The role of grain boundary scattering in reducing the thermal conductivity of polycrystalline XNiSn (X = Hf, Zr, Ti) half-Heusler alloys
Matthias Schrade, Kristian Berland, Simen N.H. Eliassen, Matylda N., Guzik, Cristina Echevarria-Bonet, Magnus H. S{\o}rby, Petra Jenus, Bj{\o}rn, C. Hauback, Raluca Tofan, Anette E. Gunn{\ae}s, Clas Persson, Ole Martin, L{\o}vvik, Terje G. Finstad

TL;DR
This study combines theoretical calculations and experimental data to identify grain boundary scattering as the primary factor reducing thermal conductivity in polycrystalline XNiSn half-Heusler alloys, aiding thermoelectric material optimization.
Contribution
It provides a comprehensive theory-experiment comparison revealing grain boundary scattering as the dominant mechanism lowering thermal conductivity in these alloys.
Findings
Grain boundary scattering significantly reduces thermal conductivity.
Good qualitative agreement between measured and calculated thermal conductivities.
Analysis explains variability in reported thermal conductivities of similar samples.
Abstract
Thermoelectric application of half-Heusler compounds suffers from their fairly high thermal conductivities. Insight into how effective various scattering mechanisms are in reducing the thermal conductivity of fabricated XNiSn compounds (X = Hf, Zr, Ti, and mixtures thereof) is therefore crucial. Here, we show that such insight can be obtained through a concerted theory-experiment comparison of how the lattice thermal conductivity kLat(T) depends on temperature and crystallite size. Comparing theory and experiment for a range of Hf0.5Zr0.5NiSn and ZrNiSn samples reported in the literature and in the present paper revealed that grain boundary scattering plays the most important role in bringing down kLat, in particular so for unmixed compounds. Our concerted analysis approach was corroborated by a good qualitative agreement between the measured and calculated kLat of polycrystalline…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
