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Derek J. Hollman Undergraduate Physics Symposium Interfacial Charge Interfacial Charge Transfer in Solar Cells: Transfer in Solar Cells: A Single Molecule A Single Molecule Perspective Perspective 8 May 08

Derek J. Hollman Undergraduate Physics Symposium Interfacial Charge Transfer in Solar Cells: A Single Molecule Perspective 8 May 08

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Derek J. HollmanUndergraduate Physics Symposium

Interfacial Charge Transfer in Solar Interfacial Charge Transfer in Solar Cells: A Single Molecule PerspectiveCells: A Single Molecule Perspective

8 May 08

Dye-Sensitized Solar Cells (DSSC)

Interfacial Dynamics Essential to Device Performance!

Understanding the DSSC

• Understanding interfacial charge transfer in DSSC complicated by heterogeneity

• Necessitates well-defined model system with controlled interface

• Bulk properties do not reveal complete dynamics in heterogeneous systems such as DSSC

• Must observe single molecules to address rates and mechanisms of charge transfer

Experimental Realization

We may observe:• Electron transfer rates

• Distance dependence

• Influence of interband states

• Influence of surface states

• Orientation dependence

System:• Perylene bisimide dye

• Gallium Nitride (GaN)

• Scandium Oxide (Sc2O3)

• Ultra-high vacuum

• Confocal Microscopy

• Thickness from 5 Å-1000 Å to slow charge transfer• Near-perfect, abrupt interface• Sc2O3 (111) grown heteroepitaxially on GaN (0001)

The Choice of Sc2O3/GaN

Chang Liu et al., APL 88 (2006), 222113

Single Molecule CT Reporter

R = -C4H9 or -C13H27

• Strong absorber ( = 75000 M-1cm-1) with unity quantum yield• Low intersystem crossing rates and short triplet lifetime• Perylene/TiO2 used in DSSC• Electronic properties tunable by bay-substitution

-1.80

Towards Single Molecule Spectroscopy in UHV

27.57

0

kcps

Photoblinking

Photobleaching

• Distinct “on” and “off” states only seen at single molecule level

Photoblinking

Objective

Histograms/distributions: P(τ)

Autocorrelation function: g(2)(τ)

• From these analyses, information about CT kinetics can be elucidated

• Simulate 2-state system, develop statistical analyses to recover rate information

MechanismMechanism!!

With kf >> kex >> kfct, 3-state system effectively becomes a 2-state system

Simulation: Signal Generation

,kefffct bctk ton, toff exp. deviate

repeat

on/offcounts

On/off Time Distributions

• On/off transitions may be Poissonian processes; on/off times are exponentially distributed

• CT kinetics may also be power-law distributed• Observing fluorescence intermittency provides information on CT kinetics• Distribution contains information on mechanism

0 20 400

2

4

6

ln(

#occ

uren

ces)

Time (ms)

Dependence on Bin Size

Ambiguity of on/off state

0 100000 2000000

3

6

cou

nts

pe

r tim

e b

in (

1/1

0

s)

Time (s)0 100000 200000

0

10

20

30

cou

nts

pe

r tim

e b

in (

1/1

00

s)

Time (s)

0 100000 2000000

60

120

cou

nts

pe

r tim

e b

in (

1/1

ms)

Time (s)

0 30 60 900

70

140

# O

ccu

ren

ces

Time (ms)

0 20 400

2

4

6

ln(

#o

ccu

ren

ces)

Time (ms)

Drawing the Line

0 10 20 30 40 500

50

100

150

200

# O

ccu

ren

ces

Time (ms)

kfct = 100Hz

krecovered = 97 ± 5 Hzoff-time histogram on-time histogram

kbct = 100Hz

Analysis:• Start clock; measure time molecule was “on” or

“off”

• When a transition occurs, record time, bin it, reset clock

• Repeat

Autocorrelation

1000 10000 1000001000000

0.0

0.5

1.0

g(2) ()

- 1

Time (s)

2)2(

)(

)()()(

tI

tItIg

• Determine correlation between pairs of photons at arbitrarily long times

Conclusions

• CT kinetics of a DSSC can be understood by analyzing single molecule fluorescence intermittency trajectories

• Experimental design allows for a good model and control of many parameters

• Simulation provides a framework for developing analyses

• Analyses can recover rates for a 2-state system

Future Simulation Work

• Fit autocorrelation functions• Power-law kinetics• Multiple dark states• Photon arrival times for additional information• Use analyses on real data!

University of ArizonaUniversity of Arizona

Dr. Oliver L. A. MontiDr. Oliver L. A. Monti

Dr. Brandon S. TackettDr. Brandon S. Tackett

Michael L. BlumenfeldMichael L. Blumenfeld

Laura K. SchirraLaura K. Schirra

Mary P. SteeleMary P. Steele

Jason M. TylerJason M. Tyler

Stefan Kreitmeier (TU MStefan Kreitmeier (TU Müünchen)nchen)

University of FloridaUniversity of Florida

Dr. Brent P. GilaDr. Brent P. Gila

Dr. Stephen J. PeartonDr. Stephen J. Pearton

DSSC – A Complex Structure

SEM micrograph of titanium oxide films. M. Grätzel et al., J. Am. Ceram. Soc. 80, 3157.

L. Kavan, M. Grätzel, S. E. Gilbert, C. Klemenz, H. J. Scheel, JACS 118, 6716

• Charge transfer in heterogeneous environment• Crystal face- and structure-dependent device

performance

Kinetics in DSSC

T. Hannappel, B. Burfeindt, W. Storck, F. Willig, JPCB 101, 6799

S.A. Haque, Y. Tachibana, D.L. Klug, J.R. Durrant, JPCB 102, 1745

Result: Non-exponential charge transfer kinetics

Ideal Model System

• Donor: Single molecule to model excited state in solar cell

• Acceptor: Single-crystalline wide bandgap semiconductor

• Spacer Layer: – Heteroepitaxial single crystalline surface– Controllably vary donor-acceptor distance– Slow down charge transfer kinetics

• Conditions: Growth and measurement in ultra-high vacuum

Experimental Realization

We may observe:• Forward and backward

electron transfer rates

• Distance dependence

• Influence of interband states

• Influence of surface states

• Orientation dependence

System: Perylene bisimide on Sc2O3 / GaN

… one molecule at a time!

Single Molecule CT Reporter

R = -C4H9 or -C13H27

• Strong absorber ( = 75000 M-1cm-1) with unity quantum yield• Low intersystem crossing rates and short triplet lifetime• Perylene/TiO2 used in DSSC• Electronic properties tunable by bay-substitution

PTCDI/Sc2O3/GaN so far

• ELUMO(PTCDI) = 0±100 meV vs. Sc2O3/GaN CBM

Excitation/Emission GaN

• There are states within the bandgap!

300

400

500

600

0.0

0.5

1.0

1.5

2.0

2.5

3.0

340

360380

400420

440460

Flu

ore

scen

ce (

AU

)

Excita

tion (n

m)

Emission (nm)

300

400

500

600

340360

380400

420440

460

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Excitation (nm)

Fluorescence Intermittency

• Single molecules exhibit “blinking”• On/Bright state: continual excitation,

fluorescence cycling• Off/Dark state: non-fluorescing state resulting

from ISC or CT event• ton, “on-time”: period of continual

excitation/fluorescing until a single molecule ISC or CT event

• toff, “off-time”: period until a charge recombination or reverse ISC event

Time Scales

• ISC events occur with low transition rate and short lifetime, typically microsecond or shorter

• CT events occur with much longer lifetimes, millisecond to seconds, also tunable (insulator layer)

• Data acquisition rate much slower than ISC event rate

• ISC events only lower average cps

What it looks like

• Distinct visible states, on and off, only seen at single molecule level

0 500000 10000000

60

120

cou

nts

pe

r tim

e b

in (

1/1

ms)

Time (s)

ton toff

Model System

• With kf >> kex >> kfct, 3-state system effectively becomes a 2-state system

• Experimental acquisition rate: 103 - 104 Hz

• kf ~ 109 Hz, kex ~ 106 Hz, kfct ~ 103 Hz

Poissonian Processes• On/off transitions are Poissonian processes• On or off times may be characterized by Poisson

distribution

ke-kt

Exponential because• Transfer of charge may be a tunneling process• Kinetics may follow well-defined rate constant

Power-law Kinetics

• CT kinetics may be power-law distributed:

• Fluctuating rate constant; molecule sampling multiple surface sites

• Observing fluorescence intermittency provides information on CT kineticsBasche, et. al

mAttP )(

Motivation for a Simulation

• Shot-noise limited signals with low S/N, need sophisticated methods of analyzing data

• Simulation provides framework for developing various analyses

• Control of input rate parameters, want to recover them

• Do not know experimental rates a priori, can not verify analyses otherwise

Simulated Fluorescence Trajectory

• Signal generated at rate much faster than real acquisition rate, then re-binned

0 200000 400000

0

8

16

cou

nts

pe

r 0

.1 m

s

Time (s)

Re-binning Simulated Trace

• Simulated data generated on 1µs time step• Real data acquisition rate closer to 0.1-1ms

0 500 10000

1

2

cou

nts

pe

r tim

e b

in (

1/1

s)

Time (s)

0 400 8000

5

10

15

cou

nts

pe

r tim

e b

in (

1/1

00

s)

Time (s)

On/off Histograms

• Will investigate dependence on threshold, bin size

0 10 20 30 40 500

50

100

150

200

# O

ccu

ren

ces

Time (ms)

off times histogram on times histogram

0 30 60 900

70

140

# O

ccu

ren

ces

Time (ms)

0 20 400

2

4

6

ln(

#o

ccu

ren

ces)

Time (ms)

krecovered = 97 ± 5 Hz

0 10 20 30 40 500

50

100

150

200

# O

ccu

ren

ces

Time (ms)

Recovery

• Fit histograms to exponential; decay rate should be input rate

• Recovery!

kfct = 100Hz

m = -0.097 ± 0.005off times histogram

Autocorrelation

1

1

1

2

1

1)2(

N

mN

tIN

tmItImNtmg

2)2(

)(

)()()(

tI

tItIg

• Determine correlation between pairs of photons at arbitrarily long times

• Shape of autocorrelation contains kinetics of system

• Algorithm implemented: