Even though the perovskite solar cell has been so popular for its skyrocketing power conversion efficiency, its further development is still roadblocked by its overall performance, in particular ...long-term stability, large-area fabrication and stable module efficiency. In essence, the soft component and ionic-electronic nature of metal halide perovskites usually chaperonage large number of anion vacancy defects that act as recombination centers to decrease both the photovoltaic efficiency and operational stability. Herein, we report a one-stone-for-two-birds strategy in which both anion-fixation and associated undercoordinated-Pb passivation are in situ achieved during crystallization by using a single amidino-based ligand, namely 3-amidinopyridine, for metal-halide perovskite to overcome above challenges. The resultant devices attain a power conversion efficiency as high as 25.3% (certified at 24.8%) with substantially improved stability. Moreover, the device without encapsulation retained 92% of its initial efficiency after 5000 h exposure in ambient and the device with encapsulation retained 95% of its initial efficiency after >500 h working at the maximum power point under continuous light irradiation in ambient. It is expected this one-stone-for-two-birds strategy will benefit large-area fabrication that desires for simplicity.
Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on ...the Earth’s surface. The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete boundary of changed area and missing detection of small targets in the final output change map. To overcome the above problems, we proposed a hierarchical attention residual nested U-Net (HARNU-Net) for remote sensing image CD. First, the backbone network is composed of a Siamese network and nested U-Net. We remold the convolution block in nested U-Net and proposed ACON-Relu residual convolution block (A-R), which reduces the missed detection rate of the backbone network in small change areas. Second, this paper proposed the adjacent feature fusion module (AFFM). Based on the adjacency fusion strategy, the module effectively integrates the details and semantic information of multi-level features, so as to realize the feature complementarity and spatial mutual enhancement between adjacent features. Finally, the hierarchical attention residual module (HARM) is proposed, which locally filters and enhances the features in a more fine-grained space to output a much better change map. Adequate experiments on three challenging benchmark public datasets, CDD, LEVIR-CD and BCDD, show that our method outperforms several other state-of-the-art methods and performs excellent in F1, IOU and visual image quality.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
X‐ray detectors have broad applications in medicine and industry. Although flexible lead‐free perovskite films are competitive because of their lightweight and low toxicity, they are less efficient ...due to low charge transport. Herein, we report low‐toxicity, flexible X‐ray detectors based on p‐type doped MA3Bi2I9 (MA=methylammonium) perovskite‐filled membranes (PFMs). Strong coordination between dopant 2,3,5,6‐tetrafluoro‐7,7,8,8‐tetracyanoquinodimethane (F4‐TCNQ) and MA3Bi2I9 and the establishment of charge‐transfer complex (CPX) improved the conductivity by four times. The flexible X‐ray detector achieved a high sensitivity of 2065 μC Gyair−1 cm−2 and an ultra‐low detection limit of 2.71 nGyair s−1, which is among the highest values in all environmentally friendly flexible X‐ray detectors. Importantly, the PFMs retained excellent charge transport under mechanical stress. All of those make flexible MA3Bi2I9 membranes more competitive as next‐generation X‐ray detection.
Molecular doping improves the conductivity of flexible lead‐free MA3Bi2I9 perovskite membranes by four times. Flexible X‐ray detector achieves a high sensitivity of 2065 μC Gyair−1 cm−2, an ultra‐low detection limit of 2.71 nGyair s−1, a high mechanical robustness, which demonstrate potential in next‐generation X‐ray detection.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Generally, growing phase pure CsPbBr3 single crystals is challenging, and CsPb2Br5 or Cs4PbBr6 by‐products are usually formed due to the different solubilities of CsBr and PbBr2 in the single ...solvent. Herein, the growth of high‐quality phase pure CsPbBr3 perovskite single crystals at room temperature by a humidity controlled solvent evaporation method is reported first. Meanwhile, the room temperature phase transition process from three dimensional (3D) cubic CsPbBr3 to two dimensional (2D) layered tetragonal CsPb2Br5 and the detailed mechanism induced by humidity are revealed. Moreover, compared with the organic–inorganic perovskite, the prepared CsPbBr3 single crystals are much more stable under high humidity, which satisfies the long‐term working conditions of X‐ray detectors. The X‐ray detectors based on CsPbBr3 single crystals show a high sensitivity and a low detection limit of 1.89 μGyair s–1, all of which meet the needs of medical diagnosis.
The mixed products of CsBr and PbBr2 are rich and diverse, which is a phenomenon of people scratching their heads. In the humidity controlled low‐temperature preparation process, the mechanism of its diversity is uncovered through the transformations of products under the influence of different conditions and a valuable guidance is provided for the subsequent crystal acquisition and growth.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
When encountering sedimentary rocks with obvious laminations or fracture development zones, the conductivity of the conductive medium in different directions will change significantly, and the ...subsurface medium will exhibit macroscopic conductivity anisotropy. To analyze the impact of electrical anisotropy on the surface–borehole transient electromagnetic exploration method, we used the finite element method to investigate the electrical anisotropy surface–borehole transient electromagnetic three-dimensional (3D) forward algorithm, in which we used a tetrahedral mesh to spatially discretize the time–domain Maxwell equation. Then, we discretized it using the second-order backward Eulerian difference method, and we obtained the fields through the PARDISO solver. The validity and correctness of the algorithm were verified through comparison of a one-dimensional (1D) anisotropic model, a complex three-dimensional (3D) isotropic model, and a three-dimensional (3D) anisotropic half-space model. A typical anisotropic geological model was constructed to analyze the effects of anisotropic strata and anomalies in the different principal axis directions on the surface–borehole transient electromagnetic response. The results show that the response of the anisotropic medium is related to the direction of the transmitting source, and the response pattern is complex and volatile. The electrical anisotropic anomaly does affect the amplitude, which should be given special attention when performing surface–borehole transient electromagnetic inversion interpretation.
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CEKLJ, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The Moore—Penrose inverse of a matrix plays a very important role in practical applications. In general, it is not easy to immediately solve the Moore—Penrose inverse of a matrix, especially for ...solving the Moore—Penrose inverse of a complex-valued matrix in time-varying situations. To solve this problem conveniently, in this paper, a novel Zhang neural network (ZNN) with time-varying parameter that accelerates convergence is proposed, which can solve Moore—Penrose inverse of a matrix over complex field in real time. Analysis results show that the state solutions of the proposed model can achieve super convergence in finite time with weighted sign-bi-power activation function (WSBP) and the upper bound of the convergence time is calculated. A related noise-tolerance model which possesses finite-time convergence property is proved to be more efficient in noise suppression. At last, numerical simulation illustrates the performance of the proposed model as well.
As an important task in the field of remote sensing image interpretation, change detection (CD) has been extensively studied by scholars in recent years. Affected by the illumination and the ...environment during bi-temporal images acquisition, there will be many pseudo-changes, and the pseudo-changes will seriously affect the effect of CD. Based on this, we propose a CD model named HMCNet, which introduces multi-layer perceptron (MLP) into a CNN-based CD model to form an MLP-CNN hybrid model. HMCNet has both the good feature extraction of CNN and the long-term dependency modeling ability of MLP, which can effectively overcome the interference of pseudo-changes. In addition, the proposed cross-axis attention MLP can induce window attention of local features through shifted windows, and at the same time form global attention to features through the interaction between information flows on the cross-axis, which effectively improves the comprehensive performance of MLP Block. Extensive experiments on three public benchmark datasets show that HMCNet can achieve better performance with fewer parameters and Flops and still maintain good generalization ability with less train data.
Understanding the interaction of T-cell receptor (TCR) with major histocompatibility-peptide (MHC-peptide) complex is extremely important in human immunotherapy and vaccine development. However, due ...to the limited available data, the performance of existing models for predicting the interaction of T-cell receptors (TCR) with major histocompatibility-peptide complexes is still unsatisfactory. Deep learning models have been applied to prediction tasks in various fields and have achieved better results compared with other traditional models. In this study, we leverage the gMLP model combined with attention mechanism to predict the interaction of MHC-peptide and TCR. Experiments show that our model can predict TCR-peptide interactions accurately and can handle the problems caused by different TCR lengths. Moreover, we demonstrate that the models trained with paired CDR3β-chain and CDR3α-chain data are better than those trained with only CDR3β-chain or with CDR3α-chain data. We also demonstrate that the hybrid model has greater potential than the traditional convolutional neural network.
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