Lectures and Scripts
Scripts and software tools
Determine_Indoor_PV_Performance The purpose of this script is to calculate the efficiency of a solar cell under a given illuminance (i.e. 200 lux) for an arbitrary light source spectrum (i.e. a certain LED). The point is to be able to compare efficiencies at different illuminances or at the same illuminance but with different light sources of e.g. different color temperature. The script needs the spectra of the light sources at least in arbitrary units (not in absolute ones) and the external quantum efficiency of the solar cell as well as fill factor and open-circuit voltage data at roughly the right illuminance. The advantage of the approach is that the script does not need an actually measured JV curve of a cell under a specific illumination but it calculates what the short-circuit current and the efficiency should be based on the external quantum efficiency. For more information on the application of the script see: Lübke, D., Hartnagel, P., Angona, J., & Kirchartz, T. (2021). Comparing and Quantifying Indoor Performance of Organic Solar Cells. Advanced Energy Materials, 11(34), 2101474. doi:https://doi.org/10.1002/aenm.202101474
Report Perovskite Solar Cell VOC Losses and QeLED This script determines the maximum achievable open circuit voltage, VOCrad (i.e. the VOC in the radiative limit), of your solar cell from the real optical response of the device by evaluating your external quantum efficiency (EQE) data. In addition, it calculates the non-radiative voltage losses (∆VOCNR) and the corresponding external luminescence quantum efficiency (QeLED) with the input of the measured VOC.
Krückemeier, L., Rau, U., Stolterfoht, M., & Kirchartz, T. (2020). How to Report Record Open-Circuit Voltages in Lead-Halide Perovskite Solar Cells. Advanced Energy Materials, 10(1), 1902573. doi:https://doi.org/10.1002/aenm.201902573
https://emerging-pv.org/data/ The emerging photovoltaics reports initiative (EPVRI) is an academic international framework for collecting, presenting and analyzing data about the best achievements in the research of emerging photovoltaic materials, e.g., organic, perovskite and dye sensitized solar cells.
It is meant to provide a reference for good practices and state-of-the art reports, summarized in periodic publications by the EPVRI organizing consortium in the journal Advanced Energy Materials (AEM).
The focus is set on the best performing devices in terms of efficiency, flexibility, transparency and photo-stability, properly described in peer-reviewed academic publications ensuring the reproducibility of the results.
For more information also see Almora et al. (2023) Device Performance of Emerging Photovoltaic Materials (Version 3). Advanced Energy Materials 13 (1) 2203313. https://doi.org/10.1002/aenm.202203313
Lectures and other presentations
Defect densities and charge carrier lifetimes in lead-halide perovskites
This talk has been part of the HOPV22 conference that took place in Valencia. In order to further improve the efficiencies and in particular the open-circuit voltages of perovskite solar cells it is important to understand non-radiative recombination and their relation to defect densities. In this talk, I will discuss our recent findings on how to measure and quantify both defect densities and decay times as an assay of the strength of recombination. Defect densities are often measured using methods that rely on measuring the charge density on these defects. Examples for such methods are capacitance-based methods as well as the measurement of unipolar current voltage curves (often called space charge limited current measurements) where the voltage onset of the trap-filled limit is evaluated. All these methods are typically done on sandwich-type devices where the volume charge densities have to compete with the charge per area on the electrodes. If the volume densities are too low to cause a Debye length that is shorter than the thickness of the semiconductor between the electrodes, the volume charge of the defects would not affect the measurement. I show that this is likely often the case in thin-film devices but not in single crystals. Therefore, these methods create an artificial bias towards assigning higher defect densities to thin films than to single crystals.1-2 A striking observation in the context of measuring recombination lifetimes is the huge discrepancies that are typically reported for lifetimes measured on films and on devices. Typically, the devices show longer lifetimes despite the fact that they have more interfaces and typically higher recombination rates at a given Fermi level splitting than well passivated films. This initially surprising feature can be understood by studying the differences between actual lifetimes due to recombination and a more general concept of decay times that may be due to recombination, extraction, injection and capacitive charging and discharging of electrodes. We develop a reasonably simple analytical set of equations to describe decay times in electrical and optical transient measurements.
An introduction to device physics of perovskite solar cells | Thomas Kirchartz
This serie of videos is aimed for researchers in the #photovoltaics community, with particular focus on #perovskite solar cells. You will find the answers to questions such as: What is a band diagram and how to draw it? How to measure the #electrostatic properties? What kinds of junctions can be found in a perovskite #solarcell? These lectures are part of the Online School on Fundamentals of Emerging Solar Cells (#PVSCHOOL) that took place from February the 10th to the 12th 2021: https://www.nanoge.org/PVSCHOOL/home Discover more at https://www.nanoge.org/
Impact of Defects on Halide Perosvkite Solar Cells
Defects are an important topic for every photovoltaic material. The talk attempts to answer three questions, namely do do defects matter in lead-halide perovskite solar cells, do they matter in the bulk or at interfaces and finally, where in the band gap are these defects? nanoGe Educational Resources are a series of materials that aim to serve researchers, teachers and students in their training process in the perovskite and photovoltaic fields. Discover more in nanoge.org
Basita Das: Parameter estimation in Perovskite solar cells using Bayesian Inference
In a perovskite solar cell, the root cause for underperformance may originate from a large variety of different phenomena such as bulk or interface recombination, transport limitations in the contact layers or bad energy alignment at interfaces. Traditionally, discriminating between these mechanisms has been done by applying a variety of different characterization methods to the samples and then performing some data analysis that often involves fitting the data with analytical equations or numerical models. Such characterizing techniques can be time consuming and often destructive to the device. Furthermore, for the sake of simplicity and to allow fitting the data with a low number of unknowns, the models used for fitting and analyzing the data are often insufficiently complex to really capture all necessary physical phenomena that are relevant to understand the measurement. Ideally one can try to fit more complex device models to the characterization data, but the sheer number of parameters that usually goes into a device simulator, and the correlation between the parameters make the problem intractable. Also, such rudimentary parameter fitting usually gives just one set of parameters that fits the data reasonably well but does not tell us if that combination of parameters is unique or if there are any correlation between the parameters. Information on correlation between parameters is not only important from a device optimization point of view but can also teach us more about the underlying physics controlling the functionality of the device. In this presentation, we introduce a fast non−destructive method of parameter estimation using Bayesian inference in combination with data current and photoluminescence-voltage measurements taken on perovskite solar cells. From the inferred parameters we identify which region or which layer in our device stack is limiting the performance of our device. Such information is useful to strategize device optimization for better performance. Bayesian inference methods have been previously used in the field of solar cells for well-studied solar cell technologies like Si solar cells as well as for material systems like SnS solar cells where the number of unknown parameters is small [1–3]. In cases where the number of unknowns are few, parameter estimation using only temperature and illumination dependent current voltage J (V, T, i) curves yielded good results. However, this is the first time we are using it for perovskite solar cells which is a bit more complicated given the number of unknown parameters are much larger than in the other solar cell technologies.
[1] R. E. Brandt, R. C. Kurchin, V. Steinmann, D. Kitchaev, C. Roat, S. Levcenco, G. Ceder, T. Unold, T. Buonassisi, Joule 2017, 1, 843.
[2] R. Kurchin, G. Romano, T. Buonassisi, Comput. Phys. Commun. 2019, 239, 161.
[3] R. C. Kurchin, J. R. Poindexter, V. Vähänissi, H. Savin, ¶ Carlos Del Cañizo, T. Buonassisi, How Much Physics Is in a Current-Voltage Curve? Inferring Defect Properties from Photovoltaic Device Measurements, 2019.
Presentations and talks
The pdf files of the presentations can be found via the Files links.