arXiv subjects

Wolfgang Tress

Publications and source records attributed to Wolfgang Tress.

3 recordsLinked to original sources

arXiv2026

Divergent Solid-state Conversion Pathways in Evaporated All-perovskite Tandem Solar Cells

Sequential thermal evaporation (sTE) is emerging as a solvent-free route to high-quality mid-bandgap perovskites, but its extension to mixed-halide wide-bandgap (WBG) and Sn-Pb narrow-bandgap (NBG) absorbers for all-perovskite tandem solar cells (TSCs) remains limited by an incomplete understanding of solid-state conversion. Here, using time-sliced ex situ analysis, we reveal divergent solid-state conversion mechanisms in sequentially evaporated WBG and NBG precursor stacks. In WBG stacks, formamidinium (FA)-containing species penetrate the inorganic template and Br/I redistribution precedes substantial three-dimensional perovskite formation. The photoactive phase then crystallizes from a chemically mixed reservoir, and absolute PbBr$_2$ thickness, rather than nominal PbBr$_2$/PbI$_2$ ratio, determines the final bandgap. In NBG stacks, by contrast, an early Pb-rich perovskite phase forms upon formamidinium iodide deposition, restricting further FA penetration into the buried SnI$_2$ precursor. Subsequent annealing promotes rapid lattice reorganization faster than Sn/Pb interdiffusion, leaving vertical compositional gradients. Guided by these insights, we develop sTE absorbers with bandgaps spanning 1.26-1.96 eV and demonstrate the first evaporated all-perovskite TSC, reaching a power conversion efficiency of 19.2%. Encapsulated tandems retain on average 80% of their initial efficiency after 1,200 h at 65 $^\circ$C (ISOS-D-2). These results establish bandgap-specific control of solid-state conversion as a design principle for sequentially evaporated perovskite tandem photovoltaics.

cond-mat.mtrl-sci↗

arXiv2026

Overcoming Transport Layer Bottlenecks to Quantify Ionic Parameters from Transient Ion Current Measurements of Perovskite Solar Cells

In perovskite solar cells (PSCs), voltage step-induced transient ion current (TIC) measurements, commonly referred to as bias-assisted charge extraction (BACE), are frequently used to quantify ion density. Drift-diffusion simulations predict that the ion density computed from TIC saturates once mobile ions screen the electric field in the perovskite. However, experimental studies often report ion densities orders of magnitude above this limit, whose physical origin remains incompletely explained in terms of transport layer (TL) properties. In this work, the capacitance of the TLs is identified to be the fundamental bottleneck: the maximum quantifiable ion density is limited to the charge that can accumulate at the perovskite/TL interfaces, so that TIC most often depends more strongly on TL properties than on the ionic properties of the perovskite. Experiments with systematically varied C$_{\rm 60}$ electron-TL thickness (p-i-n) and Spiro-OMeTAD hole-TL doping (n-i-p) confirm this dependence across architectures. To overcome this limitation, the importance of a correction based on the average ionic displacement is discussed, and it is shown how extrapolating the TL-thickness trend towards the TL-free situation yields the actual ionic conductivity of the absorber, alongside density and mobility, depending on the assumed ionic model. Simulations are also examined in which ion penetration into the TLs or initial accumulation under forward bias raise the capacitive limit and extend TIC sensitivity to higher ion densities. The slow release of trapped carriers is also discussed as a potential source of current which can inflate the TIC signal. Overall, the presented analysis provides important practical considerations for interpreting TIC and quantifying ionic properties in PSCs.

cond-mat.mtrl-sci↗

arXiv2026

AI-supported Degradation Study of Carbon-based Perovskite Solar Cells: Learning the Device Physics of Perovskite Solar Cells: A Drift-Diffusion Guided Autoencoder Approach

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 several physical parameters that contain information about charge transport and recombination using Machine Learning (ML), which allows for in situ tracking of possible signs of degradation. These results are compared to what can be classically interpreted by analysing changes in J-V curves, and the evolution of the predicted parameters is studied. The predictions are then used to simulate a digital twin of the measured devices, and their physical implications and the differences between measurements and devices are discussed.

cond-mat.mtrl-sci↗