Optical floating zone (OFZ) furnaces have had a transformative effect on many fields due to their ability to rapidly produce large and high-quality single crystals of complex materials. While it is known that the choice of process gas and its flow rate can affect the quality of grown crystals through their influence on sample temperature profiles, their impact is difficult to quantify and predict due to the complex coupling of heat absorption, heat conduction in the sample, heat transport to, from, and within the gas, as well as radiative heat loss. In this work, we first develop and parameterize a heat transfer model of the steady-state temperature profile of a tip-heated SiC rod in the gas environment within the high-temperature furnace by combining in situ experimental measurements and finite element modeling. We then use simulations with these validated models that capture gas contributions to sample thermal processes to understand how the gas environment, including the gas pressure and the choice of the gas (helium, argon, and nitrogen), affects sample temperature profiles within an OFZ furnace. In addition, we examine the large impacts that the sample radius can have on thermal profiles in different gas environments. This study provides essential insights into how the temperature profiles governing crystal growth processes in OFZ furnaces can be effectively controlled, offering the potential to enhance the quality of grown crystals through the rational design of the process gas environment.

A methodology is developed where a fundamental parameters approach (FPA) description of a laboratory powder diffraction instrument (configured in divergent-beam Bragg–Brentano geometry) is used to determine GSAS-II profile parameters for peak asymmetry and instrumental peak widths. This allows the instrumental contribution to peak shapes to be robustly determined directly from a physical description of the instrument, even though GSAS-II does not directly implement FPA for peak shape computation. The FPA-derived parameters can be used as the starting point for instrument characterization, or to characterize sample broadening without the use of a standard to determine the instrument profile function. This new method can facilitate generation of training sets for machine learning. A plot is generated that shows the differences between the two approaches, demonstrating upper bounds for the accuracy of the GSAS-II profile model for a particular instrumental configuration.

In sharp contrast to molecular synthesis, materials synthesis is generally presumed to lack selectivity. The few known methods of designing selectivity in solid-state reactions have limited scope, such as topotactic reactions or strain stabilization. This contribution describes a general approach for searching large chemical spaces to identify selective reactions. This novel approach explains the ability of a nominally “innocent” Na2CO3 precursor to enable the metathesis synthesis of single-phase Y2Mn2O7 – an outcome that was previously only accomplished at extreme pressures and which cannot be achieved with closely related precursors of Li2CO3 and K2CO3. By calculating the required change in chemical potential across all possible reactant-product interfaces in an expanded chemical space including Y, Mn, O, alkali metals, and halogens, using thermodynamic parameters obtained from density functional theory calculations, we identify reactions that minimize the thermodynamic competition from intermediates. In this manner, only the Na-based intermediates minimize the distance in the hyperdimensional chemical potential space to Y2Mn2O7, thus providing selective access to a phase which was previously thought to be metastable. Experimental evidence validating this mechanism for pathway-dependent selectivity is provided by intermediates identified from in situ synchrotron-based crystallographic analysis. This approach of calculating chemical potential distances in hyperdimensional compositional spaces provides a general method for designing selective solid-state syntheses that will be useful for gaining access to metastable phases and for identifying reaction pathways that can reduce the synthesis temperature, and cost, of technological materials.

Computational modeling is playing an increasingly important role in designing novel experiments and enhancing existing ones. However, these models require input parameters that are sufficiently accurate in order for the predictions to be quantitatively reliable. Frequently, some of these parameters are not directly measurable or have a large uncertainty. We propose a machine learning approach to automatically extract these uncertain or unknown parameters indirectly from experimental measurements that depend on the combination of parameters in a complex manner. The algorithm iteratively refines the possible range of each parameter based on the mean and standard deviation of the sampling simulations that are at the 10th percentile or lower in error until the convergence criteria is met, which is followed by a multivariate quadratic fit and minimization. To demonstrate its applicability, we apply the algorithm to determine physical parameters during the fitting of temperature profiles extracted from in situ synchrotron measurements of an optical floating zone furnace used for hightemperature crystal growth. We built a thermal transfer model in COMSOL Multiphysics (R) software that considers an Al2O3 sample that is heated by a power source and thermal conduction and cooled due to natural air convection and thermal radiation. A set of experimentally measured steady-state temperature profiles of a heated Al2O3 sample is used as training data to determine the parameter set using the algorithm, which resulted in a close match. Using this parameter set, we also simulated the time-dependent temperatures, which yielded good agreement to the corresponding experimental measurement. We conclude that the steady-state temperature profiles suffice as training data, eliminating the need for additional in situ measurements of the dynamic experimental state for robust model parameterization.

In sharp contrast to molecular synthesis, materials synthesis is generally presumed to lack selectivity. The few known methods of designing selectivity in solid-state reactions have limited scope, such as topotactic reactions or strain stabilization. This contribution describes a general approach for searching large chemical spaces to identify selective reactions. This novel approach explains the ability of a nominally "innocent" Na2CO3 precursor to enable the metathesis synthesis of single-phase Y2Mn2O7: an outcome that was previously only accomplished at extreme pressures and which cannot be achieved with closely related precursors of Li2CO3 and K2CO3 under identical conditions. By calculating the required change in chemical potential across all possible reactant-product interfaces in an expanded chemical space including Y, Mn, O, alkali metals, and halogens, using thermodynamic parameters obtained from density functional theory calculations, we identify reactions that minimize the thermodynamic competition from intermediates. In this manner, only the Na-based intermediates minimize the distance in the hyperdimensional chemical potential space to Y2Mn2O7, thus providing selective access to a phase which was previously thought to be metastable. Experimental evidence validating this mechanism for pathway-dependent selectivity is provided by intermediates identified from in situ synchrotron-based crystallographic analysis. This approach of calculating chemical potential distances in hyperdimensional compositional spaces provides a general method for designing selective solid-state syntheses that will be useful for gaining access to metastable phases and for identifying reaction pathways that can reduce the synthesis temperature, and cost, of technological materials.

Even though the growth of crystals using optical floating zone furnaces has had an immense scientific impact, the implementation of this method remains more of an art than a science due to the difficulty of obtaining quan-titative information about the sample thermal profile during crystal growth. Building on recent work demon-strating that in sita synchrotron studies can be used to map sample rod temperatures during heating, investigations were carried out to better understand how the sample environment affects the sample temperature profile. Through a combination of experimental studies and modeling efforts, it is shown that the environment in the furnace can strongly influence the sample temperature at the lamp focus, the steepness of the vertical temperature gradient, and the timescale required for sample heating and cooling - effects which can combine to produce a strong history-dependence to sample temperature profiles. It is demonstrated that the furnace effects can be effectively captured in thermal models, allowing both the steady-state and time-dependent behavior of the sample to be accurately reproduced with predictive models and providing a launching point for improved furnace designs that can more readily deliver desired thermal profiles.

The ability of optical floating zone (OFZ) furnaces to rapidly produce large single crystals of complex emerging materials has had a transformative effect on many scientific fields that require samples of this type. However, the crystal growth process within the OFZ furnace is not well understood owing to the challenges involved in monitoring the high-temperature crystal growth process. Novel beamline-compatible optical furnaces that approximate the inhomogeneous growth environment within an OFZ furnace have been fabricated and tested in high-energy synchrotron beamlines. It is demonstrated that temperature profiles can be effectively extracted from powder diffraction data collected on polycrystalline ceramic rods heated at their tip. Furthermore, these measured temperature profiles can be accurately reproduced using a heattransfer model that accounts for solid-state thermal conduction, partial sample lamp power absorption, convective air cooling and radiative cooling, allowing key thermal parameters such as thermal conductivity to be extracted from experimental data.