Vladan Mlinar 2009 Materials Research Society Spring Meeting

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Information about Vladan Mlinar 2009 Materials Research Society Spring Meeting
Technology

Published on July 5, 2010

Author: VladanMlinar

Source: slideshare.net

Description

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

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

QDs: Structure - Spectra relationship Methods for structural characterization Single-dot spectroscopy • TEM based methods • X-ray diffraction • X-STM

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!

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!

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!

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

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?

Question: What is the structural information encoded in the multiexcitonic spectra of a QD? ?

Spectral Barcoding vs. DNA Barcoding: Barcoding Barcoder Organism is identified as belonging to a particular species Sci. Am. p. 82-88 (October 2008)

Spectral Barcoding vs. DNA Barcoding: Barcoding Barcoder Organism is identified as belonging to a particular species Sci. Am. p. 82-88 (October 2008)

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).

How does the Spectral Barcoding work? Spectral barode: Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

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).

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).

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

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.

Spectral Barcoding: Consistency test! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

Spectral Barcoding: Consistency test! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

Spectral Barcoding: Consistency test! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

Spectral Barcoding: Consistency test! Validation! Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

Question: How does the deduced structure relates to the “real structure”?

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)

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)

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)

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.

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).

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).

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).

Structural motifs underlying Spectral Hard Rule: INPUT: Spectral barcoding Procedure Vladan Mlinar et al., PRB 80, 165425 (2009).

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).

Spectroscopy→Theory→Structure closes the Loop!

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).

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!

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

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?

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

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