Periodic Reporting for period 2 - OptEIon (Defect Engineering, Advanced Modelling and Characterization for Next Generation Opto-Electronic-Ionic Devices)
Période du rapport: 2021-12-01 au 2023-05-31
Photovoltaics is and will further become a major pillar for a clean and sustainable energy supply, a necessity given the rapidly progressing negative impacts of climate change. Beyond the conventional silicon solar cells, thin film technologies are highly promising due to reduced use of material and energy during production. Here, perovskite solar cells, which emerged just less than 15 years ago appear to be highly promising. Despite simple processing from solution, solar cells with efficiencies larger than 25% have been realized. One main reason is the material’s tolerance against defects and further unprecedented outstanding optoelectronic properties. However, both solar cells and light-emitting diodes suffer from “instabilities”, which are the main hurdle for their commercialization. Intriguingly, some of these “instabilities” are reversible, meaning that for instance degradation under illumination is followed by (partial) recovery when the solar cell rests in the dark.
These reversible effects mainly originating from ionic defects in the material are topic of OptEIon. The project focusses on advancing characterization and modelling of these devices. Here, one major challenge is the ambiguity of reported results regarding the quantity of these ionic defects. Since the feature sizes of the involved structures are below one micron but dominating the overall performance, nanoscale characterization is employed based on advanced microscopy. The goal is to establish a link between processes on the nanoscale and device performance. On the modeling front, we intend to merge materials science with data science by employing machine learning approaches to complement physics-based modeling. The goal here is to provide a smart tool that assists scientists working in the lab.
In a final step, we want to investigate how these commonly undesired reversible effects can be exploited in novel devices such as memristors, which show features similar to synapses. Such memristors are supposed to be essential for efficient future processors based on neuromorphic computing.
The insights gained in this project will contribute to more stable perovskite solar cells and light emitting diodes. Therefore, they contribute to the chapter Secure, clean and efficient energy in the Horizon 2020 work program. Furthermore, the new AI-based approaches in modelling and the novel devices fabricated are key ingredients to move the digitization in science and society forward.
These reversible effects mainly originating from ionic defects in the material are topic of OptEIon. The project focusses on advancing characterization and modelling of these devices. Here, one major challenge is the ambiguity of reported results regarding the quantity of these ionic defects. Since the feature sizes of the involved structures are below one micron but dominating the overall performance, nanoscale characterization is employed based on advanced microscopy. The goal is to establish a link between processes on the nanoscale and device performance. On the modeling front, we intend to merge materials science with data science by employing machine learning approaches to complement physics-based modeling. The goal here is to provide a smart tool that assists scientists working in the lab.
In a final step, we want to investigate how these commonly undesired reversible effects can be exploited in novel devices such as memristors, which show features similar to synapses. Such memristors are supposed to be essential for efficient future processors based on neuromorphic computing.
The insights gained in this project will contribute to more stable perovskite solar cells and light emitting diodes. Therefore, they contribute to the chapter Secure, clean and efficient energy in the Horizon 2020 work program. Furthermore, the new AI-based approaches in modelling and the novel devices fabricated are key ingredients to move the digitization in science and society forward.
A project team consisting of two PhD students and two part-time postdoctoral fellows was set up. Activities in the lab have been started and solar cells with an efficiency of >19% have been fabricated. At the same time, methods to characterize mobile ionic defects have been scrutineered. Regarding the nanoscale characterization, a combined optical and atomic force microscope has been set up. Furthermore, first machine-learning algorithms have been trained to identify efficiency-limiting parameters from the solar cells’ current-voltage characterization.
One reason for “instabilities” in higher-gap perovskite materials is phase segregation, which is known to limit the efficiency. We performed a study, where we characterized the emission properties of the solar cell in operando, meaning under operation, which had hardly been done before. Contrary to common belief, we found that emergence of a low-gap phase does not preliminary affect the open-circuit voltage of the solar cell but the photocurrent.
Although originally not being the main focus of OptEIon, we investigated defects and emission properties of a new generation of (double) perovskite materials, which are lead-free. Such alternative materials are more and more looked-for, since they avoid the toxic metal lead (Pb), contained in the conventional perovskites. We identified bottlenecks of the investigated material, which are hard to be overcome, concluding that the search for new materials has to continue.
Regarding nanoscale characterization we made first observations on changes in the nanoscale, which have not been reported yet and might explain some of the behavior seen when measuring solar cells.
The machine learning task is highly exciting. In a first study a machine learning algorithm was trained to identify efficiency-limiting parameters in solar cell data that was obtained from modeling. The accuracy was larger than 90% and in a next step, the algorithm has to be tested on experimental data.
Beyond solar cells, memristive devices have been fabricated with promising characteristics. Studies on the nanoscale origin of their working mechanisms are ongoing.
One reason for “instabilities” in higher-gap perovskite materials is phase segregation, which is known to limit the efficiency. We performed a study, where we characterized the emission properties of the solar cell in operando, meaning under operation, which had hardly been done before. Contrary to common belief, we found that emergence of a low-gap phase does not preliminary affect the open-circuit voltage of the solar cell but the photocurrent.
Although originally not being the main focus of OptEIon, we investigated defects and emission properties of a new generation of (double) perovskite materials, which are lead-free. Such alternative materials are more and more looked-for, since they avoid the toxic metal lead (Pb), contained in the conventional perovskites. We identified bottlenecks of the investigated material, which are hard to be overcome, concluding that the search for new materials has to continue.
Regarding nanoscale characterization we made first observations on changes in the nanoscale, which have not been reported yet and might explain some of the behavior seen when measuring solar cells.
The machine learning task is highly exciting. In a first study a machine learning algorithm was trained to identify efficiency-limiting parameters in solar cell data that was obtained from modeling. The accuracy was larger than 90% and in a next step, the algorithm has to be tested on experimental data.
Beyond solar cells, memristive devices have been fabricated with promising characteristics. Studies on the nanoscale origin of their working mechanisms are ongoing.
OptEIon will move the perovskite field forward. We will have obtained a better understanding of mobile ionic defects, which is essential for stable solar cells. On the other hand, we will have realized memristive devices and evaluated their potential usage in neuromorphic computing. Connecting the macroscopic device output with the nanoworld, we will have better understood the reasons behind reversible and irreversible degradation phenomena. Introducing AI into the device physics, we will have set a starting point for a new research direction, where algorithms based on machine learning will assist the experimental scientist of the future.