Efficient lead-free perovskite light-emitting diodes with color coordinates that fulfill Rec. 2100 requirements are demonstrated.
It remains a central challenge to the information display community ...to develop red light-emitting diodes (LEDs) that meet demanding color coordinate requirements for wide color gamut displays. Here, we report high-efficiency, lead-free (PEA)
2
SnI
4
perovskite LEDs (PeLEDs) with color coordinates (0.708, 0.292) that fulfill the Rec. 2100 specification for red emitters. Using valeric acid (VA)—which we show to be strongly coordinated to Sn
2+
—we slow the crystallization rate of the perovskite, improving the film morphology. The incorporation of VA also protects tin from undesired oxidation during the film-forming process. The improved films and the reduced Sn
4+
content enable PeLEDs with an external quantum efficiency of 5% and an operating half-life exceeding 15 hours at an initial brightness of 20 cd/m
2
. This work illustrates the potential of Cd- and Pb-free PeLEDs for display technology.
Lead halide perovskite solar cells (PSCs) have advanced rapidly in performance over the past decade. Single-crystal PSCs based on micrometers-thick grain-boundary-free films with long charge carrier ...diffusion lengths and enhanced light absorption (relative to polycrystalline films) have recently emerged as candidates for advancing PSCs further toward their theoretical limit. To date, the preferred method to grow MAPbI3 single-crystal films for PSCs involves solution processing at temperatures ≳120 °C, which adversely affects the films’ crystalline quality, especially at the surface, primarily because of methylammonium iodide loss at such high temperatures. Here we devise a solvent-engineering approach to reduce the crystallization temperature of MAPbI3 single-crystal films (<90 °C), yielding better quality films with longer carrier lifetimes. Single-crystal MAPbI3 inverted PSCs fabricated with this strategy show markedly enhanced open-circuit voltages (1.15 V vs 1.08 V for controls), leading to power conversion efficiencies of up to 21.9%, which are among the highest reported for MAPbI3-based devices.
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Defect passivation and surface modification of hybrid perovskite films are essential to achieving high power conversion efficiency (PCE) and stable perovskite photovoltaics. Here, we demonstrate a ...facile strategy that combines high PCE with high stability in CH3NH3PbI3 (MAPbI3) solar cells. The strategy utilizes inorganic perovskite quantum dots (QDs) to distribute elemental dopants uniformly across the MAPbI3 film and attach ligands to the film’s surface. Compared with pristine MAPbI3 films, MAPbI3 films processed with QDs show a reduction in tail states, smaller trap-state density, and an increase in carrier recombination lifetime. This strategy results in reduced voltage losses and an improvement in PCE from 18.3% to 21.5%, which is among the highest efficiencies for MAPbI3 devices. Ligands introduced with the aid of the QDs render the perovskite film’s surface hydrophobic—inhibiting moisture penetration. The devices maintain 80% of their initial PCE under 1-sun continuous illumination for 500 h and show improved thermal stability.
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•QDs distribute their elements and ligands to a perovskite film’s bulk and surface•Elements and ligands as bulk and surface passivators enhance device photo-voltage•MAPbI3 PSCs with CsPbBrCl2 QDs achieve improved efficiency (21.5%) and stability
Perovskite solar cells (PSCs) are one of the most compelling photovoltaic technologies because of their low cost, solution processing, and impressive PCEs. However, achieving high-performance PSCs requires processing and surface-passivation approaches for both the bulk and surface of perovskite films. Here, we use quantum dots (QDs) during device processing to deliver elemental dopants and distribute them uniformly across the perovskite film and to deliver a ligand passivation layer to the film’s surface. The approach achieves, as a result, simultaneous bulk and surface passivation. Perovskite films processed by this strategy have a significantly reduced trap-state density and yield PSCs with substantially improved PCEs of 21.5%. Ligands originating from the QDs and self-assembled on the perovskite film’s surface protect the film from degradation associated with moisture ingress and with the escape of volatile material content. This enhances the light stability and thermal stability of PSCs.
We report a facile processing strategy that utilizes perovskite quantum dots (QDs) to distribute elemental dopants uniformly across a MAPbI3 film and anchor ligands to the film’s surface—reducing the film’s trap-state density and rendering its surface hydrophobic. QD-treated MAPbI3 films yield solar cells with 21.5% power conversion efficiency (PCE) (versus 18.3% for non-QD-treated) and maintain 80% of their initial PCE under 1-sun continuous illumination for 500 h with improved thermal stability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper investigates the equivalence problem of bivariate polynomial matrices. A necessary and sufficient condition for the equivalence of a square matrix with the determinant being some power of ...a univariate irreducible polynomial and its Smith form is proposed. Meanwhile, the authors present an algorithm that reduces this class of bivariate polynomial matrices to their Smith forms, and an example is given to illustrate the effectiveness of the algorithm. In addition, the authors generalize the main result to the non-square case.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
With the advent of big data era and the enhancement of computing power, Deep Learning has swept the world. Based on Convolutional Neural Network (CNN) image classification technique broke the ...restriction of classical image classification methods, becoming the dominant algorithm of image classification. How to use CNN for image classification has turned into a hot spot. After systematically studying convolutional neural network and in-depth research of the application of CNN in computer vision, this research briefly introduces the mainstream structural models, strengths and shortcomings, time/space complexity, challenges that may be suffered during model training and associated solutions for image classification. This research also compares and analyzes the differences between different methods and their performance on commonly used data sets. Finally, the shortcomings of Deep Learning methods in image classification and possible future research directions are discussed.
The intrinsic instability of hybrid perovskite materials induced by defect states arises as one major challenge hampering the commercialization of perovskite solar cells (PSCs). Here, we report a ...facile strategy of wrapping perovskite grains within an oligomeric silica (OS) matrix in a core–shell geometry, which can synchronously passivate the defects at surfaces and grain boundaries and stabilize the grains at the nanoscale. We observe a significant reduction of trap density and elongation of carrier lifetime in OS-wrapped perovskites, which yields an increased efficiency of 21.5% for p–i–n structured PSCs with a decent open-circuit voltage of 1.15 V and a fill factor of 0.81. This all-around nanoscale grain wrapping leads to remarkable improvement of the operational stability of PSCs, sustaining 80% of the efficiency after “burn-in” under full sunlight with UV for more than 5200 h. Our findings provide a new pathway towards efficient and stable PSCs.
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Perovskite photovoltaics have been shown to recover, or heal, after radiation damage. Here, we deconvolve the effects of radiation based on different energy loss mechanisms from incident protons ...which induce defects or can promote efficiency recovery. We design a dual dose experiment first exposing devices to low-energy protons efficient in creating atomic displacements. Devices are then irradiated with high-energy protons that interact differently. Correlated with modeling, high-energy protons (with increased ionizing energy loss component) effectively anneal the initial radiation damage, and recover the device efficiency, thus directly detailing the different interactions of irradiation. We relate these differences to the energy loss (ionization or non-ionization) using simulation. Dual dose experiments provide insight into understanding the radiation response of perovskite solar cells and highlight that radiation-matter interactions in soft lattice materials are distinct from conventional semiconductors. These results present electronic ionization as a unique handle to remedying defects and trap states in perovskites.
This paper presents a framework for computational generation and conformal fabrication of woven thin-shell structures with arbitrary topology based on the foliation theory which decomposes a surface ...into a group of parallel leaves. By solving graph-valued harmonic maps on the input surface, we construct two sets of harmonic foliations perpendicular to each other. The warp and weft threads are created afterward and then manually woven to reconstruct the surface. The proposed computational method guarantees the smoothness of the foliation and the orthogonality between each pair of leaves from different foliations. Moreover, it minimizes the number of singularities to theoretical lower bound and produces the tensor product structure as globally as possible. This method is ideal for the physical realization of woven surface structures on a variety of applications, including wearable electronics, sheet metal craft, architectural designs, and conformal woven composite parts in the automotive and aircraft industries. The performance of the proposed method is demonstrated through the computational generation and physical fabrication of several free-form thin-shell structures.
•Use foliation theory for woven fabric, link free-form surface with in-plane textiles.•General to surfaces with arbitrary topologies.•Global tensor product structure with minimal singularities, orthogonal crossing angles, no dangling threads.•Computation algorithm is robust, efficient and automatic.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Tight multi-medium oil reservoirs are the main source of hydrocarbon resources around the world. Acid fracturing is the most effective technology to improve productivity in such reservoirs. As ...carbonates are primarily composed of dolomite and calcite, which are easily dissolved by hydrochloric acid, high-permeability region will be formed near the well along with the main artificial fracture when acid fracturing is implemented in tight multi-medium oil reservoirs. In this study, a comprehensive composite linear flow model was developed to simulate the transient pressure behavior of an acid fracturing vertical well in a naturally fractured vuggy carbonate reservoir. By utilizing Pedrosa's substitution, perturbation, Laplace transformation and Stehfest numerical inversion technology, the pressure behavior results were obtained in real time domain. Furthermore, the result of this model was validated by comparing with those of previous literature. Additionally, the influences of some prevailing parameters on the type curves were analyzed. Moreover, the proposed model was applied to an acid fracturing well to evaluate the effectiveness of acid fracturing measures, to demonstrate the practicability of the proposed model.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Siamese networks have drawn increasing interest in the field of visual object tracking due to their balance of precision and efficiency. However, Siamese trackers use relatively shallow backbone ...networks, such as AlexNet, and therefore do not take full advantage of the capabilities of modern deep convolutional neural networks (CNNs). Moreover, the feature representations of the target object in a Siamese tracker are extracted through the last layer of CNNs and mainly capture semantic information, which causes the tracker's precision to be relatively low and to drift easily in the presence of similar distractors. In this paper, a new nonpadding residual unit is designed and used to stack a 22-layer deep ResNet, referred as ResNet22. After utilizing ResNet22 as the backbone network, we can build a deep Siamese network, which can greatly enhance the tracking performance. Considering that the different levels of the feature maps of the CNN represent different aspects of the target object, we aggregated different deep convolutional layers to make use of ResNet22’s multilevel feature maps, which can form hyperfeature representations of targets. The final network architecture is named DSiamLA. Experimental results show that DSiamLA has achieved significant improvement on multiple benchmark datasets.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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