Ultrafast Ion Transport in 2D Confined MXene for Improved Electrochemical Performance: Boron-Atom-Substituted -OH Termination.

Regulating the surface termination of a confined space to achieve ultrafast ion transport remains an ongoing challenge. Two-dimensional (2D) MXenes possess adjustable structures and interlayer spacing, which provide an ideal platform for in-depth investigation of ion transport in 2D confined space; however, the strong interaction of the negatively charged terminations in MXenes hinders the transport of intercalated cations. In this work, we proposed a strategy that precisely regulates the surface modification of TiCT MXene with the weak polarity of boron atoms (SCB-MXene) via the distinct effect of supercritical CO.

Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA.

To increase the generalization capability of VQA systems, many recent studies have tried to de-bias spurious language or vision associations that shortcut the question or image to the answer. Despite these efforts, the literature fails to address the confounding effect of vision and language simultaneously. As a result, when they reduce bias learned from one modality, they usually increase bias from the other.

Encapsulation of Se into Cu-decorated MXene nanosheets with superior electrochemical performance for advanced Na-Se batteries.

Sodium-selenium (Na-Se) batteries are promising energy storage systems with high energy density, high safety, and low cost. However, the huge volume change of selenium, the dissolution shuttle of polyselenides, and low selenium loading need to be solved. Herein, Cu nanoparticles decorated MXene nanosheets composite (MXene/Cu) are synthesized by etching TiAlC using a molten salt etching strategy.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value.

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution.

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations.

Iron nanoparticles surface decorated MXene via molten salts etching as selenium host for ultrafast sodium ion storage.

Sodium-selenium (Na-Se) batteries have gained attention due to their high energy density and power density, resulting from the liquid-liquid reaction at the interface in the dimethoxyethane electrolyte. Nevertheless, the pronounced shuttle effect of polyselenides causes low coulomb efficiency and inadequate cycling stability for Na-Se batteries. Herein, the iron nanoparticles surface modified accordion-like TiCT MXene (MXene/Fe) synthesized via the molten salt etching is utilized as the host of Se species for high-performance Na-Se battery cathode.

MoP quantum dots based multifunctional efficient electrocatalyst for stable and long-life flexible lithium-sulfur batteries.

The commercialization of lithium-sulfur (Li-S) batteries is challenging, owing to factors like the poor conductivity of S, the 'shuttle effect', and the slow reaction kinetics. To address these challenges, MoP quantum dots were decorated on hollow carbon spheres (MoPQDs/C) in this study and used as an efficient lithium polysulfides (LiPSs) adsorbents and catalysts. In this approach polysulfides are effectively trapped through strong chemisorption and physical adsorption while simultaneously facilitating LiPSs conversion by enhancing the reaction kinetics.

Adaptive channel-modulated personalized federated learning for magnetic resonance image reconstruction.

Magnetic resonance imaging (MRI) is extensively utilized in clinical practice for diagnostic purposes, owing to its non-invasive nature and remarkable ability to provide detailed characterization of soft tissues. However, its drawback lies in the prolonged scanning time. To accelerate MR imaging, how to reconstruct MR images from under-sampled data quickly and accurately has drawn intensive research interest; it, however, remains a challenging task.

Meta-Learning-Based Degradation Representation for Blind Super-Resolution.

Blind image super-resolution (blind SR) aims to generate high-resolution (HR) images from low-resolution (LR) input images with unknown degradations. To enhance the performance of SR, the majority of blind SR methods introduce an explicit degradation estimator, which helps the SR model adjust to unknown degradation scenarios. Unfortunately, it is impractical to provide concrete labels for the multiple combinations of degradations (e.

STDAN: Deformable Attention Network for Space-Time Video Super-Resolution.

The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low-frame-rate (LFR) videos. Recent approaches based on deep learning have made significant improvements, but most of them only use two adjacent frames, that is, short-term features, to synthesize the missing frame embedding, which cannot fully explore the information flow of consecutive input LR frames. In addition, existing STVSR models hardly exploit the temporal contexts explicitly to assist high-resolution (HR) frame reconstruction.

Ultrathin δ-MnO nanoflakes with Na intercalation as a high-capacity cathode for aqueous zinc-ion batteries.

Pristine δ-MnO as the typical cathode for rechargeable zinc-ion batteries (ZIBs) suffers from sluggish reaction kinetics, which is the key issue to prepare high-performance manganese-based materials. In this work, Na incorporated into layered δ-MnO (NMO) was prepared for ZIB cathodes with high capacity, high energy density, and excellent durable stability. By an effective fabricated strategy of hydrothermal synthesis, a three-dimensional interconnected δ-MnO nanoflake network with Na intercalation showed a uniform array arrangement and high conductivity.

Residual Dense Network for Image Restoration.

Recently, deep convolutional neural network (CNN) has achieved great success for image restoration (IR) and provided hierarchical features at the same time. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images; thereby, resulting in relatively-low performance. In this work, we propose a novel and efficient residual dense network (RDN) to address this problem in IR, by making a better tradeoff between efficiency and effectiveness in exploiting the hierarchical features from all the convolutional layers.

Porous N-doped carbon nanoflakes supported hybridized SnO/CoO nanocomposites as high-performance anode for lithium-ion batteries.

Alloy-/conversion-type metal oxides usually exhibit high theoretical lithium storage capacities but suffer from the large volume change induced electrode pulverization and the poor electric conductivity, which limit their practical applications. Hybrid/mixed metal oxides with different working mechanisms/potentials can display advantageous synergistic enhancement effect if delicate structure engineering is performed. Herein, atomically hybridized SnO/CoO nanocomposites with amorphous nature are successfully cast onto the porous N-doped carbon (denoted as NC) nanoflakes through facile pyrolysis of the tin (II) 2-ethylhexanoate (CHOSn) and cobalt (II) 2-ethylhexanoate (CHOCo) mixture within NC nanoflakes in air at 300 °C for 1 h.

LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution.

In this paper, we develop a concise but efficient network architecture called linear compressing based skipconnecting network (LCSCNet) for image super-resolution. Compared with two representative network architectures with skip connections, ResNet and DenseNet, a linear compressing layer is designed in LCSCNet for skip connection, which connects former feature maps and distinguishes them from newly-explored feature maps. In this way, the proposed LCSCNet enjoys the merits of the distinguish feature treatment of DenseNet and the parametereconomic form of ResNet.

Facile preparation of protonated hexaniobate nanosheets and its enhanced photocatalytic activity.

Exfoliated hexaniobate nanosheets E-HKNbO (E-HKNO) with broad light absorption (up to 850 nm) and high adsorption properties were prepared via ion exchange and transient annealing processes with micron-size KNbO powders as the precursor. The as-prepared E-HKNO nanosheets show excellent visible light photodegradation performances when compared to degussa P25, which was evaluated in terms of degradation of Rhodamine B (Rh B). High adsorption and broad light absorption characteristics could be attributed to the exfoliation behavior and the reduction of surface Nb to Nb, which was confirmed by x-ray photoelectron spectroscopy (XPS) and Raman spectra.