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In this work, we report multiple-intercalation stages and universal Tcenhancement of superconductivity in 1T-SnSe2through Li and organic molecule co-intercalation. We observe significantly increased lattice parameters of up to 40 Å and a dramatically enlarged interlayer distance of up to ∼11 Å in Li and N,N-dimethylformamide (DMF) co-intercalated SnSe2. Well-separated co-intercalation stages with different stacking patterns have been discovered by carefully controlled reaction times and concentrations of solutions. These co-intercalation stages are superconductors showing different superconducting signals. In addition, Li and various organic species such as acetone, dimethyl sulfoxide (DMSO), and tetrahydrofuran (THF) have been co-intercalated into SnSe2crystals; all of which show an enhanced superconducting Tccompared to solely Li-intercalated SnSe2. Our findings may provide more insight into effectively tuning the electronic structure of the lamellar structure through organic molecule co-regulation and open a new strategy to engineer the physical properties of these layered materials by controlling their different intercalation stages.

Correlation between machining errors and transmission accuracy of planetary roller screw mechanism (PRSM) plays an important role in tolerance design. In this study, analytical calculations, machine learning, and experimental verification are utilized for exploring the internal correlation between the machining errors and the transmission accuracy of the PRSM. A multi-roller meshing transmission error model is established, which comprehensively considers the eccentric error, nominal diameter error, flank angle error, and cumulative pitch error of the screw, roller, and nut. The importance coefficients of various machining errors on the transmission error are determined using the random forest algorithm. A genetic algorithm-back propagation neural network algorithm-based method is utilized for training the dataset generated via analytical calculations. The results show that the proposed analytical calculation model reflects the alternate meshing characteristics of rollers during the PRSM motion, providing a more accurate prediction of the transmission error than the existing prediction methods. For an actual mean travel deviation, the most significant machining error is the cumulative pitch error of the screw, whereas for the actual bandwidth of useful travel, the most significant machining errors are the eccentric errors of the screw and nut. The proposed prediction formulae for transmission error considering the essential machining errors illustrate reasonable prediction accuracy, with an average error of 10.63% for the actual mean travel deviation and 14.27% for the actual bandwidth of useful travel compared with the experiments, which can effectively support the direct design of PRSM tolerance in engineering practice.Graphical Abstract

Remote sensing images (RSIs) in real scenes may be disturbed by multiple factors, such as optical blur, undersampling, and additional noise, resulting in complex and diverse degradation models. At present, mainstream super-resolution (SR) algorithms only consider a single and fixed degradation (such as bicubic interpolation) and cannot flexibly handle complex degradations in real scenes. Therefore, designing an SR model that can deal with various degradations has gradually attracted researchers’ attention. Some early studies estimate degradation kernels and then perform degradation-adaptive SR but face the problems of estimation error amplification and insufficient high-frequency details in the results. Although blind SR algorithms based on generative adversarial networks (GANs) have greatly improved visual quality, they still suffer from pseudotexture, mode collapse, and poor training stability. This article proposes a novel blind SR framework based on the stochastic normalizing flow (BlindSRSNF) to address the above problems. BlindSRSNF learns the conditional probability distribution over the high-resolution image space given a low-resolution (LR) image by explicitly optimizing the variational bound on the likelihood. BlindSRSNF is easy to train and can generate photorealistic SR results that outperform GAN-based models. In addition, we introduce a degradation representation strategy based on contrastive learning to avoid the error amplification problem caused by explicit degradation estimation. Comprehensive experiments show that the proposed algorithm can obtain SR results with excellent visual perception quality on both simulated LR and real-world RSIs. The code is available at https://github.com/hanlinwu/BlindSRSNF.

Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and cannot perform stepless SR, i.e., a single model can deal with any potential scale factor. Retraining the model on each scale factor wastes considerable computing resources and model storage space. To address the above problems, we propose a saliency-aware dynamic routing network (SalDRN) for lightweight and stepless SR of RSIs. First, we introduce visual saliency as an indicator of region-level SR difficulty and integrate a lightweight saliency detector into the SalDRN to capture pixel-level visual characteristics. Then, we devise a saliency-aware dynamic routing strategy that employs path selection switches to adaptively select feature extraction paths of appropriate depth according to the SR difficulty of subimage patches. Finally, we propose a novel lightweight stepless upsampling module whose core is an implicit feature function for realizing mapping from low-resolution feature space to high-resolution feature space. Comprehensive experiments verify that the SalDRN can achieve a good tradeoff between performance and complexity. The code is available at https://github.com/hanlinwu/SalDRN.

The mainstream remote sensing image (RSI) super-resolution (SR) algorithms treat tasks with different scale factors independently, and a single model can only process a fixed integer scale factor. However, in practical applications, it is important to continuously super-resolve RSIs to multiple resolutions, as different resolutions present various levels of detail. Retraining the model for each scale factor consumes huge computational resources and storage space. Existing continuous-scale SR models employ static convolutions, and most are designed for natural scenes, ignoring dynamic feature extraction needs for different scale factors and the inherent properties of RSIs. In addition, efficiently obtaining the continuous representation of RSIs and avoiding the artifacts of RSI SR results is still a challenging problem. To address the above problems, we propose a scale-aware dynamic network (SADN) for RSI continuous-scale SR. First, we devise a scale-aware dynamic convolutional (SAD-Conv) layer to handle the strong randomness of the RSI textural distribution and achieve dynamic feature extraction according to scale factors. Second, we devise a continuous-scale upsampling module (CSUM) with the multi-bilinear global implicit function (MBGIF) for any-scale upsampling. The CSUM constructs multiple feature spaces with asymptotic resolutions to approximate the continuous representation of an image, and then, the MBGIF makes full use of multiresolution features to map arbitrary coordinates to spectral values. We evaluate our SADN using various benchmarks, and the experimental results show that the CSUM can efficiently achieve continuous-scale upsampling while maintaining excellent objective and visual performance. Moreover, our SADN uses fewer parameters and even outperforms the state-of-the-art fixed-scale SR methods. The source code is available at https://github.com/hanlinwu/SADN.

The Guanxin Shutong capsule (GXST), a traditional Chinese medicine, is commonly used for treating cardiovascular disease, it has shown efficacy in improving symptoms and enhancing the quality of life for patients with heart failure (HF). However, the specific mechanism of action of GXST in HF remains unclear. In this study, we employed a comprehensive approach combining network pharmacology, molecular dynamics (MD) simulations, and in vitro validations to investigate the potential targets and molecular mechanisms of GXST against HF. We collected active ingredients and target genes of GXST, as well as related genes of HF, from multiple public databases. Using bioinformatics analysis, we constructed networks of ingredients-disease-targets and performed functional annotations of the core targets. MD simulations were conducted to verify the binding between the core protein–ligand complexes. In vitro evaluations, including cell proliferation, apoptosis, and protein expression in human umbilical vein endothelial cells (HUVECs) and H9C2 cells were treated with GXST, were performed for pharmacodynamics evaluation. Network analysis revealed 320 intersection genes and 74 active ingredients in the Herbs-ingredients-target genes-disease network. We identified key active ingredients and target genes that overlapped. The KEGG pathways of the intersection genes were primarily enriched in the PI3K-Akt signaling pathway and apoptosis. The protein–protein interaction network highlighted proteins such as AKT1, VEGFR2, and eNOS. MD simulations confirmed stable docking and lower binding energy between 4 identified ingredients (kaempferol, quercetin, (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl) chroman-4-one, and ellagic acid) and their respective core proteins (VEGFR2, eNOS, and AKT). In vitro experiments demonstrated the protective effects of GXST against H2O2-induced apoptosis in both HUVECs and H9C2 cells. Notably, consistent with the in silico predictions, GXST effectively activates the VEGFR2/AKT/eNOS signaling pathways in HUVECs. This study provides insights into the underlying mechanism of GXST’s therapeutic effects in heart failure. The involvement of the VEGFR2/AKT/eNOS signaling pathways suggests their importance in further elucidating and applying GXST in the clinical treatment of heart failure.

In this work, we have thoroughly studied the effects of flux composition and temperature on the crystal growth of the BaCu2As2compound. While Pb and CuAs self-flux produce the well-known α-phase ThCr2Si2-type structure (Z= 2), a new polymorphic phase of BaCu2As2(β-phase) with a much larger clattice parameter (Z= 10), which could be considered an intergrowth of the ThCr2Si2- and CaBe2Ge2-type structures, has been discovered via Sn flux growth. We have characterized this structure through single-crystal X-ray diffraction, transmission electron microscopy (TEM), and scanning transmission electron microscopy (STEM) studies. Furthermore, we compare this new polymorphic intergrowth structure with the α-phase BaCu2As2(ThCr2Si2type with Z= 2) and the β-phase BaCu2Sb2(intergrowth of ThCr2Si2and CaBe2Ge2types with Z= 6), both with the same space group I4/mmm. Electrical transport studies reveal p-type carriers and magnetoresistivity up to 22% at 5 K and under a magnetic field of 7 T. Our work suggests a new route for the discovery of new polymorphic structures through flux and temperature control during material synthesis.

A new ternary nonstoichiometric Zr6.5Pt6Se19has been discovered as a part of effort to dope Zr into the layered transitional metal chalcogenide PtSe2. With a new structure type (oC68), it is the first Pt-based ternary chalcogenide with group 4 elements (Ti, Zr, and Hf). The crystal structure adopts the orthorhombic space group Cmmmwith lattice parameters of a= 15.637(6) Å, b= 26.541(10) Å, c= 3.6581(12) Å, and V= 1518.2(9) Å3. This unusual structure consists of several building units: chains of edge-sharing selenium trigonal prisms and octahedra centered by zirconium atoms, chains of corner-shared square pyramid, and square planar centered by Pt atoms. The condensation of these building blocks forms a unique structure with bilayered Zr5.54Pt6Se19slabs stacking along the bdirection and large channels parallel to the cdirection within the bilayered slabs. Band structure calculations suggest that partial occupancy of Zr atoms creates a pseudo gap at the Fermi level and is likely the main cause for the stability of this new phase.

A nonstoichiometric ternary antimonide, Zr3.55Pt4Sb4, with a new structure type (hP24), has been synthesized via arc-melting. Its crystal structure was determined by single-crystal X-ray diffraction with hexagonal space group P63/mmcand lattice parameters a= 4.391(3) Å, c= 30.53(2) Å, and V= 509.7(8) Å3. It features the unique Pt4Sb4slab with Pt–Pt bonds and is reminiscent to hexagonal diamond substructures. Three different Zr atoms, occupying three different sites, aid in the close-packing of the Pt and Sb atoms. Electronic structure calculations show the half occupancy of one Zr site creates a pseudogap at the Fermi level and optimizes the Pt–Sb bonding interactions. This enhances the electronic stability and accounts for the very narrow phase width observed for this nonstoichiometric compound. Furthermore, strong Zr–Pt and Zr–Sb interactions play a crucial role in the chemical bonding of the title compound. Electrical transport measurements show metallic behavior of this compound down to 2 K, consistent with the band structure calculations.

With the rapid development of the social economy, people pay more attention to food safety, and the construction of cold stores in China has also developed rapidly. At the same time, the energy problem is increasingly serious at present, the “dual carbon” goal has made energy conservation and emission reduction become the focus of attention. This paper systematically reviews the low-carbon technology applied in cold store from two perspectives: refrigeration technology and cold storage technology. This paper introduces the development process of conventional mechanical refrigeration in cold stores and the current clean energy refrigeration technology at the forefront of cold store development. It summarizes the future development trend of conventional cold store refrigeration and the advantages and disadvantages of clean energy refrigeration. Then, combined with the dual carbon background, the advantages of cold storage technology are presented from the perspective of energy saving, cost reduction, and temperature stability improvement. It focuses on the phase change cold storage technology commonly used in cold stores and summarizes the advantages of phase change cold store at the present stage and the configuration principle of phase change materials in cold stores. Finally, the future development of cold store is prospected.