While organometal halide perovskites are promising for a variety of optoelectronic applications, the morphological and compositional defects introduced by solution processing techniques have hindered efforts at understanding their fundamental properties. To provide a detailed picture of the intrinsic carrier transport properties of methylammonium lead iodide without contributions from defects such as grain boundaries, we utilized pump-probe microscopy to measure diffusion in individual crystalline domains of a thin film. Direct imaging of carrier transport in 25 individual domains yields diffusivities between 0.74 and 1.77 cm s, demonstrating single-crystal-like, long-range transport characteristics in a thin film architecture. We also examine the effects of excitation density on carrier diffusivity, finding that transport is nearly independent of photoexcited carrier density between 6 × 10 cm and 4 × 10 cm. Transport modeling of the observed density independence suggests that strong carrier-phonon scattering coupled with a large static relative permittivity is responsible for the unusual transport characteristics of methylammonium perovskite.

Stimulated Raman scattering (SRS) microscopy is a label-free quantitative chemical imaging technique that has demonstrated great utility in biomedical imaging applications ranging from real-time stain-free histopathology to live animal imaging. However, similar to many other nonlinear optical imaging techniques, SRS images often suffer from low signal to noise ratio (SNR) due to absorption and scattering of light in tissue as well as the limitation in applicable power to minimize photodamage. We present the use of a deep learning algorithm to significantly improve the SNR of SRS images. Our algorithm is based on a U-Net convolutional neural network (CNN) and significantly outperforms existing denoising algorithms. More importantly, we demonstrate that the trained denoising algorithm is applicable to images acquired at different zoom, imaging power, imaging depth, and imaging geometries that are not included in the training. Our results identify deep learning as a powerful denoising tool for biomedical imaging at large, with potential towards in vivo applications, where imaging parameters are often variable and ground-truth images are not available to create a fully supervised learning training set.

While significant research efforts directed toward characterizing the excited-state dynamics of lead halide perovskites have enabled promising advances in photovoltaic, light-emitting diode, and laser technologies, a detailed correlation between composition and functionality in this promising class of materials remains unestablished. We use pump−probe microscopy to characterize both transport and relaxation dynamics in individual crystals of CsPbI 2 Br, a mixed halide, all-inorganic analogue to the well-studied organic−inorganic hybrid perovskites. In contrast to the methylammonium lead tri-iodide perovskite, we find excited-state dynamics that decay primarily via first-order and Auger mechanisms. By global fitting of power-dependent kinetics collected from individual domains, we find a range of Auger rate constants between 3.3 × 10 −30 and 1.5 × 10 −28 cm 6 /s, with negligible contributions from secondorder (bimolecular) processes. Direct imaging of the excited-state spatial evolution reveals an average diffusion constant of 0.27 cm 2 /s, a value that is nearly an order of magnitude smaller than that of single-crystal, organic−inorganic analogues.

Cellular imaging is an active area of research that enables researchers to monitor cellular dynamics, as well as responses to various external stimuli (physiological stress, exogenous compounds, etc.). Stimulated Raman scattering (SRS) microscopy is one popular experimental tool used to image cells, largely because of its chemical specificity, high spatial resolution, and high image acquisition speed. In this Perspective, the theoretical background and experimental implementation of SRS microscopy are discussed and recent developments in the field of cellular imaging with SRS are highlighted and summarized.

Stimulated Raman scattering (SRS) microscopy is a promising technique for studying tissue structure, physiology, and function. Similar to other nonlinear optical imaging techniques, SRS is severely limited in imaging depth due to the turbidity and heterogeneity of tissue, regardless of whether imaging in the transmissive or epi mode. While this challenge is well known, important imaging parameters (namely maximum imaging depth and imaging signal to noise ratio) have rarely been reported in the literature. It is also important to compare epi mode and transmissive mode imaging to determine the best geometry for many tissue imaging applications. In this manuscript we report the achievable signal sizes and imaging depths using a simultaneous epi/transmissive imaging approach in four different murine tissues; brain, lung, kidney, and liver. For all four cases we report maximum signal sizes, scattering lengths, and achievable imaging depths as a function of tissue type and sample thickness. We report that for murine brain samples thinner than 2 mm transmissive imaging provides better results, while samples 2 mm and thicker are best imaged with epi imaging. We also demonstrate the use of a CNN-based denoising algorithm to yield a 40 µm (24%) increase in achievable imaging depth.