Organic–inorganic
hybrid perovskite solar cells (PSCs) has
achieved the power conversion efficiency (PCE) of 25.2% in the last
10 years, and the PCE of inverted PSCs has reached >22%. The rapid
enhancement has partly benefited from the employment of suitable hole
transport layers. Especially, poly(3,4-ethenedioxythiophene):poly(styrenesulfonate)
(PEDOT:PSS) is one of the most widely used polymer hole transport
materials in inverted PSCs, because of its high optical transparency
in the visible region and low-temperature processing condition. However,
the PCE and stability of PSCs based on pristine PEDOT:PSS are far
from satisfactory, which are ascribed to low fitness between PEDOT:PSS
and perovskite materials, in terms of work function, conductivity,
film growth, and hydrophobicity. This paper summaries recent progress
regarding to modifying/remedy the drawbacks of PEDOT:PSS to improve
the PCE and stability. The systematically understanding of the mechanism
of modified PEDOT:PSS and various characteristic methods are summarized
here. This Review has the potential to guide the development of PSCs
based on commercial PEDOT:PSS.
The intrinsic magnetic topological insulator MnBi2Te4 (MBT) has provided a platform for the successful realization of exotic quantum phenomena. To broaden the horizons of MBT-based material systems, we intercalate ferromagnetic MnTe layers to construct the [(MBT)(MnTe)m]N superlattices by molecular beam epitaxy. The effective incorporation of ferromagnetic spacers mediates the anti-ferromagnetic interlayer coupling among the MBT layers through the exchange spring effect at the MBT/MnTe hetero-interfaces.2 Moreover, the precise control of the MnTe thickness enables the modulation of relative strengths among the constituent magnetic orders, leading to tunable magnetoelectric responses, while the superlattice periodicity serves as an additional tuning parameter to tailor the spin configurations of the synthesized multi-layers. Our results demonstrate the advantages of superlattice engineering for optimizing the magnetic interactions in MBT-family systems, and the ferromagnet-intercalated strategy opens up new avenues in magnetic topological insulator structural design and spintronic applications.
InSb channel. The designed device structure was later exported to the Sdevice tool, wherein the 2D Poisson solver (including both electrons and holes) was used to obtain the real-space band diagram and carrier distribution under different gate-voltage biases.
Recently, transformer architecture has gained great success in the computer vision community, such as image classification, object detection, etc. Nonetheless, its application for 3D vision remains to be explored, given that point cloud is inherently sparse, irregular, and unordered. Furthermore, existing point transformer frameworks usually feed raw point cloud of N×3 dimension into transformers, which limits the point processing scale because of their quadratic computational costs to the input size N. In this paper, we rethink the structure of point transformer. Instead of directly applying transformer to points, our network (TransLO) can process tens of thousands of points simultaneously by projecting points onto a 2D surface and then feeding them into a local transformer with linear complexity. Specifically, it is mainly composed of two components: Window-based Masked transformer with Self Attention (WMSA) to capture long-range dependencies; Masked Cross-Frame Attention (MCFA) to associate two frames and predict pose estimation. To deal with the sparsity issue of point cloud, we propose a binary mask to remove invalid and dynamic points. To our knowledge, this is the first transformer-based LiDAR odometry network. The experiment results on the KITTI odometry dataset show that our average rotation and translation RMSE achieves 0.500°/100m and 0.993% respectively. The performance of our network surpasses all recent learning-based methods and even outperforms LOAM on most evaluation sequences.Codes will be released on https://github.com/IRMVLab/TransLO.
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