Non-Equilibrium Crystallization Pathways of Manganese Oxides in Aqueous Solution

Abstract

<p>Aqueous precipitation of transition metal oxides often proceeds through non-equilibrium phases, whose appearance cannot be anticipated from traditional phase diagrams. Without a precise understanding of which metastable phases form, or their lifetimes, targeted synthesis of specific metal oxides can become a trial-and-error process. Here, we derive a new thermodynamic potential for the free-energy of a metal oxide in water, which reveals a hidden metastable energy landscape above the equilibrium Pourbaix diagram. By combining this ‘Pourbaix potential’ with classical nucleation theory, we interrogate how solution conditions can influence the multistage oxidation pathways of manganese oxides. We calculate that even within the same phase stability region of a Pourbaix diagram, subtle variations in <i>p</i>H and redox potential can redirect a crystallization pathway through different metastable phases. Our theoretical framework offers a predictive platform to navigate through the thermodynamic and kinetic energy landscape towards the rational synthesis of target metal oxide phases.</p>

Nanoparticle Size Effects on Phase Stability for Molybdenum and Tungsten Carbides

“…However, different phases may be energetically preferred at small particle sizes. We used the computational model developed by Sun et al to incorporate surface energies and obtain the size-dependent free energy using an expression similar to the Gibbs–Thomson equation: where d is the effective particle diameter (nm), η is the dimensionless shape factor (area/volume 2/3 ) of the equilibrium particle morphology, ρ is the volume normalized per mole of metal, and γ is the average surface energy. This approximation assumes that shape and average surface energy are independent of particle size, which is a reasonable approximation until particles reach sizes of approximately less than 2 nm diameter .…”

“…At small particle sizes, nanoparticles have large surface-area-to-volume ratios, causing surface energy to dominate over bulk energy. As a result, high bulk energy metastable phases with low surface energy are thermodynamically preferred at small nanoparticle sizes, , resulting in metastable phases with lower surface energy than their bulk counterparts. − Nanoparticle size and its effect on phase stability are not at present reported in TMC phase diagrams, which are extrapolated from a limited amount of experimental data. , Experimental interrogation of materials that account for particle size has demonstrated that it alters the phase boundaries for materials such as aluminas, group IV semiconductors, and hematite nanoparticles. , Recently, computational thermodynamic and kinetic models were used to accurately describe the phase-selection for manganese oxides and hydroxides, as a function of particle size, during solution-based syntheses. , These models offer guidance for the prediction of different TMC phases that account for nanoparticle size.…”

The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides

“…We note that this mechanism is particularly relevant in reactions where thermodynamic driving forces are large, such as these solidstate chemical reactions. In synthesis methods at lower temperatures and with smaller driving forces (on the order of k B T), such as in hydrothermal synthesis, structure-selection may instead be driven by size-dependent thermodynamics and competitive nucleation kinetics 14,40,41,42,43 .…”

Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature

“…Solution-based synthesis is an important area of materials synthesis [48]. It provides the ability to precisely control the morphology of the synthesized specimen [54] or synthesize metastable phases [61]. In order to extract solution-based synthesis recipes, we have built NLP/ML approaches wrapped into an automated text-mining pipeline: 1. we annotated new solution-based synthesis experimental data; 2. we incorporated a pre-trained BERT model to predict the type of synthesis described in paragraphs as well as to recognize material entities; 3. we implemented a recurrent neural network to extract synthesis actions; 4. we built chemical formulas from material entities based on chemistry knowledge; and 5. we developed a new rule-based method to extract the quantities of the materials.…”

“…For example, selecting all TiO 2 synthesized from TiCl 4 allows an exploration of how other synthesis formulations, such as synthesis actions, attributes, and quantities, affect the results. Furthermore, the materials entries in the dataset are supplied with the Materials Project [5] identifiers, thus facilitating the integration of the recipes with the thermochemical data available in the Materials Project [66,61].…”