Electrochemical energy storage devices with a high energy density are an important technology in modern society, especially for electric vehicles. The most effective approach to improve the energy ...density of batteries is to search for high‐capacity electrode materials. According to the concept of energy quality, a high‐voltage battery delivers a highly useful energy, thus providing a new insight to improve energy density. Based on this concept, a novel and successful strategy to increase the energy density and energy quality by increasing the discharge voltage of cathode materials and preserving high capacity is proposed. The proposal is realized in high‐capacity Li‐rich cathode materials. The average discharge voltage is increased from 3.5 to 3.8 V by increasing the nickel content and applying a simple after‐treatment, and the specific energy is improved from 912 to 1033 Wh kg−1. The current work provides an insightful universal principle for developing, designing, and screening electrode materials for high energy density and energy quality.
Li‐ion batteries with high energy quality require a high capacity coupled with high operating voltage. This requires the electrode materials to not only have a high specific capacity but also a high discharge voltage for cathode materials and low charge voltage for anode materials.
One great challenge in understanding the history of life is resolving the influence of environmental change on biodiversity. Simulated annealing and genetic algorithms were used to synthesize data ...from 11,000 marine fossil species, collected from more than 3000 stratigraphic sections, to generate a new Cambrian to Triassic biodiversity curve with an imputed temporal resolution of 26 ± 14.9 thousand years. This increased resolution clarifies the timing of known diversification and extinction events. Comparative analysis suggests that partial pressure of carbon dioxide (
co
) is the only environmental factor that seems to display a secular pattern similar to that of biodiversity, but this similarity was not confirmed when autocorrelation within that time series was analyzed by detrending. These results demonstrate that fossil data can provide the temporal and taxonomic resolutions necessary to test (paleo)biological hypotheses at a level of detail approaching those of long-term ecological analyses.
Lithium‐rich layered oxides with the capability to realize extraordinary capacity through anodic redox as well as classical cationic redox have spurred extensive attention. However, the ...oxygen‐involving process inevitably leads to instability of the oxygen framework and ultimately lattice oxygen release from the surface, which incurs capacity decline, voltage fading, and poor kinetics. Herein, it is identified that this predicament can be diminished by constructing a spinel Li4Mn5O12 coating, which is inherently stable in the lattice framework to prevent oxygen release of the lithium‐rich layered oxides at the deep delithiated state. The controlled KMnO4 oxidation strategy ensures uniform and integrated encapsulation of Li4Mn5O12 with structural compatibility to the layered core. With this layer suppressing oxygen release, the related phase transformation and catalytic side reaction that preferentially start from the surface are consequently hindered, as evidenced by detailed structural evolution during Li+ extraction/insertion. The heterostructure cathode exhibits highly competitive energy‐storage properties including capacity retention of 83.1% after 300 cycles at 0.2 C, good voltage stability, and favorable kinetics. These results highlight the essentiality of oxygen framework stability and effectiveness of this spinel Li4Mn5O12 coating strategy in stabilizing the surface of lithium‐rich layered oxides against lattice oxygen escaping for designing high‐performance cathode materials for high‐energy‐density lithium‐ion batteries.
A heterostructured spinel Li4Mn5O12 encapulated lithium‐rich layered oxide cathode is designed by the controlled KMnO4 oxidiation strategy. Spinel Li4Mn5O12 is chosen due to its lattice stability against oxygen release as well as a 3D lithium diffusion framework with minimal Jahn–Teller distortion. Such uniform coating can suppress lattice oxygen release, associated phase transformation, and catalytic side reactions, consequently ensuring improved electrochemical performance.
Heterojunction engineering, especially 2D/2D heterojunctions, is regarded as a quite promising strategy to manipulate the photocatalytic performance of semiconductor catalysts. In this manuscript, a ...direct Z‐scheme 2D/2D heterojunction of CsPbBr3/Bi2WO6 is designed and fabricated by a simple electrostatic self‐assembly process. By using ultrathin nanosheets with several atomic layers as the building blocks, a close CsPbBr3/Bi2WO6 heterointerface over large area with quite a short charge transport distance is obtained, which enables a valid Z‐scheme interfacial charge transfer between Bi2WO6 and CsPbBr3 and thus boosts charge separation. The CsPbBr3/Bi2WO6 heterojunction exhibits a superior photocatalytic performance toward CO2 reduction. By incorporating Pt nanoparticles as the cocatalyst, a high photoelectron consumption rate of 324.0 µmol g−1 h−1 under AM 1.5G irradiation (150 mW cm−2) is obtained, which is 12.2 fold higher than that of CsPbBr3 nanosheets. Moreover, a stable product yield of up to 1582.0 µmol g−1 and electron consumption yield of 8603.0 µmol g−1 for photocatalytic CO2 reduction to CO (11.4%) and CH4 (84.3%) can be achieved after 30 h of continuous catalytic reaction. The accelerated photogenerated charge transfer and spatial charge separation are investigated in detail by ultrafast spectra, photoelectrochemical test, and Kelvin probe force microscopy.
A Z‐Scheme 2D/2D heterojunction of CsPbBr3/Bi2WO6 is fabricated using a simple electrostatic assembly process. The as‐formed heterojunction possesses a large interface contact area and quite a short charge transport distance, which enable efficient Z‐scheme charge transfer and separation between Bi2WO6 and CsPbBr3, as well as remarkably enhanced performance toward photocatalytic CO2 reduction.
Most protein structural prediction algorithms assemble structures as reduced models that represent amino acids by a reduced number of atoms to speed up the conformational search. Building accurate ...full-atom models from these reduced models is a necessary step toward a detailed function analysis. However, it is difficult to ensure that the atomic models retain the desired global topology while maintaining a sound local atomic geometry because the reduced models often have unphysical local distortions. To address this issue, we developed a new program, called ModRefiner, to construct and refine protein structures from Cα traces based on a two-step, atomic-level energy minimization. The main-chain structures are first constructed from initial Cα traces and the side-chain rotamers are then refined together with the backbone atoms with the use of a composite physics- and knowledge-based force field. We tested the method by performing an atomic structure refinement of 261 proteins with the initial models constructed from both ab initio and template-based structure assemblies. Compared with other state-of-art programs, ModRefiner shows improvements in both global and local structures, which have more accurate side-chain positions, better hydrogen-bonding networks, and fewer atomic overlaps. ModRefiner is freely available at http://zhanglab.ccmb.med.umich.edu/ModRefiner.
Metallic lithium affords the highest theoretical capacity and lowest electrochemical potential and is viewed as a leading contender as an anode for high-energy-density rechargeable batteries. ...However, the poor wettability of molten lithium does not allow it to spread across the surface of lithiophobic substrates, hindering the production and application of this anode. Here we report a general chemical strategy to overcome this dilemma by reacting molten lithium with functional organic coatings or elemental additives. The Gibbs formation energy and newly formed chemical bonds are found to be the governing factor for the wetting behavior. As a result of the improved wettability, a series of ultrathin lithium of 10-20 μm thick is obtained together with impressive electrochemical performance in lithium metal batteries. These findings provide an overall guide for tuning the wettability of molten lithium and offer an affordable strategy for the large-scale production of ultrathin lithium, and could be further extended to other alkali metals, such as sodium and potassium.
Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, ...such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.
As one of the most promising cathodes for rechargeable sodium‐ion batteries (SIBs), O3‐type layered transition metal oxides commonly suffer from inevitably complicated phase transitions and sluggish ...kinetics. Here, a NaLi0.05Ni0.3Mn0.5Cu0.1Mg0.05O2 cathode material with the exposed {010} active facets by multiple‐layer oriented stacking nanosheets is presented. Owing to reasonable geometrical structure design and chemical substitution, the electrode delivers outstanding rate performance (71.8 mAh g−1 and 16.9 kW kg−1 at 50C), remarkable cycling stability (91.9% capacity retention after 600 cycles at 5C), and excellent compatibility with hard carbon anode. Based on the combined analyses of cyclic voltammograms, ex situ X‐ray absorption spectroscopy, and operando X‐ray diffraction, the reaction mechanisms behind the superior electrochemical performance are clearly articulated. Surprisingly, Ni2+/Ni3+ and Cu2+/Cu3+ redox couples are simultaneously involved in the charge compensation with a highly reversible O3–P3 phase transition during charge/discharge process and the Na+ storage is governed by a capacitive mechanism via quantitative kinetics analysis. This optimal bifunctional regulation strategy may offer new insights into the rational design of high‐performance cathode materials for SIBs.
An O3‐type NaLi0.05Ni0.3Mn0.5Cu0.1Mg0.05O2 cathode material with exposed {010} active facets by multiple‐layer oriented stacking nanosheets is successfully constructed via reasonable structure design and chemical substitution. An optimal bifunctional regulation is demonstrated to be an efficient strategy to restrain the unfavorable multiphase transformation and greatly improve Na+ transport kinetics resulting in excellent performance for sodium‐ion batteries.
A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. ...Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called marginal Fisher analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions
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