vladan mlinar 2009 materials research society spring meeting
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). -------------------------- My full publications list can be found at: www.vladanmlinar.com/publications.htmlTRANSCRIPT
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Deciphering Structural Information from the Multiexcitonic Spectra of a Quantum Dot
Vladan Mlinar & Alex Zunger
National Renewable Energy Laboratory
Golden, Colorado USA
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QDs: Structure - Spectra relationship
Methods for structural characterization
• TEM based methods
• X-ray diffraction
• X-STM
Single-dot spectroscopy
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QDs: Structure - Spectra relationship
(M. Bozkurt, J. M. Ulloa, & P. M. Koenraad)
Methods for structural characterization
• TEM based methods
• X-ray diffraction
• X-STM
Single-dot spectroscopy
• No atomic resolution
• All of the methods require assumption
about composition profile and/or shape!
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QDs: Structure - Spectra relationship
(M. Bozkurt, J. M. Ulloa, & P. M. Koenraad)
Methods for structural characterization
• TEM based methods
• X-ray diffraction
• X-STM
Single-dot spectroscopy
• No atomic resolution
• All of the methods require assumption
about composition profile and/or shape!
(M. Ediger &
R. J. Warburton)
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QDs: Structure - Spectra relationship
(M. Bozkurt, J. M. Ulloa, & P. M. Koenraad)
Methods for structural characterization
• TEM based methods
• X-ray diffraction
• X-STM
Single-dot spectroscopy
• No atomic resolution
• All of the methods require assumption
about composition profile and/or shape!
• Controllable number of electrons and holes
• μeV resolution
(M. Ediger &
R. J. Warburton)
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Typically, Structure is used to predict Spectra
• Since for quantum dots we do not know the structure:
Assume
or
measure
structure
Measured
emission
spectra
Calculate
resulting
spectra
Structure
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Typically, Structure is used to predict Spectra
• Since for quantum dots we do not know the structure:
Is this possible?
Assume
or
measure
structure
Measured
emission
spectra
Calculate
resulting
spectra
Structure
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Question: What is the structural information encoded in the multiexcitonic spectra of a QD?
?
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Spectral Barcoding vs. DNA Barcoding:
Barc
oder
Barc
odin
g
Organism is identified as belonging to a particular species
Sci. Am. p. 82-88 (October 2008)
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Spectral Barcoding vs. DNA Barcoding:
Barc
oder
Barc
odin
g
Organism is identified as belonging to a particular species
Sci. Am. p. 82-88 (October 2008)
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Spectral Barcoding vs. DNA Barcoding:
Barc
oder
Barc
odin
g
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).
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How does the Spectral Barcoding work?
Spectral barode:
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
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How does the Spectral Barcoding work?
Artificial Intelligence QD library
Deterministic links between
structures and spectral marker(Distilling rules from library)
Spectral barode:
Spectral barcoding
procedure
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
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How does the Spectral Barcoding work?
Artificial Intelligence QD library
Deterministic links between
structures and spectral marker(Distilling rules from library)
Structure
Structural Motifs:
• h = 2 – 3nm
• Xav(In) = 75-80%
RESULT: a set
of QD structural
motifs!
Spectral barode:
Spectral barcoding
procedure
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
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Spectral Barcoding: Data-mining of the library
Structure
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.
Lens [110]
23 4.0 80
Elong.
Lens [110]
25 5.0 90
Elong.
Lens [100]
30 6.0 100
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Spectral Barcoding: Data-mining of the library
Structure
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.
Lens [110]
23 4.0 80
Elong.
Lens [110]
25 5.0 90
Elong.
Lens [100]
30 6.0 100
Bayesian Data Reduction Algorithm:
• 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.
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Spectral Barcoding: Consistency test!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).
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Spectral Barcoding: Consistency test!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).
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Spectral Barcoding: Consistency test!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).
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Spectral Barcoding: Consistency test!
Validation!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).
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Question: How does the deduced structure relates to the “real structure”?
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Spectral Barcoding: Why is it important?
Quantum Dot
growth
Structural Characterization by X-STM
Single-dot Spectroscopy
Antonio Badolato
(ETH Zurich, Switzerland)
Theory
Collaboration with
three experimental
groups!
Many body
pseudopotential
calculations
Calculated spectra
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Spectral Barcoding: Why is it important?
Quantum Dot
growth
Structural Characterization by X-STM
Single-dot Spectroscopy
Antonio Badolato
(ETH Zurich, Switzerland)
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands)
Theory
Collaboration with
three experimental
groups!
Many body
pseudopotential
calculations
Calculated spectra
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Spectral Barcoding: Why is it important?
Quantum Dot
growth
Structural Characterization by X-STM
Single-dot Spectroscopy
Antonio Badolato
(ETH Zurich, Switzerland)
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands)
Theory
M. Ediger & R. J. Warburton
(Heriot-Watt University, UK)
Collaboration with
three experimental
groups!
Many body
pseudopotential
calculations
Calculated spectra
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Spectral Barcoding: Why is it important?
XS-2 < XT
-2 < X-1 < XX0 < X0 sequence
in measured spectra from each and
every QD studied in the ensemble is
kept.
Quantum Dot
growth
Structural Characterization by X-STM
Single-dot Spectroscopy
Antonio Badolato
(ETH Zurich, Switzerland)
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands)
Theory
M. Ediger & R. J. Warburton
(Heriot-Watt University, UK)
Collaboration with
three experimental
groups!
Many body
pseudopotential
calculations
Calculated spectra
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Spectral Barcoding: Why is it important?
XS-2 < XT
-2 < X-1 < XX0 < X0 sequence
in measured spectra from each and
every QD studied in the ensemble is
kept.
Quantum Dot
growth
Structural Characterization by X-STM
Single-dot Spectroscopy
Antonio Badolato
(ETH Zurich, Switzerland)
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands)
Theory
M. Ediger & R. J. Warburton
(Heriot-Watt University, UK)
Collaboration with
three experimental
groups!
Many body
pseudopotential
calculations
Calculated spectra
• Exciton energies
• XS-2 < XT
-2 < X-1 < XX0 < X0
sequence
?V. Mlinar, G. Bester, &
A. Zunger (NREL)
Vladan Mlinar et al., PRB 80, 165425 (2009).
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XSTM→Theory→Spectroscopy Fails to Close Loop!
Structure
• Exciton Energies:
Calculated: 1.05 -1.12 eV
Measured: 1.08-1.09 eV
Vladan Mlinar et al., PRB 80, 165425 (2009).
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XSTM→Theory→Spectroscopy Fails to Close Loop!
Structure
• Spectral Hard Rules:
All five XSTM deduced Model QDs
violate Spectroscopic Hard rules!
EXP. XS-2 < XT
-2 < X-1 < XX0 < X0
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
Vladan Mlinar et al., PRB 80, 165425 (2009).
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Structural motifs underlying Spectral Hard Rule:
Spectral barcoding
Procedure
INPUT:
Vladan Mlinar et al., PRB 80, 165425 (2009).
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Structural motifs underlying Spectral Hard Rule:
Spectral barcoding
Procedure
INPUT:
OUTPUT:
Primary structural
Motifs
1. Height (h)
2. Base-length (b)
3. Average In
composition (XIn) Vladan Mlinar et al., PRB 80, 165425 (2009).
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Spectroscopy→Theory→Structure closes the Loop!
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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).
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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.
Thank you for your attention!
Vladan Mlinar et al., PRB 80, 165425 (2009).
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
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Basic Paradigm of Spectroscopy of Molecules
Structure
• 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
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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?
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XSTM deduced Model QDs:
Model 1 Model 2
Model 5
Model 3 Model 4
• Truncated cone
• No wetting layer
• Truncated pyramid
• No wetting layer
• Truncated pyramid
• No wetting layer
• Ellipsoid
• No wetting layer
• Truncated cone
• Includes wetting layer