Surface Passivation Suppresses Local Ion Motion in Halide Perovskites.

We use scanning probe microscopy to study ion migration in formamidinium (FA)-containing halide perovskite semiconductor Cs0.22FA0.78Pb(I0.85Br0.15)3 in the presence and absence of chemical surface passivation. We measure the evolving contact potential difference (CPD) using scanning Kelvin probe microscopy (SKPM) following voltage poling. We find that ion migration leads to a ∼100 mV shift in the CPD of control films after poling with 3 V for only a few seconds. Moreover, we find that ion migration is heterogeneous, with domain interfaces leading to a larger CPD shift than domain interiors. Application of (3-aminopropyl)trimethoxysilane (APTMS) as a surface passivator further leads to 5-fold reduction in the CPD shift from ∼100 to ∼20 mV. We use hyperspectral microscopy to confirm that APTMS-treated perovskite films undergo less photoinduced halide migration than control films. We interpret these results as due to a reduction in the halide vacancy concentration after APTMS passivation.

A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data.

Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consuming to analyze and calibrate. Here, we design and train a regression neural network (NN) that accelerates and simplifies the extraction of local dynamics from SPM data directly in a cantilever-independent manner, allowing the network to process data taken with different cantilevers. We validate the NN's ability to recover local dynamics with a fidelity equal to or surpassing conventional, more time-consuming, calibrations using both simulated and real microscopy data. We apply this method to extract accurate photoinduced carrier dynamics on n = 1 butylammonium lead iodide, a halide perovskite semiconductor film that is of interest for applications in both solar photovoltaics and quantum light sources. Finally, we use SHapley Additive exPlanations to evaluate the robustness of the trained model, confirm its cantilever-independence, and explore which parts of the trEFM signal are important to the network.