them into a sequence of phase evolution steps that can be modeled using thermodynamic calculations. − To design synthesis routes for new materials, it is essential to understand why certain conditions are preferred and develop models for predicting these conditions for synthesis (e.g., temperature, time). While thermodynamic calculations have been used to rationalize synthesis conditions in specific chemical systems, , a synthesis condition predictor with broad applicability for general inorganic compounds is still elusive.…”

Machine-learning rationalization and prediction of solid-state synthesis conditions

“…While solid-state synthesis is the prevailing approach for making inorganic solids, the determination of synthesis conditions for new solids is mostly based on heuristics and humanacquired experiences, with no analytical predictive approaches. 1,2 Recent work has focused on rationalizing solid-state reaction pathways observed in in-situ experiments, [3][4][5][6][7] by decomposing them into a sequence of phase evolution steps 1 that can be modeled using thermodynamic calculations. [8][9][10][11] To design synthesis routes for new materials, it is essential to understand why certain conditions are preferred and develop models for predicting these conditions for synthesis (e.g., temperature, time).…”