Minimizing surface defect is vital to further improve power conversion efficiency (PCE) and stability of inorganic perovskite solar cells (PSCs). Herein, we designed a passivator trifluoroacetamidine (TFA) to suppress...
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Minimizing surface defect is vital to further improve power conversion efficiency (PCE) and stability of inorganic perovskite solar cells (PSCs). Herein, we designed a passivator trifluoroacetamidine (TFA) to suppress CsPbI 3− x Br x film defects. The amidine group of TFA can strongly chelate onto the perovskite surface to suppress the iodide vacancy, strengthened by additional hydrogen bonds. Moreover, three fluorine atoms allow strong intermolecular connection via intermolecular hydrogen bonds, thus constructing a robust shield against moisture. The TFA-treated PSCs exhibit remarkably suppressed recombination, yielding the record PCEs of 21.35 % and 17.21 % for 0.09 cm 2 and 1.0 cm 2 device areas, both of which are the highest for all-inorganic PSCs so far. The device also achieves a PCE of 39.78 % under indoor illumination, the highest for all-inorganic indoor photovoltaic devices. Furthermore, TFA greatly improves device ambient stability by preserving 93 % of the initial PCE after 960 h.
In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective L...
In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.
The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the tra...
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In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accuratel...
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—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense network...
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In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accuratel...
In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.
HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their...
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HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Å); Rg (nm); PSA (Å); SASA (Å), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where -amyrin scored the most significant values in all aspects.
It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correla...
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Wide-band gap (1.68 eV) perovskite solar cells (PSCs) are important components of perovskite/Si tandem devices. However, the efficiency of wide band gap PSCs has been limited by their huge open-circuit voltage ( V oc ...
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Wide-band gap (1.68 eV) perovskite solar cells (PSCs) are important components of perovskite/Si tandem devices. However, the efficiency of wide band gap PSCs has been limited by their huge open-circuit voltage ( V oc ) deficit due to non-radiative recombination. Deep-level acceptor defects are identified as the major killers of V oc , and they can be effectively improved by passivation with ammonium salts. Theoretical calculation predicts that increasing the distance between F and −NH 3 + of fluorinated ammonium can dramatically enhance the electropositivity of −NH 3 + terminals, thus providing strong adsorption onto the negatively charged I A and I Pb anti-site defects. Characterizations further confirm that surface gradient passivation employing p -FPEAI demonstrates the most efficient passivation effect. Consequently, a record-efficiency of 21.63 % with the smallest V oc deficit of 441 mV is achieved for 1.68 eV-band gap inverted PSCs. Additionally, a flexible PSC and 1 cm 2 opaque device also deliver the highest PCEs of 21.02 % and 19.31 %, respectively.
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