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Overcoming Transport Layer Bottlenecks to Quantify Ionic Parameters from Transient Ion Current Measurements of Perovskite Solar Cells

This paper identifies the capacitance of transport layers as the fundamental bottleneck causing transient ion current (BACE) measurements in perovskite solar cells to overestimate ion density, and proposes correction methods based on ionic displacement and transport layer thickness extrapolation to accurately quantify ionic parameters.

Original authors: Shudi Jiao, Miguel Torre Cachafeiro, Huagui Lai, Fuxiang Ji, Tristan Sachsenweger Ballantyne, Sharun Parayil Shaji, Matthias Diethelm, Fan Fu, Wei E. I. Sha, Wolfgang Tress

Published 2026-09-10

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CC BY 4.0

Original authors: Shudi Jiao, Miguel Torre Cachafeiro, Huagui Lai, Fuxiang Ji, Tristan Sachsenweger Ballantyne, Sharun Parayil Shaji, Matthias Diethelm, Fan Fu, Wei E. I. Sha, Wolfgang Tress

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Solar cells made from a class of materials called perovskites have promised to revolutionize how we capture sunlight, offering a cheaper and more flexible alternative to traditional silicon panels. However, these materials have a hidden quirk: inside the crystal structure, tiny charged particles known as ions are constantly on the move. When an electric field is applied, these ions drift toward the edges of the cell, much like a crowd of people shifting to one side of a room when a door opens. This movement creates a temporary screening effect that can distort the cell's performance and cause its efficiency to fluctuate over time. To build better solar cells, scientists need to know exactly how many of these mobile ions are present and how fast they move. For years, researchers have relied on a specific electrical test to count them, but the results have been confusing, often suggesting there are far more ions than physics should allow.

A team of researchers has now uncovered why these measurements have been misleading. They discovered that the test does not actually count the ions inside the solar cell's core as reliably as once thought. Instead, the measurement is often bottlenecked by the thin layers of material that sit on either side of the perovskite to help collect electricity. These layers act like capacitors, which are components that store electrical charge. The researchers found that the amount of charge these side layers can hold sets a hard limit on what the test can detect. If the solar cell contains more ions than the side layers can store, the test hits a ceiling and stops registering the extra ions, leading to a false reading. It is similar to trying to measure the volume of water in a massive ocean by looking only at how much water a small cup can hold; if the ocean is deeper than the cup is tall, the cup will always appear full, regardless of how much more water is actually below.

To solve this puzzle, the team combined advanced computer simulations with real-world experiments on solar cells built in two different configurations. They systematically changed the thickness of the electron-collecting layer in one set of cells and the chemical doping of the hole-collecting layer in another. By observing how the electrical signals changed as these layers grew thicker or more conductive, they confirmed that the measured ion count was directly tied to the properties of these side layers, not just the ions inside the perovskite. The simulations showed that once the ions move far enough to screen the electric field, the current measured by the device depends almost entirely on the capacitance of the transport layers. This explained why previous studies reported ion densities that were orders of magnitude higher than what standard physics models predicted; the test was simply measuring the storage capacity of the side layers rather than the true population of ions.

The researchers then developed a method to bypass this limitation. By measuring cells with different layer thicknesses and mathematically projecting what the result would be if the side layers were removed entirely, they could extract the true ionic properties of the perovskite material itself. This approach allowed them to calculate the actual density and mobility of the ions without the interference of the surrounding layers. Their findings suggest that in many previous experiments, the reported high ion counts were not real increases in ion numbers, but rather artifacts of the measurement setup. Furthermore, they explored scenarios where ions might penetrate into the side layers or where trapped electrical charges could release slowly, both of which could artificially inflate the signal and explain the unusually high values seen in some older, thicker-layered devices.

This work provides a crucial correction for the field, offering a clear path to accurately quantify the ionic behavior that dictates the stability and performance of perovskite solar cells. By understanding that the measurement is often limited by the storage capacity of the transport layers, scientists can now interpret their data correctly and avoid overestimating the number of mobile ions. The study confirms that to get a true picture of the material's health, one must account for the electrostatic influence of the layers surrounding it. This insight is essential for optimizing these solar cells, ensuring that future designs are based on accurate physical data rather than measurement artifacts, paving the way for more stable and efficient energy harvesting.

Technical Summary: Overcoming Transport Layer Bottlenecks in Transient Ion Current Measurements of Perovskite Solar Cells

Problem Statement

Transient Ion Current (TIC) measurements, often referred to as Bias-Assisted Charge Extraction (BACE), are widely used to quantify mobile ion density ( $N_{ion}$ ) in Metal Halide Perovskite Solar Cells (PSCs). However, a significant discrepancy exists between simulation predictions and experimental reports. Drift-diffusion simulations suggest that once mobile ions screen the internal electric field, the quantifiable ion density saturates at a limit determined by the electrode charge density (typically around $10^{16} - 10^{17} \text{ cm}^{-3}$ ). Conversely, experimental studies frequently report ion densities orders of magnitude higher (exceeding $10^{18} \text{ cm}^{-3}$ ), particularly in aged devices. The physical origin of this discrepancy and the fundamental limitations of the TIC technique regarding Transport Layer (TL) properties have not been systematically understood. Specifically, it remains unclear whether these high values reflect true ionic accumulation or artifacts arising from the device architecture.

Methodology

The authors employed a combined approach of drift-diffusion simulations and experimental validation to decouple ionic properties from transport layer effects.

Simulations:

Modeling: Used the commercial drift-diffusion solver Setfos to simulate PSCs with varying TL parameters (thickness $d_{TL}$ , dielectric constant $\varepsilon_{TL}$ , doping density $N_{TL}$ , and energy band offsets).
Ionic Models: Compared a "two-ion" model (mobile cations and anions) and a "one-ion" model (mobile cations with a static negative background).
Extensions: Investigated mechanisms that could bypass standard limits, including:
- Permeable TLs: Allowing ions to penetrate the TLs.
- Trapped States: Introducing shallow interfacial defects that release electronic charges during the transient.
- Preconditioning Effects: Analyzing the discharge of ions accumulated under forward bias ( $V_{pre} > V_{min,Q}$ ).
Correction Factor ( $f_d$ ): Calculated a correction factor based on the average ionic displacement ( $\Delta \bar{x}$ ) to account for incomplete ion redistribution when the bulk is screened.

Experimental Validation:

Device Architectures: Fabricated both p-i-n (ITO/4PADCB/Perovskite/C60/BCP/Cu) and n-i-p (FTO/SnO2/Perovskite/Spiro-OMeTAD/Au) devices.
Systematic Variation:
- p-i-n: Varied the C60 electron transport layer (ETL) thickness (2.5 nm to 25 nm).
- n-i-p: Varied the Li-TFSI doping concentration in the Spiro-OMeTAD hole transport layer (HTL).
Measurements: Performed TIC measurements in the dark, J-V hysteresis characterization, and Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) with in-situ biasing to detect ion migration into transport layers.

Key Contributions and Results

1. Identification of the Transport Layer Capacitance Bottleneck

The study identifies the capacitance of the transport layers ( $C_{TL}$ ) as the fundamental bottleneck for TIC measurements.

Capacitive Limit: The maximum charge that can be displaced and measured is limited by the charge the TLs can accumulate at the perovskite/TL interfaces ( $Q_{max} \approx C_{TL} \cdot V_{bi}$ ).
Dependence on TL Properties: Simulations and experiments confirm that the computed $N_{ion}$ $N_{i o n}$ is strongly dependent on TL thickness and doping, often more so than on the intrinsic ionic properties of the perovskite.
- Increasing C60 thickness (p-i-n) or decreasing HTL doping (n-i-p) reduces $C_{TL}$ , leading to a lower measured $N_{ion}$ , even if the actual ion density remains constant.
- Experimental data for p-i-n devices showed a monotonic decrease in computed $N_{ion}$ with increasing C60 thickness, aligning with the capacitive limit prediction.

2. The $f_d$ Correction and TL-Free Extrapolation

To overcome the capacitance limit, the authors propose a rigorous correction protocol:

Correction Factor ( $f_d$ ): The standard calculation assumes ions drift the full perovskite thickness ( $f_d = 1$ ). However, when ions screen the field, the average displacement decreases. The authors derive $f_d$ from the shift of the density-weighted centroid. Applying this correction allows for the recovery of the true $N_{ion}$ in capacitance-limited regimes.
Extrapolation Protocol: By systematically varying the TL thickness (or doping) and extrapolating the trends (initial current $J_{initial}$ $J_{ini t ia l}$ and integrated charge) toward a "TL-free" limit ( $d_{TL} \to 0$ $d_{T L} \to 0$ or $C_{TL} \to \infty$ $C_{T L} \to \infty$ ), the actual ionic conductivity ( $\sigma_{ion}$ $σ_{i o n}$ ), density, and mobility can be quantified.
- Results: For p-i-n devices, extrapolation yielded an ionic conductivity of $\approx 7 \times 10^{-9} \text{ S cm}^{-1}$ . The extrapolated $N_{ion}$ was $3.5 \times 10^{17} \text{ cm}^{-3}$ (two-ion model) or $1.8 \times 10^{18} \text{ cm}^{-3}$ (one-ion model with $f_d$ correction).

3. Explaining Anomalously High Ion Densities

The paper provides physical mechanisms explaining why experimental TIC values often exceed the standard capacitive limit:

Ion Permeation: If ions penetrate the TLs (reducing the effective insulating thickness), the capacitive limit increases. ToF-SIMS data showed evidence of Iodide and Li+ presence in HTL/ETL layers, supporting the possibility of ion permeation.
Trapped Charge Release: Slow release of electronic charges from interfacial traps during the transient can inflate the current signal, mimicking higher ion densities.
Preconditioning Artifacts: If the preconditioning voltage ( $V_{pre}$ ) is significantly higher than the flat-band voltage ( $V_{min,Q}$ ), the initial signal may originate from the discharge of accumulated ions with reversed polarity. This discharge process can generate currents far exceeding the standard charging limit, leading to overestimation of $N_{ion}$ .

Significance and Claims

The paper claims that the interpretation of TIC measurements has been fundamentally limited by a lack of consideration for transport layer electrostatics. The authors assert that:

TL Dominance: In many cases, TIC signals reflect the capacitance of the transport layers rather than the intrinsic ion density of the perovskite absorber.
Quantification Protocol: The proposed method of varying TL parameters and extrapolating to a TL-free limit, combined with the $f_d$ correction, provides a practical and reliable route to quantify true ionic parameters (conductivity, density, mobility) in PSCs.
Re-evaluation of Literature: The anomalously high ion densities reported in previous studies may not necessarily indicate a genuine increase in mobile ions but could be artifacts of TL properties, ion permeation, or specific preconditioning protocols.
Practical Guidance: The work offers essential guidelines for researchers to avoid misinterpreting TIC data, emphasizing that changes in TIC signals do not automatically equate to changes in mobile ion density unless TL electrostatics are accounted for.

The study concludes that accurate quantification of ionic properties is only possible when the mean displacement of mobile ions is reliably approximated and the influence of the transport layer capacitance is either corrected or eliminated via extrapolation.