Related papers: Inversion of the impedance response towards physic…
Interpreting the impedance response of perovskite solar cells (PSC) is significantly more challenging than for most other photovoltaics. This is for a variety of reasons, of which the most significant are the mixed ionic-electronic…
Impedance spectroscopy (IS) is a relatively straightforward experimental technique that is commonly used to obtain information about the physical and chemical characteristics of photovoltaic devices. However, the non-standard physical…
Rational design of interface passivators for perovskite solar cells is hindered by the entanglement of intrinsic molecular efficacy with extrinsic platform-dependent performance - a confounding factor that obscures true chemical advances.…
Fundamental electronic processes such as charge-carrier transport and recombination play a critical role in determining the efficiency of hybrid perovskite solar cells. The presence of mobile ions complicates the development of a clear…
The discovery of effective molecular modulators is essential for advancing perovskite solar cells (PSCs), but the research process is hindered by the vastness of chemical space and the time-consuming and expensive trial-and-error…
The significant advancements in perovskite solar cell (PSC) research and development have reignited optimism for the practicality of solar energy. A deeper comprehension of the fundamental mechanisms is essential for enhancing the current…
Fundamental working mechanisms of perovskite solar cells remain an elusive topic of research. Impedance Spectroscopy (IS) application to perovskite-based devices generates uncommon features and misleading outputs, mainly due to the lack of…
Drift-diffusion model is an indispensable modeling tool to understand the carrier dynamics (transport, recombination, and collection) and simulate practical-efficiency of solar cells (SCs) through taking into account various carrier…
Metal halide perovskite solar cells have gained widespread attention due to their high efficiency and high defect tolerance. The absorbing perovskite layer is as a mixed electron-ion conductor that supports high rates of ion and charge…
Many recent advances in metal halide perovskite solar cell (PSC) performance are attributed to surface treatments which passivate interfacial trap states, minimise charge recombination and boost photovoltages. Surprisingly, these…
One of the key challenges for future development of efficient and stable metal halide perovskite solar cells is related to the migration of ions in these materials. Mobile ions have been linked to the observation of hysteresis in the…
Perovskite thin films hold great promise for optoelectronic applications, such as solar cells and light emitting diodes. A challenge is that defects are unavoidably formed in the material. Thorough understanding of the defect formation and…
Carbon-electrode-based PSC devices are stressed under 1 Sun equivalent illumination in a stability setup, and different scan-speed dependent current-voltage (J-V) curves are measured during aging. The collected data is used to estimate…
Perovskite solar cells (PSC) are shown to behave as coupled ionic-electronic conductors with strong evidence that the ionic environment moderates both the rate of electron-hole recombination and the band offsets in planar PSC. Numerous…
Due largely to challenges associated with physical interpretability of machine learning (ML) methods, and because model interpretability is key to credibility in management applications, many scientists and practitioners are hesitant to…
We examine the optical properties of a system of nano and micro particles of varying size, shape, and material (including metals and dielectrics, and sub-wavelength and super-wavelength regimes). Training data is generated by numerically…
Metal halide perovskites (MHPs) are nowadays one of the most studied semiconductors due to their exceptional performance as active layers in solar cells. Although MHPs are excellent solid-state semiconductors, they are also ionic compounds,…
Machine learning has emerged as a promising approach for estimating material parameters in solar cells. Traditional methods for parameter extraction often rely on time-consuming numerical simulations that fail to capture the full complexity…
While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, inductive reasoning and knowledge extraction remain elusive tasks, in part because of the difficulty extracting fungible knowledge…
Perovskite solar cells (PSCs) without a hole transport layer (HTL) offer a cost-effective and stable alternative to conventional architectures, utilizing only an absorber layer and an electron transport layer (ETL). This study presents a…