https://researchdata.se/en/catalogue Search results en Densified biochar particles emerge as a promising carbon-negative insulation solution for building constructions. Their effective thermal conductivity (ETC) is a critical parameter, but has not been systematically investigated in dependence of particle size, shape and packed bed porosity. The provided data sets allows the computation of a fully experiment-based ETC, using the approach from the article "Effective thermal conductivity of packed beds made of cubical particles" (10.1016/j.ijheatmasstransfer.2022.122994), as well as model-based ETC using the Zehner-Bauer-Schlünder approach.   Biochar particles of dried spruce bark chips were pyrolyzed in a pilot-scale rotary kiln reactor at around 350-400 °C with a residence time of ca. 45 min. Biochar particles were subsequently mixed with a binder and densified with a vacuum extruding process to a cylindrical shape before being crushed into smaller particles to eliminate the anisotropic pore structure and thermal conductivity inside individual particles.  The data includes measured data of particle thermal conductivity and heat capacity, using LFA 467HT from NETZSCH GmbH, Germany, and coin-shaped samples with diameters of 12.5±0.2 mm and thicknesses of 2.5±0.5 mm for the measurement. The particle size distributions were obtained from Camsizer from Microtrac Retsch GmbH, Germany. The particle shape, i.e. sphericity, was obtained from X-ray microtomography (XMT) using a Zeiss Xradia 510 Versa tomograpy and cylindrical packed beds with an inner diameter of 23 mm and a height of 18 mm. The tube voltage was 40 kV and a tube power of 3 W to reduce beam hardening artifacts. Each scan collected 1601 projections over a full 360° rotation. Image resolutions were adjusted based on the particle sizes, with voxel sizes of 5 μm for packed beds containing smaller particles of around 0.315 mm and 23 μm for all other samples. The number of particles in the samples ranged from about 70 to 200,000. The X-ray microtomography (XMT) raw data can be viewed using the open-source software Fiji (https://imagej.net/software/fiji/). Tue, 18 Aug 2026 06:29:17 GMT https://researchdata.se/en/catalogue/dataset/2026-204 https://researchdata.se/en/catalogue/dataset/2026-204 Luleå University of Technology Nicole Vorhauer-Huget Zahra Ghasemi Monfared Kazemi Neda Eduardo Arango Durango Evangelos Tsotsas Kentaro Umeki