Enhanced stability and photovoltage for inverted perovskite solar cells <i>via</i> precursor engineering

Liu, Changwen; Kong, Weiguang; Li, Wang; Chen, Hong; Li, Dedi; Xu, Baomin; Cheng, Chun; Jen, Alex K.‐Y.

doi:10.1039/c9ta03454d

Cited by 23 publications

(24 citation statements)

References 37 publications

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“…Further increasing the bias voltage leads to a sharp rise in the current, which suggests that the injected electrons start to fill up the trap states of perovskite, that is, the trap‐filled limit (TFL) region, until reaching the SCLC region. As is known, the onset voltage of TFL region ( V TFL ) correlates tightly with the trap density in the perovskite film, and a smaller trap density is normally accompanied by a smaller V TFL . As shown in Figure b, it is evident that the V TFL is smaller in the MAPbI 3‐x Cl x ‐PCBM composite‐based device, which is in agreement with the result from Figure a.…”

Section: Resultssupporting

confidence: 86%

“…To confirm this, admittance spectroscopy was further measured (Figure c), which has been widely applied as a powerful technique to quantitatively study the trap density ( N T ) of perovskite . From the admittance spectroscopy, N T can be calculated from the equation described as,

N_{T} (E_{ω}) = - \frac{V_{bi}}{italicqW} \frac{italicdC}{d ω} \frac{ω}{k_{B} T}

where

ω

, C , and E

E_{ω}

are angular frequency, capacitance, and variance between trap energy and valence band edge, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…From the admittance spectroscopy, N T can be calculated from the equation described as,

N_{T} (E_{ω}) = - \frac{V_{bi}}{italicqW} \frac{italicdC}{d ω} \frac{ω}{k_{B} T}

where

ω

, C , and E

E_{ω}

are angular frequency, capacitance, and variance between trap energy and valence band edge, respectively. The built‐in potential ( V bi ) and depletion width ( W ) are obtained from the Mott–Schottky analysis as described elsewhere . The calculated results shown in Figure c demonstrate that N T is reduced significantly in CPSC, with the reduction of one order of magnitude (

E_{ω}

= 0.15 eV) in comparison with that from 4L‐SPSC.…”

Section: Resultsmentioning

confidence: 99%

“…The MAPbI 3‐x Cl x /PCBM composite of 4L‐SPSC exhibits a tetragonal perovskite structure with the diffraction peaks at 14.2°, 28.5°, and 32.0° being assigned to (110), (220), and (310) lattice plane, respectively. Moreover, it can be seen that an evident peak appears at 12.7°, which is belonged to the (001) plane of cubic PbI 2 . The appearance of PbI 2 peak is mainly due to the volatile nature of the organic cation CH 3 NH 3 + (MA) that easily escapes from the perovskite crystal lattice during thermal annealing process .…”

Section: Resultsmentioning

confidence: 99%

“…As all the photogenerated charge carriers eventually recombine with each other under open‐circuit condition, the variation of V oc upon I can provide the information about recombination characteristics within devices. The relationship between I and V oc is described as,

\frac{d V_{oc}}{d lg I} = 2.303 \frac{n k_{normalB} T}{q},

where n , k B , T , and q are ideality factor, Boltzmann constant, absolute temperature, and elementary charge, respectively. By linear fitting of V oc upon I (Figure a), n was calculated with the value of 1.34 and 1.80 for CPSC and 4L‐SPSC, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Simplified Compact Perovskite Solar Cells with Efficiency of 19.6% via Interface Engineering

Liu

Chen

et al. 2020

Energy & Environ Materials

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For the commercialization of perovskite solar cells (PSCs), it is more appealing to develop high‐performance simplified PSCs where perovskite films are just sandwiched between the back and front electrodes, in order to simplify the fabrication process and to reduce the cost. However, to date, this kind of devices shows rather low performance, and there are few researches on this subject. Herein, we report on a kind of compact PSCs (CPSCs) that are free of independent charge transport layers (CTLs). The devices are realized by the use of organic monolayer‐modified effective electrodes, along with the use of [6,6]‐phenyl‐C61‐butyric acid methyl ester (PCBM)‐assisted anti‐solvent technique to obtain ultra‐thin (~10 nm) PCBM‐embedded perovskite films. Compared to control devices, CPSCs achieve a promising champion power conversion efficiency of 19.6% with largely reduced hysteresis. Moreover, the unencapsulated CPSC shows good stability under ambient atmosphere, with only 10% efficiency loss after 60 days’ storage. This work indicates that, by delicate design, CPSCs with smaller materials consumption in device architecture can perform competitively as conventional PSCs. Further reduction in the actual usage of costly CTL materials can be expected upon our CPSCs by developing more facile and economic methods to prepare ultra‐thin CTLs.

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Section: Resultssupporting

confidence: 86%

N_{T} (E_{ω}) = - \frac{V_{bi}}{italicqW} \frac{italicdC}{d ω} \frac{ω}{k_{B} T}

where

ω

, C , and E

E_{ω}

are angular frequency, capacitance, and variance between trap energy and valence band edge, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…From the admittance spectroscopy, N T can be calculated from the equation described as,

N_{T} (E_{ω}) = - \frac{V_{bi}}{italicqW} \frac{italicdC}{d ω} \frac{ω}{k_{B} T}

where

ω

, C , and E

E_{ω}

E_{ω}

= 0.15 eV) in comparison with that from 4L‐SPSC.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

\frac{d V_{oc}}{d lg I} = 2.303 \frac{n k_{normalB} T}{q},

Section: Resultsmentioning

confidence: 99%

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Simplified Compact Perovskite Solar Cells with Efficiency of 19.6% via Interface Engineering

Liu

Chen

et al. 2020

Energy & Environ Materials

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Exploring Transport Behavior in Hybrid Perovskites Solar Cells via Machine Learning Analysis of Environmental‐Dependent Impedance Spectroscopy

et al. 2021

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Hybrid organic–inorganic perovskites are one of the promising candidates for the next‐generation semiconductors due to their superlative optoelectronic properties. However, one of the limiting factors for potential applications is their chemical and structural instability in different environments. Herein, the stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times (DRTs) are used to effectively analyze impedance spectra. DRT is further analyzed via machine learning workflow based on the non‐negative matrix factorization of reconstructed relaxation time spectra. This exploration provides the interplay of charge transport dynamics and recombination processes under environment stimuli and illumination. The results reveal that in the dark, oxygen atmosphere induces an increased hole concentration with less ionic character while ionic motion is dominant under ambient air. Under 1 Sun illumination, the environment‐dependent impedance responses show a more striking effect compared with dark conditions. In this case, the increased transport resistance observed under oxygen atmosphere in equivalent circuit analysis arises due to interruption of photogenerated hole carriers. The results not only shed light on elucidating transport mechanisms of perovskite solar cells in different environments but also offer an effective interpretation of impedance responses.

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A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells

et al. 2021

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A library of metal oxide-conjugated polymer composites was synthesized, encompassing of WO3-polyaniline (PANI), WO3poly(N-methylaniline) (PMANI), WO3-poly(2-fluoroaniline) (PFANI), WO3-polythiophene (PTh), WO3-polyfuran (PFu) and WO3-poly(3,4ethylenedioxythiophene) (PEDOT). These composites were probed as hole selective layers for perovskite solar cells (PSCs) fabrication. We adopted machine learning approaches to predict and compare PSCs performances with the developed WO3 and its composites. The experimental and theoretical results are coherent, when the electrooptical properties of PSC were computed. Notably, for the evaluation of PSCs performance, decision tree model is the ideal for WO3-PEDOT composite, while random forest model was found to be suitable for WO3-PMANI, WO3-PFANI, WO3-PFu. While in the case for WO3, WO3-PANI and WO3-PTh, K Nearest Neighbors model was appropriate. Machine learning models can be a pioneering prediction models for the PSCs performance and its validation.

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Enhanced stability and photovoltage for inverted perovskite solar cells via precursor engineering

Abstract: Precursor engineering toward enhanced Voc and stability of inverted perovskite solar cells with a champion power conversion efficiency of 20.7%.

Cited by 23 publications

References 37 publications

Simplified Compact Perovskite Solar Cells with Efficiency of 19.6% via Interface Engineering

Simplified Compact Perovskite Solar Cells with Efficiency of 19.6% via Interface Engineering

Exploring Transport Behavior in Hybrid Perovskites Solar Cells via Machine Learning Analysis of Environmental‐Dependent Impedance Spectroscopy

A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells

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