Lithium metal has been regarded as the future anode material for high-energy-density rechargeable batteries due to its favorable combination of negative electrochemical potential and high theoretical ...capacity. However, uncontrolled lithium deposition during lithium plating/stripping results in low Coulombic efficiency and severe safety hazards. Herein, we report that nanodiamonds work as an electrolyte additive to co-deposit with lithium ions and produce dendrite-free lithium deposits. First-principles calculations indicate that lithium prefers to adsorb onto nanodiamond surfaces with a low diffusion energy barrier, leading to uniformly deposited lithium arrays. The uniform lithium deposition morphology renders enhanced electrochemical cycling performance. The nanodiamond-modified electrolyte can lead to a stable cycling of lithium | lithium symmetrical cells up to 150 and 200 h at 2.0 and 1.0 mA cm
, respectively. The nanodiamond co-deposition can significantly alter the lithium plating behavior, affording a promising route to suppress lithium dendrite growth in lithium metal-based batteries.Lithium metal is an ideal anode material for rechargeable batteries but suffer from the growth of lithium dendrites and low Coulombic efficiency. Here the authors show that nanodiamonds serve as an electrolyte additive to co-deposit with lithium metal and suppress the formation of dendrites.
Feature extraction is of significance for hyperspectral image (HSI) classification. Compared with conventional hand-crafted feature extraction, deep learning can automatically learn features with ...discriminative information. However, two issues exist in applying deep learning to HSIs. One issue is how to jointly extract spectral features and spatial features, and the other one is how to train the deep model when training samples are scarce. In this paper, a deep convolutional neural network with two-branch architecture is proposed to extract the joint spectral-spatial features from HSIs. The two branches of the proposed network are devoted to features from the spectral domain as well as the spatial domain. The learned spectral features and spatial features are then concatenated and fed to fully connected layers to extract the joint spectral-spatial features for classification. When the training samples are limited, we investigate the transfer learning to improve the performance. Low and mid-layers of the network are pretrained and transferred from other data sources; only top layers are trained with limited training samples extracted from the target scene. Experiments on Airborne Visible/Infrared Imaging Spectrometer and Reflective Optics System Imaging Spectrometer data demonstrate that the learned deep joint spectral-spatial features are discriminative, and competitive classification results can be achieved when compared with state-of-the-art methods. The experiments also reveal that the transferred features boost the classification performance.
2D transition metal carbides and nitrides, named MXenes, are attracting increasing attentions and showing competitive performance in energy storage devices including electrochemical capacitors, ...lithium‐ and sodium‐ion batteries, and lithium–sulfur batteries. However, similar to other 2D materials, MXene nanosheets are inclined to stack together, limiting the device performance. In order to fully utilize MXenes' electrochemical energy storage capability, here, processing of 2D MXene flakes into hollow spheres and 3D architectures via a template method is reported. The MXene hollow spheres are stable and can be easily dispersed in solvents such as water and ethanol, demonstrating their potential applications in environmental and biomedical fields as well. The 3D macroporous MXene films are free‐standing, flexible, and highly conductive due to good contacts between spheres and metallic conductivity of MXenes. When used as anodes for sodium‐ion storage, these 3D MXene films exhibit much improved performances compared to multilayer MXenes and MXene/carbon nanotube hybrid architectures in terms of capacity, rate capability, and cycling stability. This work demonstrates the importance of MXene electrode architecture on the electrochemical performance and can guide future work on designing high‐performance MXene‐based materials for energy storage, catalysis, environmental, and biomedical applications.
Hollow Ti3C2Tx spheres and 3D macroporous MXene films are fabricated using a sacrificial template approach. The 3D MXene films are free‐standing, flexible, and highly conductive. They can serve directly as electrodes for Na‐ion storage and exhibit high capacities accompanied with excellent stabilities and rate performance.
Large scale synthesis and delamination of 2D Mo2CT
x
(where T is a surface termination group) has been achieved by selectively etching gallium from the recently discovered nanolaminated, ternary ...transition metal carbide Mo2Ga2C. Different synthesis and delamination routes result in different flake morphologies. The resistivity of free‐standing Mo2CT
x
films increases by an order of magnitude as the temperature is reduced from 300 to 10 K, suggesting semiconductor‐like behavior of this MXene, in contrast to Ti3C2T
x
which exhibits metallic behavior. At 10 K, the magnetoresistance is positive. Additionally, changes in electronic transport are observed upon annealing of the films. When 2 μm thick films are tested as electrodes in supercapacitors, capacitances as high as 700 F cm−3 in a 1 m sulfuric acid electrolyte and high capacity retention for at least 10,000 cycles at 10 A g−1 are obtained. Free‐standing Mo2CT
x
films, with ≈8 wt% carbon nanotubes, perform well when tested as an electrode material for Li‐ions, especially at high rates. At 20 and 131 C cycling rates, stable reversible capacities of 250 and 76 mAh g−1, respectively, are achieved for over 1000 cycles.
2D Mo2C (MXene) is produced using different synthesis routes, which lead to different flake morphologies. Mo2C exhibits a semiconductor‐like increase in resistivity from 300 to 10 K. Mo2C electrodes in a supercapacitor achieve 700 F cm−3 capacitance in 1 m H2SO4 for 10,000 cycles. Mo2C–Carbon nano tube (CNT) electrodes possess 250 mAh g−1 capacity for 1000 cycles, showing promise as anode for batteries and Li‐ion capacitors.
Hyperspectral image (HSI) denoising is an essential preprocess step to improve the performance of subsequent applications. For HSI, there is much global and local redundancy and correlation (RAC) in ...spatial/spectral dimensions. In addition, denoising performance can be improved greatly if RAC is utilized efficiently in the denoising process. In this paper, an HSI denoising method is proposed by jointly utilizing the global and local RAC in spatial/spectral domains. First, sparse coding is exploited to model the global RAC in the spatial domain and local RAC in the spectral domain. Noise can be removed by sparse approximated data with learned dictionary. At this stage, only local RAC in the spectral domain is employed. It will cause spectral distortion. To compensate the shortcoming of local spectral RAC, low-rank constraint is used to deal with the global RAC in the spectral domain. Different hyperspectral data sets are used to test the performance of the proposed method. The denoising results by the proposed method are superior to results obtained by other state-of-the-art hyperspectral denoising methods.
2D Nb2CTx MXene flakes are produced using an amine‐assisted delamination process. Upon mixing with carbon nanotubes and filtration, freestanding, flexible paper is produced. The latter exhibits high ...capacity and excellent stability when used as the electrode for Li‐ion batteries and capacitors.
Although the mechanism of DNA methylation‐mediated gene silencing is extensively studied, relatively little is known about how promoter methylated genes are protected from transcriptional silencing. ...SUVH1, an Arabidopsis Su(var)3‐9 homolog, was previously shown to be required for the expression of a few promoter methylated genes. By chromatin immunoprecipitation combined with sequencing, we demonstrate that SUVH1 binds to methylated genomic loci targeted by RNA‐directed DNA methylation. SUVH1 and its homolog SUVH3 function partially redundantly and interact with three DNAJ domain‐containing homologs, SDJ1, SDJ2, and SDJ3, thus forming a complex which we named SUVH‐SDJ. The SUVH‐SDJ complex components are co‐localized in a large number of methylated promoters and are required for the expression of a subset of promoter methylated genes. We demonstrate that the SUVH‐SDJ complex components have transcriptional activation activity. SUVH1 and SUVH3 function synergistically with SDJ1, SDJ2, and SDJ3 and are required for plant viability. This study reveals how the SUVH‐SDJ complex protects promoter methylated genes from transcriptional silencing and suggests that the transcriptional activation of promoter methylated genes mediated by the SUVH‐SDJ complex may play a critical role in plant growth and development.
DNA methylation within promoters typically leads to transcriptional silencing. But little is known about how genes with methylated promoters are protected from transcriptional silencing. This study identifies a protein complex which binds to methylated promoters and is required for transcriptional activation.
Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution spatial-spectral ...information of the scene. Existing methods mostly based on spectral unmixing and sparse representation are often developed from a low-level vision task perspective, they cannot sufficiently make use of the spatial and spectral priors available from higher-level analysis. To this issue, this paper proposes a novel HSI super-resolution method that fully considers the spatial/spectral subspace low-rank relationships between available HR-MSI/LR-HSI and latent HSI. Specifically, it relies on a new subspace clustering method named "structured sparse low-rank representation" (SSLRR), to represent the data samples as linear combinations of the bases in a given dictionary, where the sparse structure is induced by low-rank factorization for the affinity matrix. Then we exploit the proposed SSLRR model to learn the SSLRR along spatial/spectral domain from the MSI/HSI inputs. By using the learned spatial and spectral low-rank structures, we formulate the proposed HSI super-resolution model as a variational optimization problem, which can be readily solved by the ADMM algorithm. Compared with state-of-the-art hyperspectral super-resolution methods, the proposed method shows better performance on three benchmark datasets in terms of both visual and quantitative evaluation.
In this paper, we develop the theory of basic reproduction ratios
R
0
for abstract functional differential systems in a time-periodic environment. It is proved that
R
0
-
1
has the same sign as the ...exponential growth bound of an associated linear system. Then we apply it to a time-periodic Lyme disease model with time-delay and obtain a threshold type result on its global dynamics in terms of
R
0
.
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