Using Large Datasets of Organic Photovoltaic Performance Data to Elucidate Trends in Reliability Between 2009 and 2019

The application of data analytical approaches to understand long-term stability trends of organic photovoltaics (OPVs) is presented. Nearly 1900 OPV data points have been catalogued, and multivariate analysis has been applied in order to identify patterns, produce models that quantitatively compare different internal and external stress factors, and subsequently enable predictions of OPV stability to be achieved. Analysis of the weights associated with the acquired predictive model shows that for light stability (ISOS-L) testing, the most significant factor for increasing the time taken to reach 80% of the initial performance (T80) is the substrate and top electrode selection, and the best light stability is achieved with a small molecule active layer. The weights for damp-heat (ISOS-D) testing shows that the type of encapsulation is the primary factor affecting the degradation to T80. The use of data analytics and potentially machine learning can provide researchers in this area new insights into degradation patterns and emerging trends

Development of an Improved Computer Model for Organic Photovoltaic Cells

This paper reports on an improved diode approximation-based model for PVs which is tested on three different organic PV (OPV) modules: AgGrid, AgNW and Carbon OPV. The model can emulate the electrical characteristics of the three cells accurately, facilitating the deployment in system models. Analytical I-V and P-V curves obtained with the model are compared with outdoor test data and demonstrate high correlation

The effect of OPV module size on stability and diurnal performance: outdoor tests and application of a computer model

The outdoor performance of large area Organic Photovoltaics (OPVs) is investigated in this work. Initially, the diurnal performance of the three modules is determined and found to be similar. Subsequently module degradation is monitored, and it is found that the larger area module displays a significantly greater stability as compared to the smallest area module; in fact the larger module displays a T50% (time to fall to 50% of its original value) of 191 days whilst the smallest module displays a T50% of 57 days. This is attributed to an increased level of water infiltration due to a larger perimeter-to-area ratio. These findings are then used to verify a computer simulation model which allows the model parameters, series and shunt resistances, to be calculated. It is determined that the series resistance is not an obvious obstruction at these module sizes. The findings of this work provide great promise for the application of OPV technology on a larger scale

Application of large datasets to assess trends in the stability of perovskite photovoltaics through machine learning

Current trends in manufacturing indicate that optimised decision making using new state-of-the-art machine learning (ML) technologies will be used. ML is a versatile technique that rapidly and accurately generates new insights from multifactorial data. The ML approach has been applied to a perovskite solar cell (PSC) database to elucidate trends in stability and forecast the stability of new configurations. A database consisting of 6038 entries of device characteristics, performance, and stability data was utilised, and a sequential minimal optimisation regression (SMOreg) model was employed to determine the most influential factors governing solar cell stability. When considering sub-sections of data, it was found that pin-device architectures provided the best model fittings with a training correlation efficiency of 0.963, compared to 0.699 for all device architectures. By establishing models for each PSC architecture, the analysis allows the identification of materials that can lead to improvements in stability. This paper also attempts to summarise some key challenges and trends in the current research methodologies

Design for a sustainability approach to organic solar cell design: the use of machine learning to quantify the trade-off between performance, stability and environmental impact

Organic photovoltaics (OPVs) are considered one of the best-performing photovoltaic (PV) technologies from an environmental point of view. Many of the constituent materials possess low embodied energies, which can generally be processed and disposed of in a less energy-intensive manner than other PV technologies. There has been an enormous range of materials used in OPVs; however, identification of the optimal materials and device architectures that provide the best environmental profile within this large search space has yet to have been conducted. This is a non-trivial task because the selection of these materials not only impacts the environmental profile but also on the solar cell efficiency and its operational stability. Here, we have developed a methodology that enables rapid assessment of the trade-off between efficiency, stability, and embodied energy of an OPV using machine learning. To achieve this, a database of OPV data was used, which has been acquired from the literature between 2011 and 2020 and consists of 1580 device data points. Our results highlight the importance of focusing activity on particular transport layers, substrates, and active layer materials, which are discussed further in the manuscript. We demonstrate that the trained and validated models can predict, with a high degree of confidence, the efficiency, stability, and embodied energy of an OPV. The methodology set out in this work provides a means of identifying optimum device configurations in a rapid manner such that the net energy production is maximized, whereas the environmental impact of OPVs is minimized. Materials which show promise toward delivering a positive net energy are PET + barrier layers, PET (substrates), NiOx, ZrOx, CrO2, ZnO, LiF, and MoO3 (transport layers). Active layer materials which show promise for delivering a positive net energy are DRCN7T, DR3TSBDT, ZnPc, PDPP4T-2F, PCDTBT (donors), and IT-4F, C61 (acceptors)

Enhancing the stability of perovskite solar cells through functionalisation of metal oxide transport layers with self-assembled monolayers

A systematic study of the application of self-assembly monolayers (SAMs) onto electron and hole transporting layers for perovskite solar cells (PSCs) stability is reported. Cs0.05FA0.83MA0.17Pb(I0.87Br0.13)3 (FMC) perovskite films were deposited onto tin oxide (SnO2) and nickel oxide (NiOx) layers that were functionalized with ethylphosphonic acid (EPA) and 4-bromobenzoic acid (BBA) SAMs. X-ray diffractometry measurements were performed on these films shortly after they were deposited. The diffractograms agree with the positions reported in the literature for the crystal structure of the FMC. The results show that the deposition of SAMs on the metal oxide layers yields positive improvements in the FMC film stability and in the device stability when using FMC as the active layer. The work shows that by adopting SAMs, the long-term stability of PSCs cells under accelerated test conditions can be enhanced, and this provides one step on the way to making this technology a commercial reality

Predicting diurnal outdoor performance and degradation of organic photovoltaics via machine learning; relating degradation to outdoor stress conditions

Accurate prediction of the future performance and remaining useful lifetime of next-generation solar cells such as organic photovoltaics (OPVs) is necessary to drive better designs of materials and ensure reliable system operation. Degradation is multifactorial and difficult to model deterministically; however, with the advent of machine learning, data from outdoor performance monitoring can be used for understanding the relative impact of stress factors and could provide a powerful method to interpret large quantities of outdoor data automatically. Here, we propose the use of artificial neural networks and regression models for forecasting OPV module performance and their degradation as a function of climatic conditions. We demonstrate their predictive capability for short-term energy forecasting of OPV modules, showing that energy yield can be predicted if climatic conditions are known. In addition, the model has been extended so that the impact of climatic conditions on degradation can be predicted. The combined model has been validated on unseen OPV module data and is able to predict energy yield to within 4% accuracy