Today's commercial X-ray micro computed tomography (CT) specimen systems are based on microfocus sources, 2D pixel array cameras and short source-to-detector distances (i.e. cone-beam configurations). High resolution is achieved by means of geometric magnification. The further development of such devices to acquire phase and scattering contrast images can dramatically enhance their range of applications. Due to the compact geometries, which imply a highly diverging beam, the gratings must be curved to maintain highest imaging performance over a large field of view. We report about the implementation of extremely compact Talbot and Talbot-Lau type grating interferometers which are compatible to the geometry of typical micro CT systems. For the analytical description of the imaging system, formulas are presented describing the dependency of the sensitivity on geometric parameters, camera and source parameters. Further, the imaging pipeline consisting of the data acquisition protocol, radiographic phase retrieval and tomographic image reconstruction is illustrated. The reported methods open the way for an immediate integration of phase and scattering contrast imaging on table top X-ray micro CT scanners.

The optimization of compact X-ray grating interferometry systems is crucial for the progress of this technique in industrial devices. Here, an analytical formulation for the sensitivity of the phase contrast image acquisition is derived using previous results from noise analyses. Furthermore, experimental measurements of the sensitivity for different configurations are compared, providing further insight into the dependence on polychromatic radiation. Finally, strategies for the geometrical optimization are given.

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Abstract. Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parametrizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow data set from the MOSAiC expedition, we have evaluated for the first time the seasonal evolution of the snow's thermal conductivity and thermal resistance on different ice ages (leads, first and second-year ice) and topographic features (ridges). Combining different measurement parametrizations and assessing the robustness against spatial variability, we infer and quantify a hitherto undocumented feature in the seasonal dynamics of snow on sea ice. We observe an increase in thermal conductivity up to March and a decrease thereafter, both on first-year and second-year ice before the melt period started. Since a similar non-monotonic behaviour is extracted for the snow depth, the thermal resistance of snow on level sea ice remains approximately constant with a value of 515 ± 404 m2 K W−1 on first-year ice and 660 ± 475m2 K W−1 on second-year ice. We found approximately three times higher thermal resistance on ridges (1411 ± 910 m2 K W−1). Our findings are that the micropenetrometer-derived thermal conductivities give accurate values, and confirm that spatial variability of the snow cover is vertically and horizontally large. The implications of our findings for Arctic sea ice are discussed.

Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.