Shanghai University of Engineering Science

Abstract Zn‐based batteries are safe, low cost, and environmentally friendly, as well as delivering the highest energy density of all aqueous battery systems. However, the application of Zn‐based batteries is being seriously hindered by the uneven electrostripping/electroplating of Zn on the anodes, which always leads to enlarged polarization (capacity fading) or even cell shorting (low cycling stability). How a porous nano‐CaCO 3 coating can guide uniform and position‐selected Zn stripping/plating on the nano‐CaCO 3 ‐layer/Zn foil interfaces is reported here. This Zn‐deposition‐guiding ability is mainly ascribed to the porous nature of the nano‐CaCO 3 ‐layer, since similar functionality (even though relatively inferior) is also found in Zn foils coated with porous acetylene black or nano‐SiO 2 layers. Furthermore, the potential application of this strategy is demonstrated in Zn|ZnSO 4 +MnSO 4 |CNT/MnO 2 rechargeable aqueous batteries. Compared with the ones with bare Zn anodes, the battery with a nano‐CaCO 3 ‐coated Zn anode delivers a 42.7% higher discharge capacity (177 vs 124 mAh g −1 at 1 A g −1 ) after 1000 cycles.

The screening of novel materials with good performance and the modelling of quantitative structure-activity relationships (QSARs), among other issues, are hot topics in the field of materials science. Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations. Thus, it is imperative to develop a new method of accelerating the discovery and design process for novel materials. Recently, materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy. In this review, we first outline the typical mode of and basic procedures for applying machine learning in materials science, and we classify and compare the main algorithms. Then, the current research status is reviewed with regard to applications of machine learning in material property prediction, in new materials discovery and for other purposes. Finally, we discuss problems related to machine learning in materials science, propose possible solutions, and forecast potential directions of future research. By directly combining computational studies with experiments, we hope to provide insight into the parameters that affect the properties of materials, thereby enabling more efficient and target-oriented research on materials discovery and design. Machine learning provides a new means of screening novel materials with good performance, developing quantitative structure-activity relationships (QSARs) and other models, predicting the properties of materials, discovering new materials and performing other materials-relateds studies. • The typical mode of and basic procedures for applying machine learning in materials science are summarized and discussed. • For various points of application, the machine learning methods used for different purposes are comprehensively reviewed. • Existing problems are discussed, possible solutions are proposed and potential directions of future research are suggested.

Abstract Rechargeable aqueous Zn‐ion batteries promise high capacity, low cost, high safety, and sustainability for large‐scale energy storage. The Zn metal anode, however, suffers from the dendrite growth and side reactions that are mainly due to the absence of an appropriate solid electrolyte interphase (SEI) layer. Herein, the in situ formation of a dense, stable, and highly Zn 2+ ‐conductive SEI layer (hopeite) in aqueous Zn chemistry is demonstrated, by introducing Zn(H 2 PO 4 ) 2 salt into the electrolyte. The hopeite SEI (≈140 nm thickness) enables uniform and rapid Zn‐ion transport kinetics for dendrite‐free Zn deposition, and restrains the side reactions via isolating active Zn from the bulk electrolyte. Under practical testing conditions with an ultrathin Zn anode (10 µm), a low negative/positive capacity ratio (≈2.3), and a lean electrolyte (9 µL mAh −1 ), the Zn/V 2 O 5 full cell retains 94.4% of its original capacity after 500 cycles. This work provides a simple yet practical solution to high‐performance aqueous battery technology via building in situ SEI layers.

Abstract Restructuring-induced catalytic activity is an intriguing phenomenon of fundamental importance to rational design of high-performance catalyst materials. We study three copper-complex materials for electrocatalytic carbon dioxide reduction. Among them, the copper(II) phthalocyanine exhibits by far the highest activity for yielding methane with a Faradaic efficiency of 66% and a partial current density of 13 mA cm −2 at the potential of – 1.06 V versus the reversible hydrogen electrode. Utilizing in-situ and operando X-ray absorption spectroscopy, we find that under the working conditions copper(II) phthalocyanine undergoes reversible structural and oxidation state changes to form ~ 2 nm metallic copper clusters, which catalyzes the carbon dioxide-to-methane conversion. Density functional calculations rationalize the restructuring behavior and attribute the reversibility to the strong divalent metal ion–ligand coordination in the copper(II) phthalocyanine molecular structure and the small size of the generated copper clusters under the reaction conditions.

Abstract A competitive complexation strategy has been developed to construct a novel electrocatalyst with Zn‐Co atomic pairs coordinated on N doped carbon support (Zn/CoN‐C). Such architecture offers enhanced binding ability of O 2 , significantly elongates the O−O length (from 1.23 Å to 1.42 Å), and thus facilitates the cleavage of O−O bond, showing a theoretical overpotential of 0.335 V during ORR process. As a result, the Zn/CoN‐C catalyst exhibits outstanding ORR performance in both alkaline and acid conditions with a half‐wave potential of 0.861 and 0.796 V respectively. The in situ XANES analysis suggests Co as the active center during the ORR. The assembled zinc–air battery with Zn/CoN‐C as cathode catalyst presents a maximum power density of 230 mW cm −2 along with excellent operation durability. The excellent catalytic activity in acid is also verified by H 2 /O 2 fuel cell tests (peak power density of 705 mW cm −2 ).

Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.

Based upon advances in theoretical algorithms, modeling and simulations, and computer technologies, the rational design of materials, cells, devices, and packs in the field of lithium-ion batteries is being realized incrementally and will at some point trigger a paradigm revolution by combining calculations and experiments linked by a big shared database, enabling accelerated development of the whole industrial chain. Theory and multi-scale modeling and simulation, as supplements to experimental efforts, can help greatly to close some of the current experimental and technological gaps, as well as predict path-independent properties and help to fundamentally understand path-independent performance in multiple spatial and temporal scales.

Abstract Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization 1–5 . First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

We report a bio-inspired design strategy for constructing an in situ polymeric SEI in aqueous Zn chemistry. This SEI can restrain interfacial side reactions, facilitate a uniform Zn 2+ flux, and consequently endow a highly stable Zn metal anode.

An electronegativity-dominant high-entropy atomic environment regulation strategy was developed to manipulate the electrocatalytic properties by tailoring the competitive adsorption sites in HEA NPs.

, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

This article comprehensively reviews the recent progress in the development of key components of lithium-ion batteries, including positive/negative electrodes, electrolytes and separators. The necessity of developing batteries with high energy/power density and long cycle-life is emphasized both in terms of industrial and academic perspectives.

Abstract Dendrite and interfacial reactions have affected zinc (Zn) metal anodes for rechargeable batteries many years. Here, these obstacles are bypassed via adopting an intrinsically safe trimethyl phosphate (TMP)‐based electrolyte to build a stable Zn anode. Along with cycling, pristine Zn foil is gradually converted to a graphene‐analogous deposit via TMP surfactant and a Zn phosphate molecular template. This novel Zn anode morphology ensures long‐term reversible plating/stripping performance over 5000 h, a rate capability of 5 mA cm −2 , and a remarkably high Coulombic efficiency (CE) of ≈99.57% without dendrite formation. As a proof‐of‐concept, a Zn–VS 2 full cell demonstrates an ultralong lifespan, which provides an alternative for electrochemical energy storage devices.

Notice of Violation of IEEE Publication Principles<br><br>After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. <br/> This paper is concerned with dissipativity-based fuzzy integral sliding mode control (FISMC) of continuous-time Takagi-Sugeno (T-S) fuzzy systems with matched/unmatched uncertainties and external disturbance. To better accommodate the characteristics of T-S fuzzy models, an appropriate integral-type fuzzy switching surface is put forward by taking the state-dependent input matrix into account, which is the key contribution of the paper. Based on the utilization of Lyapunov function and property of the transition matrix for unmatched uncertainties, sufficient conditions are presented to guarantee the asymptotic stability of corresponding sliding mode dynamics with a strictly dissipative performance. A FISMC law is synthesized to drive system trajectories onto the fuzzy switching surface despite matched/unmatched uncertainties and external disturbance. A modified adaptive FISMC law is further designed for adapting the unknown upper bound of matched uncertainty. Two practical examples are provided to illustrate the effectiveness and advantages of developed FISMC scheme.

In this technical note, the sliding-mode control (SMC) problem is investigated for T-S fuzzy-model-based nonlinear Markovian jump singular systems subject to matched/unmatched uncertainties. To accommodate the model characteristics of such a hybrid system, a novel integral-type fuzzy sliding surface is put forward by taking the singular matrix and state-dependent projection matrix into account simultaneously, which is the key contribution of the note. The designed surface contains two important features: 1) local input matrices for different subsystems in the same system mode are allowed to be different; and 2) the matched uncertainties are completely compensated, and the unmatched ones are not amplified during sliding motion. Sufficient conditions for the stochastic admissibility of the corresponding sliding-mode dynamics are presented, and a fuzzy SMC law is constructed to ensure the reaching condition despite uncertainties. The applicability and effectiveness of our approach are verified by simulations on an inverted pendulum system.

Abstract Rechargeable aqueous Zn/MnO 2 batteries are very attractive large‐scale energy storage technologies, but still suffer from limited cycle life and low capacity. Here the novel adoption of a near‐neutral acetate‐based electrolyte (pH ≈ 6) is presented to promote the two‐electron Mn 4+ /Mn 2+ redox reaction and simultaneously enable a stable Zn anode. The acetate anion triggers a highly reversible MnO 2 /Mn 2+ reaction, which ensures high capacity and avoids the issue of structural collapse of MnO 2 . Meanwhile, the anode‐friendly electrolyte enables a dendrite‐free Zn anode with outstanding stability and high plating/stripping Coulombic efficiency (99.8%). Hence, a high capacity of 556 mA h g −1 , a lifetime of 4000 cycles without decay, and excellent rate capability up to 70 mA cm −2 are demonstated in this new near‐neutral aqueous Zn/MnO 2 battery by simply manipulating the salt anion in the electrolyte. The acetate anion not only modifies the surface properties of MnO 2 cathode but also creates a highly compatible environment for the Zn anode. This work provides a new opportunity for developing high‐performance Zn/MnO 2 and other aqueous batteries based on the salt anion chemistry.

The MnO(x) and CeO(x) were in situ supported on carbon nanotubes (CNTs) by a poly(sodium 4-styrenesulfonate) assisted reflux route for the low-temperature selective catalytic reduction (SCR) of NO with NH(3). X-Ray diffraction (XRD), transmission electron microscopy (TEM), high-resolution TEM (HRTEM), X-ray photoelectron spectroscopy (XPS), H(2) temperature-programmed reduction (H(2)-TPR) and NH(3) temperature-programmed desorption (NH(3)-TPD) have been used to elucidate the structure and surface properties of the obtained catalysts. It was found that the in situ prepared catalyst exhibited the highest activity and the most extensive operating-temperature window, compared to the catalysts prepared by impregnation or mechanically mixed methods. The XRD and TEM results indicated that the manganese oxide and cerium oxide species had a good dispersion on the CNT surface. The XPS results demonstrated that the higher atomic concentration of Mn existed on the surface of CNTs and the more chemisorbed oxygen species exist. The H(2)-TPR results suggested that there was a strong interaction between the manganese oxide and cerium oxide on the surface of CNTs. The NH(3)-TPD results demonstrated that the catalysts presented a larger acid amount and stronger acid strength. In addition, the obtained catalysts exhibited much higher SO(2)-tolerance and improved the water-resistance as compared to that prepared by impregnation or mechanically mixed methods.

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.

Potassium-ion batteries are promising for low-cost and large-scale energy storage applications, but the major obstacle to their application is the lack of safe and effective electrolytes. A phosphate-based fire retardant such as triethyl phosphate is now shown to work as a single solvent with potassium bis(fluorosulfonyl)imide at 0.9 m, in contrast to previous Li and Na systems where phosphates cannot work at low concentrations. This electrolyte is optimized at 2 m, where it exhibits the advantages of low cost, low viscosity, and high conductivity, as well as the formation of a uniform and robust salt-derived solid-electrolyte interphase layer, leading to non-dendritic K-metal plating/stripping with Coulombic efficiency of 99.6 % and a highly reversible graphite anode.

In recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. Recent advances in this topic are dominantly led by deep learning-based methods. In this paper, we provide a survey covering various aspects ranging from indirect segmentation to direct segmentation. Firstly, we review methods of indirect segmentation based on multi-views and voxel grids, as well as direct segmentation methods from different perspectives including point ordering, multi-scale, feature fusion and fusion of graph convolutional neural network (GCNN). Then, the common datasets for point cloud segmentation are exposed to help researchers choose which one is the most suitable for their tasks. Following that, we devote a part of the paper to analyze the quantitative results of these methods. Finally, the development trend of point cloud semantic segmentation technology is prospected.