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Doped strontium vanadate:
Computational design of a stable, low
work function material
Ryan Jacobs
(rjacobs3@wisc.edu)
Joh...
Current cathodes suffer from shortcomings
2
• W-BaO dispenser1, mixed-matrix2, top layer3, Scandate2,3,4
• All rely on coa...
Perovskites: tunable properties, including Φ
3
• Composition space: {Sr,La}{Sc,Ti,V,Cr,Mn,Fe,Co,Ni}O3
• Perovskites: wide ...
Work functions of 20 (001)-oriented perovskites
• SrVO3 and LaMnO3 have low AO Φ’s of 1.86 and 1.76 eV, respectively
4
[1]...
Work functions of 20 (001)-oriented perovskites
5
• AO Φ’s are dominated by positive surface dipoles
• Suggests Φ can be l...
SrVO3: a low Φ, stable, conductive material
• SrVO3 is the most promising new emission material, Φ = 1.86 eV
• Experiments...
Ba in SrVO3 is more stable than W, scandate cathodes
7
• Compare Ba residence lifetime on representative cathode surfaces ...
Optimizing SrVO3: can we stabilize further?
8
• Materials analysis with Python modules1,2
• Grand potential phase diagram ...
Future outlook: Materials design in silico
9
Generate thousands of
perovskite
compositions and
calculate properties
with D...
Summary
10
• Ba in SrVO3 surface segregates and binds
more strongly than in W and scandate
cathodes, opening the possibili...
Summary
11
• Ba in SrVO3 surface segregates and binds
more strongly than in W and scandate
cathodes, opening the possibili...
Acknowledgement
COMPUTATIONAL MATERIALS GROUP
Faculty
* Izabela Szlufarska * Dane Morgan
Postdocs
* Georgios Bokas * Guang...
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Doped strontium vanadate: Computational design of a stable, low work function material

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Presentation at the IEEE-IVEC conference in 2016 on computational modeling of perovskite work function physics and discovery of low work function materials

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Doped strontium vanadate: Computational design of a stable, low work function material

  1. 1. Doped strontium vanadate: Computational design of a stable, low work function material Ryan Jacobs (rjacobs3@wisc.edu) John Booske, Dane Morgan 2016 IEEE-IVEC Meeting Session 10: Scandate/Dispenser cathodes April 20th, 2016 Jacobs, R. M., Booske, J., Morgan, D. “Understanding and controlling the work function of perovskite oxides using Density Functional Theory”, Advanced Functional Materials (2016)
  2. 2. Current cathodes suffer from shortcomings 2 • W-BaO dispenser1, mixed-matrix2, top layer3, Scandate2,3,4 • All rely on coated metal or oxide layers and volatile surface species Sc2O3 Sc2O3 +BaO • Shortcomings that can be mitigated with materials change: lifetime, off gassing, emission nonuniformity, etc. • Perovskite oxides: possible intrinsic, low work function without needing to replenish surface dipole emission layer [1] Vlahos, V., Booske, J.H., Morgan, D., Phys. Rev. B. 81 (2010). [2] H. Yuan, X. Gu, K. Pan, Y. Wang, W. Liu, K. Zhang, J. Wang, M. Zhou, and J. Li, Appl. Surf. Sci. 251, (2005). [3] J.M. Vaughn, C. Wan, K.D. Jamison, and M.E. Kordesch. IBM J. Res. & Dev. 55 (2011) [4] Jacobs, R.M. , Booske, J.H., Morgan, D., J. Phys. Chem. C (2014) Sc O Ba [011]
  3. 3. Perovskites: tunable properties, including Φ 3 • Composition space: {Sr,La}{Sc,Ti,V,Cr,Mn,Fe,Co,Ni}O3 • Perovskites: wide composition range = tunable properties • Density Functional Theory (with VASP)1,2: obtain Φ for 20 systems (40 surfaces) • HSE functionals for accurate electronic structure3,4 • (001) surfaces with BO2- and AO- orientations5,6 [1] Y. Wang and J.P. Perdew, Phys. Rev. B 44 (1991). [2] Kresse, G. and J. Furthmuller, Phys. Rev. B, 54 (1996). [3] Franchini, C., J. Phys.: Condens. Matter 26 (2014). [4] He, J., Franchini, C., Phys. Rev. B, 86 (2012) [5] Kilner, J., Druce, J., et. al., Energy & Env. Sci (2014). [6] Liu, F., Ding, H., et. al., Phys. Chem. Chem. Phys. (2013).
  4. 4. Work functions of 20 (001)-oriented perovskites • SrVO3 and LaMnO3 have low AO Φ’s of 1.86 and 1.76 eV, respectively 4 [1] Suntivich, J., Hong, W. T., Lee, Y.-L., Rondinelli, J. M., Yang, W., Goodenough, J. B., Dabrowski, B., Freeland, J. W., and Shao-Horn, Y. Journal of Physical Chemistry C 118 (2014). • Band insulators have nearly the same work functions, and have highest values • BO2 Φ’s increase with increased 3d band filling and increased hybridization of 3d and O 2p bands1 • AO Φ’s nearly insensitive to 3d band filling, instead dominated by positive surface dipoles
  5. 5. Work functions of 20 (001)-oriented perovskites 5 • AO Φ’s are dominated by positive surface dipoles • Suggests Φ can be lowered if electropositive dopants constrained to surface layer
  6. 6. SrVO3: a low Φ, stable, conductive material • SrVO3 is the most promising new emission material, Φ = 1.86 eV • Experiments on powders3,4 and (001) films1 show high conductivity on order of Pt1 and good stability at T > 1000 °C in reducing H2/Ar atmosphere2,3,4 6 Ba-doped SrVO3 has ultra-low Φ of 1.07 eV, Ba segregates due to larger size, enhances dipole [1] Engel-Herbert, R., et. al.. Advanced Materials (2013). [2] Hui, S., Petric, A. Solid State Ionics (2001). • Dope SrVO3 with alkaline metals to investigate if Φ can be lowered further: [3] Maekawa, T, et. al., Journal of Alloys and Compounds (2006). [4] Nagasawa, H., at. Al., Solid State Communications (1991).
  7. 7. Ba in SrVO3 is more stable than W, scandate cathodes 7 • Compare Ba residence lifetime on representative cathode surfaces (Ba Ebind) • SrVO3 : Ba binds more strongly than W and scandate cathodes. • Possibility for ultra low Φ, long lifetime thermionic emitters with SrVO3. [1] Vlahos, V., Booske, J.H., Morgan, D., Phys. Rev. B. 81 (2010). [2] Jacobs, R. M. , Booske, J. H., Morgan, D., J. Phys. Chem. C (2014) [3] Jacobs, R. M., Booske, J. H., Morgan, D., Advanced Functional Materials (2016)
  8. 8. Optimizing SrVO3: can we stabilize further? 8 • Materials analysis with Python modules1,2 • Grand potential phase diagram analysis • Examine stability under operating conditions: (T, P) = (1073 K, 10-10 Torr) [1] Ong, S. P., et. al. Computational Materials Science (2013) [2] Jain, A., et. al. Applied Physics Letters: Materials (2013) • Transition metals Cr, Fe, Mn, Mo, Nb and Ta may increase the stability of SrVO3 under operating conditions • These elements support high oxidation states (give off e-), stabilize under reducing conditions.
  9. 9. Future outlook: Materials design in silico 9 Generate thousands of perovskite compositions and calculate properties with Density Functional Theory and high- throughput methods High bulk stability in vacuum at 1000°C Elimination of potential compounds. Converge on most promising for testing SrVO3 What other potential compounds exist? ?? Experimental evaluation New computationally predicted, experimentally validated material
  10. 10. Summary 10 • Ba in SrVO3 surface segregates and binds more strongly than in W and scandate cathodes, opening the possibility for very long cathode lifetimes. • Φ of 20 perovskite systems (40 surfaces) calculated. SrVO3 has pure (Ba doped) Φ of 1.86 eV (1.07 eV). • Bulk SrVO3 may be further stabilized with other transition metal dopants, such as Mo, Nb, Ta, and Fe
  11. 11. Summary 11 • Ba in SrVO3 surface segregates and binds more strongly than in W and scandate cathodes, opening the possibility for very long cathode lifetimes. • Φ of 20 perovskite systems (40 surfaces) calculated. SrVO3 has pure (Ba doped) Φ of 1.86 eV (1.07 eV). • Bulk SrVO3 may be further stabilized with other transition metal dopants, such as Mo, Nb, Ta, and Fe High chemical stability High emitted current density Ultra-long lifetimes Lower operating temperature Reduced operational and replacement costs Low work function Materials design of novel perovskite cathodes
  12. 12. Acknowledgement COMPUTATIONAL MATERIALS GROUP Faculty * Izabela Szlufarska * Dane Morgan Postdocs * Georgios Bokas * Guangfu Luo * Henry Wu * Jia-Hong Ke * Mahmood Mamivand * Ryan Jacobs * Shipeng Shu * Wei Xie * Yueh-Lin Lee Graduate Students * Amy Kaczmarowski * Ao Li * Austin Way * Benjamin Afflerbach * Chaiyapat Tangpatjaroen * Cheng Liu * Franklin Hobbs * Hao Jiang * Huibin Ke * Hyunseok Ko * Jie Feng * Lei Zhao * Mehrdad Arjmand * Shenzen Xu * Shuxiang Zhou * Tam Mayeshiba * Xing Wang * Yipeng Cao * Zhewen Song Visiting and Undergraduate Students * Aren Lorenson * Benjamin Anderson * Haotian Wu * Jason Maldonis * Josh Perry * Jui-Shen Chang * Liam Witteman * Tom Vandenberg * Zachary Jensen We gratefully acknowledge funding from the US Air Force Office of Scientific Research through Grant #FA9550-11-1- 0299, NSF Software Infrastructure for Sustained Innovation (SI2) award #1148011, and compute resources of the UW-Madison Center for High Throughput Computing (CHTC)

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