| Slide 2 Dr Jonathan M. Skelton 34 % 26 % 19 % 18 % 3 % 1000 MW nuclear power plant: o 650 MW waste heat o 3 % โ 20 MW โ 50,000 homes 300-500 W from exhaust gases: o 2 % lower fuel consumption o 2.4 Mt reduction in CO2 Thermoelectric generators allow waste heat to be recovered as electricity TEGs with ~3 % energy recovery (๐๐ = 1) are considered industrially viable 1. Provisional UK greenhouse gas emissions national statistics (published June 2020) 2. EPSRC Thermoelectric Network Roadmap (2018)
3 Dr Jonathan M. Skelton ๐๐ = ๐2๐ ๐ ele + ๐ lat ๐ ๐ - Seebeck coefficient ๐ - electrical conductivity ๐ ele - electronic thermal conductivity ๐ lat - lattice thermal conductivity G. Tan et al., Chem. Rev. 116 (19), 12123 (2016)
Slide 5 Dr Jonathan M. Skelton A. Togo et al., Phys. Rev. B 91, 094306 (2015) ๐ฟlatt (๐) = 1 ๐๐0 เท ๐ ๐ฟ๐ (๐) 1 ๐๐0 เท ๐ ๐ถ๐ (๐)๐๐ โ ๐๐ ๐๐ (๐) The simplest model for ๐ latt is the single-mode relaxation time approximation (SM-RTA) - a closed solution to the phonon Boltzmann transport equations Modal heat capacity Mode group velocity ๐๐ฮป ๐๐ช Average over phonon modes ฮป Phonon MFP Mode lifetime ๐ฮป = 1 2ฮฮป ๐ฒ๐ ๐ = ๐๐ ๐๐ ๐
Slide 6 Dr Jonathan M. Skelton A. Togo et al., Phys. Rev. B 91, 094306 (2015) J. Tang and J. M. Skelton, J. Phys.: Condens. Matter 33 (16), 164002 (2021) CoSb3
Aug 2022 | Slide 14 Dr Jonathan M. Skelton ๐ฟ (W m-1 K-1) ฮค ๐ฟ ๐๐๐๐๐ (W m-1 K-1 ps-1) ๐๐๐๐๐ (ps) d-Si 136.24 5.002 27.24 oC24 40.92 2.295 17.83 K-II / C-I 43.54 0.829 52.52 K-V / C-VI 36.29 0.815 44.53 K-VII / C-V 31.16 0.770 40.45 C-II 6.33 0.458 13.81 Spacegroup ๐๐ ๐น๐เดค 3๐ 2 ๐ถ๐๐๐ 12 ๐๐เดค 3๐ 46 ๐ถ๐๐๐ 40 ๐63 /๐๐๐ 68 ๐น๐เดค 3๐ 34 With the exception of the Clathrate-II structure, the harmonic ฮค ๐ฟ ๐CRTA term correlates with: (1) the size of the primitive cell (๐a ); and (2) the spacegroup (crystal symmetry) Implies low group velocities are favoured by complex structures with large primitive cells and/or low symmetry B. Wei et al., in preparation
Dr Jonathan M. Skelton ๐ฟlatt (๐) ฮค ๐ฟ ๐CRTA ๐CRTA เดฅ ๐2 เทจ ๐ Phonopy + Phono3py A. Togo and I. Tanka, Scr. Mater. 108, 1 (2015) A. Togo et al., Phys. Rev. B 91, 094306 (2015)
Aug 2022 | Slide 19 Dr Jonathan M. Skelton ๐ [W m-1 K-1] ฮค ๐ ๐๐๐๐๐ [W m-1 K-1 ps-1] ๐๐๐๐๐ [ps] Si 136.24 5.002 27.2 SnS 2.15 0.718 3.00 SnSe 1.58 0.372 4.23 CoSb3 9.98 0.273 36.6 Bi2 S3 (Pnma) 0.90 0.423 2.14 Bi2 Se3 (R-3m) 1.82 0.293 6.20 Bi2 Te3 (R-3m) 0.87 0.199 4.41 J. M. Skelton, J. Mater. Chem. C 9, 11772 (2021) J. Tang and J. M. Skelton, J. Phys.: Condens. Matter 33 (16), 164002 (2021) J. Cen, I. Pallikara and J. M. Skelton, Chem. Mater. 33 (21), 8404 (2021) B. Wei et al., in preparation
2022 | Slide 23 Dr Jonathan M. Skelton Schwartz and Walker, Phys. Rev. B 155, 959 (1967) E. S. Toberer et al., J. Mater. Chem. 21, 15843 (2011) J. Tang and J. M. Skelton, J. Phys.: Condens. Matter 33 (16), 164002 (2021) โOne phononโ model for resonant scattering: ๐โ1 = เท ๐ ๐๐ ๐2๐2 ๐๐ 2 โ ๐2 2 + ๐พ๐ ๐๐ 2๐2
Dr Jonathan M. Skelton ๐ฟlatt (๐) ฮค ๐ฟ ๐CRTA ๐CRTA เดฅ ๐2 เทจ ๐ ๐บ(๐, ๐) ๐(๐, ๐) ๐ฟel (๐, ๐) Phonopy + Phono3py AMSET ๐๐(๐, ๐) A. Togo and I. Tanka, Scr. Mater. 108, 1 (2015) A. Togo et al., Phys. Rev. B 91, 094306 (2015) A. M. Ganose et al., Nature Comm. 12, 2222 (2021)
Slide 28 Dr Jonathan M. Skelton W. Rahim et al., J. Mater. Chem. A 8, 16405 (2020) W. Rahim et al., J. Mater. Chem. A 9, 20417 (2021) K. Brlec et al., J. Mater. Chem. A 10, 16813 (2022) ๐ผ-Bi2 Sn2 O7 ๐ = 1.73 ร 1019 cm-3 ๐๐ = 0.36 (385 K) Ca4 Sb2 O / Ca4 Bi2 O ๐ = 4.64 / 2.15 ร 1019 cm-3 ๐๐ = 1.58 / 2.14 (1000 K) Y2 Ti2 O5 S2 ๐ = 2.37 ร 1020 cm-3 ๐๐ = 1.18 (1000 K)
Slide 30 Dr Jonathan M. Skelton Some questions to consider (definitely not exhaustive...): 1. Is there a โkillerโ application of TEs that we should target? 2. If so, what are the parameters? โข Operating temperature and target ๐๐? โข Target cost per device? โข Constraints on elemental composition? โข Constraints on synthesis/device fabrication for scale up? 3. How can theory and experiment best work together? โข Is modelling in a position to provide actionable suggestions to improve ๐๐? โข Would it be possible(/useful) to use modelling to screen candidates to narrow the focus of experiments? โข Are existing theoretical techniques sufficient, or is more development needed (e.g. faster, cheaper, easier to use, capability to model new things ...)?