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Vladan Mlinar 2009 Materials Research Society Spring Meeting

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For more information about the Spectral Barcoding and establishing structure-spectra relationship in quantum dots, see the following publications: …

For more information about the Spectral Barcoding and establishing structure-spectra relationship in quantum dots, see the following publications:

- Vladan Mlinar and Alex Zunger, Phys. Rev. B 80, 035328 (2009).
- Vladan Mlinar et al. Phys. Rev. B 80, 165425 (2009).

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My full publications list can be found at:
www.vladanmlinar.com/publications.html

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  • 1. Deciphering Structural Information from the Multiexcitonic Spectra of a Quantum Dot Vladan Mlinar & Alex Zunger National Renewable Energy Laboratory Golden, Colorado USA Vladan.Mlinar@nrel.gov
  • 2. QDs: Structure - Spectra relationship Methods for structural characterization Single-dot spectroscopy • TEM based methods • X-ray diffraction • X-STM
  • 3. QDs: Structure - Spectra relationship Methods for structural characterization Single-dot spectroscopy • TEM based methods • X-ray diffraction • X-STM (M. Bozkurt, J. M. Ulloa, & P. M. Koenraad) • No atomic resolution • All of the methods require assumption about composition profile and/or shape!
  • 4. QDs: Structure - Spectra relationship Methods for structural characterization Single-dot spectroscopy • TEM based methods • X-ray diffraction • X-STM (M. Ediger & R. J. Warburton) (M. Bozkurt, J. M. Ulloa, & P. M. Koenraad) • No atomic resolution • All of the methods require assumption about composition profile and/or shape!
  • 5. QDs: Structure - Spectra relationship Methods for structural characterization Single-dot spectroscopy • TEM based methods • X-ray diffraction • X-STM (M. Ediger & R. J. Warburton) (M. Bozkurt, J. M. Ulloa, & P. M. Koenraad) • No atomic resolution • Controllable number of electrons and holes • All of the methods require assumption • μeV resolution about composition profile and/or shape!
  • 6. Typically, Structure is used to predict Spectra Assume Calculate or resulting measure spectra structure • Since for quantum dots we do not know the structure: Measured emission Structure spectra
  • 7. Typically, Structure is used to predict Spectra Assume Calculate or resulting measure spectra structure • Since for quantum dots we do not know the structure: Measured emission Structure spectra Is this possible?
  • 8. Question: What is the structural information encoded in the multiexcitonic spectra of a QD? ?
  • 9. Spectral Barcoding vs. DNA Barcoding: Barcoding Barcoder Organism is identified as belonging to a particular species Sci. Am. p. 82-88 (October 2008)
  • 10. Spectral Barcoding vs. DNA Barcoding: Barcoding Barcoder Organism is identified as belonging to a particular species Sci. Am. p. 82-88 (October 2008)
  • 11. Spectral Barcoding vs. DNA Barcoding: Barcoding Barcoder Organism is identified as belonging to a particular species ? QD is identified as belonging to a group of QDs with common structural motifs. Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 12. How does the Spectral Barcoding work? Spectral barode: Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 13. How does the Spectral Barcoding work? Spectral barcoding procedure Spectral barode: Artificial Intelligence QD library (Distilling rules from library) Deterministic links between structures and spectral marker Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 14. How does the Spectral Barcoding work? Spectral barcoding procedure Spectral barode: Artificial Intelligence QD library (Distilling rules from library) Deterministic links between structures and spectral marker RESULT: a set Structural Motifs: of QD structural motifs! Structure • h = 2 – 3nm • Xav(In) = 75-80% Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 15. Spectral Barcoding: Data-mining of the library QD structure is discretized into a set of Ns=5 structural motifs, each taking up one of Nv possible values: Motifs: Shape b (nm) h (nm) XIn (%) profile Trun.Cone 12 2.0 50 Homog. Trun. Pyr. 18 3.0 60 Linear Lens 20 3.5 70 Elong. 23 4.0 80 Lens [110] Elong. 25 5.0 90 Lens [110] Elong. 30 6.0 100 Lens [100] Structure
  • 16. Spectral Barcoding: Data-mining of the library QD structure is discretized into a set of Ns=5 structural motifs, each taking up one of Nv possible values: Motifs: Shape b (nm) h (nm) XIn (%) profile Trun.Cone 12 2.0 50 Homog. Trun. Pyr. 18 3.0 60 Linear Lens 20 3.5 70 Elong. 23 4.0 80 Lens [110] Elong. 25 5.0 90 Lens [110] Elong. 30 6.0 100 Lens [100] Bayesian Data Reduction Algorithm: Structure • Training: Testing how each structural motif and its corresponding values influences the barcode • Result: Identifies the set of structural motifs that are responsible for a given spectral barcode sequence.
  • 17. Spectral Barcoding: Consistency test! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 18. Spectral Barcoding: Consistency test! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 19. Spectral Barcoding: Consistency test! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 20. Spectral Barcoding: Consistency test! Validation! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
  • 21. Question: How does the deduced structure relates to the “real structure”?
  • 22. Spectral Barcoding: Why is it important? Collaboration with three experimental groups! Structural Characterization by X-STM Quantum Dot Theory growth Many body pseudopotential calculations Single-dot Spectroscopy Calculated spectra Antonio Badolato (ETH Zurich, Switzerland)
  • 23. Spectral Barcoding: Why is it important? Collaboration with three experimental groups! Structural Characterization by X-STM Quantum Dot Theory growth M. Bozkurt, J. M. Ulloa, & P. M. Koenraad (TU Eindhoven, The Netherlands) Many body pseudopotential calculations Single-dot Spectroscopy Calculated spectra Antonio Badolato (ETH Zurich, Switzerland)
  • 24. Spectral Barcoding: Why is it important? Collaboration with three experimental groups! Structural Characterization by X-STM Quantum Dot Theory growth M. Bozkurt, J. M. Ulloa, & P. M. Koenraad (TU Eindhoven, The Netherlands) Many body pseudopotential calculations Single-dot Spectroscopy Calculated spectra Antonio Badolato (ETH Zurich, Switzerland) M. Ediger & R. J. Warburton (Heriot-Watt University, UK)
  • 25. Spectral Barcoding: Why is it important? Collaboration with three experimental groups! Structural Characterization by X-STM Quantum Dot Theory growth M. Bozkurt, J. M. Ulloa, & P. M. Koenraad (TU Eindhoven, The Netherlands) Many body pseudopotential calculations Single-dot Spectroscopy Calculated spectra Antonio Badolato (ETH Zurich, Switzerland) M. Ediger & R. J. Warburton XS-2 < XT-2 < X-1 < XX0 < X0 sequence (Heriot-Watt University, UK) in measured spectra from each and every QD studied in the ensemble is kept.
  • 26. Spectral Barcoding: Why is it important? Collaboration with three experimental groups! Structural Characterization by X-STM Quantum Dot Theory growth M. Bozkurt, J. M. Ulloa, & P. M. Koenraad (TU Eindhoven, The Netherlands) Many body pseudopotential calculations Single-dot Spectroscopy ? Calculated spectra V. Mlinar, G. Bester, & A. Zunger (NREL) Antonio Badolato (ETH Zurich, Switzerland) • Exciton energies M. Ediger & R. J. Warburton XS-2 < XT-2 < X-1 < XX0 < X0 sequence • XS-2 < XT-2 < X-1 < XX0 < X0 (Heriot-Watt University, UK) in measured spectra from each and sequence every QD studied in the ensemble is kept. Vladan Mlinar et al., PRB 80, 165425 (2009).
  • 27. XSTM→Theory→Spectroscopy Fails to Close Loop! • Exciton Energies: Calculated: 1.05 -1.12 eV Structure Measured: 1.08-1.09 eV Vladan Mlinar et al., PRB 80, 165425 (2009).
  • 28. XSTM→Theory→Spectroscopy Fails to Close Loop! • Spectral Hard Rules: EXP. XS-2 < XT-2 < X-1 < XX0 < X0 Structure Model 1 XS-2 < X0 < XX0 < X-1 < XT-2 Model 2 XS-2 < X0 < XX0 < XT-2 < X-1 Model 3 X0 < XX0 < XS-2 < X-1 < XT-2 Model 4 X0 < XS-2 < XX0 < X-1 < XT-2 Model 5 XS-2 < XX0 < X0 < X-1 < XT-2 All five XSTM deduced Model QDs violate Spectroscopic Hard rules! Vladan Mlinar et al., PRB 80, 165425 (2009).
  • 29. Structural motifs underlying Spectral Hard Rule: INPUT: Spectral barcoding Procedure Vladan Mlinar et al., PRB 80, 165425 (2009).
  • 30. Structural motifs underlying Spectral Hard Rule: INPUT: Spectral barcoding Procedure OUTPUT: Primary structural Motifs 1. Height (h) 2. Base-length (b) 3. Average In composition (XIn) Vladan Mlinar et al., PRB 80, 165425 (2009).
  • 31. Spectroscopy→Theory→Structure closes the Loop!
  • 32. Spectroscopy→Theory→Structure closes the Loop! • More than one dot can be constructed! • Spectral Hard Rules are satisfied by the construction! Vladan Mlinar et al., PRB 80, 165425 (2009).
  • 33. Conclusions: Spectral Barcoding: Procedure for deciphering structural motifs from the multiexcitonic spectra • We established missing structural basis for QD spectroscopy • We offer spectroscopically-derived structural motifs that combined with X-STM measurements give more realistic QD structure. Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009). Vladan Mlinar et al., PRB 80, 165425 (2009). Thank you for your attention!
  • 34. Basic Paradigm of Spectroscopy of Molecules • To understand the spectra one must know the structure (hence symmetry) of the molecule • Structure-spectra relationship in molecules has historically been facilitated by the accumulated knowledge on electronic and vibrational spectral fingerprints of specific groups making up the molecules • Deliberate design of molecules with given properties Structure
  • 35. Spectroscopic vs. Geometrical QD size: Can we construct a model QD that has geometrical size as extracted from XSTM, but spectroscopic size as deduced by spectral barcoding?
  • 36. XSTM deduced Model QDs: Model 1 Model 2 Model 3 Model 4 • Truncated cone • Truncated pyramid • Truncated pyramid • Ellipsoid • No wetting layer • No wetting layer • No wetting layer • No wetting layer Model 5 • Truncated cone • Includes wetting layer