Showing papers by "City University of Hong Kong published in 2020"
TL;DR: It is suggested that this transparent, mechanically robust, self-cleaning glass could help to negate the dust-contamination issue that leads to a loss of efficiency in solar cells and could also guide the development of other materials that need to retain effective self- Cleaning, anti-fouling or heat-transfer abilities in harsh operating environments.
Abstract: The ability of superhydrophobic surfaces to stay dry, self-clean and avoid biofouling is attractive for applications in biotechnology, medicine and heat transfer1–10. Water droplets that contact these surfaces must have large apparent contact angles (greater than 150 degrees) and small roll-off angles (less than 10 degrees). This can be realized for surfaces that have low-surface-energy chemistry and micro- or nanoscale surface roughness, minimizing contact between the liquid and the solid surface11–17. However, rough surfaces—for which only a small fraction of the overall area is in contact with the liquid—experience high local pressures under mechanical load, making them fragile and highly susceptible to abrasion18. Additionally, abrasion exposes underlying materials and may change the local nature of the surface from hydrophobic to hydrophilic19, resulting in the pinning of water droplets to the surface. It has therefore been assumed that mechanical robustness and water repellency are mutually exclusive surface properties. Here we show that robust superhydrophobicity can be realized by structuring surfaces at two different length scales, with a nanostructure design to provide water repellency and a microstructure design to provide durability. The microstructure is an interconnected surface frame containing ‘pockets’ that house highly water-repellent and mechanically fragile nanostructures. This surface frame acts as ‘armour’, preventing the removal of the nanostructures by abradants that are larger than the frame size. We apply this strategy to various substrates—including silicon, ceramic, metal and transparent glass—and show that the water repellency of the resulting superhydrophobic surfaces is preserved even after abrasion by sandpaper and by a sharp steel blade. We suggest that this transparent, mechanically robust, self-cleaning glass could help to negate the dust-contamination issue that leads to a loss of efficiency in solar cells. Our design strategy could also guide the development of other materials that need to retain effective self-cleaning, anti-fouling or heat-transfer abilities in harsh operating environments. Water-repellent nanostructures are housed within an interconnected microstructure frame to yield mechanically robust superhydrophobic surfaces.
889 citations
TL;DR: The gasdermin E protein is shown to act as a tumour suppressor: it is cleaved by caspase 3 and granzyme B and leads to pyroptosis of cancer cells, provoking an immune response to the tumour.
Abstract: Cleavage of the gasdermin proteins to produce pore-forming amino-terminal fragments causes inflammatory cell death (pyroptosis)1. Gasdermin E (GSDME, also known as DFNA5)—mutated in familial ageing-related hearing loss2—can be cleaved by caspase 3, thereby converting noninflammatory apoptosis to pyroptosis in GSDME-expressing cells3–5. GSDME expression is suppressed in many cancers, and reduced GSDME levels are associated with decreased survival as a result of breast cancer2,6, suggesting that GSDME might be a tumour suppressor. Here we show that 20 of 22 tested cancer-associated GSDME mutations reduce GSDME function. In mice, knocking out Gsdme in GSDME-expressing tumours enhances, whereas ectopic expression in Gsdme-repressed tumours inhibits, tumour growth. This tumour suppression is mediated by killer cytotoxic lymphocytes: it is abrogated in perforin-deficient mice or mice depleted of killer lymphocytes. GSDME expression enhances the phagocytosis of tumour cells by tumour-associated macrophages, as well as the number and functions of tumour-infiltrating natural-killer and CD8+ T lymphocytes. Killer-cell granzyme B also activates caspase-independent pyroptosis in target cells by directly cleaving GSDME at the same site as caspase 3. Uncleavable or pore-defective GSDME proteins are not tumour suppressive. Thus, tumour GSDME acts as a tumour suppressor by activating pyroptosis, enhancing anti-tumour immunity. The gasdermin E protein is shown to act as a tumour suppressor: it is cleaved by caspase 3 and granzyme B and leads to pyroptosis of cancer cells, provoking an immune response to the tumour.
711 citations
TL;DR: It is shown that spreading of an impinged water droplet on the device bridges the originally disconnected components into a closed-loop electrical system, transforming the conventional interfacial effect into a bulk effect, and so enhancing the instantaneous power density by several orders of magnitude over equivalent devices that are limited by interfacial effects.
Abstract: Extensive efforts have been made to harvest energy from water in the form of raindrops1–6, river and ocean waves7,8, tides9 and others10–17. However, achieving a high density of electrical power generation is challenging. Traditional hydraulic power generation mainly uses electromagnetic generators that are heavy, bulky, and become inefficient with low water supply. An alternative, the water-droplet/solid-based triboelectric nanogenerator, has so far generated peak power densities of less than one watt per square metre, owing to the limitations imposed by interfacial effects—as seen in characterizations of the charge generation and transfer that occur at solid–liquid1–4 or liquid–liquid5,18 interfaces. Here we develop a device to harvest energy from impinging water droplets by using an architecture that comprises a polytetrafluoroethylene film on an indium tin oxide substrate plus an aluminium electrode. We show that spreading of an impinged water droplet on the device bridges the originally disconnected components into a closed-loop electrical system, transforming the conventional interfacial effect into a bulk effect, and so enhancing the instantaneous power density by several orders of magnitude over equivalent devices that are limited by interfacial effects. A device involving a polytetrafluoroethylene film, an indium tin oxide substrate and an aluminium electrode allows improved electricity generation from water droplets, which bridge the previously disconnected circuit components.
699 citations
TL;DR: This paper constructs an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images and proposes an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs).
Abstract: Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html .
697 citations
TL;DR: Challenges and possible research directions for each mainstream approach of ensemble learning are presented and an extra introduction is given for the combination of ensemblelearning with other machine learning hot spots such as deep learning, reinforcement learning, etc.
Abstract: Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data. In this context, it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model. Ensemble learning, as one research hot spot, aims to integrate data fusion, data modeling, and data mining into a unified framework. Specifically, ensemble learning firstly extracts a set of features with a variety of transformations. Based on these learned features, multiple learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way. In this paper, we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics. In addition, we present challenges and possible research directions for each mainstream approach of ensemble learning, and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning, reinforcement learning, etc.
649 citations
TL;DR: This work developed and determined carbon nanosheets embedded with nitrogen and phosphorus dual-coordinated iron active sites that were favorable for oxygen intermediate adsorption/desorption, resulting in accelerated reaction kinetics and promising catalytic oxygen reduction activity.
Abstract: Atomically dispersed transition metal active sites have emerged as one of the most important fields of study because they display promising performance in catalysis and have the potential to serve as ideal models for fundamental understanding. However, both the preparation and determination of such active sites remain a challenge. The structural engineering of carbon- and nitrogen-coordinated metal sites (M-N-C, M = Fe, Co, Ni, Mn, Cu, etc.) via employing new heteroatoms, e.g., P and S, remains challenging. In this study, carbon nanosheets embedded with nitrogen and phosphorus dual-coordinated iron active sites (denoted as Fe-N/P-C) were developed and determined using cutting edge techniques. Both experimental and theoretical results suggested that the N and P dual-coordinated iron sites were favorable for oxygen intermediate adsorption/desorption, resulting in accelerated reaction kinetics and promising catalytic oxygen reduction activity. This work not only provides efficient way to prepare well-defined single-atom active sites to boost catalytic performance but also paves the way to identify the dual-coordinated single metal atom sites.
548 citations
Geophysical Fluid Dynamics Laboratory1, Lamont–Doherty Earth Observatory2, City University of Hong Kong3, Massachusetts Institute of Technology4, Seoul National University5, National Oceanic and Atmospheric Administration6, India Meteorological Department7, University of Tokyo8, University of Melbourne9, Nanjing University of Information Science and Technology10
TL;DR: In this article, model projections of tropical cyclone activity response to anthropogenic warming in climate models are assessed and observations, theory, and models, with increasing robustness, indicate that tropical cyclones respond well to global warming.
Abstract: Model projections of tropical cyclone (TC) activity response to anthropogenic warming in climate models are assessed. Observations, theory, and models, with increasing robustness, indicate ...
536 citations
TL;DR: This review highlights various aqueous rechargeable metal-ion batteries with focuses on their voltage characteristics and strategies that can effectively raise battery voltage, as well as potential directions for further improvements and future perspectives of this thriving field.
Abstract: Over the past two decades, a series of aqueous rechargeable metal-ion batteries (ARMBs) have been developed, aiming at improving safety, environmental friendliness and cost-efficiency in fields of consumer electronics, electric vehicles and grid-scale energy storage. However, the notable gap between ARMBs and their organic counterparts in energy density directly hinders their practical applications, making it difficult to replace current widely-used organic lithium-ion batteries. Basically, this huge gap in energy density originates from cell voltage, as the narrow electrochemical stability window of aqueous electrolytes substantially confines the choice of electrode materials. This review highlights various ARMBs with focuses on their voltage characteristics and strategies that can effectively raise battery voltage. It begins with the discussion on the fundamental factor that limits the voltage of ARMBs, i.e., electrochemical stability window of aqueous electrolytes, which decides the maximum-allowed potential difference between cathode and anode. The following section introduces various ARMB systems and compares their voltage characteristics in midpoint voltage and plateau voltage, in relation to respective electrode materials. Subsequently, various strategies paving the way to high-voltage ARMBs are summarized, with corresponding advancements highlighted. The final section presents potential directions for further improvements and future perspectives of this thriving field.
452 citations
TL;DR: This dendrite issue in Zn anodes, with regard to fundamentals, protection strategies, characterization techniques, and theoretical simulations, is systematically discussed and comprehensively summarized to generate an overview of respective superiorities and limitations of various strategies.
Abstract: Aqueous Zn batteries that provide a synergistic integration of absolute safety and high energy density have been considered as highly promising energy-storage systems for powering electronics. Despite the rapid progress made in developing high-performance cathodes and electrolytes, the underestimated but non-negligible dendrites of Zn anode have been observed to shorten battery lifespan. Herein, this dendrite issue in Zn anodes, with regard to fundamentals, protection strategies, characterization techniques, and theoretical simulations, is systematically discussed. An overall comparison between the Zn dendrite and its Li and Al counterparts, to highlight their differences in both origin and topology, is given. Subsequently, in-depth clarifications of the specific influence factors of Zn dendrites, including the accumulation effect and the cathode loading mass (a distinct factor for laboratory studies and practical applications) are presented. Recent advances in Zn dendrite protection are then comprehensively summarized and categorized to generate an overview of respective superiorities and limitations of various strategies. Accordingly, theoretical computations and advanced characterization approaches are introduced as mechanism guidelines and measurement criteria for dendrite suppression, respectively. The concluding section emphasizes future challenges in addressing the Zn dendrite issue and potential approaches to further promoting the lifespan of Zn batteries.
452 citations
14 Jun 2020
TL;DR: A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and shows that it generalizes well to diverse lighting conditions.
Abstract: The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed.
447 citations
TL;DR: A review of hydrogel-based biomaterial inks and bioinks for 3D printing can be found in this paper, where the authors provide a comprehensive overview and discussion of the tailorability of material, mechanical, physical, chemical and biological properties.
Abstract: 3D printing alias additive manufacturing can transform 3D virtual models created by computer-aided design (CAD) into physical 3D objects in a layer-by-layer manner dispensing with conventional molding or machining. Since the incipiency, significant advancements have been achieved in understanding the process of 3D printing and the relationship of component, structure, property and application of the created objects. Because hydrogels are one of the most feasible classes of ink materials for 3D printing and this field has been rapidly advancing, this Review focuses on hydrogel designs and development of advanced hydrogel-based biomaterial inks and bioinks for 3D printing. It covers 3D printing techniques including laser printing (stereolithography, two-photon polymerization), extrusion printing (3D plotting, direct ink writing), inkjet printing, 3D bioprinting, 4D printing and 4D bioprinting. It provides a comprehensive overview and discussion of the tailorability of material, mechanical, physical, chemical and biological properties of hydrogels to enable advanced hydrogel designs for 3D printing. The range of hydrogel-forming polymers covered encompasses biopolymers, synthetic polymers, polymer blends, nanocomposites, functional polymers, and cell-laden systems. The representative biomedical applications selected demonstrate how hydrogel-based 3D printing is being exploited in tissue engineering, regenerative medicine, cancer research, in vitro disease modeling, high-throughput drug screening, surgical preparation, soft robotics and flexible wearable electronics. Incomparable by thermoplastics, thermosets, ceramics and metals, hydrogel-based 3D printing is playing a pivotal role in the design and creation of advanced functional (bio)systems in a customizable way. An outlook on future directions of hydrogel-based 3D printing is presented.
TL;DR: The proposed UWCNN model directly reconstructs the clear latent underwater image, which benefits from the underwater scene prior which can be used to synthesize underwater image training data, and can be easily extended to underwater videos for frame-by-frame enhancement.
TL;DR: The advantageous properties and potential of phthalocyanines as advanced photosensitisers for photodynamic therapy of cancer are highlighted in this tutorial review.
Abstract: Phthalocyanines exhibit superior photoproperties that make them a surely attractive class of photosensitisers for photodynamic therapy of cancer. Several derivatives are at various phases of clinical trials, and efforts have been put continuously to improve their photodynamic efficacy. To this end, various strategies have been applied to develop advanced phthalocyanines with optimised photoproperties, dual therapeutic actions, tumour-targeting properties and/or specific activation at tumour sites. The advantageous properties and potential of phthalocyanines as advanced photosensitisers for photodynamic therapy of cancer are highlighted in this tutorial review.
TL;DR: Results show that media richness negatively predicts citizen engagement through government social media, but dialogic loop facilitates engagement, and all relationships were contingent upon the emotional valence of each Weibo post.
TL;DR: This article sought to fill the gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information and found specific features in predicting the reposted amount of each type of information.
Abstract: During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.
TL;DR: It is shown that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough.
TL;DR: In this paper, a comprehensive theory of the through-space conjugation (TSC) for these chromophores is proposed, and various applications have been envisioned, for example in the areas of process monitoring, structural visualization, sensors, and probes.
14 Jun 2020
TL;DR: This paper proposes a new framework, which is referred to as collaborative class conditional generative adversarial net, to bypass the dependence on the source data and achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.
Abstract: In this paper, we investigate a challenging unsupervised domain adaptation setting --- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.
TL;DR: This work develops the first full-reference image quality model with explicit tolerance to texture resampling, using a convolutional neural network to construct an injective and differentiable function that transforms images to multi-scale overcomplete representations.
Abstract: Objective measures of image quality generally operate by making local comparisons of pixels of a "degraded" image to those of the original. Relative to human observers, these measures are overly sensitive to resampling of texture regions (e.g., replacing one patch of grass with another). Here we develop the first full-reference image quality model with explicit tolerance to texture resampling. Using a convolutional neural network, we construct an injective and differentiable function that transforms images to a multi-scale overcomplete representation. We empirically show that the spatial averages of the feature maps in this representation capture texture appearance, in that they provide a set of sufficient statistical constraints to synthesize a wide variety of texture patterns. We then describe an image quality method that combines correlation of these spatial averages ("texture similarity'') with correlation of the feature maps ("structure similarity''). The parameters of the proposed measure are jointly optimized to match human ratings of image quality, while minimizing the reported distances between subimages cropped from the same texture images. Experiments show that the optimized method explains human perceptual scores, both on conventional image quality databases and texture databases. The measure also offers competitive performance on texture classification and retrieval, and show the robustness to geometric transformations. Code is available at https://github.com/dingkeyan93/DISTS.
14 May 2020
TL;DR: Recently, phase has emerged as an important structural parameter that determines the properties and functionalities of nanomaterials, in addition to composition, morphology, architecture, facet, size and dimensionality as discussed by the authors.
Abstract: Phase has emerged as an important structural parameter — in addition to composition, morphology, architecture, facet, size and dimensionality — that determines the properties and functionalities of nanomaterials. In particular, unconventional phases in nanomaterials that are unattainable in the bulk state can potentially endow nanomaterials with intriguing properties and innovative applications. Great progress has been made in the phase engineering of nanomaterials (PEN), including synthesis of nanomaterials with unconventional phases and phase transformation of nanomaterials. This Review provides an overview on the recent progress in PEN. We discuss various strategies used to synthesize nanomaterials with unconventional phases and induce phase transformation of nanomaterials, by taking noble metals and layered transition metal dichalcogenides as typical examples. Moreover, we also highlight recent advances in the preparation of amorphous nanomaterials, amorphous–crystalline and crystal phase-based hetero-nanostructures. We also provide personal perspectives on challenges and opportunities in this emerging field, including exploration of phase-dependent properties and applications, rational design of phase-based heterostructures and extension of the concept of phase engineering to a wider range of materials. Properties of nanomaterials respond to changes in the material’s phase, as well as changes in size, composition and morphology. This Review discusses the most recent developments in phase engineering of nanomaterials to afford conventional and unconventional crystal phases, amorphous phases and amorphous–crystalline heterophases.
TL;DR: Micrometre-sized light-emitting diodes (LEDs) based on quantum dots (QDs) will propel the next generation of display technologies, a review by leading researchers shows.
Abstract: Micro-light-emitting diodes (μ-LEDs) are regarded as the cornerstone of next-generation display technology to meet the personalised demands of advanced applications, such as mobile phones, wearable watches, virtual/augmented reality, micro-projectors and ultrahigh-definition TVs. However, as the LED chip size shrinks to below 20 μm, conventional phosphor colour conversion cannot present sufficient luminance and yield to support high-resolution displays due to the low absorption cross-section. The emergence of quantum dot (QD) materials is expected to fill this gap due to their remarkable photoluminescence, narrow bandwidth emission, colour tuneability, high quantum yield and nanoscale size, providing a powerful full-colour solution for μ-LED displays. Here, we comprehensively review the latest progress concerning the implementation of μ-LEDs and QDs in display technology, including μ-LED design and fabrication, large-scale μ-LED transfer and QD full-colour strategy. Outlooks on QD stability, patterning and deposition and challenges of μ-LED displays are also provided. Finally, we discuss the advanced applications of QD-based μ-LED displays, showing the bright future of this technology. Micrometre-sized light-emitting diodes (LEDs) based on quantum dots (QDs) will propel the next generation of display technologies, a review by leading researchers shows. Conventional LED designs, with phosphor coatings that convert light to different colours, are difficult to make smaller than 20 micrometres. Jr-Hau He at City University of Hong Kong and co-workers explain how this problem can be tackled using QDs, tiny particles whose optical properties can be tuned by varying their size, providing brighter and more precise colours. Ultra-high-resolution displays based on phospholuminescent QD-LEDs are now being released to the market thanks to finely-controlled methods for synthesising QDs and depositing them onto films. Further research should focus on the best ways to stabilise and protect QD films within LEDs, and to continue developing electroluminescent QD-LEDs, which could potentially outperform their phospholuminescent cousins.
TL;DR: An in situ polyaniline (PANI) intercalation strategy is developed to facilitate the Zn2+ (de)intercalation kinetics in V2 O5, exhibiting a stable and highly reversible electrochemical reaction during repetitive Zn 2+ insertion and extraction.
Abstract: Rechargeable zinc-ion batteries (ZIBs) are emerging as a promising alternative for Li-ion batteries. However, the developed cathodes suffer from sluggish Zn2+ diffusion kinetics, leading to poor rate capability and inadequate cycle life. Herein, an in situ polyaniline (PANI) intercalation strategy is developed to facilitate the Zn2+ (de)intercalation kinetics in V2 O5 . In this way, a remarkably enlarged interlayer distance (13.90 A) can be constructed alternatively between the VO layers, offering expediting channels for facile Zn2+ diffusion. Importantly, the electrostatic interactions between the Zn2+ and the host O2- , which is another key factor in hindering the Zn2+ diffusion kinetics, can be effectively blocked by the unique π-conjugated structure of PANI. As a result, the PANI-intercalated V2 O5 exhibits a stable and highly reversible electrochemical reaction during repetitive Zn2+ insertion and extraction, as demonstrated by in situ synchrotron X-ray diffraction and Raman studies. Further first-principles calculations clearly reveal a remarkably lowered binding energy between Zn2+ and host O2- , which explains the favorable kinetics in PANI-intercalated V2 O5 . Benefitting from the above, the overall electrochemical performance of PANI-intercalated V2 O5 electrode is remarkable improved, exhibiting excellent high rate capability of 197.1 mAh g-1 at current density of 20 A g-1 with capacity retention of 97.6% over 2000 cycles.
TL;DR: This is the first demonstration of an all-solid-state Zn-ion battery based on a newly developed electrolyte, which meanwhile solves the deep-seated hydrogen evolution and dendrite growth problem in traditional ZN-ion batteries.
Abstract: An ionic-liquid-based Zn salt electrolyte is demonstrated to be an effective route to solve both the side-reaction of the hydrogen evolution reaction (HER) and Zn-dendrite growth in Zn-ion batteries. The developed electrolyte enables hydrogen-free, dendrite-free Zn plating/stripping over 1500 h cycle (3000 cycles) at 2 mA cm-2 with nearly 100% coulombic efficiency. Meanwhile, the oxygen-induced corrosion and passivation are also effectively suppressed. These features bring Zn-ion batteries an unprecedented long lifespan over 40 000 cycles at 4 A g-1 and high voltage of 2.05 V with a cobalt hexacyanoferrate cathode. Furthermore, a 28.6 µm thick solid polymer electrolyte of a poly(vinylidene fluoride-hexafluoropropylene) film filled with poly(ethylene oxide)/ionic-liquid-based Zn salt is constructed to build an all-solid-state Zn-ion battery. The all-solid-state Zn-ion batteries show excellent cycling performance of 30 000 cycles at 2 A g-1 at room temperature and withstand high temperature up to 70 °C, low temperature to -20 °C, as well as abuse test of bending deformation up to 150° for 100 cycles and eight times cutting. This is the first demonstration of an all-solid-state Zn-ion battery based on a newly developed electrolyte, which meanwhile solves the deep-seated hydrogen evolution and dendrite growth problem in traditional Zn-ion batteries.
TL;DR: The studies reveal that the nitrile (C-N) groups on the small molecule effectively reduce the trap density of the perovskite film and thus significantly suppresses the non-radiative recombination in the derived PVSC by passivating the Pb-exposed surface, resulting in an improved open-circuit voltage from 1.10 V to 1.16”V after passivation.
Abstract: All-inorganic perovskite solar cells (PVSCs) have drawn increasing attention because of their outstanding thermal stability. However, their performance is still inferior than the typical organic-inorganic counterparts, especially for the devices with p-i-n configuration. Herein, we successfully employ a Lewis base small molecule to passivate the inorganic perovskite film, and its derived PVSCs achieved a champion efficiency of 16.1% and a certificated efficiency of 15.6% with improved photostability, representing the most efficient inverted all-inorganic PVSCs to date. Our studies reveal that the nitrile (C-N) groups on the small molecule effectively reduce the trap density of the perovskite film and thus significantly suppresses the non-radiative recombination in the derived PVSC by passivating the Pb-exposed surface, resulting in an improved open-circuit voltage from 1.10 V to 1.16 V after passivation. This work provides an insight in the design of functional interlayers for improving efficiencies and stability of all-inorganic PVSCs. There has been a hot competition to optimize the device performance for all-inorganic perovskite solar cells. Here Wang et al. employ a Lewis base molecule to suppresses the non-radiative recombination in the inverted device and achieve a champion efficiency of 16.1%.
TL;DR: Chlorine-functionalized graphdiyne (GCl) is successfully applied as a multifunctional solid additive to fine-tune the morphology and improve device efficiency as well as reproductivity for the first time and confirms the efficacy of GCl to enhance device performance.
Abstract: Morphology tuning of the blend film in organic solar cells (OSCs) is a key approach to improve device efficiencies. Among various strategies, solid additive is proposed as a simple and new way to enable morphology tuning. However, there exist few solid additives reported to meet such expectations. Herein, chlorine-functionalized graphdiyne (GCl) is successfully applied as a multifunctional solid additive to fine-tune the morphology and improve device efficiency as well as reproductivity for the first time. Compared with 15.6% efficiency for control devices, a record high efficiency of 17.3% with the certified one of 17.1% is obtained along with the simultaneous increase of short-circuit current (Jsc ) and fill factor (FF), displaying the state-of-the-art binary organic solar cells at present. The redshift of the film absorption, enhanced crystallinity, prominent phase separation, improved mobility, and decreased charge recombination synergistically account for the increase of Jsc and FF after introducing GCl into the blend film. Moreover, the addition of GCl dramatically reduces batch-to-batch variations benefiting mass production owing to the nonvolatile property of GCl. All these results confirm the efficacy of GCl to enhance device performance, demonstrating a promising application of GCl as a multifunctional solid additive in the field of OSCs.
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TL;DR: Zhang et al. as discussed by the authors proposed a zero-reference deep curve estimation (Zero-DCE) method, which formulates light enhancement as a task of image-specific curve estimation with a deep network.
Abstract: The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed. Code and model will be available at this https URL.
TL;DR: Results show that non-self-reactive virus-neutralizing mAbs elicited during SARS-CoV-2 infection are a promising therapeutic strategy and should be guided by immunization strategies.
TL;DR: This review discusses the fundamental interaction mechanisms underpinning the spectacular wet adhesion in natural mussels and mussel-inspired materials, and the key routes to engineering hydrogels by leveraging on the interactions of mussels-inspired building blocks.
Abstract: Mussel-inspired chemistry, owing to its unique and versatile functions to manipulate dynamic molecular-scale interactions, has emerged as a powerful tool for the rational design and synthesis of new hydrogels. In particular, possessing a myriad of unique advantages that are otherwise impossible by conventional counterparts, mussel-inspired hydrogels have been widely explored in numerous fields such as biomedical engineering, soft electronics and actuators, and wearable sensors. Despite great excitement and vigor, a comprehensive and timely review on this emerging topic is missing. In this review, we discuss (1) the fundamental interaction mechanisms underpinning the spectacular wet adhesion in natural mussels and mussel-inspired materials; (2) the key routes to engineering hydrogels by leveraging on the interactions of mussel-inspired building blocks; (3) the emerging applications of mussel-inspired hydrogels, especially in the areas of flexible electronics and biomedical engineering; (4) the future perspectives and unsolved challenges of this multidisciplinary field. We envision that this review will provide an insightful perspective to stimulate new thinking and innovation in the development of next-generation hydrogels and beyond.
TL;DR: A multicomponent FeCoCrNi alloy with dynamically formed Ni4+ species to offer high catalytic activity via lattice oxygen activation mechanism to offer highly intrinsic activity at low applied potentials is reported.
Abstract: Anodic oxygen evolution reaction (OER) is recognized as kinetic bottleneck in water electrolysis. Transition metal sites with high valence states can accelerate the reaction kinetics to offer highly intrinsic activity, but suffer from thermodynamic formation barrier. Here, we show subtle engineering of highly oxidized Ni4+ species in surface reconstructed (oxy)hydroxides on multicomponent FeCoCrNi alloy film through interatomically electronic interplay. Our spectroscopic investigations with theoretical studies uncover that Fe component enables the formation of Ni4+ species, which is energetically favored by the multistep evolution of Ni2+→Ni3+→Ni4+. The dynamically constructed Ni4+ species drives holes into oxygen ligands to facilitate intramolecular oxygen coupling, triggering lattice oxygen activation to form Fe-Ni dual-sites as ultimate catalytic center with highly intrinsic activity. As a result, the surface reconstructed FeCoCrNi OER catalyst delivers outstanding mass activity and turnover frequency of 3601 A gmetal−1 and 0.483 s−1 at an overpotential of 300 mV in alkaline electrolyte, respectively. Electrocatalytic water oxidation is facilitated by high valence states, but these are challenging to achieve at low applied potentials. Here, authors report a multicomponent FeCoCrNi alloy with dynamically formed Ni4+ species to offer high catalytic activity via lattice oxygen activation mechanism.
TL;DR: In order to improve the electrochemical performance of various kinds of rechargeable batteries, such as lithium-ion batteries, lithium-sulfur batteries, sodium- ion batteries, and other types of emerging batteries, the strategies for the design and fabrication of layered TMD-based electrode materials are discussed.
Abstract: The rapid development of electrochemical energy storage (EES) systems requires novel electrode materials with high performance. A typical 2D nanomaterial, layered transition metal dichalcogenides (TMDs) are regarded as promising materials used for EES systems due to their large specific surface areas and layer structures benefiting fast ion transport. The typical methods for the preparation of TMDs and TMD-based nanohybrids are first summarized. Then, in order to improve the electrochemical performance of various kinds of rechargeable batteries, such as lithium-ion batteries, lithium-sulfur batteries, sodium-ion batteries, and other types of emerging batteries, the strategies for the design and fabrication of layered TMD-based electrode materials are discussed. Furthermore, the applications of layered TMD-based nanomaterials in supercapacitors, especially in untraditional supercapacitors, are presented. Finally, the existing challenges and promising future research directions in this field are proposed.