Search

Abstract To satisfy the needs of modern intelligent society for power supplies with long-endurance ability, Li-rich Mn-based layered oxides (LRMOs) are receiving much attention because of their ultrahigh capacity. However, their real-world implementation is hindered by the serious voltage decay, which results in a continuous decrease in energy density. The understanding on voltage decay still remains a mystery due to the complicated hybrid cationic-anionic redox and the serious surface-interface reactions in LRMOs. Moreover, some of the mechanisms are occasionally contradictory, indicating that the origin of voltage decay is still unclear. As a result, none of the innovative strategies proposed on the basis of mechanisms has effectively alleviated the problem of voltage decay, and voltage decay becomes a long-term distress of LRMOs. Therefore, it is particularly crucial to sort out the mutual relation of various mechanisms, which helps to go back to the source of voltage decay. In this review, we summarize the current mechanisms of voltage decay as structural evolution and oxygen chemistry, and attempt to trace the origin of voltage decay for LRMOs. In addition, we discuss how current researches address the issue with generalized guidance in designing appropriate strategies based on mechanisms.

Abstract Although graph convolutional networks have achieved good performances in skeleton‐graph‐based action recognition, there are still some problems which include the incomplete utilization of skeleton graph features and the lacking of logical adjacency information between nodes in adjacency matrix. In this article, a human action recognition algorithm is proposed based on multiple features from the skeleton graph to solve these problems. More specifically, an improved adjacency matrix is constructed to make full use of the multiple skeleton graph features. These features include local differential features, multi‐scale edge features, features of the original skeleton graph, nodal features, and nodal motion features. Extensive results are conducted on four standard datasets (NTU RGB‐D 60, NTU RGB‐D 120, Kinetics, and Northwestern‐UCLA). The experimental results show that the proposed algorithm outperforms the SOTA action recognition algorithms.

Abstract Electronic waste (e‐waste) contains substantial quantities of valuable precious metals, particularly gold (Au). However, inefficient metal recovery leads to these precious metals being discarded in landfills, causing significant water and environmental contamination. This study introduces a two‐dimensional (2D) electrode with a layered graphene oxide membrane functionalized by chitosan (GO/CS). The GO/CS membrane acts as an ion‐selective layer and demonstrates capabilities in the electrochemical extraction and reduction of Au ions. The multiple functional groups of GO and CS offer high cooperativity in ion extraction and reduction, achieving 95 wt.% extraction efficiency within 10 min. The simultaneous extraction and electrocatalytic reduction of Au ions within the membrane leads to the formation of ready‐to‐use metallic Au forms such as chips and sensors. Such an approach eliminates the processing steps required to convert extracted gold into functional products, reducing time, cost, and energy. This direct formation of usable Au components enhances the efficiency of the recovery process, making it economically viable and environmentally sustainable. The gold mining market is projected to be valued at $270 billion by 2032, with the recycling segment reaching $10.8 billion, highlighting the substantial benefits and economic potential of efficient e‐waste recycling technologies.

In the realm of photoenergy conversion, the scarcity of efficient light-driven semiconductors poses a significant obstacle to the advancement of photocatalysis, highlighting the critical need for researchers to explore novel semiconductor materials. Herein, we present the inaugural synthesis of a novel semiconductor, CdNCN, under mild conditions, while shedding light on its formation mechanism. By effectively harnessing the [NCN]2⁻ moiety in the thiourea process, we successfully achieve the one-pot synthesis of CdNCN-CdS heterostructure photocatalysts. Notably, the optimal CdNCN-CdS sample demonstrates a hydrogen evolution rate of 14.7 ​mmol ​g−1 ​h−1 under visible light irradiation, establishing itself as the most efficient catalyst among all reported CdS-based composites without any cocatalysts. This outstanding hydrogen evolution performance of CdNCN-CdS primarily arises from two key factors: i) the establishment of an atomic-level N-Cd-S heterostructure at the interface between CdNCN and CdS, which facilitating highly efficient electron transfer; ii) the directed transfer of electrons to the (110) crystal plane of CdNCN, promoting optimal hydrogen adsorption and active participation in the hydrogen evolution reaction. This study provides a new method for synthesizing CdNCN materials and offers insights into the design and preparation of innovative atomic-level composite semiconductor photocatalysts.

Abstract BiVO4 is one of the most promising photoanode materials for photoelectrochemical (PEC) solar energy conversion, but it still suffers from poor photocurrent density due to insufficient light‐harvesting efficiency (LHE), weak photogenerated charge separation efficiency (ΦSep), and low water oxidation efficiency (ΦOX). Herein, we tackle these challenges of the BiVO4 photoanodes using systematic engineering, including catalysis engineering, bandgap engineering, and morphology engineering. In particular, we deposit a NiCoOx layer onto the BiVO4 photoanode as the oxygen evolution catalyst to enhance the ΦOX of Fe‐g‐C3N4/BiVO4 for PEC water oxidation, and incorporate Fe‐doped graphite‐phase C3N4 (Fe‐g‐C3N4) into the BiVO4 photoanode to optimize the bandgap and surface areas to subsequently expand the light absorption range of the photoanode from 530 to 690 nm, increase the LHE and ΦSep, and further improve the oxygen evolution reaction activity of the NiCoOx catalytic layer. Consequently, the maximum photocurrent density of the as‐prepared NiCoOx/Fe‐g‐C3N4/BiVO4 is remarkably boosted from 4.6 to 7.4 mA cm−2. This work suggests that the proposed systematic engineering strategy is exceptionally promising for improving LHE, ΦSep, and ΦOX of BiVO4‐based photoanodes, which will substantially benefit the design, preparation, and large‐scale application of next‐generation high‐performance photoanodes.

Abstract Electrochemical conversion of nitrate to ammonia offers an efficient approach to reducing nitrate pollutants and a potential technology for low-temperature and low-pressure ammonia synthesis. However, the process is limited by multiple competing reactions and NO3 − adsorption on cathode surfaces. Here, we report a Fe/Cu diatomic catalyst on holey nitrogen-doped graphene which exhibits high catalytic activities and selectivity for ammonia production. The catalyst enables a maximum ammonia Faradaic efficiency of 92.51% (−0.3 V(RHE)) and a high NH3 yield rate of 1.08 mmol h−1 mg−1 (at − 0.5 V(RHE)). Computational and theoretical analysis reveals that a relatively strong interaction between NO3 − and Fe/Cu promotes the adsorption and discharge of NO3 − anions. Nitrogen-oxygen bonds are also shown to be weakened due to the existence of hetero-atomic dual sites which lowers the overall reaction barriers. The dual-site and hetero-atom strategy in this work provides a flexible design for further catalyst development and expands the electrocatalytic techniques for nitrate reduction and ammonia synthesis.

Highlights The state-of-the-art development in electrochemical H2O2 production via the two-electron oxygen reduction reaction is reviewed with emphasis on material science, reaction mechanisms, and fundamental factors that govern the reaction route. General principles and strategies for catalyst design are summarized to understand the inherent relationships between the catalyst properties and electrocatalytic performances. Perspectives and challenges are presented to get insights into the large-scale manufacturing of H2O2 via the electrochemical routes.

A mechanically strong binder with polar functional groups could overcome the dilemma of the large volume change during charge/discharge processes and poor cyclability of lithium-sulfur batteries (LSBs). In this work, for the first time, we report the use of poly(thiourea triethylene glycol) (PTTG) as a multifunctional binder for sulfur cathodes to enhance the performance of LSBs. As expected, the PTTG binder facilitates the high performance and stability delivered by the Sulfur-PTTG cathode, including a higher reversible capacity of 825 mAh g−1 at 0.2 C after 80 cycles, a lower capacity fading (0.123% per cycle) over 350 cycles at 0.5 C, a higher areal capacity of 2.5 mAh cm−2 at 0.25 mA cm−2, and better rate capability of 587 mAh g−1 at 2 C. Such superior electrochemical performances could be attributed to PTTG's strong chemical adsorption towards polysulfides which may avoid the lithium polysulfide shuttle effect and excellent mechanical characteristics which prevents electrode collapse during cycling and allows the Sulfur-PTTG electrode to maintain robust electron and ion migration pathways for accelerated redox reaction kinetics.

Abstract One of the most unique properties of two‐dimensional carbides and nitrides of transition metals (MXenes) is their excellent water dispersibility and yet possessing superior electrical conductivity but their industrial‐scale application is limited by their costly chemical synthesis methods. In this work, the niche feature of MXenes was capitalized in the packed‐bed electrochemical reactor to produce MXenes at an unprecedented reaction rate and yield with minimal chemical waste. A simple NH4F solution was employed as the green electrolyte, which could be used repeatedly without any loss in its efficacy. Surprisingly, both fluoride and ammonium were found to play critical roles in the electrochemical etching, functionalization, and expansion of the layered parent materials (MAXs) through which the liberation of ammonia gas was observed. The electrochemically produced MXenes with excellent conductivity, applied as supercapacitor electrodes, could deliver an ultrahigh volumetric capacity (1408 F cm−3) and a volumetric energy density (75.8 Wh L−1). This revolutionary green, energy‐efficient, and scalable electrochemical route will not only pave the way for industrial‐scale production of MXenes but also open up a myriad of versatile electrochemical modifications for improved functional MXenes.

Abstract Three‐dimensional (3D) printing has gained popularity in a variety of applications, particularly in the manufacture of wearable devices. Aided by the large degree of freedom in customizable fabrication, 3D printing can cater towards the practical requirements of wearable devices in terms of light weight and flexibility. In particular, this focus review aims to cover the important aspect of wearable energy storage devices (WESDs), which is an essential component of most wearable devices. Herein, the topics discussed are the fundamentals of 3D printing inks used, the optimizing strategies in improving the mechanical and electrochemical properties of wearable devices and the recent developments and challenges of wearable electrochemical systems such as batteries and supercapacitors. It can be expected that, with the development of 3D printing technology, realization of the full potential of WESDs and seamless integration into smart devices also needs further in‐depth investigations.

Abstract High energy and power density alkali‐ion (i.e., Li+, Na+, and K+) batteries (AIBs), especially lithium‐ion batteries (LIBs), are being ubiquitously used for both large‐ and small‐scale energy storage, and powering electric vehicles and electronics. However, the increasing LIB‐triggered fires due to thermal runaways have continued to cause significant injuries and casualties as well as enormous economic losses. For this reason, to date, great efforts have been made to create reliable fire‐safe AIBs through advanced materials design, thermal management, and fire safety characterization. In this review, the recent progress is highlighted in the battery design for better thermal stability and electrochemical performance, and state‐of‐the‐art fire safety evaluation methods. The key challenges are also presented associated with the existing materials design, thermal management, and fire safety evaluation of AIBs. Future research opportunities are also proposed for the creation of next‐generation fire‐safe batteries to ensure their reliability in practical applications.

Abstract Aqueous zinc-ion batteries (AZIBs) can be one of the most promising electrochemical energy storage devices for being non-flammable, low-cost, and sustainable. However, the challenges of AZIBs, including dendrite growth, hydrogen evolution, corrosion, and passivation of zinc anode during charging and discharging processes, must be overcome to achieve high cycling performance and stability in practical applications. In this work, we utilize a dual-functional organic additive cyclohexanedodecol (CHD) to firstly establish [Zn(H2O)5(CHD)]2+ complex ion in an aqueous Zn electrolyte and secondly build a robust protection layer on the Zn surface to overcome these dilemmas. Systematic experiments and theoretical calculations are carried out to interpret the working mechanism of CHD. At a very low concentration of 0.1 mg mL−1 CHD, long-term reversible Zn plating/stripping could be achieved up to 2200 h at 2 mA cm−2, 1000 h at 5 mA cm−2, and 650 h at 10 mA cm−2 at the fixed capacity of 1 mAh cm−2. When matched with V2O5 cathode, the resultant AZIBs full cell with the CHD-modified electrolyte presents a high capacity of 175 mAh g−1 with the capacity retention of 92% after 2000 cycles under 2 A g−1. Such a performance could enable the commercialization of AZIBs for applications in grid energy storage and industrial energy storage.

Abstract Two‐dimensional (2D) MXene‐based nanofiltration membranes could sustainably and rapidly produce clean water. However, the challenges related to the severe swelling of the MXene membranes in water should be overcome before the practical application. In this work, the negatively charged MXene‐based membrane is modified by positively charged polyethyleneimine (PEI) with various PEI:MXene ratios. The PEI‐MXene is deposited on a polyacrylonitrile (PAN) substrate as a top membrane to prepare an anti‐swelling composite PEI‐MXene/PAN membranes with outstanding mechanical strength and stability. Interestingly, when the interlayer spacing of the top Mxene layers are tuned between ca. 14.15 and ca. 14.52 Å, the resultant PEI‐MXene/PAN membranes exhibit the excellent ability of size sieving. For instance, PEI‐MXene(1:1)/PAN membrane presents high flux performance to Acid Blue 90 (AB90), Congo Red (CR), and Direct Red 80 (DR80) (i.e., 563.9, 882.6, and 633.6 L m−2 h−1, respectively) at 0.2 MPa, and remarkable rejection rates, that is, 99.3%, 99.82%, and 99.9%, respectively. Besides, the PEI‐MXene(0.5:1)/PAN possesses the highest flux of 1528.1 L m−2 h−1 and outstanding rejection rate of 99.9% to dye of AB8GX. Moreover, the proposed strategy can be extended to the modification of other 2D material nanofiltration membranes.

Abstract Solid polymer electrolytes (SPEs) have become increasingly attractive in solid‐state lithium‐ion batteries (SSLIBs) in recent years because of their inherent properties of flexibility, processability, and interfacial compatibility. However, the commercialization of SPEs remains challenging for flexible and high‐energy‐density LIBs. The incorporation of functional additives into SPEs could significantly improve the electrochemical and mechanical properties of SPEs and has created some historical milestones in boosting the development of SPEs. In this study, we review the roles of additives in SPEs, highlighting the working mechanisms and functionalities of the additives. The additives could afford significant advantages in boosting ionic conductivity, increasing ion transference number, improving high‐voltage stability, enhancing mechanical strength, inhibiting lithium dendrite, and reducing flammability. Moreover, the application of functional additives in high‐voltage cathodes, lithium–sulfur batteries, and flexible lithium‐ion batteries is summarized. Finally, future research perspectives are proposed to overcome the unresolved technical hurdles and critical issues in additives of SPEs, such as facile fabrication process, interfacial compatibility, investigation of the working mechanism, and special functionalities.

Infrared images have been widely used in many research areas, such as target detection and scene monitoring. Therefore, the copyright protection of infrared images is very important. In order to accomplish the goal of image-copyright protection, a large number of image-steganography algorithms have been studied in the last two decades. Most of the existing image-steganography algorithms hide information based on the prediction error of pixels. Consequently, reducing the prediction error of pixels is very important for steganography algorithms. In this paper, we propose a novel framework SSCNNP: a Convolutional Neural-Network Predictor (CNNP) based on Smooth-Wavelet Transform (SWT) and Squeeze-Excitation (SE) attention for infrared image prediction, which combines Convolutional Neural Network (CNN) with SWT. Firstly, the Super-Resolution Convolutional Neural Network (SRCNN) and SWT are used for preprocessing half of the input infrared image. Then, CNNP is applied to predict the other half of the infrared image. To improve the prediction accuracy of CNNP, an attention mechanism is added to the proposed model. The experimental results demonstrate that the proposed algorithm reduces the prediction error of the pixels due to full utilization of the features around the pixel in both the spatial and the frequency domain. Moreover, the proposed model does not require either expensive equipment or a large amount of storage space during the training process. Experimental results show that the proposed algorithm had good performances in terms of imperceptibility and watermarking capacity compared with advanced steganography algorithms. The proposed algorithm improved the PSNR by 0.17 on average with the same watermark capacity.

Abstract Low cost and green fabrication of high-performance electrocatalysts with earth-abundant resources for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are crucial for the large-scale application of rechargeable Zn–air batteries (ZABs). In this work, our density functional theory calculations on the electrocatalyst suggest that the rational construction of interfacial structure can induce local charge redistribution, improve the electronic conductivity and enhance the catalyst stability. In order to realize such a structure, we spatially immobilize heterogeneous CoS/CoO nanocrystals onto N-doped graphene to synthesize a bifunctional electrocatalyst (CoS/CoO@NGNs). The optimization of the composition, interfacial structure and conductivity of the electrocatalyst is conducted to achieve bifunctional catalytic activity and deliver outstanding efficiency and stability for both ORR and OER. The aqueous ZAB with the as-prepared CoS/CoO@NGNs cathode displays a high maximum power density of 137.8 mW cm−2, a specific capacity of 723.9 mAh g−1 and excellent cycling stability (continuous operating for 100 h) with a high round-trip efficiency. In addition, the assembled quasi-solid-state ZAB also exhibits outstanding mechanical flexibility besides high battery performances, showing great potential for applications in flexible and wearable electronic devices.

Abstract Carbon nitrides (including CN, C2N, C3N, C3N4, C4N, and C5N) are a unique family of nitrogen-rich carbon materials with multiple beneficial properties in crystalline structures, morphologies, and electronic configurations. In this review, we provide a comprehensive review on these materials properties, theoretical advantages, the synthesis and modification strategies of different carbon nitride-based materials (CNBMs) and their application in existing and emerging rechargeable battery systems, such as lithium-ion batteries, sodium and potassium-ion batteries, lithium sulfur batteries, lithium oxygen batteries, lithium metal batteries, zinc-ion batteries, and solid-state batteries. The central theme of this review is to apply the theoretical and computational design to guide the experimental synthesis of CNBMs for energy storage, i.e., facilitate the application of first-principle studies and density functional theory for electrode material design, synthesis, and characterization of different CNBMs for the aforementioned rechargeable batteries. At last, we conclude with the challenges, and prospects of CNBMs, and propose future perspectives and strategies for further advancement of CNBMs for rechargeable batteries.

Abstract Sustainable, conductive, and porous carbon materials are ideal for energy storage materials. In this study, honeycomb‐like carbon materials (HCM) are synthesized via a “salty” thermal treatment of abundant and sustainable coffee extract. Systematic materials characterization indicates that the as‐prepared HCM consists of heteroatoms (N and O, etc.) doped ultra‐thin carbon framework, possesses remarkable specific surface area, and excellent electrical conductivity. Such properties bestow HCM outstanding materials to be the blocking layer for Li‐I2 battery, significantly eliminating the dissolution of I2 in the cathode region and stopping the I2 from shutting to anode compartment. Furthermore, our electrochemical investigation suggests that HCM could incur surface pseudo‐capacitive iodine‐ions charge storage and contribute additional energy storage capacity. As a result, the resultant Li‐I2 battery achieves a robust and highly reversible capacity of 224.5 mAh·g−1 at the rate of 10 C. Even under a high rate of 50 C, the remarkable capacity of the as‐prepared Li‐I2 battery can still be maintained at 120.2 mAh·g−1 after 4000 cycles.

With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot attacks, such as different distances and capturing angles of the screenshots. In this paper, a fast and robust high-capacity flexible watermarking algorithm for screenshot images is proposed. Firstly, Oriented FAST and Rotated BRIEF (ORB) feature points are extracted from the input image. Secondly, the feature points are then sorted in a descending order according to their response values. Then, the first five non-overlapping feature points are selected for the embedding by using Hamming window-based filtering method. Furthermore, we exploit the multi-resolution property of Discrete Wavelet Transform (DWT) and energy compaction property of Singular Value Decomposition (SVD) to embed the watermark. Therefore, the classical DWT combined with Singular Value Decomposition (SVD) are adopted to improve the robustness and capacity of the proposed watermarking algorithm. At the extraction side, the sum of the response values for the three RGB channels of the color-ripped image is calculated to improve the feature point localization accuracy. Experimental results show that the proposed screen-shooting watermarking algorithm improves running speed while ensuring the robustness. Furthermore, it has less time complexity and high robustness compared with the state-of-the-art watermarking algorithms against different screenshot attacks.

Abstract The sulfur cathode of lithium‐sulfur (Li‐S) batteries suffers from inherent problems of insufficient mechanical strength and the dissolution of sulfur and polysulfides. Inspired by the extraordinarily resilient and strong binding force of the Great Wall binder, that is, the sticky rice mortar, we extracted highly branched amylopectin (HBA), the effective ingredient, as a low‐cost, nontoxic and environmentally benign aqueous binder for the sulfur cathode. The HBA‐based cells outperform the Li‐S batteries based on the traditional polyvinyldene diflouride (PVDF) binder and a lowly branched polysaccharide binder. The improved electrochemical performance in the HBA‐based cell could be attributed to two mechanisms. First, the branched structure of the HBA provides enhanced mechanical and adhesive properties, which allow for a robust electronic and ionic conductive framework to be maintained throughout the cathode after extended cycling. Second, the HBA shows enhanced polysulfide retention due to the polymer's abundant lone‐pair rich hydroxyl groups and the formation of C─S bonds between the HBA and polysulfides prohibits the shuttle effect of polysulfides. The improved mechanical properties and polysulfide retention function of the HBA binder facilitate the HBA‐based Li‐S battery to deliver a long cycle life of 500 cycles at 2 C while only displaying a capacity fading of 0.104% per cycle.

Abstract Electrolyte engineering is considered as an effective strategy to establish stable solid electrolyte interface (SEI), and thus to suppress the growth of lithium dendrites. In a recent study reported in Advanced Functional Materials by Ma group, discovered that strong coordination force could be founded between 15-Crown-5 ether (15-C-5) and Li+, which facilitates the crown ether (15-C-1) to participate in the solvation structure of Li+ in the electrolyte for the same purpose. Such a novel strategy might impact the design of high-performance and safe lithium metal batteries (LMBs).

Here the authors construct carbon nanofiber interpenetrated graphene architecture with in-situ grown MoS2 nanoflakes alongside the framework. The design combines exceptional mechanical integrity and excellent electronic conductivity, enabling outstanding electrochemical performance in sodium-ion battery.

An efficient data hiding method with modulo-2 operation and Hamming code (3, 2) based on absolute moment block truncation coding (AMBTC) is proposed. In order to obtain good data hiding performance, different textures are assigned to different embedding strategies. The AMBTC compressed codes are divided into smooth and complex blocks according to texture. In the smooth block, the secret data and the four most significant bits plane of the two quantization levels are calculated using modulo-2 operation to replace the bitmap in order to improve the security of data transmission. Moreover, Hamming code (3, 2) is used to embed the two additional secret bits in the three significant bits planes of the two quantization levels. In the complex block, one secret bit is embedded by swapping the order of two quantization levels and flipping the bitmap. Experimental results show that the proposed method achieves higher capacity than the existing data hiding methods and maintains good visual quality.

Effective recycling technologies represent a solution to the sustainability and environmental consequences of spent rechargeable batteries. Here, the authors show a bipolar electrode design that allows not only good electrochemical performance but a closed loop of material use for sodium ion batteries.

Since most of the previous video watermark algorithms regard a video as a series of consecutive images, the embedding and extraction of watermark are performed on these images, and the correlation and redundancy among frames of a video are not considered. Such algorithms are weak in protecting against frame attacks. In order to improve the robustness, we take into consideration the correlation and redundancy among the frames of a video to propose a blind video watermark algorithm based on tensor decomposition. First, a grayscale video is represented as a 3-order tensor, and the core tensor is obtained by tensor decomposition. Second, the watermark embedding position is selected based on the stability of the maximum value in the core tensor because the core tensor represents the main energy of a video. Then, the watermark is embedded by quantifying the maximum value in the core tensor. Finally, the watermark is uniformly distributed across frames of a video by inverse tensor decomposition. The experiments show that our algorithm based on tensor decomposition has better imperceptibility and robustness against common video attacks.

Silicon is a promising anode material for rechargeable Li-ion battery (LIB) due to its high energy density and relatively low operating voltage. However, silicon based electrodes suffer from rapid capacity degradation during electrochemical cycling. The capacity decay is predominantly caused by (i) cracking due to large volume variations during lithium insertion/extraction and (ii) surface degradation due to excessive solid electrolyte interface (SEI) formation. In this work, we demonstrate that coating of a-Si thin film with a Li-active, nanoporous SiOx layer can result in exceptional electrochemical performance in Li-ion battery. The SiOx layer provides improved cracking resistance to the thin film and prevent the active material loss due to excessive SEI formation, benefiting the electrode cycling stability. Half-cell experiments using this anode material show an initial reversible capacity of 2173 mAh g−1 with an excellent coulombic efficiency of 90.9%. Furthermore, the electrode shows remarkable capacity retention of ∼97% after 100 cycles at C/2 charging rate. The proposed anode architecture is free from Li-inactive binders and conductive additives and provides mechanical stability during the charge/discharge process. Keywords: Amorphous silicon, Thin film, Solid electrolyte interphase, Silicon oxide, Anode, Li-ion battery

Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.

Mobile devices have been increasingly used to take pictures without leaving a trace. However, the application system can lead to confidential information leaks. A framework for screen-shooting-resilient watermarking via deep networks (SSDeN) in the frequency domain is put forward in this study to solve this problem. The proposed framework can extract the watermark from the leaked photo for copyright protection. SSDeN is an end-to-end process that combines convolutional neural network (CNN) with residual block to embed and extract watermarks in the DCT domain. We simulate some screen-shooting attacks to ensure the networks embed the watermark robustly. Our framework achieves the state-of-the-art performance on existing learning architectures for screen-shooting-resilient watermarking.

Abstract Extensive efforts have been devoted to the design of micro-, nano-, and/or molecular structures of sulfur hosts to address the challenges of lithium–sulfur (Li–S) batteries, yet comparatively little research has been carried out on the binders in Li–S batteries. Herein, we systematically review the polymer composite frameworks that confine the sulfur within the sulfur electrode, taking the roles of sulfur hosts and functions of binders into consideration. In particular, we investigate the binding mechanism between the binder and sulfur host (such as mechanical interlocking and interfacial interactions), the chemical interactions between the polymer binder and sulfur (such as covalent bonding, electrostatic bonding, etc.), as well as the beneficial functions that polymer binders can impart on Li–S cathodes, such as conductive binders, electrolyte intake, adhesion strength etc. This work could provide a more comprehensive strategy in designing sulfur electrodes for long-life, large-capacity and high-rate Li–S battery.

Camera shakes cause video motion blur. Video deblurring has been studied for years, and however, there are still unresolved problems, such as video frame alignment, frame selection, and frame ambiguity evaluation. We propose a video deblurring algorithm based on the motion vector and an encoder-decoder network. Our algorithm consists of four steps: first, the blurry image blocks in a video frame are located using a blurred image quality evaluation algorithm based on a response function of singular values. Second, the corresponding candidates of the blurry image block in the consecutive frames are searched using the motion vector, and the optimal candidate blocks are obtained using an objective function. Third, the blurry image block and the optimal candidate blocks are served as samples, which are inputted to an encoder-decoder network, so that the blurry image block is repaired. Finally, all blurry image blocks are replaced with the repaired ones, the boundary artifacts are eliminated, and the entire video frame is repaired. The experiments show that our algorithm yields sharper repair results, and the overall performance of our algorithm is better than other related algorithms.

On account of the high theoretical capacity, high corrosion resistance, environmental benignity, abundant availability and low cost, the research on α-Fe2O3 has been gradually fastened on as promising anodes materials toward lithium-ion batteries (LIBs). A high-performance anode for LIBs based on α-Fe2O3 nanoplates have been selectively prepared. The α-Fe2O3 nanoplates can be synthesized with iron ion-based ionic liquid as iron source and template. The α-Fe2O3 nanoplates as the anode of LIBs can display high capacity of around 1950 mAh g−1 at 0.5 A g−1 which have exceeded the theoretical capacity of α-Fe2O3. On account of unique nanoplate structures and gum arabic as binder, the α-Fe2O3 nanoplates also exhibit high rate capability and excellent cycling performance. Keywords: α-Fe2O3, Nanoplates, Li-ion battery, High reversible capacity, Anode

In the original publication, the label text.

High-voltage transmission towers are built to supply electricity for campuses and local residents. In order to guarantee the power supply and safety for remote campus, the unmanned aerial vehicles (UAVs) are used to take the images of high power lines to alarm potential malfunction. A novel method of bird's nest images detection based on cascade classifier and combination features is proposed. Different features of the bird's nest and iron tower are analyzed, and the following four novel features are proposed: proportion of white area (PWA), ratio of white pixels (RWP) in each lap, projection feature (PF), and improved burr feature (IBF). The combined features are used to describe the characteristics of the bird's nest backbone area and the edges, respectively. Furthermore, the cascade classifier combined with the four proposed features is used for the further classification of bird's nest region. The proposed detection process mainly consists of three stages. First, the suspected bird's nest region is obtained by template convolution. Second, PWA and RWP with low dimensionality and high discrimination are used to classify the sample set of suspected nest region. Third, based on the previous classification results with positive and negative samples, PF and IBF are adopted to further conduct the secondary classification in order to reduce the misclassified samples, and the final classification label is determined by the second classification results. Experimental results show that the proposed algorithm can accurately detect the nest and achieve good performance.

This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications.

To protect the copyright of the color image, a color image watermarking scheme based on quaternion discrete Fourier transform (QDFT) and tensor decomposition (TD) is presented. Specifically, the cover image is partitioned into non-overlapping blocks, and then QDFT is performed on each image block. Then, the three imaginary frequency components of QDFT are used to construct a third-order tensor. The third-order tensor is decomposed by Tucker decomposition and generates a core tensor. Finally, an improved odd–even quantization technique is employed to embed a watermark in the core tensor. Moreover, pseudo-Zernike moments and multiple output least squares support vector regression (MLS–SVR) network model are used for geometric distortion correction in the watermark extraction stage. The scheme utilizes the inherent correlations among the three RGB channels of a color image, and spreads the watermark into the three channels. The experimental results indicate that the proposed scheme has better fidelity and stronger robustness for common image-processing and geometric attacks, can effectively resist each color channel exchange attack. Compared with the existing schemes, the presented scheme achieves better performance.

To understand the role of ubiquitous phototrophic periphyton in aquatic ecosystem on the biogeochemical cycling of organic phosphorus, the conversion and removal kinetic characteristics of organic phosphorus (Porg) such as adenosine triphosphate (ATP) were investigated in the presence of the periphyton cultured in artificial non-point source wastewater. The preliminary results showed that the periphyton was very powerful in converting Porg evidenced by the fact that inorganic phosphorus (Pinorg) content in solution increased from about 0.7 to 14.3 mg P L(-1) in 48 hours in the presence of 0.6 g L(-1) periphyton. This was because the periphyton could produce abundant phosphatases that benefited the conversion of Porg to Pinrog. Moreover, this conversion process was described more suitable by the pseudo-first-order kinetic model. The periphyton was also effective in removing Porg, which showed that the Porg can be completely removed even when the initial Porg concentration was as high as 13 mg P L(-1) in 48 hours in the presence of 1.6 g L(-1) periphyton. Furthermore, it was found that biosorption dominated the Porg removal process and exhibited the characteristics of physical adsorption. However, this biosorption process by the periphyton was significantly influenced by biomass (absorbent dosage) and temperature. This work provides insights into Porg biogeochemical circulation of aquatic ecosystem that contained the periphyton or similar microbial aggregates.

The effects of climate change are just beginning to be felt, and as such, society must work towards strategies of reducing humanity’s impact on the environment. Due to the fact that energy production is one of the primary contributors to greenhouse gas emissions, it is obvious that more environmentally friendly sources of power are required. Technologies such as solar and wind power are constantly being improved through research; however, as these technologies are often sporadic in their power generation, efforts must be made to establish ways to store this sustainable energy when conditions for generation are not ideal. Battery storage is one possible supplement to these renewable energy technologies; however, as current Li-ion technology is reaching its theoretical capacity, new battery technology must be investigated. Lithium–sulphur (Li–S) batteries are receiving much attention as a potential replacement for Li-ion batteries due to their superior capacity, and also their abundant and environmentally benign active materials. In the spirit of environmental harm minimization, efforts have been made to use sustainable carbonaceous materials for applications as carbon–sulphur (C–S) composite cathodes, carbon interlayers, and carbon-modified separators. This work reports on the various applications of carbonaceous materials applied to Li–S batteries, and provides perspectives for the future development of Li–S batteries with the aim of preparing a high energy density, environmentally friendly, and sustainable sulphur-based cathode with long cycle life.