Multivariate Approach for Studying the Degradation of Perovskite Solar Cells.

Despite the progress in the performance of perovskite solar cells (PSCs), the absorber layer degradation during prolonged exposure to multiple environmental conditions is still a major issue. As the degradation depends upon many intrinsic and extrinsic factors, the need to adopt a multivariate testing protocol, which provides rapid assessment of device stability, is required. To do this, a Plackett Burman (PB) screening design has been used to analyze 9 different factors that affect the PSC stability; including four extrinsic factors (oxygen, moisture, UV exposure and temperature) and five intrinsic factors (selection of hole transport layer and electron transport layer, absorber layer thickness, halide type and perovskite deposition process). This approach allows us to rank the relative severity of these factors and can be used to narrow the scope of materials and device architectures to be modified, by identifying materials and configurations, which are the most stable. The least and most stable device configurations have been identified and the success of the screening approach has been demonstrated by testing the optimized configurations under ISOS-D1 and –L2 protocols. Importantly, only 12 experiments are needed to establish the most stable combination from the 9 factors thus providing a rapid assessment. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) measurements of perovskite absorber films have been performed in order to understand the degradation pathways and to support the conclusion of PB screening technique

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

Enhancing the stability of Organic Photovoltaics through Machine Learning

A machine learning approach for extracting information from organic photovoltaic (OPV) solar cell data is presented. A database consisting of 1850 entries of device characteristics, performance and stability data is utilised and a sequential minimal optimisation regression (SMOreg) model is employed as a means of determining the most influential factors governing the solar cell stability and power conversion efficiency (PCE). This is achieved through the analysis of the acquired SMOreg model in terms of the attribute weights. Significantly, the analysis presented allows for identification of materials which could lead to improvements in stability and PCE for each thin film in the device architecture, as well as highlighting the role of different stress factors in the degradation of OPVs. It is found that, for tests conducted under ISOS-L protocols the choice of light spectrum and the active layer material significantly govern the stability, whilst for tests conducted under ISOS-D protocols, the primary attributes are material and encapsulation dependent. The reported approach affords a rapid and efficient method of applying machine learning to enable material identification that possess the best stability and performance. Ultimately, researchers and industries will be able to obtain invaluable information for developing future OPV technologies so that can be realised in a significantly shorter period by reducing the need for time-consuming experimentation and optimisation

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 value of source data verification in a cancer clinical trial

Background Source data verification (SDV) is a resource intensive method of quality assurance frequently used in clinical trials. There is no empirical evidence to suggest that SDV would impact on comparative treatment effect results from a clinical trial. Methods Data discrepancies and comparative treatment effects obtained following 100% SDV were compared to those based on data without SDV. Overall survival (OS) and Progression-free survival (PFS) were compared using Kaplan-Meier curves, log-rank tests and Cox models. Tumour response classifications and comparative treatment Odds Ratios (ORs) for the outcome objective response rate, and number of Serious Adverse Events (SAEs) were compared. OS estimates based on SDV data were compared against estimates obtained from centrally monitored data. Findings Data discrepancies were identified between different monitoring procedures for the majority of variables examined, with some variation in discrepancy rates. There were no systematic patterns to discrepancies and their impact was negligible on OS, the primary outcome of the trial (HR (95% CI): 1.18(0.99 to 1.41), p = 0.064 with 100% SDV; 1.18(0.99 to 1.42), p = 0.068 without SDV; 1.18(0.99 to 1.40), p = 0.073 with central monitoring). Results were similar for PFS. More extreme discrepancies were found for the subjective outcome overall objective response (OR (95% CI): 1.67(1.04 to 2.68), p = 0.03 with 100% SDV; 2.45(1.49 to 4.04), p = 0.0003 without any SDV) which was mostly due to differing CT scans. Interpretation Quality assurance methods used in clinical trials should be informed by empirical evidence. In this empirical comparison, SDV was expensive and identified random errors that made little impact on results and clinical conclusions of the trial. Central monitoring using an external data source was a more efficient approach for the primary outcome of OS. For the subjective outcome objective response, an independent blinded review committee and tracking system to monitor missing scan data could be more efficient than SDV

Multiple treatment comparisons in epilepsy monotherapy trials

© 2007 Smith et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

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

Systematic techniques for assisting recruitment to trials (START): study protocol for embedded, randomized controlled trials

BACKGROUND: Randomized controlled trials play a central role in evidence-based practice, but recruitment of participants, and retention of them once in the trial, is challenging. Moreover, there is a dearth of evidence that research teams can use to inform the development of their recruitment and retention strategies. As with other healthcare initiatives, the fairest test of the effectiveness of a recruitment strategy is a trial comparing alternatives, which for recruitment would mean embedding a recruitment trial within an ongoing host trial. Systematic reviews indicate that such studies are rare. Embedded trials are largely delivered in an ad hoc way, with interventions almost always developed in isolation and tested in the context of a single host trial, limiting their ability to contribute to a body of evidence with regard to a single recruitment intervention and to researchers working in different contexts. METHODS/DESIGN: The Systematic Techniques for Assisting Recruitment to Trials (START) program is funded by the United Kingdom Medical Research Council (MRC) Methodology Research Programme to support the routine adoption of embedded trials to test standardized recruitment interventions across ongoing host trials. To achieve this aim, the program involves three interrelated work packages: (1) methodology - to develop guidelines for the design, analysis and reporting of embedded recruitment studies; (2) interventions - to develop effective and useful recruitment interventions; and (3) implementation - to recruit host trials and test interventions through embedded studies. DISCUSSION: Successful completion of the START program will provide a model for a platform for the wider trials community to use to evaluate recruitment interventions or, potentially, other types of intervention linked to trial conduct. It will also increase the evidence base for two types of recruitment intervention. TRIAL REGISTRATION: The START protocol covers the methodology for embedded trials. Each embedded trial is registered separately or as a substudy of the host trial