The study of perovskite solar cells degradation is a complex issue due to the multitude of phenomena that can contribute to it. Normally the degradation can produce two main impacts, decrease of charge collection (lowering photocurrent) or increase of recombination (lowering photovoltage). We need to find dynamical signatures of the phenomena causing these effects to discover the physical reasons for the devaluated performance. Recently, we have obtained new insights using a model that combines several electronic and ionic processes, that can produce capacitive and inductive response in different circumstances. These models are very successful to describe huge memory effects and hysteresis in perovskite memristors, by the combination of different techniques: current-voltage scan, time transients, and impedance spectroscopy. Here we show the changes of impedance spectroscopy and time transients as a diagnosis of evolution of degradation in the perovskite solar cells. This analysis expands the tools available for understanding the transformation of perovskite solar cells under working conditions.

Electrochemical impedance spectroscopy measurements of perovskite solar cells (PSCs) show characteristic features at low frequencies, such as a large illumination-dependent capacitance [1,2]. While this effect is well known, the debate on its origin persists [3,4]. An illumination-dependent increase in the conductivity of ionic charge carriers was suggested early on [2]. However, experiments to elucidate the presence of such a photo-conductive effect require special devices or measurement techniques and neglect possible influences of the enhanced electronic charge concentrations. Thus, only a few studies investigated this effect in detail.

Using drift-diffusion simulations and two novel techniques to analyze the simulation results, we show how the illumination-dependent part of the capacitance originates from electronic currents that are amplified due to the screening of the alternating electric field by the ions [5]. This is inherently caused by the mixed electronic-ionic interaction. Counter-intuitively, an illumination-dependent ion conductivity even reduces the magnitude of the capacitance increase.

As the presence and magnitude of a low-frequency capacitance increase by itself are unsuited to assess the presence of illumination-dependent ion conductivity, we propose a novel characterization technique based solely on capacitance measurements at short-circuit on fully integrated devices. The frequency shift of the onset in capacitance is extracted at varying illumination intensity. This quantity shows a distinct qualitative difference depending on whether the ion conductivity depends on illumination or is constant. As these measurements can be performed on unaltered, fully integrated devices and with standard equipment, the method is well suited for widespread investigation of a photo-conductive effect in different materials and devices or in response to degradation.

The method is applied to a range of perovskite solar cells with different active layer materials. Remarkably, all measured samples show a clear signature of photoenhanced ion conductivity, posing fundamental questions on the underlying nature of the photosensitive mechanism.

Semitransparent perovskite solar cells are emerging as a promising technology for applications such as energy-generating windows and integrated photovoltaics, where a balance between efficiency, transparency, and aesthetics is essential. [1-5] Designing such devices demands a deep understanding of light absorption, reflection, and transmission within the cell structure. In this work, we outline a simulation-based methodology for optimizing these devices, beginning with the calculation of optical constants for each layer in the cell, which is key not only for understanding the limitations associated to different devices, but also for accurately predicting the best design for each application. [6-7] We show the importance of validating the accuracy of the optical model by comparing simulated results with experimental data of full devices, performing global fits of different devices which ensures the model reliability. Finally, we explore various optimization strategies aimed at achieving specific performance targets, such as maximizing power conversion efficiency while maintaining desired levels of transparency and color rendering properties. By integrating accurate material characterization, rigorous simulation validation, and strategic optimization, this approach accelerates the development of high-performance, semitransparent perovskite solar cells tailored for diverse applications. This framework offers a practical pathway for bridging the gap between fundamental research and scalable device design.

In this talk we set out to explain the physics that gives rise to two surprising phenomena that are frequently observed in perovskite solar cells, namely inverted hysteresis (IH) and the appearance of a third (intermediate frequency) feature in PSC impedance plots. Both these phenomena are caused by the leakage of charge carriers from one of the transport layers into the perovskite in sufficient numbers that they are able to partially screen electric fields in the interior of the perovskite layer.  In order to better understand these phenomena, we analyze the standard drift-diffusion model of a planar three-layer PSC, using asymptotic techniques, to derive a reduced order model capable of describing the screening effects in the perovskite layers arising both from the presence of ions (which redistribute slowly) and from that of charge carriers (which adjust almost instantaneously). This approximate reduced order model shows excellent agreement with numerical simulations to the full drift-diffusion model and provides fundamental insights into the causes of inverted hysteresis reconciling the alternative explanations of this phenomenon found in the literature. Furthermore it can be used to explain the appearance of the third (intermediate frequency) feature in PSC impedance plots. Understanding why these atypical phenoneman occur is important for the device physicist because their appearance can be used to diagnose certain properties of the cell. 

In the last 10+ years of development, perovskite solar cells have achieved excellent efficiencies and a more gradual improvement in stability. Perovskite materials for photovoltaics are mixed electronic-ionic conductors [1]. It is therefore essential to consider the density and mobility of ionic defects in continuum-level models of perovskite-based devices, including tandem cells. Ionic defects impact both the steady-state and dynamic behaviour of perovskite cells [2] by modulating the electric field and charge carrier recombination rates.

An initial density of ionic defects is formed during cell fabrication; however studies suggest that additional defects can form during operation, under bias and illumination. Accumulation of mobile ion defects at the perovskite/transport layer interfaces results in undesirable degradation and performance loss over a timescale of hundreds of hours [3].

Improved modelling and simulation is required to understand the process of defect generation and quantify its impact on device characteristics over relevant timescales. We extend the charge-transport model that underpins our open-source IonMonger tool [4] and perform simulations to investigate the impact of defect generation and migration on perovskite solar cell performance.

Zinc Phosphide (Zn₃P₂) is a promising earth abundant absorber for photovoltaics, offering direct bandgap of (1.5 eV), high optical absorption coefficient in the visible range (10⁴ - 10⁵ cm^-1) and long carrier diffusion length, making it ideal for thin-film solar cells.

Monocrystalline Zn₃P₂, grown via Selective Area Epitaxy (SAE) naturally forms textured films with periodic pyramid-shaped nanostructures [1], [2], [3]. The growth mechanism necessitates a SiO₂ patterned substrate, where Zn₃P₂ can grow on selectively exposed area of the substrate.

These naturally occurring nanostructures can be controlled in height and periodicity, depending on the opening dimension, and offer potential light management benefits to minimize in-coupling and out-coupling losses. The patterned substrate is a consequence of the growth mechanism; however, it provides the advantages of shaping and reducing the p-n junction contact area with valuable prospects for enhanced carrier management.

In this study, we present comprehensive device modelling of textured Zn₃P₂-based solar cells using coupled 3D optical and electrical simulations, employing FDTD and a Schrödinger-Poisson drift-diffusion solver to optimize the solar cell design.

Optical simulations are used to study the optical phenomena occurring within this complex structure including Mie modes, Rayleigh anomalies, and Fabry-Perot resonances. These effects are analyzed as a function of the pyramid height, periodicity, and thickness of the thin film beneath the pyramid. By tuning these modes and their interactions through geometric adjustments, the photocurrent is optimized to approach up to 89% of the Lambertian limit.

Electrical simulations are employed to study the effect of the reduced p-n junction contact area between Zn₃P₂ and the substrate.  The junction area fraction was varied from 100% (continuous interface) down to 1.4%.  Analysis of the simulated JV curves under both illumination and dark conditions highlighted the benefits of reducing the junction area fraction, for enhancing the open-circuit voltage (V_oc) up to 0.12V.

This optical-electrical model exploits the potential for Zn₃P₂-based solar cells grown by SAE combining advanced light management and optimized junction design to enhance performance.

We present a tour through the many and varied impedance spectra observed in perovskite solar cells, including loops, mid-frequency features, and the so-called ‘giant’ and ‘negative’ capacitances. Beginning with single-arc spectra, progressing through double- and triple-arcs, and finishing with discussion of the effects of degradation, we classify observed spectra into generic types, named for animals resembling their Nyquist plot. Remarkably, all of these spectral ‘animals’ can be faithfully replicated using the well-established ionic-electronic drift-diffusion model with a single mobile ion species, eliminating the need for speculative physics.  Perovskite solar cell spectra often defy traditional interpretations, prompting increasingly intricate equivalent circuit models comprising elements without a sensible physical meaning. However, our animal-inspired framework offers a simpler, more intuitive approach to spectral analysis. This ‘spotter’s guide’ allows researchers to identify spectral features and their underlying physical origins based only shape recognition from a safe distance, unlocking insights without the need to venture into the wilds of computational modelling.

Ion migration lies at the heart of perovskite solar cell (PSC) performance and stability. Different experimental techniques have been used to better understand the puzzling performance of ion migration in PSCs. In this talk, I will present our last results on the characterization of ion migration by two characterization measurements, X-Ray Photoemission Spectroscopy (XPS) [1] and impedance spectroscopy (IS) measurements [2] interpreted by drift-diffusion (DD) simulations.

For the characterization of the electronic and chemical properties of halide perovskites surface and interfaces to adjacent layers, XPS is a versatile technique. We use a lateral microstructure in which two different charge transport layers in co-planar contact configuration are separated by a perovskite channel constituting a lateral solar cell [1]. We apply reverse and forward bias with typical PSC operational conditions to the lateral microstructure to analyze the performance of the PSCs focusing on the evolution of band bending. 

 On the other hand, Impedance Spectroscopy is a consolidated tool to analyze the opto-electronic response of PSCs. Lately, it has been proved the proof of concept of the coupling of DD simulations with IS for a better understanding of device degradation [2]. I will present our last results on numerical DD simulations, and in particular, the study case of NiOx-based PSCs with various interface passivation treatments [2]. Our simulations approach several experimental measurements of IS under short-circuit conditions at different illumination intensities, along with bias-stress accelerated operational stability tests under constant illumination. Drift-diffusion simulations suggest that interface modification with the hole transport material may modify ion mobility within the perovskite layer. Our findings provide a systematic approach for characterizing instability mechanisms in PSCs using IS under short-circuit conditions.

Dye-sensitized solar cells (DSSCs) are promising for glazing applications due to their potential for semi-transparency. However, their photovoltaic performance and light transmittance are largely determined by the dye and electrolyte used, both of which are fixed during the manufacturing process. Electrolytes are critical to energy technologies, yet their optimization is challenging due to the complexity of their formulations and the multitude of interacting chemical components. Typically, optimization requires numerous experiments, as the effects of these components are often correlated and difficult to analyze independently.

In this study, we employed a design of experiments (DoE) methodology combined with machine learning (ML) to design electrolytes that effectively balance two typically conflicting properties: visible transparency and power conversion efficiency (PCE). The model required only a limited number of experiments for training and exhibited excellent predictive agreement with experimental results.

First, we optimized iodine-based electrolytes to fabricate solar cells with a visible transparency range of 34% and a maximum PCE of 2.94%. We then extended this approach to electrolytes based on alternative redox systems. Using our data-driven modeling approach, we optimized a TEMPO-based electrolyte, achieving photochromic semi-transparent cells with a 42% transmittance variation and a PCE of 2.16%. For opaque cells, this novel electrolyte delivered a PCE of 3.46% with a photochromic dye and an impressive 7.64% PCE when paired with a non-photochromic dye.

An efficient way to substantially increase the surface area coverage of photovoltaic (PV) modules, while maintaining the existent electricity infrastructure, is to integrate such modules into the roofs and facades of buildings. Aside from the practical technical requirements, such as high power conversion efficiency (PCE), low cost, and long lifetime, photovoltaic modules for building applications also necessitate an aesthetically attractive design, which can be achieved by finetuning their color according to the specific architectural needs of the building [1,2].

For some PV technologies, intrinsic coloration with a limited selection of colors can be attained by selecting absorbing materials with specific spectral absorption behavior [3,4]. To achieve a broader range of colors typically requires the addition of colored encapsulants, printed glass covers or interlayers in front of the PV module. Most commonly, pigments or chemical colorants are employed to accomplish such coloration, but they tend to absorb a significant portion of the solar spectrum, drastically reducing the PCE of the resulting PV module. A promising alternative to overcome such issues is to take advantage of the interference effects between non-absorbing dielectric materials with contrasting refractive indexes to design PV modules with vivid structural colors and low optical losses [5,6].

Periodic distributed Bragg reflectors (DBRs) have been considered in some previous works to realize structural coloration in PV modules [7,8], but they typically fail to reproduce some colors, especially reds, owing to the appearance of higher-order interference peaks in the reflectance spectra. To reach a broader color gamut thus requires breaking up the periodicity inherent to DBRs in a controlled way, such that the optical response of the PV module is tuned to achieve the desired coloration. In the present work, a numerical approach combining the electromagnetic description of light propagation together with the use of optimization algorithms is considered to optimize the configuration of a dielectric multilayer structure deposited directly on top of a polymer foil interlayer that is placed in between the substrate and the PV cell, targeting different structural colors for the PV module. As it will be demonstrated, the non-trivial aperiodic structures obtained from this method cover a broader color gamut than the simpler DBRs, and are key to achieve different red hues, including the ones of commonly used raw construction materials, such as clay or brick. As a proof-of-concept, a mini-module with a selected color is assembled by depositing an aperiodic multilayer structure with a numerically optimized configuration on top of a polymeric foil, using a roll-to-roll physical vapor deposition method. This allows not only to validate the numerical predictions regarding the structural color achieved, but also to demonstrate the low photovoltaic loss associated with this sort of multilayers and the scalability of the processes used to fabricate such colored modules.

Extracting relevant material properties from experimental measurement is challenging, especially in the field of organic semiconductors. The models used to fit and reproduce experimental results are complex with multiple correlated parameters, which render the use of such model to extract relevant material properties very complicated. To overcome such limitations, we consider in this work the use of Bayesian inference for parameter estimation. Bayesian inference is a powerful tool to extract parameters distribution considering the experimental observations and the models considered [1].

 In this study, we apply Bayesian inference techniques to analyze temperature-dependent photoluminescence spectra obtained from organic solar cells. We model the photoluminescence spectra of organic semiconductor films using a semi-classical marcus-levich-jortner expression [2, 3]. We model the spectra under different temperature and reproduce the change in spectral shape and relative intensity. Using the model and a Bayesian inference approach, we extract distributions for the different relevant properties of interest such as: Energy of the first excited state, the static disorder in energy, the reorganization energies (low and high frequency) as well as the dominant frequency mode. Our approach provides robust parameter estimation and quantifies uncertainties, enabling more accurate characterization of organic semiconductor materials. The results demonstrate the effectiveness of Bayesian inference in unraveling complex material properties and guiding future research in renewable energy applications.

Organic light-emitting diodes have been successfully commercialized by the display industry, yet there are still basic challenges in modeling their operation and degradation. In this talk, I will highly recent work establishing the thermodynamic limit of OLEDs, which shows that strong exciton binding in these devices requires a higher voltage to achieve the same luminance as a comparable inorganic LED, and that the best OLEDs reported to date have likely reached this limit. I will discuss how the well-known Shockley-Read-Hall (SRH) expression for trap-mediated recombination in OLEDs is modified to account for the finite lifetime of dopant excitons and its implication for minimizing OLED drive voltage. Finally, I will discuss recent work focused on understanding blue OLED degradation where exciton-polaron-based degradation kinetics are implemented into a drift-diffusion-based device model.  The results suggest that OLED luminance loss and voltage rise largely originate from different sets of degradation-induced defect states formed in the emissive and transport layers, respectively, which opens up new opportunities to optimize the performance and lifetime of these devices.