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Poly (9-vinylcarbazole) (PVK) is an attractive hole transport material for organometallic-halide perovskite solar cell (PSC) because of its excellent hole transport ability and great film-forming property. Nowadays, the PVK hole-transport-layer (HTL) based PSC suffered from low power conversion efficiency (PCE), which mainly caused by bad interface contact between PVK and perovskite film, including the energy level mismatch. Doing PVK is an effective and facial method to modify the buried interface of perovskite film. In this work, 1,1-bis [(di-4-tolylamino)phenyl]cyclohexane (TAPC) was added into PVK HTL. Experimental results show that a highest occupied molecular orbital (HOMO) level elevation was achieved after doping TAPC. Meanwhile perovskite films with increased crystallinity, reduced defect density and enhanced light absorbance were obtained. Finally, the PCE of PSCs were enhanced from 10.81% of the control device to 15.65% of PVK + 40% TAPC device. The reason that responsible for the energy level lifting, morphology improvement and performance enhancement were thoroughly investigated and analyzed. This work provides an alternative option for the dopant in PVK to improve the buried interface of perovskite film, and therefore, the performance of PSCs. • TAPC was doped in PVK to modify the buried interface of perovskite film. • The HOMO level of HTL was lifted after doping TAPC in PVK. • A reduction in defect density and an enhancement in crystallinity of perovskite film were achieved. • The PCE of PSC was enhanced from 10.98% to 15.35%. [ABSTRACT FROM AUTHOR]

Computational spectrometers are explored to overcome the disadvantages of large size, narrow bandwidth and low spectral resolution suffered by conventional spectrometers. However, expensive spectral encoders and unstable algorithms impede widespread applications of the computational spectrometers. In this paper, we propose a neural network (NN)-based miniaturized spectrometer with a frosted glass as its spectral encoder. The frosted glass has the merits of easy fabrication, low loss, and high throughput. In order to evaluate the reconstruction ability, several frequently used algorithms such as the multilayer perceptron (MLP), convolutional neural network (CNN), residual convolutional neural network (ResCNN), and Tikhonov regularization are adopted to reconstruct different types of spectra in sequence. Experimental results show that the reconstruction performance of the MLP is better than other algorithms. By using the MLP network, the average mean squared error is 1.38 × 10 -3 and the reconstruction time is 16 µs. At the same time, a spectral resolution of 1.4 nm and a wavelength detection range of 420 nm-700 nm are realized. The effectiveness of this approach is also demonstrated by implementing a reconstruction for an unseen multi-peak spectrum. Equipped with the size, low cost, real time, broad-band, and high-resolution spectrometer, one may envision many portable wavelength analysis applications.