<div>The manufacturing cost of solar cells along with their efficiency and reliability define the levelized cost of electricity (LCOE). One needs to reduce LCOE to make solar cells cost competitive compared to other sources of electricity. After a sustained decrease since 2001 the manufacturing cost of the dominant photovoltaic technology based on c-Si solar cells has recently reached a plateau. Further reduction in LCOE is only possible by increasing the efficiency and/or reliability of c-Si cells. Among alternate technologies, organic photovoltaics (OPV) has reduced manufacturing cost, but they do not offer any LCOE gain because their lifetime and efficiency are significantly lower than c-Si. Recently, perovskite solar cells have showed promising results in terms of both cost and efficiency, but their reliability/stability is still a concern and the physical origin of the efficiency gain is not fully understood.</div><div><br></div>In this work, we have collaborated with scientists industry and academia to explain the origin of the increased cell efficiency of bulk solution-processed perovskite cells. We also explored the possibility of enhancing the efficiency of the c-Si and perovskite cells by using them in a tandem configuration. To improve the intrinsic reliability, we have investigated 2D-perovskite cells with slightly lower efficiency but longer lifetime. We interpreted the behavior of the 2D-perovskite cells using randomly stacked quantum wells in the absorber region. We studied the reliability issues of c-Si modules and correlated series resistance of the modules directly to the solder bond failure. We also found out that finger thinning of the contacts at cell level manifests as a fake shunt resistance but is distinguishable from real shunt resistance by exploring the reverse bias or efficiency vs. irradiance. Then we proposed a physics-based model to predict the energy yield and lifetime of a module that suffers from solder bond failure using real field data by considering the statistical nature of the failure at module level. This model is part of a more comprehensive model that can predict the lifetime of a module that suffers from more degradation mechanisms such as yellowing, potential induced degradation, corrosion, soiling, delamination, etc. simultaneously. This method is called forward modeling since we start from environmental data and initial information of the module, and then predict the lifetime and time-dependent energy yield of a solar cell technology. As the future work, we will use our experience in forward modeling to deconvolve the reliability issues of a module that is fielded since each mechanism has a different electrical signature. Then by calibrating the forward model, we can predict the remaining lifetime of the fielded module. This work opens new pathways to achieve 2030 Sunshot goals of LCOE below 3c/kWh by predicting the lifetime that the product can be guaranteed, helping financial institutions regarding the risk of their investment, or national laboratories to redefine the qualification and reliability protocols.<br>

Der Schwerpunkt der vorliegenden Arbeit liegt in der Charakterisierung der elektronischen Eigenschaften von hybriden organisch-anorganischen Perowskit (HOIP)-Schichten während der Schichtbildung und in verschiedenen Umgebungen mittels Photoelektronenspektroskopie (PES). Insbesondere wird der Methylammonium-Blei-Iodid-Chlorid-Perowskit (MAPbI3-xClx) untersucht. Als erstes werden Änderungen in den elektronischen Eigenschaften, der Zusammensetzung, sowie der Kristallstruktur mittels PES, Flugzeit-Sekundärionenmassenspektrometrie, sowie Röntgendiffraktometrie mit streifendem Einfall analysiert. Die Resultate weisen auf die entscheidende Rolle von Chlor im texturierten Wachstum der Perowskitschicht hin. Die auskristallisierte Perowskitschicht weist eine stärkere n-Typ Eigenschaft auf, welche auf die Änderung der Zusammensetzung während der Schichtbildung zurückgeführt werden kann. Außerdem beweisen die Ergebnisse eindeutig die Ablagerung von Chlor an der Grenzfläche zwischen der Perowskitschicht und dem Substrat. Zweitens werden die separaten Einflüsse von Wasser, Sauerstoff, und Umgebungsluft auf die elektronischen Eigenschaften von MAPbI3-xClx-Schichtoberflächen untersucht. Bereits geringste Wassermengen ähnlich wie im Hochvakuum oder in inerter Umgebung können eine reversible Reduzierung der Austrittsarbeit hervorrufen. Höherer Wasserdampf-Partialdruck führt zu einer Verschiebung des Valenzbandmaximums (VBM) weit vom Fermi-Niveau, sowie zu einer Reduzierung der Austrittsarbeit. Im Gegensatz dazu führt eine Sauerstoffexposition zu einer Verschiebung des VBM in Richtung des Fermi-Niveaus und zu einer Steigerung der Austrittsarbeit. Analog kommt es zu einer Verschiebung von bis zu 0.6 eV bei einer Exposition gegenüber Umgebungsluft, was den vorwiegenden Einfluss von Sauerstoff demonstriert. Die vorliegenden Untersuchungen betonen den kritischen Einfluss der Schichtbildung, der Zusammensetzung, sowie der Umgebungsbedingungen auf die elektronischen Eigenschaften von HOIP. / The present thesis aims at characterizing the electronic properties of solution-processed hybrid organic-inorganic perovskites (HOIPs) in general, and the HOIP methyl ammonium (MA) lead iodide-chloride (MAPbI3-xClx) films, in particular, at different stages, namely from its formation to its degradation, by means of photoelectron spectroscopy (PES). Firstly, the formation of MAPbI3-xClx films upon thermal annealing is monitored by a combination of PES, time-of-flight secondary ion mass spectrometry, and grazing incidence X-ray diffraction for disclosing changes in electronic properties, film composition, and crystal structure, respectively. Overall, the results point to the essential mediating role of chlorine in the formation of a highly textured perovskite film. The film formation is accompanied by a change of composition which leads to the film becoming more n-type. The accumulation of chlorine at the interface between perovskite and the underlying substrate is also unambiguously revealed. Secondly, the separate effects of water and oxygen on the electronic properties of MAPbI3-xClx film surfaces are investigated by PES. Already low water exposure – as encountered in high vacuum or inert conditions – appears to reversibly impact the work function of the film surfaces. Water vapor in the mbar range induces a shift of the valence band maximum (VBM) away from the Fermi level accompanied by a decrease of the work function. In contrast, oxygen leads to a VBM shift towards the Fermi level and a concomitant increase of the work function. The effect of oxygen is found to predominate in ambient air with an associated shift of the energy levels by up to 0.6 eV. Overall, the findings contribute to an improved understanding of the structure-property relationships of HOIPs and emphasize the impact of least variation in the environmental conditions on the reproducibility of the electronic properties of perovskite materials.

<p>A wide range of optoelectronic applications demand semiconductors optimized for purpose.</p> <p>My research focused on data-driven identification of ABX3 Halide perovskite compositions for optimum photovoltaic absorption in solar cells.</p> <p>I trained machine learning models on previously reported datasets of halide perovskite band gaps based on first principles computations performed at different fidelities.</p> <p>Using these, I identified mixtures of candidate constituents at the A, B or X sites of the perovskite supercell which leveraged how mixed perovskite band gaps deviate from the linear interpolations predicted by Vegard's law of mixing to obtain a selection of stable perovskites with band gaps in the ideal range of 1 to 2 eV for visible light spectrum absorption.</p> <p>These models predict the perovskite band gap using the composition and inherent elemental properties as descriptors.</p> <p>This enables accurate, high fidelity prediction and screening of the much larger chemical space from which the data samples were drawn.</p> <p><br></p> <p>I utilized a recently published density functional theory (DFT) dataset of more than 1300 perovskite band gaps from four different levels of theory, added to an experimental perovskite band gap dataset of \textasciitilde{}100 points, to train random forest regression (RFR), Gaussian process regression (GPR), and Sure Independence Screening and Sparsifying Operator (SISSO) regression models, with data fidelity added as one-hot encoded features.</p> <p>I found that RFR yields the best model with a band gap root mean square error of 0.12 eV on the total dataset and 0.15 eV on the experimental points.</p> <p>SISSO provided compound features and functions for direct prediction of band gap, but errors were larger than from RFR and GPR.</p> <p>Additional insights gained from Pearson correlation and Shapley additive explanation (SHAP) analysis of learned descriptors suggest the RFR models performed best because of (a) their focus on identifying and capturing relevant feature interactions and (b) their flexibility to represent nonlinear relationships between such interactions and the band gap.</p> <p>The best model was deployed for predicting experimental band gap of 37785 hypothetical compounds.</p> <p>Based on this, we identified 1251 stable compounds with band gap predicted to be between 1 and 2 eV at experimental accuracy, successfully narrowing the candidates to about 3% of the screened compositions.</p>