modelling excitonic solar cells alison walker department of physics
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Modelling excitonic solar cellsModelling excitonic solar cells
Alison Walker
Department of Physics
How can modelling help?
• Materials
• Patterning, Self-organisation, Fabrication
• Device Physics
• Characterization
Outline
• Dynamic Monte Carlo Simulation
• Energy transport
• Charge transport
Dynamic Monte Carlo Simulation
Excitons generated throughoutElectrons confined to green regionsHoles confined to red regions
P K Watkins, A B Walker, G L B Verschoor Nano Letts 5, 1814 (2005)
Disordered morphology
(a) Interfacial area 3106 nm2
(b) Interfacial area 1106 nm2
(c) Interfacial area 0.2106 nm2
Modelled Morphology
• Hopping sites on a cubic latticewith lattice parameter a = 3 nm
• Sites are either electron transporting polymer (e) or hole transporting polymer (h)
Ising Model
• Ising energy for site i isi = -½J [(si, sj) – 1]
• Summation over 1st and 2nd nearest neighbours
• Spin at site i si = 1 for e site, 0 for h site
• Exchange energy J = 1
• Chose neighbouring pair of sites l, m and findenergy difference = l - m
• Spins swopped with probability )(exp1
)(exp)(
Tk
TkP
B
B
IQE measures exciton harvesting efficiency
Exciton dissociation efficiency e = no of dissociated excitons no of absorbed photons
Charge transport efficiency c = no of electrons exiting device no of dissociated excitons
Internal quantum efficiency IQE = no of electrons exiting device = e c no of absorbed photons
NB Assume all charges reaching electrodes exit device
Internal quantum efficiency IQE
External quantum efficiency EQE For illumination with spectral density S()
JSC = qd EQE S()where external quantum efficiency
EQE = no of electrons flowing in external circuit no of photons incident on cell
= AIQE
photon absorption efficiency
A = no of absorbed photons no of photons incident on cell internal quantum efficiency
IQE = no of electrons flowing in external circuit no of absorbed photons
Possible reactions
• Exciton creation on either e or h site• Exciton hopping between sites of same type• Exciton dissociation at interface between e and h
sites• Exciton recombination
• Electron(hole) hopping between e(h) sites • Electron(hole) extraction• Charge recombination
Generation of morphologies with varying interfacial area
• Start with a fine scale of interpenetration, corresponding to a large interfacial area
• As time goes on, free energy from Ising model is lowered, favouring sites with neighbours that are the same type
• Hence interfacial area decreases• Systems with different interfacial areas are
morphologies at varying stages of evolution
Challenges• Several interacting particle species• Many possible interactions:
GenerationHoppingRecombinationExtraction
• Wide variation in time scales• Two site types
Why use Monte Carlo ?
• Do not have (or want) detailed information about particle trajectories on atomic length scales nor reaction rates
• Thus can only give probabilities for reaction times
• These can be obtained by solving the Master equation but this is computationally costly for 3D systems
Dynamical Monte Carlo Model
• Many different methods • These can all be shown to solve the Master
Equation (Jansen*)• First Reaction Method has been used to
simulate electrons only in dye-sensitized solar cells
*A P J Jansen Phys Rev B 69, 035414 (2004) A P J Jansen http://ar.Xiv.org/, paper no. cond-matt/0303028
Master equation
dP
dt= (W P- W P)
, are configurationsP, P are their probabilities
W are the transition rates
Consider a reaction with a transition rate k.
Probability that a reaction occurs in time intervalt t + dt dp = (Probability reaction does not occur before t) (Probability reaction occurs in dt) = - p(t) k dt Hence probability distribution P(t) of times at which reaction occurs normalised such that P(t)dt = 1 is the Poisson distributionP(t) = kexp(-kt)
Simple derivation of Poisson Distribution
R Hockney, J W Eastwood Computer simulation using particles
IoP Publishing, Bristol, 1988
Integrating dc = dp = P(t) dt givescumulative probabilityc(t) = 0
t P(t)dt The reaction has not occurred at t = 0 but will occur some time, soc(0) = 0 c 1 = c()If the value of c is set equal to a random number r chosen from a uniform distribution in the range 0 r 1, the probability of selectinga value in the range c c + dc is dcHencer = c(t) = 0
t P(t)dt
Selecting waiting times
eg for a distribution peaked at x0, most values of r will give values of x close to x0
x
f
x0
For Poisson distribution,
t
P
tt0
r1
c
0
F
x
r
x0
1
0
To select times with Poisson distribution from random numbers ri distributed uniformly between 0 and 1, use
r1 = 0t kexp(-kt)dt
Hence
t = -1 ln(1-r1) = -1 ln(r2)k k
• Each reaction i with rate wi has a waiting time from a uniformly distributed random number r
First Reaction Method
• List of reactions created in order of increasing i • First reaction in list takes place if enabled• List then updated
i = -1 ln(r)wi
Flow ChartCreate a queue of reactions i and associated waiting times i.
Set simulation time t = 0.Select reaction at top of queue
Do top reaction Remove this reaction from queue
Set t = t + top
Set i = i - top
Add enabled reactions
Top reaction enabled?
Yes
Remove from queueNo
Simulation details• Hops allowed to the 122 neighbours within 9
nm cutoff distance• Exclusion principle applies ie hops disallowed
to occupied sites• Periodic boundary conditions in x and y
• Site energies Ei are all zero for excitons
• For charge transport, Ei include(i) Coulomb interactions(ii) external field due to built-in potential and external voltage
• Electron(hole) hopping between e(h) sites wij = w0exp[-2Rij]exp[-(Ej – Ei)/(kBT)] if Ej > Ei
w0exp[-2Rij] if Ej < Ei
w0 = [6kBT/(qa2)]exp[-2a] e = h = 1.10-3 cm2/(Vs) = 2 nm-1
• Electron(hole) recombination ratewce = 100 s-1
allows peak IQE to exceed 50% for idealisedmorphology
• Electron(hole) extractionwce = if electron next to anode/hole next to cathode
wce = 0 otherwise
Reaction rates• Exciton creation on either e or h site
S = 2.4102 nm-2s-1
• Exciton hopping between sites of same typewij = we(R0/Rij)6 weR0
6 = 0.3 nm6s-1 gives diffusion length of 5nm
• Exciton dissociation at interface between e and h sites wed = if exciton on an interface site wed = 0 otherwise
Disordered morphology
(a) Interfacial area 3106 nm2
(b) Interfacial area 1106 nm2
(c) Interfacial area 0.2106 nm2
Efficiencies (disordered morphology)
a
b
c
At large interfacial area ie small scale phaseseparation:
• excitons more likely to find an interface before recombining• thus exciton dissociation efficiency increases• charges follow more tortuous routes to get to electrodes • charge densities are higher • charge recombination greater• thus charge transport efficiency decreases• Net effect is a peak in the internal quantum efficiency
Sensitivity of IQE to input parameters
a) As the exciton generation rate increases, IQE decreases at all interfacial areas due to enhanced charge recombination
b) For larger external biases, the peak IQE increases and shifts to larger interfacial areas
c) Similar changes to (b) seen for larger charge mobilities and if charge mobilities differ
Ordered morphology
Achievable with diblock copolymers
Efficiencies (ordered morphology)
• As for disordered morphologies, see a peak in IQE, here at a width of 15 nm
• Maximum IQE is larger by a factor of 1.5 than
for disordered morphologies • Peak is sharper since at large interfacial areas,
excitons less likely to find an interface and the charges are confined to narrow regions so there is a large recombination probability.
• Continuous charge transport pathways, no disconnected or ‘cul-de-sac’ features• Free from islands• A practical way of achieving a similar efficiency to the rods?
Gyroids
Geminate recombinationUnexpected difference between rod structures and the others.
Recombination
Bimolecular recombinationNovel structures show little advantage over blends (even at 5 suns). Islands and disconnected pathways not responsible for inefficiency as previously thoughtRod structures significantly better, even at small feature sizes-Short, direct pathways to electrodes- Can keep charges entirely isolated
Angle ηgr
0° ~22%
90° ~26%
180° ~83% E
• Most time is spent tracking at the interface.
• A polymer with a range of interface angles is far less
efficient than a vertical structure.
• Feature size dependence of fill factor, shift in optimum feature size when examining complete J-V performance.
• Islands shift the perceived optimum feature size.
• New morphologies not as efficient as hoped, despite absence of islands and disconnected pathways.
• Morphology can still inhibit geminate separation at large feature sizes.
• Rods have noticeably lower geminate and bimolecular recombination, but for different reasons.
• Angle of interface is critical, morphologies with a range of angles less efficient than vertical structures.
Dynamical Monte Carlo Summary
Dynamical Monte Carlo methods are a useful way of modelling polymer blend organic solar cells because (i) they are easy to implement, (ii) they can handle interacting particles (iii) they can be used with widely varying time scales
Energy transport
Stavros Athanasopoulos, David Beljonne, Evgenia EmilianovaUniversity of Mons-HainautLuca Muccioli, Claudio ZannoniUniversity of Bologna
electronic properties
Chemical structure Physical morphology
• Polyphenylenes eg PFO used for blue emissive layers in blue OLEDs but emission maxima close to violet
• Polyindenofluorenes intermediate between PFO and LPPP show purer blue emission
• The solid state luminescence output has been related to the microscopic morphology
Experimental background
SolidSolutionP
L in
tens
ity
Indenofluorene chromophoresPerylene end-caps
(nm)
Spectroscopy on end-capped polymers
• Transfer rates from chromophore to perylene are much faster than those between chromophores
• Different spectra are observed for the polymer in solution, and as a film
Morphology
P3HT- crystalline, high mobility (~0.1 cm2/Vs)
Disorder could occur parallel to plane of substrate
Electron micrographof PF2/6:Liquid-crystalline state lamellae separatedby disordered regions;molecules inside lamellaeseparate according tolengths
Ordered regions also seen in PIF copolymers
Energetic disorder
Numbers of chromophores per chain, and lengths of individual chromophores are assigned specified distributions:
• Exciton diffusion takes place within a realistic morphology consisting of a 3D array of PIF chains
• Excitons hop between chromophores• Averaging over many exciton trajectories,
properties such as diffusion length, ratio of numbers of intrachain to interchain hops, spectra etc are explored
Key Features of our Model
Quantum Chemical Calculation of Hopping Rates
• Mons provide rates of exciton transfer between chromophores
• They use quantum chemical calculations employing the distributed monopole method
• This takes into account the shape of donor and acceptor chromophores in calculating the electronic coupling Vda
• The hopping rate from donor to acceptor is
Electronic coupling Overlap factor
Trajectories of individual particles
are averaged to obtain quantities of interest
(note periodic boundary conditions)
• Intrachain hops are less common (No. interchain hops) / (No. intrachain hops) 7
• Yet motion parallel to the chain axes is more prevalent: why?
– Intrachain hops involve long distances
– Also, the more numerous interchain hops can involve a non-negligible z component
yx
z
Mean absolute value = 1.6 nm
Mean absolute value = 4.5 nm
2
2exp
22tN E
g E
rF = 3.1 nmNt = 1 nm-3
Summary for exciton transport
• A physically valid method of simulating transport in conjugated polymers (towards a multiscale approach)
• Advantages over cubic-lattice approaches
• Energetic disorder is crucial
Charge transport
Jarvist Frost, James Kirkpatrick, Jenny NelsonImperial College London
• The waiting time before a hop from site i to a neighbouring site j is
ij = -1 ln(r)
wij
where wij is the hopping rate between sites i and j, and r is a random number uniformly distributed between 0 and 1.
• When the exciton hops, we always choose the hop with the shortest waiting time ij
Dynamical Monte Carlo Migration Algorithm
Ordered chains
Time of flight (ToF) experiment
= d E
• Localized polarons on single conjugated segments
• Alternative is Gaussian disorder model which involves hopping between sites on a cubic lattice subject to some disorder
Questions:1. Chemical structure?2. Molecular packing?
Our Model
Field parallel to the chains leads to higher
mobility
=> Intra chain transfer dominates
Relaxed Geometry
Marcus theory
Reorganisation energy
intra = intra(A1) + intra
(D2)
J-L. Brédas et al Chemical Reviews104 4971 (2004)
Donor
E
QD
Acceptor
intra(D2)
i
ii
1
2
intra(D1)
intra(A1) = E(A1)(A+) – E(A1)(A)
intra(D2) = E(D2)(D) – E(D2)(D+)
QA
QA
ii
i
intra(A2)
intra(A1)
1
2
E
D + A+ → D+ + A
Transfer rates
kDA = 2V2 exp - (G + )2
ħ(4kBT) (4kBT)
Electronic coupling potential V from INDOG is change in free energy
from Density Functional Theory (B3LYP)
Simulated transient current
Charge transfer in aligned PFOH
ole
mob
ility
(cm
2 V-1s
-1)
(Field)1/2 (V1/2 m-1/2)
Summary for charge transport
• We can relate charge transport to chemical structure – up to a point
• The fact that intrachain transport is much faster than interchain transport is crucial to understand charge mobilities in polymer films
• Good agreement with experimental ToF hole mobility data for aligned films
Where next?
• Improved charge and exciton transfer and recombination rates
• Include triplet excitons• Different morphologies• Other systems eg display devices
ToRisto, Martti, Adam,Arkady,Mikko,Teemu
Thanks!!!Thanks!!!