Lead halide perovskites have emerged as promising absorber materials over the last decade to increase the efficiency of photovoltaics beyond its current limits. However, to further optimize the performance of perovskites more detailed studies need to be performed, which allow for the correlation of film morphology and local electronic properties at the nanoscale. Here, we present a scanning tunneling microscopy (STM) approach to probe the effect of an applied electric field of a methylammonium formamidinium lead triiodide perovskite thin film on the film response by current–voltage spectroscopy, current imaging tunneling spectroscopy, differential conductance mapping, and x-ray absorption spectroscopy by means of synchrotron x-ray STM. We find a strong correlation between the measurement conditions and the obtained current–voltage characteristics when imaging under opposite bias polarities. In particular, we find similarities to already observed poling effects for lead halide perovskites, which result in either a positively or negatively charged interface due to ion and vacancy migration. Our results provide insight into the influence of measurement conditions such as bias polarity on the performance assessment of perovskite thin films by STM.

Lead halide perovskites have emerged over the last decade as a promising absorber material for applications in photovoltaics (PVs) or light-emitting diodes where bandgap tunability, long carrier diffusion lengths and minority carrier lifetimes, or high absorption coefficients are prerequisites for a successful incorporation into device structures.1–6 However, to further optimize the long-term performance of perovskite materials by assessing inhomogeneities due to film processing conditions and film (in)stabilities under realistic operating conditions, more detailed studies need to be performed, which allow for the correlation of film morphology and local electronic properties at the nanoscale. Scanning probe techniques provide the required resolution and performance-related properties of perovskite materials can be obtained simultaneously. In particular, Kelvin probe force microscopy (KPFM) and conductive atomic force microscopy (c-AFM) have been successfully employed to study surface phenomenon such as work function variations or conductance differences across the surface which could be ascribed to differences in film composition and grain facets or inhomogeneous current collection and photovoltages within grains and at grain boundaries.7–13 

On the other hand, scanning tunneling microscopy (STM) can achieve high resolutions up to the sub-Ångstrom level enabling real-space visualization of surface features such as defects or extrinsic and intrinsic surface states.14 Several STM-related studies on perovskite thin films and single crystals have been reported in the literature revealing insight into the lattice structure on the atomic scale.15–21 The coexistence of a zigzag pattern and dimer rows were imaged for the first time on the atomic scale for an in situ cleaved methylammonium lead bromide (MAPbBr3) single crystal.15 It was shown that the patterns are formed by the topmost halide layers and that it originated due to the different orientations of the MA+ cations. Similar patterns were found for methylammonium lead iodide (MAPbI3) thin films supported on Au(111), indicating that this is an inherent property of the material surface.16 Additionally, STM studies revealed insight into the variations in the local density of states (LDOS) of a MAPbI3 thin film, which were attributed to different grains and grain facets.22 

However, despite these recent advances, the interpretation of STM images and scanning tunneling spectroscopy (STS) spectra is non-trivial because STM relies on the tunneling process, which always involves a bias voltage either applied to the tip or sample, hence strong electric fields emerge at the tip–sample junction on the order of MV/m or even GV/m.23 This makes it particularly challenging for investigations of these semiconductor perovskite films due to the ionic bonding character of the lead halide octahedral sub-lattice which result in anharmonic, mechanically soft, and highly dynamic crystal structures.24–26 

In this contribution, we prepare a mixed-cation lead halide perovskite thin film based on methylammonium formamidinium lead triiodide (MA0.83FA0.17PbI3) on ITO-coated boro-aluminosilicate. The perovskite thin film is imaged in air under ambient conditions by STM to gain insight into the effect of the applied bias voltage by current–voltage (IV) spectroscopy, current imaging tunneling spectroscopy (CITS), and differential tunneling conductance mapping (dI/dV). We find a strong correlation between the measurement conditions and the obtained IV and conductance spectra depending on the strength and polarity of the applied electric field. Our results suggest that mobile species, particularly iodide ions and vacancies, are either attracted or repelled from the surface depending on the polarity of the electric field. To support this hypothesis, we employ synchrotron x-ray STM (SX-STM) to probe the iodine M4,5 edge under different electric field polarities. We see a change in the rising edge and main edge feature which is indicative of an electronic structural change and a resulting change in the local environment of iodine. These combined results show the importance of measurement conditions on the performance assessment of perovskite thin films by STM.

Lead halide perovskite thin films were prepared as previously described.27–29 Briefly, the perovskite film was spin-coated on ITO-coated glass supported on boro-aluminosilicate glass (8–10 ohm/sq). The perovskite precursor solution was prepared from a 1.2 M PbI2 (TCI) stock solution, 1.2 M MAI (Dyenamo), and 1.2 M FAI (Dyenamo). The PbI2 stock solution was mixed with MAI or FAI in a 1.09:1 ratio to achieve an overstoichiometric ratio.30 PbI2 was dissolved in anhydrous DMF:DMSO (9:1 v:v). The perovskite layer was then spin-coated in a two-step program: 1000 rpm for 10 s and 5000 rpm for 30 s. Chlorobenzene was used as antisolvent during the second spin-coating process. The film was then annealed at 100 °C for 10 min under a nitrogen atmosphere.

An OceanOptics spectrometer (HR2000 + ES) under continuous 405 nm excitation (PicoQuant, LDH-D-C-405) was used to measure steady-state photoluminescence spectra. Absorption spectra were measured using a UV–vis spectrometer (Shimadzu UV-2450).

STM measurements were performed with an RHK STM in a constant current mode in air under ambient conditions. The STM tips were prepared by mechanically cutting a Pt/Ir wire (80:20%, Goodfellow). All STM images are taken in the dark without light exposure. Current–voltage and dI/dV curves were recorded with a sampling time of 40 ms and a sweep rate of 250 mV/s. dI/dV maps were acquired in parallel to the topography images with an oscillation amplitude of 40 mV at 1.23 kHz. All STM images were processed using the open source software Gwyddion.31 

SX-STM measurements were carried out in ultrahigh vacuum (UHV) at room temperature at the XTIP beamline (sector 4-ID-E) of the Advanced Photon Source at Argonne National Laboratory.32 Prior to the XAS measurement, the sample was held at the respective bias for 10 min.

In the past, STM studies have been used to study Fermi-level pinning and reveal atomic insight into lead halide perovskite structures.15,16,18,19,22,33 In the following, we seek to tackle the following questions: (i) Is there a difference in the obtained STM images based on the bias polarity (positive or negative), and the intrinsic surface states that can be imaged? (ii) How does a change in the tunneling direction influence the STM measurements, particularly STS measurements and differential conductance maps? (iii) How does the local differential conductance evolve over time? And (iv) how does the local environment of the halide species changes under an applied electric field?

To start to answer these questions, we will first give more insight into the tunneling process between the sample and the tip.34 By applying a bias voltage to either the tip or the sample, the Fermi energies are offset, and a tunneling current results due to a potential difference. In the case of terminated semiconductor surfaces, band bending occurs at the interface to account for charge neutrality even without applying a bias voltage. Here, charge carriers accumulate at the interface and induce an electric field also known as space charge region, which screens the applied bias voltage.

To understand the influence of the applied bias shifting the sample Fermi level with respect to the Fermi level of the STM tip, we start our investigation by freshly preparing a MA0.83FA0.17PbI3 perovskite film by spin-coating on ITO-coated glass supported on boro-aluminosilicate. Figure 1(a) shows the STM topography image measured in air where the single grains are clearly visible. We calculate an average root mean square roughness value of 5 nm, which indicates a smooth surface.

FIG. 1.

Characterization of the MA0.83FA0.17PbI3 film deposited onto ITO-coated boro-aluminosilicate. (a) Constant current STM topography image taken in air, scanning conditions: Vt = 0.8 V and It = 100 pA. Averaged (b) IV and (c) normalized dI/dV spectra for an alternating sweep direction at a sweep rate of ∼250 mV/s for ∼300 spectra taken at an area of 300 × 300 nm2. (d) PL and absorbance spectra. The PL spectra was taken under 405 nm excitation showing the peak maximum at 1.6 ± 0.05 eV.

FIG. 1.

Characterization of the MA0.83FA0.17PbI3 film deposited onto ITO-coated boro-aluminosilicate. (a) Constant current STM topography image taken in air, scanning conditions: Vt = 0.8 V and It = 100 pA. Averaged (b) IV and (c) normalized dI/dV spectra for an alternating sweep direction at a sweep rate of ∼250 mV/s for ∼300 spectra taken at an area of 300 × 300 nm2. (d) PL and absorbance spectra. The PL spectra was taken under 405 nm excitation showing the peak maximum at 1.6 ± 0.05 eV.

Close modal

To gain more insight into the LDOS at the perovskite surface, we perform current imaging tunneling spectroscopy (CITS). A 16 × 16 pixel grid was overlaid on the topography image (300 × 300 nm2), and IV and dI/dV curves were measured at every grid pixel. Here, at every grid pixel, an alternating bias sweep direction between −3 V and +3 V was used with a sweep rate of ∼250 mV/s to measure an IV curve in the forward (+3 V to −3 V) and reverse (−3 V to +3 V) scan direction. For clarification, only the region between −1.5 V and 1.5 V is shown in Figs. 1(b) and 1(c) due to a tunneling current saturation during the spectroscopy at higher biases. To obtain dI/dV spectra, a small bias voltage modulation at 1.23 kHz was used while sweeping the bias voltage. The averaged and normalized IV and (dI/dV)/(I/V¯) spectra for the perovskite film are shown in Figs. 1(b) and 1(c), respectively (∼300 spectra, excluding spectra with an unstable tip–sample distance). The obtained averaged dI/dV spectra are normalized by dividing the differentiated tunneling conductance by the total conductance to account for the contribution of the transmission function and the divergence at the band states onsets.35,36 A bandgap of Eg = 1.2 eV is obtained for the perovskite film with an asymmetry around zero for the conduction band (CB) and valence band (VB) onsets. The Fermi-level position is defined at zero voltage, thus the perovskite surface is n-type-like terminated, which is in agreement with other STM reports exploring similar perovskite compositions.22 We observe a peak in the CB at approximately 1 V, which has been attributed to the projected density of states of Pb-6p orbitals.37–39 Another interesting feature is related to the sharp CB onset, while there is only a shallow slope at the onset of the VB. A similar behavior of this shallow onset at the VB has been seen experimentally and has been calculated theoretically in MAPbI3 and MAPbBr3 films where the effect was attributed to the dispersion at the band edge mainly due to the strong coupling between I 5p and Pb 6s antibonding orbitals.38,40 We also note small spikes within the bandgap related to electronic trap states due to possible deep defect states, vacancies, dislocations, or grain boundaries.21 

However, we observe an unexpected discrepancy between the measured bandgap obtained by STS and optical methods such as absorption and steady-state photoluminescence (PL) as seen in Fig. 1(d), whereas an apparent bandgap of 1.2 eV is obtained by STS measurements, a bandgap of ∼1.6 eV with a full width at half maximum (FWHM) of 0.05 eV is found based on our PL experiments.

To unravel the underlying cause of this effect, first, we study the influence of the bias polarity on STS experiments. It is known that perovskite samples are susceptible to changes in the applied voltage especially when the sweep direction is alternated for perovskite solar cells.41–44 The root cause for the difference is believed to originate from ion migration within the perovskite sample, which results in changes in the internal electric field causing hysteresis.45,46 Additionally, electric-field-induced poling effects have been observed in perovskite-based devices.47,48 Depending on the poling direction, a field-switchable photovoltaic effect has been observed, which has been attributed to ion drift in the perovskite layer. These observations led to the consensus that perovskite films cannot be regarded as static systems rather the perovskite lattice has to be considered as a soft and dynamic crystal structure due to the ionic binding character of the perovskite lattice units. Figure 2(a) shows representative IV curves between −2 V and 2 V within a single grain of a fresh perovskite film measured in the dark. The normalized dI/dV curves are shown in Fig. 2(b). The curves were measured between −3 V and 3 V; however, for clarification, only the region of interest between −2 V and 2 V is shown. The forward direction is shown in light blue while the reverse direction is shown in dark blue. To obtain a statistical distribution, we averaged 50 curves measured at a sweep rate of 250 mV/s for the forward and reverse directions.

FIG. 2.

(a) Current–voltage and (b) normalized dI/dV spectra. The spectra were taken within a single grain and averaged over 50 spectra at a sweep rate of ∼250 mV/s. To distinguish the influence of the sweep direction, the spectra were either recorded only in positive forward direction (light blue) or negative reverse direction (dark blue).

FIG. 2.

(a) Current–voltage and (b) normalized dI/dV spectra. The spectra were taken within a single grain and averaged over 50 spectra at a sweep rate of ∼250 mV/s. To distinguish the influence of the sweep direction, the spectra were either recorded only in positive forward direction (light blue) or negative reverse direction (dark blue).

Close modal

For the single sweep directions, we obtain a bandgap of 1.55 eV (reverse) and 1.44 eV (forward), which is closer to the optical bandgap (∼1.6 eV). In addition, the onsets for the CB and VB are shifted to lower voltages for the forward and the reverse directions, respectively. In particular, in regard to the Fermi-level pinning, the position of the CB is close to the Fermi level for the forward direction, while the position of the VB is close to the Fermi level in the reverse direction. We attribute this discrepancy in STS measurements compared to optical experiments to the difference in the measurement methods resulting in optical and electronic transitions, respectively: defect states are known to exist within the optical bandgap of perovskites and manifest in sub-bandgap absorption.49–51 However, these states commonly do not exhibit radiative recombination and, therefore, do not contribute to the PL. Rather these states are depopulated either non-radiatively or via “single-photon upconversion,” which refers to thermally activated PL from the bandgap despite sub-bandgap excitation into defect states. However, these defect states contribute to the electronic conductivity, and therefore, can artificially reduce the observed bandgap. Comparing the results obtained for one sweep direction for our IV and dI/dV curves in Fig. 2 with the averaged curves measured in Figs. 1(b) and 1(c), we can conclude that hysteresis has a big impact on the extracted electronic bandgap in STM experiments and should be considered. Here, the “average” bandgap shown in Fig. 1(b) is artificially reduced due to a shift in the VB and CB with respect to the pinned Fermi level.

To gain further insight into the underlying changes in the electronic structure based on the applied bias polarity, we measure differential conductance maps. Here, a small harmonic modulation with a constant frequency is applied to the bias voltage. A lock-in amplifier is used to extract the additional tunneling modulation signal; hence, the change in the differential conductance is proportional to the LDOS at a set energy E and can yield insight into the variation and distribution across the sample surface. Figure 3 shows the STM topography images of the perovskite film measured under a positive [Fig. 3(a)] and negative [Fig. 3(b)] applied voltage to the sample. The voltages of 0.8 V and −1 V were chosen to match the dI/dV spectra as shown in Fig. 2(b) where a high LDOS can be expected. No change in the perovskite film topography can be seen. The simultaneously obtained conductance maps are shown in Figs. 3(c) and 3(d) for the positive and negative bias, respectively. To better visualize the STM topography, the grain boundary outlines in the bottom right corner are superimposed on the differential conductance maps. A homogenous distribution of the LDOS can be seen in both images independent of the bias polarity. However, when a positive bias is applied [Fig. 3(c)], a higher LDOS can be found at grain boundaries compared to the interior of the grains. On the contrary, when the film is imaged under a negative bias, a weaker contrast is observed at grain boundaries [Fig. 3(d)]. By collecting both forward and reverse STM scans during the measurement, we can exclude that these observed features are due to tip artefacts or drift during the measurements.

FIG. 3.

(a) and (b) Representative STM topography images of a fresh perovskite film measured under a positive and negative applied voltage. (c) and (d) Conductance maps of the same area for the opposite tunneling directions with the topography underlaid for clarity. Maps measured at [(a) and (c)] Vt = 0.8 V and It = 100 pA and [(b) and (d)] Vt = −1 V and It = 100 pA. Schematic of ion drift under (e) positive and (f) negative applied voltages to the sample.

FIG. 3.

(a) and (b) Representative STM topography images of a fresh perovskite film measured under a positive and negative applied voltage. (c) and (d) Conductance maps of the same area for the opposite tunneling directions with the topography underlaid for clarity. Maps measured at [(a) and (c)] Vt = 0.8 V and It = 100 pA and [(b) and (d)] Vt = −1 V and It = 100 pA. Schematic of ion drift under (e) positive and (f) negative applied voltages to the sample.

Close modal

In general, a difference in the LDOS can be correlated to a variation in the local composition due to a change in work function, defect concentration, and surface termination or different grain facets. One of the hypotheses underlying these changes is related to electric-field-induced poling which we believe has also a major influence on the conductance maps measured here. As mentioned, negative or positive poling results in ion drift, which modifies the accessible intrinsic surface states in STM experiments due to a change in activation energies of vacancies and interstitials compared to ions.52,53 In particular, positive poling results in positively charged I vacancies VI at the interface and attracts positive ions such as Pb2+, MA+, and FA+ [Fig. 3(e)]. It has been suggested that the underlying mechanism for I migration is based on a vacancy-assisted hopping mechanism in which the migration originates from VI due to the lower activation energy compared to Pb2+ or MA+.52,54 This hints to that these iodine vacancies are preferably located at grain boundaries which is also supported by several studies in the literature where a higher ionic diffusivity was found at grain boundaries based on faster I ion movements.12,55–58 On the other hand, when a negative bias is applied, negatively charged vacancies such as VPb, VMA, and ions (I) accumulate at the surface while positive ions are repelled [Fig. 3(f)].12,47

To shed more light onto the observed changes in the LDOS under opposite bias voltage polarities, we employ SX-STM to probe the M-edge of iodine.59,60 In our SX-STM experiments, the sample is kept under an UHV environment at room temperature. Instead of standard chemically etched STM tips, we utilize specially fabricated coaxial tips, “smart tips,” made of tungsten wire.60 Throughout the experiments, the tip is placed ∼1 μm away from the sample surface and the synchrotron x-ray beam is focused onto the tip/sample junction illuminating an area of 10 × 10 μm2. We either apply a positive (+1 V) or negative (−1 V) voltage to the sample and collect the changes in the I M4,5 absorption edge under 0 V by sweeping the x-ray beam energy between 610 and 670 eV with a step size of 0.2 eV at a resolving power E/ΔE of about 4000.32 We collect the x-ray generated photocurrent at the sample, which corresponds to the total electron yield (TEY) using a lock-in amplifier. Figure 4 shows the obtained x-ray absorption (XAS) spectra for the iodine M4,5 edge obtained at an applied bias of +1 V [Fig. 4(a)] and −1 V [Fig. 4(b)]. We observe a change in the rising edge and main edge feature and the shape of the spectra under a positive and negative bias which supports the hypotheses that we indeed probe a different environment of iodine in our STM experiments.

FIG. 4.

XAS obtained by SX-STM collected at the M4,5-edge of iodine by SX-STM at an applied bias of (a) +1 V and (b) −1 V, showing the change in the local environment of the iodine species. The dotted lines are a guide to the eye to visualize the change in the spectra.

FIG. 4.

XAS obtained by SX-STM collected at the M4,5-edge of iodine by SX-STM at an applied bias of (a) +1 V and (b) −1 V, showing the change in the local environment of the iodine species. The dotted lines are a guide to the eye to visualize the change in the spectra.

Close modal

To understand how the differential conductance is influenced by the electric field over time, we perform a time series of consecutive differential conductance maps for the perovskite film at a positive [Fig. 5(a)] and negative [Fig. 5(b)] applied bias. We track the evolution of the change in the differential conductance over 32 h. We see a similar initial behavior as in Figs. 3(c) and 3(d) where the change in the differential conductance maps starts at grain boundaries or grain interiors, respectively. Over time, these changes grow into larger patches of the sample independent of the applied bias polarity resulting in an overall higher conductance compared to t = 0 and a more homogenous distribution within the single grains. Although the origin of the different bias polarities varies, we observe a homogenous distribution of the differential conductance after several hours t = 32 h.

FIG. 5.

Time series of consecutive differential conductance maps for a perovskite film imaging (a) unoccupied states and (b) occupied states of the sample. Maps measured at Vt = 0.8 V and It = 100 pA (a) and Vt = −1 V and It = 100 pA (b).

FIG. 5.

Time series of consecutive differential conductance maps for a perovskite film imaging (a) unoccupied states and (b) occupied states of the sample. Maps measured at Vt = 0.8 V and It = 100 pA (a) and Vt = −1 V and It = 100 pA (b).

Close modal

In general, the conductivity of a sample is defined as the ratio of the current and voltage: I/V. For our experiments, we obtain a higher conductivity when we apply a positive bias compared to a negative bias. Note, the same dynamic range is shown for both bias polarities in Figs. 5(a) and 5(b) with an applied bias of 0.8 V and −1 V, respectively. Two scenarios could be imagined explaining these observations: (i) Due to the different applied bias polarities, an additional external field is added to the already existing built-in potential of the perovskite sample based on the terminated surface. This effect could result in an increased or decreased overall field strength. (ii) The origin could be related to be an intrinsic phenomenon to STM measurements. When a positive bias is applied to the sample, elastic tunneling mainly occurs between tip states located near the Fermi energy and unoccupied states in the sample. In contrast, when a negative bias is applied to the sample, the tunneling current is dominated by states near the Fermi level of the sample; thus, only occupied states with the highest energy are imaged preferentially resulting in an overall lower tunneling current.61 We also note that since all measurements are performed in air under ambient conditions, the influence of adsorbate layers mostly based on water molecules or contaminants cannot be excluded to slightly change the obtained values. Hence, we only compare the results qualitatively rather than providing absolute values.

Our obtained results suggest that the bias polarity has a major impact on the obtained IV curves and conductance maps. In particular, a shift in the VB and CB to higher voltages is observed depending on the voltage sweep direction which hints that various local density of states are available for the tunneling process. By using conductance maps, we were able to study the change in conductance over time. An increase in conductance is observed for both bias polarities which might be explained by a reorganization of the crystal lattice due to the applied electric field. We note that a similar effect is observed under illumination in optical spectroscopy, in which perovskite films “photobrighten” over time due to a reduction in non-radiative decay channels. The influence of light on the perovskite performance can be probed by a combination of optical spectroscopy with STM, which provides a means to gain further insight onto the underlying mechanisms.23,62–64 Additionally, SX-STM can be used to map the perovskite surface under a constant x-ray beam energy to give information on the local environment of the halide species under various applied electric fields. These experimental approaches will be of interest for future studies.

In conclusion, we have imaged a mixed-cation lead halide perovskite thin film based on MA0.83FA0.17PbI3 on ITO-coated boro-aluminosilicate by current–voltage spectroscopy, current imaging tunneling spectroscopy, differential tunneling conductance mapping, and x-ray absorption spectroscopy. We find a strong correlation between the applied bias polarity and the performance of the perovskite film. In particular, we find a shift in the VB and CB onsets of the perovskite sample depending on the sweep direction of the bias, resulting in an artificially reduced extracted electrical bandgap due to hysteresis. While the origin of an enhanced conductivity can be traced back to grain interiors or grain boundaries for opposite tunneling directions, an overall improvement is observed when the surface is biased for several hours similar to the photobrightening effect seen in PL experiments. By performing SX-STM experiments, we could further show that the local environment of iodine is changed when we apply a positive or negative electric field. These results demonstrate the importance of measurement conditions such as bias directions on the performance assessment of perovskite thin films by STM.

S.W. and L.N. gratefully acknowledge Florida State University startup funds. This work was performed in part at the Advanced Photon Source and the Center for Nanoscale Materials, U. S. Department of Energy Office of Science User Facilities, and supported by the U. S. Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357. The authors thank D. Rosenmann and Y. Liu at CNM for support with smart tip fabrication.

The authors declare no competing financial interests.

1.
D. P.
McMeekin
,
G.
Sadoughi
,
W.
Rehman
,
G. E.
Eperon
,
M.
Saliba
,
M. T.
Hörantner
,
A.
Haghighirad
,
N.
Sakai
,
L.
Korte
,
B.
Rech
,
M. B.
Johnston
,
L. M.
Herz
, and
H. J.
Snaith
,
Science
351
,
151
(
2016
).
2.
J.
Berry
,
T.
Buonassisi
,
D. A.
Egger
,
G.
Hodes
,
L.
Kronik
,
Y.-L.
Loo
,
I.
Lubomirsky
,
S. R.
Marder
,
Y.
Mastai
,
J. S.
Miller
,
D. B.
Mitzi
,
Y.
Paz
,
A. M.
Rappe
,
I.
Riess
,
B.
Rybtchinski
,
O.
Stafsudd
,
V.
Stevanovic
,
M. F.
Toney
,
D.
Zitoun
,
A.
Kahn
,
D.
Ginley
, and
D.
Cahen
,
Adv. Mater.
27
,
5102
(
2015
).
3.
S. D.
Stranks
,
G. E.
Eperon
,
G.
Grancini
,
C.
Menelaou
,
M. J. P.
Alcocer
,
T.
Leijtens
,
L. M.
Herz
,
A.
Petrozza
, and
H. J.
Snaith
,
Science
342
,
341
(
2013
).
4.
Y.
Dong
,
Y.
Fang
,
Y.
Shao
,
P.
Mulligan
,
J.
Qiu
,
L.
Cao
, and
J.
Huang
,
Science
347
,
967
(
2015
).
5.
G.
Xing
,
N.
Mathews
,
S.
Sun
,
S. S.
Lim
,
Y. M.
Lam
, and
M.
Grätzel
,
Science
342
,
344
(
2013
).
6.
W.
Tress
,
N.
Marinova
,
O.
Inganäs
,
M. K.
Nazeeruddin
,
S. M.
Zakeeruddin
, and
M.
Graetzel
,
Adv. Energy Mater.
5
,
1400812
(
2014
).
7.
J. L.
Garrett
,
E. M.
Tennyson
,
M.
Hu
,
J.
Huang
,
J. N.
Munday
, and
M. S.
Leite
,
Nano Lett.
17
,
2554
(
2017
).
8.
S.
Wieghold
,
J.-P.
Correa-Baena
,
L.
Nienhaus
,
S.
Sun
,
K. E.
Shulenberger
,
Z.
Liu
,
J. S.
Tresback
,
S. S.
Shin
,
M. G.
Bawendi
, and
T.
Buonassisi
,
ACS Appl. Energy Mater.
1
,
6801
(
2018
).
9.
S.
Wieghold
,
J.
Tresback
,
J.-P.
Correa-Baena
,
N. T. P.
Hartono
,
S.
Sun
,
Z.
Liu
,
M.
Layurova
,
Z. A.
VanOrman
,
A. S.
Bieber
,
J.
Thapa
,
B.
Lai
,
Z.
Cai
,
L.
Nienhaus
, and
T.
Buonassisi
,
Chem. Mater.
31
,
3712
(
2019
).
10.
S. Y.
Leblebici
,
L.
Leppert
,
Y.
Li
,
S. E.
Reyes-Lillo
,
S.
Wickenburg
,
E.
Wong
,
J.
Lee
,
M.
Melli
,
D.
Ziegler
,
D. K.
Angell
,
F.
Ogletree
,
P.
Ashby
,
F. M.
Toma
,
J. B.
Neaton
,
I. D.
Sharp
, and
A.
Weber-Bargioni
,
Nat. Energy
1
,
16093
(
2016
).
11.
K.
Kutes
,
Y.
Zhou
,
J. L.
Bosse
,
J.
Steffes
,
N. P.
Padture
, and
B. D.
Huey
,
Nano Lett.
16
,
3434
(
2016
).
12.
J. S.
Yun
,
J.
Seidel
,
J.
Kim
,
A. M.
Soufiani
,
S.
Huang
,
J.
Lau
,
N. J.
Jeon
,
S. I.
Seok
,
M. A.
Green
, and
A.
Ho-Baillie
,
Adv. Energy Mater.
6
,
1600330
(
2016
).
13.
J. S.
Yun
,
A.
Ho-Baillie
,
S.
Huang
,
S. H.
Woo
,
Y.
Heo
,
J.
Seidel
,
F.
Huang
,
Y.-B.
Cheng
, and
M. A.
Green
,
J. Phys. Chem. Lett.
6
,
875
(
2015
).
14.
S.
Wieghold
and
L.
Nienhaus
,
Joule
4
,
524
(
2020
).
15.
R.
Ohmann
,
L. K.
Ono
,
H.-S.
Kim
,
H.
Lin
,
M. V.
Lee
,
Y.
Li
,
N.-G.
Park
, and
Y.
Qi
,
J. Am. Chem. Soc.
137
,
16049
(
2015
).
16.
L.
She
,
M.
Liu
, and
D.
Zhong
,
ACS Nano
10
,
1126
(
2016
).
17.
B.
Murali
,
S.
Dey
,
A. L.
Abdelhady
,
W.
Peng
,
E.
Alarousu
,
A. R.
Kirmani
,
N.
Cho
,
S. P.
Sarmah
,
M. R.
Parida
,
M. I.
Saidaminov
,
A. A.
Zhumekenov
,
J.
Sun
,
M. S.
Alias
,
E.
Yengel
,
B. S.
Ooi
,
A.
Amassian
,
O. M.
Bakr
, and
O. F.
Mohammed
,
ACS Energy Lett.
1
,
1119
(
2016
).
18.
Y.
Liu
,
K.
Palotas
,
X.
Yuan
,
T.
Hou
,
H.
Lin
,
Y.
Li
, and
S.-T.
Lee
,
ACS Nano
11
,
2060
(
2017
).
19.
J.
Hieulle
,
X.
Wang
,
C.
Stecker
,
D.-Y.
Son
,
L.
Qiu
,
R.
Ohmann
,
L. K.
Ono
,
A.
Mugarza
,
Y.
Yan
, and
Y.
Qi
,
J. Am. Chem. Soc.
141
,
3515
(
2019
).
20.
H.-C.
Hsu
,
B.-C.
Huang
,
S.-C.
Chin
,
C.-R.
Hsing
,
D.-L.
Nguyen
,
M.
Schnedler
,
R.
Sankar
,
R. E.
Dunin-Borkowski
,
C.-M.
Wei
,
C.-W.
Chen
,
P.
Ebert
, and
Y.-P.
Chiu
,
ACS Nano
13
,
4402
(
2019
).
21.
L. K.
Ono
and
Y.
Qi
,
J. Phys. Chem. Lett.
7
,
4764
(
2016
).
22.
T.
Gallet
,
D.
Grabowski
,
T.
Kirchartz
, and
A.
Redinger
,
Nanoscale
11
,
16828
(
2019
).
23.
L.
Nienhaus
,
S.
Wieghold
,
D.
Nguyen
,
J. W.
Lyding
,
G. E.
Scott
, and
M.
Gruebele
,
ACS Nano
9
,
10563
(
2015
).
24.
D. A.
Egger
,
A. M.
Rappe
, and
L.
Kronik
,
Acc. Chem. Res.
49
,
573
(
2016
).
25.
F.
Brivio
,
J. M.
Frost
,
J. M.
Skelton
,
A. J.
Jackson
,
O. J.
Weber
,
M. T.
Weller
,
A. R.
Goñi
,
A. M. A.
Leguy
,
P. R. F.
Barnes
, and
A.
Walsh
,
Phys. Rev. B
92
,
144308
(
2015
).
26.
A.
Marronnier
,
G.
Roma
,
S.
Boyer-Richard
,
L.
Pedesseau
,
J.-M.
Jancu
,
Y.
Bonnassieux
,
C.
Katan
,
C. C.
Stoumpos
,
M. G.
Kanatzidis
, and
J.
Even
,
ACS Nano
12
,
3477
(
2018
).
27.
S.
Wieghold
,
A. S.
Bieber
,
Z. A.
VanOrman
,
L.
Daley
,
M.
Leger
,
J.-P.
Correa-Baena
, and
L.
Nienhaus
,
Matter
1
,
705
(
2019
).
28.
S.
Wieghold
and
L.
Nienhaus
,
J. Phys. Chem. Lett.
11
,
601
(
2020
).
29.
S.
Wieghold
,
A. S.
Bieber
,
Z. A.
VanOrman
, and
L.
Nienhaus
,
J. Phys. Chem. Lett.
10
,
3806
(
2019
).
30.
J.-P.
Correa-Baena
,
Y.
Luo
,
T. M.
Brenner
,
J.
Snaider
,
S.
Sun
,
X.
Li
,
M. A.
Jensen
,
N. T. P.
Hartono
,
L.
Nienhaus
,
S.
Wieghold
,
J. R.
Poindexter
,
S.
Wang
,
Y. S.
Meng
,
T.
Wang
,
B.
Lai
,
M. V.
Holt
,
Z.
Cai
,
M. G.
Bawendi
,
L.
Huang
,
T.
Buonassisi
, and
D. P.
Fenning
,
Science
363
,
627
(
2019
).
31.
D.
Nečas
and
P.
Klapetek
,
Cent. Eur. J. Phys.
10
,
181
(
2012
).
32.
V.
Rose
,
N.
Shirato
,
M.
Bartlein
,
A.
Deriy
,
T.
Ajayi
,
D.
Rosenmann
,
S.-W.
Hla
,
M.
Fisher
, and
R.
Reininger
,
J. Synchrotron Radiat.
27
,
836
(
2020
).
33.
L.
She
,
M.
Liu
,
X.
Li
,
Z.
Cai
, and
D.
Zhong
,
Surf. Sci.
656
,
17
(
2017
).
34.
S.
Lounis
, arXiv:1404.0961 (
2014
).
35.
M.
Prietsch
,
A.
Samsavar
, and
R.
Ludeke
,
Phys. Rev. B
43
,
11850
(
1991
).
36.
J. A.
Stroscio
,
R. M.
Feenstra
, and
A. P.
Fein
,
Phys. Rev. Lett.
57
,
2579
(
1986
).
37.
J.
Haruyama
,
K.
Sodeyama
,
L.
Han
, and
Y.
Tateyama
,
J. Phys. Chem. Lett.
5
,
2903
(
2014
).
38.
J.
Endres
,
D. A.
Egger
,
M.
Kulbak
,
R. A.
Kerner
,
L.
Zhao
,
S. H.
Silver
,
G.
Hodes
,
B. P.
Rand
,
D.
Cahen
,
L.
Kronik
, and
A.
Kahn
,
J. Phys. Chem. Lett.
7
,
2722
(
2016
).
39.
W.
Li
,
J.
Liu
,
F.-Q.
Bai
,
H.-X.
Zhang
, and
O. V.
Prezhdo
,
ACS Energy Lett.
2
,
1270
(
2017
).
40.
H.
Kawai
,
G.
Giorgi
,
A.
Marini
, and
K.
Yamashita
,
Nano Lett.
15
,
3103
(
2015
).
41.
H.-S.
Kim
and
N.-G.
Park
,
J. Phys. Chem. Lett.
5
,
2927
(
2014
).
42.
S.-H.
Turren-Cruz
,
M.
Saliba
,
M. T.
Mayer
,
H.
Juárez-Santiesteban
,
X.
Mathew
,
L.
Nienhaus
,
W.
Tress
,
M. P.
Erodici
,
M.-J.
Sher
,
M. G.
Bawendi
,
M.
Grätzel
,
A.
Abate
,
A.
Hagfeldt
, and
J.-P.
Correa-Baena
,
Energy Environ. Sci.
11
,
78
(
2018
).
43.
J.
Shi
,
H.
Zhang
,
X.
Xu
,
D.
Li
,
Y.
Luo
, and
Q.
Meng
,
Small
12
,
5288
(
2016
).
44.
S. A. L.
Weber
,
I. M.
Hermes
,
S.-H.
Turren-Cruz
,
C.
Gort
,
V. W.
Bergmann
,
L.
Gilson
,
A.
Hagfeldt
,
M.
Graetzel
,
W.
Tress
, and
R.
Berger
,
Energy Environ. Sci.
11
,
2404
(
2018
).
45.
46.
R. A.
Belisle
,
W. H.
Nguyen
,
A. R.
Bowring
,
P.
Calado
,
X.
Li
,
S. J. C.
Irvine
,
M. D.
McGehee
,
P. R. F.
Barnes
, and
B. C.
O’Regan
,
Energy Environ. Sci.
10
,
192
(
2017
).
47.
Z.
Xiao
,
Y.
Yuan
,
Y.
Shao
,
Q.
Wang
,
Q.
Dong
,
C.
Bi
,
P.
Sharma
,
A.
Gruverman
, and
J.
Huang
,
Nat. Mater.
14
,
193
(
2015
).
48.
Y.
Yuan
,
T.
Li
,
Q.
Wang
,
J.
Xing
,
A.
Gruverman
, and
J.
Huang
,
Sci Adv.
3
,
e1602164
(
2017
).
49.
S.
Nah
,
B.
Spokoyny
,
X.
Jiang
,
C.
Stoumpos
,
C. M. M.
Soe
,
M. G.
Kanatzidis
, and
E.
Harel
,
Nano Lett.
18
,
827
(
2018
).
50.
C. M.
Sutter-Fella
,
D. W.
Miller
,
Q. P.
Ngo
,
E. T.
Roe
,
F. M.
Toma
,
I. D.
Sharp
,
M. C.
Lonergan
, and
A.
Javey
,
ACS Energy Lett.
2
,
709
(
2017
).
51.
X.
Jiang
,
J.
Hoffman
,
C. C.
Stoumpos
,
M. G.
Kanatzidis
, and
E.
Harel
,
ACS Energy Lett.
4
,
1741
(
2019
).
52.
J. M.
Azpiroz
,
E.
Mosconi
,
J.
Bisquert
, and
F.
De Angelis
,
Energy Environ. Sci.
8
,
2118
(
2015
).
53.
J.-W.
Lee
,
S.-G.
Kim
,
J.-M.
Yang
,
Y.
Yang
, and
N.-G.
Park
,
Appl. Phys. Lett. Mater.
7
,
041111
(
2019
).
54.
C.
Eames
,
J. M.
Frost
,
P. R. F.
Barnes
,
B. C.
O’Regan
,
A.
Walsh
, and
M. S.
Islam
,
Nat. Commun.
6
,
7497
(
2015
).
55.
Y.
Shao
,
Y.
Fang
,
T.
Li
,
Q.
Wang
,
Q.
Dong
,
Y.
Deng
,
Y.
Yuan
,
H.
Wei
,
M.
Wang
,
A.
Gruverman
,
J.
Shield
, and
J.
Huang
,
Energy Environ. Sci.
9
,
1752
(
2016
).
56.
Y.
Yuan
and
J.
Huang
,
Acc. Chem. Res.
49
,
286
(
2016
).
57.
J.-J.
Li
,
J.-Y.
Ma
,
Q.-Q.
Ge
,
J.-S.
Hu
,
D.
Wang
, and
L.-J.
Wan
,
Appl. Mater. Interfaces
7
,
28518
(
2015
).
58.
T. S.
Sherkar
,
C.
Momblona
,
L.
Gil-Escrig
,
J.
Ávila
,
M.
Sessolo
,
H. J.
Bolink
, and
L. J. A.
Koster
,
ACS Energy Lett.
2
,
1214
(
2017
).
59.
M. L.
Cummings
,
T. Y.
Chien
,
C.
Preissner
,
V.
Madhavan
,
D.
Diesing
,
M.
Bode
,
J. W.
Freeland
, and
V.
Rose
,
Ultramicroscopy
112
,
22
(
2012
).
60.
N.
Shirato
,
M.
Cummings
,
H.
Kersell
,
Y.
Li
,
B.
Stripe
,
D.
Rosenmann
,
S.-W.
Hla
, and
V.
Rose
,
Nano Lett.
14
,
6499
(
2014
).
61.
P.
Sutter
, in
Springer Handbook of Microscopy
, edited by
P. W.
Hawkes
and
J. C. H.
Spence
(
Springer International Publishing
,
Cham
,
2019
), p.
2
.
62.
L.
Nienhaus
,
J. J.
Goings
,
D.
Nguyen
,
S.
Wieghold
,
J. W.
Lyding
, and
M.
Gruebele
,
J. Am. Chem. Soc.
137
,
14743
(
2015
).
63.
S.
Wieghold
,
L.
Nienhaus
,
F. L.
Knoller
,
F. F.
Schweinberger
,
J. J.
Shepherd
,
J. W.
Lyding
,
U.
Heiz
,
M.
Gruebele
, and
F.
Esch
,
Phys. Chem. Chem. Phys.
19
,
30570
(
2017
).
64.
L.
Nienhaus
,
G. E.
Scott
,
R. T.
Haasch
,
S.
Wieghold
,
J. W.
Lyding
, and
M.
Gruebele
,
J. Phys. Chem. C
118
,
13196
(
2014
).