Formamidinium lead triiodide (FAPbI 3 ) has been demonstrated as the most efficient perovskite system to date, due to its excellent thermal stability and an ideal bandgap approaching the Shockley-Queisser limit. Whereas, there are intrinsic quantum confinement effects in FAPbI 3 , which lead to unwanted non-radiative recombination. Additionally, the black α-phase of FAPbI 3 is unstable under room temperature due to the significant residual tensile stress in the film. To simultaneously address the above issues, a thermallyactivated delayed fluorescence polymer P1 is designed in the study to modify the FAPbI 3 film. Owing to the spectral overlap between the photoluminescence of P1 and absorption of the above-bandgap quantum wells of FAPbI 3 , the Förster energy transfer occurs at the P1/FAPbI 3 interface, which further triggers the Dexter energy transfer within FAPbI 3 . The exciton "recycling" can thus be realized, which reduces the non-radiative recombination losses in perovskite solar cells (PSCs). Moreover, P1 is found to introduce compressive stress into FAPbI 3 , which relieves the tensile stress in perovskite. Consequently, the PSCs with P1 treatment achieve an outstanding power conversion efficiency (PCE) of 23.51%. Moreover, with the alleviation of stress in the perovskite film, flexible PSCs (f-PSCs) also deliver a high PCE of 21.40%.
With rapid development of photovoltaic technology, flexible perovskite solar cells (f-PSCs) have attracted much attention for their light weight, high flexibility and portability. However, the power conversion efficiency (PCE) achieved so far is not yet comparable to that of rigid devices. This is mainly due to the great challenge of depositing homogeneous and high-quality perovskite films on flexible substrate. In this study, the pre-buried 3-aminopropionic acid hydroiodide (3AAH) additives into the electron transport layer (ETL) and modified the ETL/perovskite (PVK) interface by a bottom-up strategy. 3AAH treatment induced a templated perovskite grain growth and improved the quality of the ETL. By this, the residual stresses generated in PVK during the annealing-cooling process are released and converted into micro-compressive stresses. As a result, the defect density of f-PSCs with pre-buried 3AAH is reduced and the photovoltaic performance is greatly improved, reaching an exceptional PCE of 23.36%. This strategy provides a new idea to bridge the gap between flexible and rigid devices.
With the advent of large and dense seismic arrays, novel, cheap, and fast imaging and inversion methods are needed to exploit the information captured by stations in close proximity to each other and produce results in near real time. We have developed a sequence of fast seismic acquisition for dispersion curve extraction and inversion for 3D seismic models, based on wavefield gradiometry, wave equation inversion, and machine-learning technology. The seismic array method that we use is Helmholtz wave equation inversion using measured wavefield gradients, and the dispersion curve inversions are based on a mixture of density neural networks (NNs). For our approach, we assume that a single surface wave mode dominates the data. We derive a nonlinear relationship among the unknown true seismic wave velocities, the measured seismic wave velocities, the interstation spacing, and the noise level in the signal. First with synthetic and then with the field data, we find that this relationship can be solved for unknown true seismic wave velocities using fixed point iterations. To estimate the noise level in the data, we need to assume that the effect of noise varies weakly with the frequency and we need to be able to calibrate the retrieved average dispersion curves with an alternate method (e.g., frequency wavenumber analysis). The method is otherwise self-contained and produces phase velocity estimates with tens of minutes of noise recordings. We use NNs, specifically a mixture density network, to approximate the nonlinear mapping between dispersion curves and their underlying 1D velocity profiles. The networks turn the retrieved dispersion model into a 3D seismic velocity model in a matter of seconds. This opens the prospect of near-real-time near-surface seismic velocity estimation using dense (and potentially rolling) arrays and only ambient seismic energy.
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