Searcharxiv⌕ Search

arXiv subjects

Maria J. Gendron Romero

Publications and source records attributed to Maria J. Gendron Romero.

2 recordsLinked to original sources

Synthesis and Structural Analysis of an Emissive Colloidal Argyrodite Nanocrystal: Canfieldite Ag8SnS6

We resolve a phase identification controversy in the Ag-Sn-S material system by unraveling the polymorphic structure of nanocrystals within the argyrodite material family. Argyrodites are a class of superionic materials used in their bulk form for applications in solid-state batteries and thermoelectrics, where their advantageous properties relate to their polymorphism. However, despite their well-studied bulk applications, the limited exploration at the nanoscale has left considerable potential for the discovery of emerging properties due to size effects. Further, phase identification presents a prominent challenge to the study of polymorphs in superionic conductors and related mate-rials. In this work, we synthesize canfieldite-like (Ag8SnS6) nanocrystals to understand their formation and structural behavior at the nanoscale. We observe the emergence of emissive, meta-stable, cluster-like species. Then, high-resolution transmission electron microscopy reveals indistinguishable polymorphs of canfieldite due to identical heavy-atom frameworks. However, using synchrotron X-ray total scattering for pair distribution function analysis, we uncover structural distortions, showing a pseudo-orthorhombic configuration that likely gives rise to the red emission. Further, we investigate the optical properties and structure of Ag8SnS6 nanocrystals upon the addition of Zn2+, the cation of interest in the canfieldite vs. pirquitasite (Ag2ZnSnS4) phase identification controversy. We show that Zn2+ is incorporated in the canfieldite-like structure through the replacement of Ag+, boosting the emission. Our results solve a standing phase identification challenge and uncover fundamental insights for the synthesis and structure of canfieldite nanocrystals, laying the ground for the exploration of other argyrodite materials with emerging properties at the nanoscale.

cond-mat.mtrl-sci↗

Artificial Intelligence-Enabled Optimization of Battery-Grade Lithium Carbonate Production

By 2035, the need for battery-grade lithium is expected to quadruple. About half of this lithium is currently sourced from brines and must be converted from a chloride into lithium carbonate (Li2CO3) through a process called softening. Conventional softening methods using sodium or potassium salts contribute to carbon emissions during reagent mining and battery manufacturing, exacerbating global warming. This study introduces an alternative approach using carbon dioxide (CO2(g)) as the carbonating reagent in the lithium softening process, offering a carbon capture solution. We employed an active learning-driven high-throughput method to rapidly capture CO2(g) and convert it to lithium carbonate. The model was simplified by focusing on the elemental concentrations of C, Li, and N for practical measurement and tracking, avoiding the complexities of ion speciation equilibria. This approach led to an optimized lithium carbonate process that capitalizes on CO2(g) capture and improves the battery metal supply chain's carbon efficiency.

cond-mat.mtrl-sci↗