Device physics of hybrid perovskite solar cells

De vraag naar schone en duurzame energie is op dit moment groter dan ooit, en er wordt verwacht dat het gebruik van fossiele brandstoffen zal dalen door toenemende kosten vanwege uitputting en schadelijke effecten voor het milieu als gevolg van het gebruik van fossiele brandstoffen. De energie die kan worden opgevangen van de zon, een onuitputtelijke bron van schone energie, kan worden omgezet in bruikbare energie door middel van zonnecellen. De recent ontdekte zonnecellen gemaakt van organische/anorganische hybride perovskiet, zijn zeer aantrekkelijk voor het genereren van bruikbare energie. Het commercie ̈le succes van de perovskiet zonnecellen is afhankelijk van toenemende efficiëntie en verbeterde stabiliteit. Om deze doelen te verwezenlijken is beter begrip van de fundamentele fysische processen die plaatsvinden in de hybride perovskiet nodig. Dit proefschrift richt zich op de onderliggende fysica van perovskieten en geeft inzicht in de prestatie-beperkende fysieke processen en manieren om deze te identificeren

Targeting VEGF to design pyrimidines against breast cancer and diabetic retinopathy

A library of 900 pyrimidine derivatives was screened virtually on vascular endothelial growth factor (VEGF) using Vlife MDS 4.1 software to identify potential candidates with anticancer and anticataract activity. A series of 2,4,6-substituted pyrimidine derivatives were synthesized in good yields from chalcones, where chalcones have been prepared according to claisen - schimidt condensation by condensing various ketones with aromatic aldehyde in presence of ethanol and sodium or potassium hydroxide. Their structures were confirmed by  IR, 1H NMR and mass spectra. Biological screening of the potential candidates was done for anticancer and anticataract activity. The present study reveals that a derivative of pyrimidine shows activity against breast cancer and diabetic retinopathy through inhibition of VEGF

Identification of the dominant recombination process for perovskite solar cells based on machine learning

Over the past decade, perovskite solar cells have become one of the major research interests of the photovoltaic community, and they are now on the brink of catching up with the classical inorganic solar cells, with efficiency now reaching up to 25%. However, significant improvements are still achievable by reducing recombination losses. The aim of this work is to develop a fast and easy-to-use tool to pinpoint the main losses in perovskite solar cells. We use large-scale drift-diffusion simulations to get a better understanding of the light intensity dependence of the open-circuit voltage and how it correlates to the dominant recombination process. We introduce an automated identification tool using machine learning methods to pinpoint the dominant loss using the light intensity-dependent performances as an input. The machine learning was trained using >2 million simulations and gives an accuracy of the prediction up to 82%. Le Corre et al. demonstrate the application of machine learning methods to identify the dominant recombination process in perovskite solar cells with 82% accuracy. The machine learning algorithms are trained and tested using large-scale drift-diffusion simulations, and their applicability on real solar cells is also demonstrated on devices previously reported

Can Ferroelectric Polarization Explain the High Performance of Hybrid Halide Perovskite Solar Cells?

The power conversion efficiency of photovoltaic cells based on the use of hybrid halide perovskites, CH3NH3PbX3 (X = Cl, Br, I), now exceeds 20%. Recently, it was suggested that this high performance originates from the presence of ferroelectricity in the perovskite, which is hypothesized to lower charge recombination in the device. Here, we investigate and quantify the influence of mesoscale ferroelectric polarization on the device performance of perovskite solar cells. We implement a 3D drift diffusion model to describe the solar cell operation. To account for the mesoscale ferroelectricity, we incorporate domains defined by polarization strength, P, in 3D space, forming different polarization landscapes or microstructures. Study of microstructures with highly-ordered polarized domains shows that charge transport and recombination in the solar cell depends significantly on the polarization landscape viz. the orientation of domain boundaries and the size of domains. In the case of the microstructure with random correlated polarization, a realistic scenario, we find indication of the existence of channels for efficient charge transport in the device which leads to lowering of charge recombination, as evidenced by the high fill factor (FF). However, the high open-circuit voltage (VOC), which is typical of high performance perovskite solar cells, is unlikely to be explained by the presence of ferroelectric polarization in the perovskite

Recombination in Perovskite Solar Cells:Significance of Grain Boundaries, Interface Traps, and Defect Ions

Trap-assisted recombination, despite being lower as compared with traditional inorganic solar cells, is still the dominant recombination mechanism in perovskite solar cells (PSCs) and limits their efficiency. We investigate the attributes of the primary trap-assisted recombination channels (grain boundaries and interfaces) and their correlation to defect ions in PSCs. We achieve this by using a validated device model to fit the simulations to the experimental data of efficient vacuum-deposited p–i–n and n–i–p CH₃NH₃PbI₃ solar cells, including the light intensity dependence of the open-circuit voltage and fill factor. We find that, despite the presence of traps at interfaces and grain boundaries (GBs), their neutral (when filled with photogenerated charges) disposition along with the long-lived nature of holes leads to the high performance of PSCs. The sign of the traps (when filled) is of little importance in efficient solar cells with compact morphologies (fused GBs, low trap density). On the other hand, solar cells with noncompact morphologies (open GBs, high trap density) are sensitive to the sign of the traps and hence to the cell preparation methods. Even in the presence of traps at GBs, trap-assisted recombination at interfaces (between the transport layers and the perovskite) is the dominant loss mechanism. We find a direct correlation between the density of traps, the density of mobile ionic defects, and the degree of hysteresis observed in the current–voltage (<i>J</i>–<i>V</i>) characteristics. The presence of defect states or mobile ions not only limits the device performance but also plays a role in the <i>J</i>–<i>V</i> hysteresis

Quantifying Efficiency Loss of Perovskite Solar Cells by a Modified Detailed Balance Model

A modified detailed balance model is built to understand and quantify efficiency loss of perovskite solar cells. The modified model captures the light-absorption dependent short-circuit current, contact and transport-layer modified carrier transport, as well as recombination and photon-recycling influenced open-circuit voltage. Our theoretical and experimental results show that for experimentally optimized perovskite solar cells with the power conversion efficiency of 19%, optical loss of 25%, non-radiative recombination loss of 35%, and ohmic loss of 35% are the three dominant loss factors for approaching the 31% efficiency limit of perovskite solar cells. We also find that the optical loss will climb up to 40% for a thin-active-layer design. Moreover, a misconfigured transport layer will introduce above 15% of energy loss. Finally, the perovskite-interface induced surface recombination, ohmic loss, and current leakage should be further reduced to upgrade device efficiency and eliminate hysteresis effect. The work contributes to fundamental understanding of device physics of perovskite solar cells. The developed model offers a systematic design and analysis tool to photovoltaic science and technology.Comment: 21 pages, 9 figures, 3 table