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Surfaces, Interfaces, and Catalysis; Physical Properties of Nanomaterials and Materials

Graphene Field-Effect Transistor as a High-Throughput Platform to Probe Charge Separation at Donor-Acceptor Interfaces Bhupal Kattel, Liang Qin, Tika R. Kafle, and Wai-Lun Chan J. Phys. Chem. Lett., Just Accepted Manuscript • Publication Date (Web): 13 Mar 2018 Downloaded from http://pubs.acs.org on March 13, 2018

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The Journal of Physical Chemistry Letters

Graphene Field-Effect Transistor as a High-Throughput Platform to Probe Charge Separation at Donor-Acceptor Interfaces Bhupal Kattel,1 Liang Qin,1,2 Tika R. Kafle,1 Wai-Lun Chan1,* 1. Department of Physics and Astronomy, University of Kansas, Lawrence, KS 66045, US 2. Key Laboratory of Luminescence and Optical Information, Ministry of Education, Beijing JiaoTong University, Beijing 100044, China

Abstract In organic and low-dimensional materials, electrons and holes are bound together to form excitons. Effective exciton dissociation at interfaces is essential for applications such as photovoltaics and photosensing. Here, we present an interface-sensitive, time-resolved method that utilizes graphene field effect transistor as an electric-field sensor to measure the charge separation dynamics and yield at donor-acceptor interfaces. Compared to other interfacesensitive spectroscopy techniques, our method has a much reduced measurement time and can be easily adapted to different material interfaces. Hence, it can be used as a high throughput screening tool to evaluate the charge separation efficiency in a large number of systems. By using zinc phthalocyanine/fullerene interface, we demonstrate how this method can be used to quantify the charge separation dynamics and yield at a typical organic donor-acceptor interface.

*

Email: [email protected]

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Table of Content (TOC) graphics


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In photovoltaics and opto-electronic devices, charge transfer (CT) and charge separation (CS) at donor-acceptor interfaces or p-n junctions are important steps for generating free carriers. Many efforts have been devoted to understand CS in materials such as organic semiconductors and nanomaterials in which electrons and holes are tightly bound.1-2 Time-resolved optical pump-probe spectroscopy has often been employed to study the CT and CS processes. One of the challenges in using optical spectroscopy to study interfacial processes is to isolate the signal contributed by the interface from that contributed by the bulk because many optical probes are not selective to interfaces. Advanced spectroscopy techniques use optical probes that are sensitive to the interface or to the E-field produced by the CS. Examples of these techniques include non-linear spectroscopy,3-5 time-resolved electro-absorption spectroscopy,6-8 E-field induced

second

harmonic

generation

(EFISH)9-14

and

interface-sensitive

vibrational

spectroscopy.15-16 However, these methods often involve complex optical setups, long measurement times and sophisticated procedures for data interpretation. In some cases, bulkheterojunction (BHJ) samples are needed for the measurement. Due to the structural complexity of the BHJ, it is difficult to correlate the observed dynamics with the interfacial structure. Surface sensitive techniques such as time-resolved two photon photoemission spectroscopy has been used to measure the CT dynamics and delocalization size of CT excitons at interfaces.17-21 However, it, again, requires the use of complex instrumentation. Because of the complexity of the above methods, in many cases, only a limited number of model systems have been studied by a particular method. At organic interfaces, it is known that a small change in the molecular orientation, crystal packing or microstructure on the nanoscale would significantly impact the energetics of CT states22 and the CS kinetics.23-24 Different interfaces would have distinct CS mechanisms. For example, there has been a continual debate on whether the excess energy possessed by hot CT excitons is necessary for effective CS.25-28 Studies on different interfaces can have seemingly contradictory conclusions. A selected model system, after all, may not be a good representation 3 ACS Paragon Plus Environment


to a wide range of donor-acceptor interfaces. Therefore, conventional approaches in which a model system is studied in details by a spectroscopy technique may not be sufficient to understand the CS mechanism in complex systems. On the other hand, a versatile and highthroughput method that can probe a large number of interfaces in a short period of time can be used to search for the optimum structure for CS. A high-throughput approach can supplement time-consuming, complex spectroscopy techniques. Indeed, high-throughput and combinatorial approaches29 have been used in recent years to understand and optimize the performance of many complex materials ranging from catalysts30-31 to sensing materials.32 This work presents a high-throughput and versatile tool that can be used to quantify the CS yield and its temporal dynamics at material interfaces used for PV and optoelectronic applications. Our method utilizes the E-field sensitive nature of graphene33-35 to measure the CS dynamics and quantify the carrier generation yield. It selectively probes the amount of free carriers generated from the CS process. To this end, its capability is similar to that of the timeresolved microwave conductivity method,36-38 which is often used to study charge generation and recombination dynamics. However, our method directly measures the concentration of free carriers instead of the conductivity (i.e. the product of the carrier mobility and the carrier concentration), which is an advantage as compared to the microwave technique. Comparing to other pump-probe spectroscopy techniques, our method cannot temporally resolve sub-ps charge dynamics, but it has several advantages which enable it to be used as a high throughput and versatile characterization tool. For instance, the experimental setup is relative simple and no time-delayed pulse is required. The measurement time (~ 10 – 100 seconds) is orders of magnitude shorter compared to typical time-resolved spectroscopy techniques. The short measurement time allows it to be used for samples that are susceptible to laser damages. The same data analysis procedure can be applied to different material interfaces. Hence, the method can be easily adapted to different material interfaces. The method is sensitive enough to capture the CS from a single interface, although it can also be used to study charge generation from BHJ 4 ACS Paragon Plus Environment

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structures.39 Therefore, the CS dynamics and yield can be correlated easily with the interfacial structure. We will first discuss the basic principle of this method before the results are presented. Because of the low density of states (DOS) of graphene near the Fermi level (Ef),40 the graphene conductivity is very sensitive to doping. A small amount of doping can effectively shift Ef with respect to the Dirac point (ED),33,

41-43

which in turn can modify the DOS at Ef and the

conductivity of graphene. This mechanism has been used for photosensing in graphene field effect transistors (GFET).35, 44 In a GFET sensor, a photo-active semiconductor is deposited on graphene. Photoexcitation creates electron-hole pairs in the semiconductor. In some systems, one of the charged-species (e.g. electron) preferentially transfers to graphene while its counterpart (hole) is trapped within the semiconductor. The net charges trapped inside the semiconductor induce doping in graphene via capacitive coupling. This modifies the graphene’s conductivity, which can then be detected by a simple circuit. Here, GFET devices will be adapted to measure the CS dynamics and yield at donoracceptor interfaces. The donor-acceptor bilayer under study is deposited on a GFET device. In this work, zinc phthalocyanine (ZnPc) and fullerene (C60) are chosen as the donor and acceptor respectively. The ZnPc/C60/graphene multilayer structure used in the experiment is shown in Figure 1a. In the experiment, we selectively excite the donor (ZnPc) by a 700 nm pump pulse. The pump pulse cannot create singlet exciton in C60 because the photon energy is below the bandgap of C60. The CS occurred at the ZnPc/C60 interface generates free holes and electrons in the ZnPc and C60 layers respectively. The holes remain trapped inside the ZnPc layer while electrons can transport through the C60 layer to the C60/graphene interface, and subsequently inject into graphene. The trapped holes in the topmost donor layer will create an E-field that induces electron doping in graphene, which modifies the conductivity of the graphene channel. As in a parallel-plate capacitor, the number of electrons doped in graphene is equal to the number of holes trapped in the donor layer. The energy level diagram and charge separation 5 ACS Paragon Plus Environment


processes are summarized schematically in Figure 1b. The temporal change in the graphene’s conductivity can be measured by the simple circuit shown in Figure 1c. Compared to other optical pump-electrical probe methods,45-48 our method probes the voltage change instead of the current change. Hence, the time resolution can reach that of a fast oscilloscope without the use of time-delayed pulses. This significantly simplifies the optical setup and reduces the measurement time. In this work, our oscilloscope has a time resolution of ~ 2 ns. However, a time resolution down to ~ 10 – 100 ps can be achieved by using GHz sampling oscilloscopes. As we will show, by modulating the graphene conductivity with a back gate (Vg), the absolute number of separated free carriers can also be determined.

Figure 1: (a) Schematic diagrams showing the CS at the donor-acceptor interface can induce a conductivity change in the graphene channel. (b) The energy level diagram of the donor/acceptor/graphene sample. (c) The circuit used for measuring the photo-induced conductivity change in the graphene channel. The GFET device used in this work has a channel size of 1 mm × 1 mm, which can be fabricated on CVD grown graphene using shadow masks. The ZnPc/C60 bilayer is deposited on top of a graphene channel inside an ultrahigh vacuum (UHV) chamber. The sample preparation procedure is described in the method section. The sample is kept inside a high vacuum cryostat (pressure ~ 10-6 Torr) during the experiment. The singlet (S1) exciton in the ZnPc layer is excited resonantly by femtosecond laser pulses (~ 20 fs, ~ 1.75 eV). Because the photon energy is smaller than the bandgap of C60, the C60 layer is not excited. As discussed above, CS at the ZnPc/C60 interface and the subsequent electron injection into graphene creates holes in the 6 ACS Paragon Plus Environment

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topmost ZnPc layer. This induces electron doping in graphene via capacitive coupling. In our setup, the graphene channel is connected, in series, to an external, fixed resistor R0 and a constant voltage power supply Vs (Figure 1c). The voltage V(t) across R0 is measured by an oscilloscope. The resistance of the graphene channel RG(t) can then be calculated from the measured voltage V(t) by: ܴீ ሺ‫ݐ‬ሻ = ܴ଴ ቀ

௏ೞ

௏ሺ௧ሻ

− 1ቁ.

(1)

The diffential change in the graphene resistance, which is defined as: ୼ோಸ ሺ௧ሻ

ோಸ ሺ௧ୀ଴ሻ

=

ோಸ ሺ௧ሻିோಸ ሺ௧ୀ଴ሻ ோಸ ሺ௧ୀ଴ሻ

,

(2)

provides a quantiative measure of the amount of holes trapped within the ZnPc layer. For a small photo-induced doping, ∆RG is roughly proportional to the amount of doping. This is because the slope of the resistance versus doping concentration curve changes slowly with the doping concentration except at energies close to the Dirac point.33,

44

In most practical cases and

including ours, the graphene has intrinsic doping and the Ef is not located at the Dirac point. After the CS and prior to the electron-hole recombination (~1 ns < t < ~100 ns), the number of holes trapped in ZnPc is equal to the number of holes generated from the CS process. Figure 2a shows the result from a ZnPc (15 nm)/C60 (2 nm)/graphene sample, and results from control experiments done with ZnPc (15 nm)/graphene, C60 (8 nm)/graphene and grapheneonly samples. The transient change in the graphene’s resistance (∆RG/RG) observed in the ZnPc/C60 sample is significantly larger than the signal observed in other samples. The signal rise is attributed to CS at the ZnPc/C60 interface and the subsequent electron injection into graphene. The holes in ZnPc are essentially trapped because the band offset at the ZnPc/C60 interface (Fig. 1b) prohibits hole transfer to C60. As a result, the signal has an extremely long lifetime (10 – 100 µs). The decay of the signal indicates that these trapped holes eventually recombine with electrons in graphene. There would be multiple pathways for this slow recombination. For



example, as trapped holes diffuse along the ZnPc-C60 interface, it would encounter defect sites or grain boundaries at which hole transfer to the C60 layer and graphene can occur via gap states.

Figure 2: (a) The temporal change of ∆RG/RG of the bilayer ZnPc/C60 sample as compared to the monolayer samples. “Gr” in the legend represents graphene. (b) The voltage change ∆V measured by the oscilloscope for different supplied voltage Vs. The sample used is 15 nm ZnPc/2 nm C60/graphene. (c) The ∆RG/RG calculated with the ∆V shown in (b). As expected, the result is independent of Vs. (d) The normalized ∆RG/RG for ZnPc/C60 samples with different C60 thicknesses. The de-trapping of holes from the ZnPc layer is slower with a thicker C60 layer. (e) The normalized ∆RG/RG for different ZnPc thicknesses. The inset shows the signal amplitude near time zero as a function of the ZnPc thickness. A much weaker signal is observed in the ZnPc(15 nm)/graphene sample (blue curve in Figure 2a). When ZnPc is in contact with graphene, both electron and hole can transfer to graphene. The weak signal can be explained by an imbalance between electron and hole injection, which results in a small portion of holes trapped within the ZnPc layer. This imbalance in charge injection can be attributed to the interfacial band-bending and is often observed in semiconductor-GFET light sensors.44,

49-50

However, this effect is relative weak and is not

presence in our bilayer samples because the C60 layer will block the direct electron and hole 8 ACS Paragon Plus Environment

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injection from ZnPc to graphene. Finally, as expected, no signal is observed in the C60/graphene (the pump photon energy is below the bandgap of C60) and the graphene only samples. To further show that the signal is originated from a change in the graphene’s resistance instead of a photovoltage induced by the photoexcitation, we study how the signal varies with the voltage of the power supply Vs (Figure 1c). If the signal is originated from a change in the graphene resistance, the measured voltage change ∆V(t) should increase roughly linearly with Vs because the potential drop across any individual resistor in the circuit increases linearly with Vs. On the other hand, if the observed signal is a photovoltage induced by the light illumination, ∆V(t) should be independent of Vs. As shown in Figure 2b, the raw signal ∆V(t) increases linearly with Vs. Indeed, we do not expect the GFET device to produce a photovoltage because the two electrical contacts on the GFET are symmetric and identical. Using Eq. (1), the same ∆RG/RG is found regardless of the Vs used in the experiment. As shown in Figure 2c, the ∆RG/RG curves overlap with each other. This is expected because the number of separated carriers should not vary with the Vs used in the measurement. When the C60 thickness is increased, the recombination of ZnPc’s holes with electrons in graphene is expected to be slower because the C60 layer acts as a barrier for recombination. Hence, the signal decay should be slower for a larger C60 thickness. Figure 2d compares the normalized signal for the ZnPc (15nm)/C60(2nm) and the ZnPc (15 nm)/C60(10 nm) samples. As expected, the signal lifetime of the 10 nm sample is larger as compared to the 2 nm sample. For a fixed C60 thickness (2 nm), we also vary the ZnPc thickness. The results are shown in Figure 2e. All the curves show the same signal decay rate, which is consistent with a fixed C60 thickness. In contrast, the signal amplitude should increase with the ZnPc thickness because of the increase in the light absorption. The signal amplitude near time zero as a function of ZnPc thickness is shown in the inset. For small thicknesses, the signal increases linearly with the thickness until it saturates at ~ 5 – 10 nm, which corresponds to the exciton diffusion length in ZnPc. In our samples, the ZnPc grows on C60 with an edge-on orientation.17 Hence, the pi-stacking direction is 9 ACS Paragon Plus Environment


parallel to the surface and the exciton diffusion along the surface normal direction is expected to be slow. The observed diffusion length is similar to those found in polycrystalline organic thin films.51

Figure 3: (a) Top: The graphene can be doped independently by using a Si/SiO2 back gate. Bottom: A schematic shows the graphene conductivity as a function of Vg. Under light illumination, the curve shifts to the left because of the additional doping induced by the optical excitation. (b) The results of the 5 nm ZnPc/2 nm C60 sample. The thickness of the SiO2 is 285 nm. The voltage shift ∆Vg can be used to calculate the amount of doping induced by the light illumination. (c) The results of the 5 nm ZnPc/10 nm C60 sample. The amount of voltage shift is smaller, which indicates a smaller CS yield. In order to quantify the number and the polarity of injected carriers, we use a back-gate voltage (Vg) to modulate the graphene conductivity.44,

50

The graphene is transferred onto a

SiO2(285 nm)/Si substrate and Vg is applied via the highly-doped Si substrate (Figure 3a). Note that Vg is electrically insulated from the graphene, but the E-field created by the gate will induce doping in graphene by capacitive coupling. The graphene and the Si back gate act as two conducting plates in a parallel-plate capacitor.42 In a parallel-plate capacitor, the E-field is confined between the two conducting plates across the SiO2. Outside the conducting plates, the E-field created by the two plates cancels each other. Therefore, the E-field will not penetrate into the donor-acceptor bilayer located on top of the graphene. Indeed, the optical-induced CS from the top and the electrical gating from the bottom can change the graphene doping independently. This is similar to cases in which the graphene doping can be controlled independently by a top 10 ACS Paragon Plus Environment

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and a bottom gates.33, 43 The doping level of graphene (i.e. the position of Ef with respect to ED) can be varied by changing Vg. For instance, a positive Vg induces electron doping in the graphene, which shifts Ef to a higher energy. By scanning Vg while measuring the channel conductivity, a curve tracing the DOS of graphene can be obtained (Figure 3a, bottom). If the organic bilayers is excited optically, additional electron doping is induced in graphene (Figure 1a). This essentially shifts the curve in Figure 3a to the left. Similar optically-induced shift is commonly observed in GFET photodetectors.44, 49-50 The direction and magnitude of this voltage shift can be used to quantify the sign and the amount of separated carriers injected into graphene. Figure 3b shows the result obtained from a ZnPc (5 nm)/C60(2 nm) sample. In this plot, the inverse of the resistance (proportional to the conductivity) of the graphene channel is shown as a function of Vg. The resistance of the channel prior to the laser irradiation (t < 0 ns, black curve) and right after the laser irradiation (red curve) are shown. For the red curve, the resistance at the early time (t < 100 ns), when the ∆RG/RG is at its maximum, is used. Because our device is relative large (1 mm × 1 mm), the probability for dielectric breakdown in the SiO2 or current leakage from the gate to the channel is largely increased52 compared to typical µm-sized devices. Hence, the range of Vg that can be used in our device is limited and we are only able to capture a portion of the full Dirac curve. Moreover, it is common that these devices can have intrinsic doping and Ef is not located at ED.35, 44, 50 In our case, the Dirac point is located at Vg > 0, which is outside our measurement range. Nevertheless, the shift of the curve to the left, which corresponds to electron doping in the graphene channel, is clearly observed upon photoexcitation. To determine the amount of the optically-induced doping, i.e. the amount of separated carriers, quantitatively, we note that at Vg = 0 (no back gate), optical excitation induces a conductivity change by the amount indicated by the red arrow (path 1) in Figure 3b. An equal amount of conductivity change can be induced in the same device by applying a back gate voltage ∆Vg in the absence of the optical excitation. This is shown by the black arrow (path 2) in 11 ACS Paragon Plus Environment


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Figure 3b. The amount of carriers induced by the electrical gating, ng, can be determined simply by using the equation for a parallel plate capacitor:42 ݊௚ = Δܸ௚

఑ఌబ ஺ ଵ ௗ

௘

.

(3)

In Eq. (3), κ and d are the dielectric constant and the thickness of SiO2 respectively. ε0 is the vacuum permittivity, A is the area of the channel and e is the electron charge. As illustrated in Figure 3b, path 2 (electrical gating only) and path 1 (optical excitation only) change the graphene’s conductivity by the same amount. Hence, the number of doped carriers induced by optical excitation nop (at Vg = 0) is equal to ng (laser off; Vg = ∆Vg), and nop can be calculated quantitatively using Eq. (3) without the use of any fitting parameter. This method allows us to determine the absolute number of separated carriers without knowing the defect density and the initial doping concentration of graphene a priori. As a result, the CS yield obtained from different samples can be compared quantitatively despite different graphene samples can have slightly different defect densities and initial doping concentrations. For the data shown in Figure 3b, using Eq. (3), the total amount of separated carriers is determined to be 5.5×1011 cm-2. To determine the number of photons absorbed by the organic bilayer, we measure the optical absorption of serval ZnPc films with different ZnPc thicknesses. Then, we model the data by solving the Fresnel equation using a multi-layer optical model (see the supporting information) that accounts for the reflection and transmission at all interfaces, and the interference effect.53-54 For the ZnPc (5 nm)/C60 (2 nm)/graphene/SiO2 (285 nm)/Si sample, we find that the number of photons absorbed by ZnPc is 3.6×1012 cm-2. Hence, the internal quantum efficiency (IQE) for carrier generation from the donor-acceptor interface is ~15 %. This value is reasonable because unlike usual PV cells, no E-field is presence within the organic layers to drive the CS and to bias the electron diffusion in the C60 layer.55 Indeed, our experimental condition is similar to the open circuit condition in a PV cell. We note that similar


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experimental conditions are commonly found in ultrafast spectroscopy experiments done with bilayer samples. This method will allow us to compare the CS yield for different material interfaces quantitatively, which will be useful for understanding the CS mechanism. To illustrate this, the same experiment is performed on a ZnPc (5 nm)/C60(10 nm) sample. The result is shown in Figure 3c. Compared to Figure 3b, a smaller voltage shift is observed. The IQE can be calculated using the same procedure, which is ~ 4.7 %. The result indicates that the ZnPc (5nm)/C60 (2nm) sample has a larger CS yield compared to the ZnPc (5 nm)/C60 (10 nm) sample. To explain this observation, we note that our previous TR-TPPE experiment has shown that the delocalization size of hot CT excitons at this interface is ~ 4 nm.17 A C60 thickness that is smaller than the delocalization size increases the CS yield because a hot CT exciton can transfer its electron directly and coherently to graphene before it relaxes to bound CT excitons. This eliminates the need for overcoming the Coulombic barrier encountered in the separation of bound CT excitons.56 In contrast, for the ZnPc (5 nm)/C60 (10 nm) sample, delocalized CT excitons can relax to bound CT excitons within ~ 2 ps after photoexcitation.17 The dissociation of bound CT excitons requires thermal activation, which can significantly lower the CS yield.

Figure 4: (a) The CS dynamics for two ZnPc(1.5 nm)/C60/graphene samples with different C60 thicknesses. The dashed line shows that minimum rise time that can be measured by the oscilloscope. (b) The signal rise time as a function of the C60 thickness.



We can study the dynamics of the CS by measuring the rise time of the signal. The temporal resolution of our circuit is optimized (see supporting information) so that it approaches the temporal resolution of our oscilloscope. With the optimized setup, we can compare the signal rise times for different samples. Figure 4 compares the rise time of two samples: 1.5 nm ZnPc/2 nm C60 and 1.5 nm ZnPc/10 nm C60. In these samples, a monolayer-thick ZnPc layer with an edge-on orientation is used so that the rise time is not slowed down by the exciton diffusion in ZnPc. When the C60 thickness (2 nm) is smaller than the delocalization size of hot, delocalized CT excitons,17 a rise time of ~ 2 ns is observed, which is close to the minimum measureable rise time of our oscilloscope (~1.75 ns - dashed line). Hence, the actual CS time can even be smaller. The fast rise time can be attributed to the electron transfer from hot, delocalized CT states at the interface to graphene (electron delocalization size in C60 is ~ 4 nm)17, which results in the ultrafast CS. Note that the fast rise time cannot be explained by a direct contact between the ZnPc layer and the graphene. If there is direct contacts between ZnPc and graphene due to a discontinuous C60 layer, the signal should be largely diminished because both electron and hole can transfer from ZnPc into graphene (Figure 2a). However, this is not the case here. Indeed, the sample with a 2 nm C60 layer actually has a stronger signal than the sample with a 10 nm C60 layer (Figure 3). When the C60 thickness increases to 10 nm, the rise time increases to ~5 ns. For a C60 thickness that is larger than the size of CT excitons, hot CT excitons relax to bound CT excitons within a few ps after photoexcitation.17 Hence, the increase in the signal rise time can be attributed to a slower CS process. Note that the slower rise time can also be contributed by the electron diffusion across the thicker C60 layer. The two contributions can be separated by measuring the rise time as a function of the C60 thickness. The result is shown in Figure 4b. A rather abrupt increase in the rise time can be observed at 4 nm. This thickness agrees well with the delocalized size of hot CT excitons reported in our previous work.17 Hence, this abrupt increase in the rise time can be attributed to the switching from the direct dissociation of 14 ACS Paragon Plus Environment

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delocalized and hot CT excitons to the thermal dissociation of localized and bound CT excitons. The latter pathway is expected to have a longer CS time. At larger C60 thicknesses, a gradual increase in the rise time as a function of thickness is observed, which can be attributed to the electron diffusion across the C60 layer. To determine the diffusivity, we fit the rise times to a convolution of the instrumental resolution τres, the thermal dissociation time of bound CT excitons τth and the carrier diffusion time across the C60 layer τdiff. The total rise time τt is expressed as: ߬௧ ଶ = ߬௥௘௦ ଶ + ߬௧௛ ଶ + ߬ௗ௜௙௙ ଶ

(4)

Note that only theτdiff term depends on the C60 thickness l. This term can be written as τௗ௜௙௙ =

௟మ ஽

, where D is the diffusion constant. By using τres = 1.75 ns, we find that τth = 3.4 ns and D = 8 × 10-4 cm2 s-1. The fit is shown as the dashed line in Figure 4b. The electron mobility can be determined by the Einstein relationship, which is 0.03 cm2 s-1 V-1. This is smaller than the electron mobility found in C60 single crystals (~ 1 cm2 s-1 V-1),57 but is reasonable for a polycrystalline thin film. We like to comment on the observed dissociation time τth of bound CT excitons. First, our previous time-resolved photoemission (TR-TPPE) study17 shows that a manifold of bound CT states with binding energies in the range of 0.2 - 0.6 eV coexist at the interface. The coexistence of CT states with different binding energies can be originated from the intermolecular coupling in the C60 crystal. The thermal dissociation of these CT states is likely to proceed via configurations with lower binding energies. Using a typical attempt frequency of 1013 s-1 (Ref. [58]) and an energy barrier of 0.2 eV, an Arrhenius rate on the order of 109 s-1 can be obtained. This is consistent with the 3.4 ns dissociation time observed in the experiment. The effective activation barrier would also be lowered by other factors such as entropy.59 Second, our previous TR-TPPE study shows that the population of the bound CT state decays with a 400 ps time constant. This short CT exciton lifetime seems to contradict with the 3.4 ns CS time 15 ACS Paragon Plus Environment


observed in this work. However, we note that the TR-TPPE study only captures dynamics up to 300 ps. The bound CT state would have a multi-exponential decay dynamics that is not fully captured by the TR-TPPE experiment. It has shown that C60 has a spin relaxation time of 650 ps.36 CT excitons that survive the first few hundred ps can convert into spin triplet CT states which can have a longer recombination time. The spin flipping process would not be captured by the TR-TPPE experiment because the energy of the CT state is not sensitive to the spin.60 A time-resolved microwave study on ZnPc-C60, which resolves the ns charge dynamics, has shown that the electrons and holes have a recombination time of few ns.36 A few ns recombination time can allow a portion of CT excitons to separate prior to recombination and it is consistent with the rise time observed in Figure 4b.

Figure 5: The carrier generation dynamics from a CH3NH3PbI3-PCBM BHJ. The thickness of the layer is ~ 400 nm. Finally, in order to demonstrate that our method can be used to measure the charge generation dynamics in typical BHJ films, we have performed measurements on CH3NH3PbI3 films mixed with a small concentration of phenyl-C61-butyric acid methyl ester (PCBM). This is a BHJ structure used in some high performance organometallic halide perovskite solar cells.61 The details on sample preparation and characterization are reported in the method section and in Ref. [39]. In this BHJ structure, the PCBM within the CH3NH3PbI3 acts as the electron acceptor.


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After the CS, the holes can transfer from CH3NH3PbI3 to graphene. Figure 5 shows the result obtained from one of these BHJ samples. The signal rise time is ~ 160 ns. Because the BHJ film has a total thickness of ~ 400 nm, the relative long signal rise time can be attributed to the hole transport within the CH3NH3PbI3 layer. A negative signal is observed because holes (in the case of CH3NH3PbI3) instead of electrons (in the case of ZnPc/C60) are injected into graphene. For future works, the BHJ layer would be sandwiched by an electron and a hole transport layers, which mimics the structure of a realistic solar cell with graphene as one of the electrodes. This will allow us to measure the charge generation dynamics in active layers of typical solar cells. In summary, we have presented a high-throughput technique that can resolve the CS dynamics and yield at donor-acceptor interfaces. The relative simple experimental setup, fast data acquisition time and easy adaption to different material interfaces make it an ideal tool for high-throughput combinatorial measurements. This method can be useful for studying complex material interfaces in which the electronic properties is sensitive to the interfacial structure. In these materials, a universal model that can predict the behavior of interfaces with different structures may not exist. With many possible interfacial structures, a high-throughput method is necessary to determine the optimum structure for CS.

METHOD Single layer graphene on SiO2/Si was bought from Graphene Supermarket. The graphene was grown by the chemical vapor deposition (CVD) method. For graphene on glass, the graphene was transferred from graphene/Cu (Graphene Supermarket) to the glass substrate using the standard poly(methyl methacrylate) (PMMA) method.62 The graphene channel was patterned on the substrate using Ar ion sputtering through a shadow mask. Two Cu-electrodes (~ 30 nm thick) were deposited on the 2 sides of the channel using sputter deposition. The GFET device was then outgassed inside an UHV chamber with base pressure ~10-9 Torr at ~ 400 °C for ~ 10 hours prior to the deposition of organic films. Our previous photoemission work has shown that this procedure can produce a clean graphene surface for effective CT.18 The C60 and ZnPc films were deposited using thermal evaporation. The deposition rate is kept at 1 Å/min and was determined using a quartz crystal microbalance.



The sample was then loaded into a high vacuum cryostat (pressure ~ 10-6 Torr). The electrodes of the device were connected to the circuit outside the vacuum through BNC feedthroughs. The sample was excited by femtosecond laser pulses (Light Conversion Pharos 10 W, Orpheus-N2H). The wavelength, pulse width and repetition rate used were 700 nm, 20fs and 1 kHz respectively. The pulse energy was ~ 340 nJ and the beam had a full-width half maxima size of ~ 1.2 mm. The laser beam roughly covered the whole channel. The voltage drop across the resistor was measured by a 200 MHz oscilloscope (Keysight DSOX2024A) using a 300 MHz passive probe. A portion of the laser beam was split from the main beam using a quartz window, which was directed to a photodiode used for triggering the oscilloscope. The final signal was obtained by averaging the traces obtained from 128 – 1024 laser pulses. The CH3NH3PbI3-PCBM BHJ is fabricated by spin coating. The details can be found in Ref. [39]. The CH3NH3PbI3 precursor solution was prepared by mixing lead iodide (Alfa Aesar, 99.9985%) and methylammonium iodide (Luminescence technology, 99.5%) in a stoichiometric ratio in DMF (Sigma-Aldrich, 99.8%) with a concentration of 0.75 M. The solution was stirred at 70°C overnight before spin-coating. Then, PCBM (Luminescence technology, 99.5%), with a weight ratio of 0.1 % was mixed into the perovskite solution. PCBM was first dissolved into chlorobenzene solution. The PCBM to perovskite ratio was controlled by mixing the required volume of the PCBM chlorobenzene solution and the CH3NH3PbI3 precursor solution. The perovskite-PCBM film was deposited on a pre-heated graphene substrate (at 80 °C) by spincoating at 500 rpm for 30 s and then at 3000 rpm for 60 s in a nitrogen glovebox. During the spin-coating, the anti-solvent (isopropyl alcohol) was dropped onto the film. Then, the perovskite film was annealed at 70°C for 20 min, and 100°C for 10 min to remove the residual solvent.

ASSOCIATED CONTENT Supporting Information Modeling of the optical absorption, Additional data on the temporal resolution of the setup. AUTHOR INFORMATION Corresponding Author * [email protected] (W. –L. C.) Notes The authors declare no competing financial interest. ACKNOWLEDGEMENT We acknowledge the support by US National Science Foundation, grant DMR-1351716. This investigation was also supported by the University of Kansas New Faculty General Research Fund allocation #2302027.


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