Active Reaction Control of Cu Redox State Based on Real-Time Feedback from In Situ Synchrotron Measurements

Thermochemical Mechanism of Optimized Lanthanum Chromite Heaters for High-Pressure and High-Temperature Experiments

High-pressure heaters in large volume presses must reconcile potentially contradictory properties, and the whole high-pressure and high-temperature (HPHT) community has been engaged for years to seek a better heater. LaCrO₃ (LCO)-based ceramic heaters have been widely applied in multianvil apparatus; however, their performance is far from satisfactory, motivating further research on the chemical optimization strategy and corresponding thermochemical mechanism. Here, we adopted a chemical-screening strategy and manufactured tubular heaters using the electrically, chemically, and mechanically optimized Sr-Cu codoped La_0.9Sr_0.1Cr_0.8Cu_0.2O_3-δ (LSCCuO-9182). HPHT examinations of cylindrical LSCCuO-9182 heaters on Walker-type multianvil apparatuses demonstrated a small temperature gradient, robust thermochemical stability, and excellent compatibility with high-pressure assemblies below 2273 K and 10 GPa. Thermochemical mechanism analysis revealed that the temperature limitation of the LSCCuO-9182 heater was related to the autoredox process of the Cu dopant and Cr and the exchanging ionic migration of Cu and Mg between the LSCCuO-9182 heater and the MgO sleeve. Our combinatorial strategy coupled with thermochemical mechanism analysis makes the prioritization of contradictory objectives more rational, yields reliable LCO heaters, and sheds light on further improvement of the temperature limitation and thermochemical stability.

A multimodal analytical toolkit to resolve correlated reaction pathways: the case of nanoparticle formation in zeolites

Unraveling the complex, competing pathways that can govern reactions in multicomponent systems is an experimental and technical challenge. We outline and apply a novel analytical toolkit that fully leverages the synchronicity of multimodal experiments to deconvolute causal from correlative relationships and resolve structural and chemical changes in complex materials. Here, simultaneous multimodal measurements combined diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and angular dispersive X-ray scattering suitable for pair distribution function (PDF), X-ray diffraction (XRD) and small angle X-ray scattering (SAXS) analyses. The multimodal experimental data was interpreted via multi-level analysis; conventional analyses of each data series were integrated through meta-analysis involving non-negative matrix factorization (NMF) as a dimensional reduction algorithm and correlation analysis. We apply this toolkit to build a cohesive mechanistic picture of the pathways governing silver nanoparticle formation in zeolite A (LTA), which is key to designing catalytic and separations-based applications. For this Ag-LTA system, the mechanisms of zeolite dehydration, framework flexing, ion reduction, and cluster and nanoparticle formation and transport through the zeolite are elucidated. We note that the advanced analytical approach outline here can be applied generally to multimodal experiments, to take full advantage of the efficiencies and self-consistencies in understanding complex materials and go beyond what can be achieved by conventional approaches to data analysis.

Multimodal in situ experimental data probing a complex reaction have been integrated via a multi-level analysis involving non-negative matrix factorization and correlation analysis. This strategy can be applied generally to multimodal experiments.

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

Validation of non-negative matrix factorization for rapid assessment of large sets of atomic pair distribution function data

The use of the non-negative matrix factorization (NMF) technique is validated for automatically extracting physically relevant components from atomic pair distribution function (PDF) data from time-series data such as in situ experiments. The use of two matrix-factorization techniques, principal component analysis and NMF, on PDF data is compared in the context of a chemical synthesis reaction taking place in a synchrotron beam, applying the approach to synthetic data where the correct composition is known and on measured PDFs from previously published experimental data. The NMF approach yields mathematical components that are very close to the PDFs of the chemical components of the system and a time evolution of the weights that closely follows the ground truth. Finally, it is discussed how this would appear in a streaming context if the analysis were being carried out at the beamline as the experiment progressed.