Publication date: 21st July 2025
Photoluminescence imaging techniques are commonly used to investigate the optoelectronic and transport properties of halide perovskite absorbers and devices [1]. However, obtaining precise spatial maps of key physical parameters—such as carrier lifetime, quasi-Fermi level splitting (QFLS), and bandgap energy (Eg)—requires a high local signal-to-noise ratio (SNR), typically achieved through long acquisition times. Prolonged measurements can compromise data integrity due to changes occurring during acquisition. This limitation is particularly critical in operando experiments conducted under elevated humidity or temperature, where shortened acquisition times are essential to track dynamic changes in the material’s properties in real time.
To address this challenge, we previously demonstrated a denoising strategy based on Total Variation Regularization (TVR) [2], enabling the extraction of high-quality lifetime images from quickly acquired, noisy time-resolved fluorescence imaging (TR-FLIM) datasets [3]. In this work, we present a significant advancement by applying a tailored version of the Noise2Noise (N2N) algorithm [4] to denoise multidimensional datasets. The main advantage of N2N lies in its unsupervised learning framework, which makes it particularly well-suited to complex, real-world image denoising situations compared to TVR.
Using this approach, we performed in-situ TR-FLIM data acquisitions on halide perovskite thin films—specifically triple-cation compositions—under controlled humidity conditions (relative humidity XX%). This allowed, for the first time, micrometer-scale tracking of local carrier lifetime degradation during environmental exposure. The reduced acquisition time made possible by N2N denoising enabled the resolution of spatial heterogeneity in perovskite degradation.
In conclusion, coupling advanced PL imaging techniques with unsupervised denoising approaches like N2N opens new avenues for accelerated, high-resolution characterization of halide perovskites. This methodology not only deepens our understanding of material stability under realistic conditions but also holds broader potential for studying other beam-sensitive materials and for developing fast, reliable imaging workflows for operando and accelerated experiments.
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