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Akin, Minta C.

Publications and source records attributed to Akin, Minta C..

Executive summary of error sources in dynamic surface temperature measurements

Obtaining bulk T requires measuring apparent sample interface T surface , knowledge of any window conditions T window , and knowledge of thermal transport from the sample to the window. To obtain bulk T with uncertainties below 5% requires relatively small uncertainties in each of these areas, and large uncertainties in one area require smaller uncertainties in others to maintain the error budget. For example, if T window is known to 10%, a 5% total uncertainty can be obtained if T surface is known to 1% and combined transport uncertainties are known to 20%. If T surface can only be measured to 2%, combined transport uncertainties must be 17% to reach the same overall uncertainty. As the form of transport is unknown at high pressure, and window temperatures are difficult to measure by their very nature, reducing surface T uncertainties is the practical first step. Here we will discuss various error sources in the measurement of apparent T surface , and which ones must be correctly obtained prior to the experiment.

36 MATERIALS SCIENCE↗

The right conditions for high-precision dynamic temperature and heat capacity measurement via pyrometry and conductivity

The pursuit of accurate bulk temperature T under extreme conditions has been a long-standing goal of the high pressure science community, complicated by a lack of data to inform models. To reach these extremely high-pressure, high-temperature (high P − T) conditions, a combination of dynamic and heated static experiments (e.g., diamond or gem anvil cel experiments) are used. For example, in a diamond anvil cell (DAC) experiment, a sample placed in the DAC is first pressurized. Following pressurization, the sample T is increased either by heating the entire DAC (usually using resistive heating, and limited to ∼1000K) or by applying intense laser power to the sample surfaces. In a dynamic experiment, the process of pressurizing the sample also heats it. In the case of shock physics experiments, such heating is substantial, easily reaching thousands of Kelvin; in our work we have seen T ∼17000K. Most methods of measuring temperature at ambient are not compatible with experiments under these high-pressure, high-temperature conditions: thermocouples break, melt, or have conductivity properties that differ from ambient where they are calibrated; thermometers would melt; both are too slow. As a result most methods are based on non-contact techniques such as x-ray diffraction broadening, neutron scattering, or optical methods. Of these, optical methods using the visible and near-infrared region of the spectrum are the most commonly used as the sources and detectors are readily available. In the case of optical methods the optical depth, and therefore the measurement location, is limited to the surface. When a window or anvil material is used, heat flows from the sample into the window/anvil. Likewise, if the sample undergoes a change in thermodynamic state, such as expansion upon release, different T may be expected. As a result, the surface or apparent temperature T app measurement will differ from the bulk or interior temperature that is desired. This surface measurement must be related to the bulk measurement using thermal transport models and material models. While it is tempting to conclude that one should just use x-ray methods that directly probe the interior, even these methods have been shown to depend on thermal transport and material models. Regardless of the method used to create the high P − T condition, therefore, we must understand the role of thermal transport and material models upon our interpretation of the T measurement, as well as the errors and uncertainties associated with the choice of models used in the analysis. This is a substantial area of research and this paper is by no means a complete survey of the relevant sources of uncertainty. For example, we have yet to begin to address alternate transport models in a detailed manner (e.g., Tan-Ahrens), or the many models that use additional layers to approximate melting, turbulence, or epitaxial phenomena). Likewise, we have not explored the impact upon uncertainty of thermal models that use temperature-dependent thermal transport coefficients, or the wide range of material models that can be applied. Instead, this paper focuses on using one simple model, the Urtiew-Grover model, to understand the sources of error in T measurement so that we may identify how best to focus future research efforts to return the best improvements and avoid working on over-optimizing a single type of measurement. To this end, we work through some of the best and worst case scenarios for T measurement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

X-ray diffraction from shock driven Sn microjets

Here, in situ x-ray diffraction was performed on shock-generated microjets composed of Sn and Sn–4Ag. Under low pressure drives (~21 GPa), a significant fraction of the jet volume was found to be in the β-Sn phase, and these crystallites were much smaller than the initial grain size of the material. Significant quantities of amorphous (molten) material were observed for higher drive pressures (~25–35 GPa). The extent of melting at these pressures was greater than would be predicted for uniaxial shock loading. Diffraction patterns from the Sn–4Ag alloy showed a peak that is consistent with the expected Ag 3 Sn intermetallic phase. This peak remained evident under drive conditions where the sample was otherwise fully amorphous. This indicates a slushy or a mixed phase of liquid Sn and solid Ag 3 Sn. Given the eutectic character of this alloy, this observation is attributed to a kinetic limitation on the dissolution of Ag 3 Sn. This implies that a much broader range of drive conditions will lead to mixed phase jets and ejecta than would be predicted from the equilibrium melt boundary of such alloys.

36 MATERIALS SCIENCE↗

Temperature distribution in a laser-heated diamond anvil cell as described by finite element analysis

Finite element analysis (FEA) is a powerful tool for numerically solving partial differential equations over complex geometries and is thus useful for analyzing heat transport in laser-heated diamond anvil cell (LHDAC) experiments. Our models expand on previously published simulations by calculating the volume-averaged temperatures of both the sample and insulation/pressure media under steady-state heating to determine the thermal pressure of the hot sample. Our goal is to produce an accurate relationship between the measured surface temperature of the absorbing sample and the temperature of the transparent insulating media, which is used to determine thermal pressure but susceptible to steep temperature gradients. We find that in doing so, our FEA models of temperature within the pressure/insulation media can differ from simplified estimates of temperature gradients by more than a factor of 2. We also explore temperature-dependent and temperature-independent thermal conductivity models and find that the volume-averaged temperatures differ by up to a factor of 1.3, forcing the predicted thermal pressures determined to also differ by up to a factor of 1.5 at a temperature of 2000 K at 50 GPa for neon. Higher temperatures exacerbate this difference. We also find that unintentional asymmetric sample insertion and sample heating, which are common in LHDAC experiments, do not have a first-order effect on volume-averaged temperatures. The FEA models, available in both Python and FlexPDE, are versatile across different sample geometries, materials, and heat source laser shapes.

Farah, Frederick↗