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High-energy-density lithium (Li)–air cells have been considered a promising energy-storage system, but the liquid electrolyte-related safety and side-reaction problems seriously hinder their development. To address these above issues, solid-state Li–air batteries have been widely developed. However, many commonly-used solid electrolytes generally face huge interface impedance in Li–air cells and also show poor stability towards ambient air/Li electrodes. Herein, we fabricate a differentiating surface-regulated ceramic-based composite electrolyte (DSCCE) by constructing disparately LiI-containing polymethyl methacrylate (PMMA) coating and Poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) layer on both sides of Li1.5Al0.5Ge1.5(PO4)3 (LAGP). The cathode-friendly LiI/PMMA layer displays excellent stability towards superoxide intermediates and also greatly reduces the decomposition voltage of discharge products in Li–air system. Additionally, the anode-friendly PVDF-HFP coating shows low-resistance properties towards anodes. Moreover, Li dendrite/passivation derived from liquid electrolyte-induced side reactions and air/I-attacking can be obviously suppressed by the uniform and compact composite framework. As a result, the DSCCE-based Li–air batteries possess high capacity/low voltage polarization (11,836 mA h g−1/1.45 V under 500 mA g−1), good rate performance (capacity ratio under 1000 mA g−1/250 mA g−1 is 68.2%) and long-term stable cell operation (∼300 cycles at 750 mA g−1 with 750 mAh g−1) in ambient air.

This study delved into the potential application of Platanus acerifolia leaf extract (PALE) as an environmentally benign corrosion inhibitor. Evaluation of corrosion inhibition was conducted using electrochemical methods, field emission scanning electron microscopy equipped with energy dispersive spectrometer. The results reveal that PALE offers efficient protection for Q235 steel across the temperature range of 298 K–318 K, achieving a maximum corrosion inhibition efficiency of approximately 93%. Furthermore, Density functional theory (DFT) calculations and molecular dynamics (MD) simulations were performed to elucidate the spontaneous and stable adsorption of PALE molecules onto the surface of Q235 steel. This adsorption impedes the diffusion of corrosive substances towards the steel surface, thereby endowing Q235 steel with robust corrosion resistance.

Abstract Transition metal oxides (TMOs) exhibit fascinating physicochemical properties, which originate from the diverse coordination structures between the transition metal and oxygen atoms. Accurate determination of such structure-property relationships of TMOs requires to correlate structural and electronic properties by capturing the global parameters with high resolution in energy, real, and momentum spaces, but it is still challenging. Herein, we report the determination of characteristic electronic structures from diverse coordination environments on the prototypical anatase-TiO2(001) with (1 × 4) reconstruction, using high-resolution angle-resolved photoemission spectroscopy and scanning tunneling microscopy/atomic force microscopy, in combination with density functional theory calculation. We unveil that the shifted positions of O 2s and 2p levels and the gap-state Ti 3p levels can sensitively characterize the O and Ti coordination environments in the (1 × 4) reconstructed surface, which show distinguishable features from those in bulk. Our findings provide a paradigm to interrogate the intricate reconstruction-relevant properties in many other TMO surfaces.

Highlights A Cr5Te8@expanded graphite heterostructure is fabricated by chemical vapor deposition, exhibiting remarkable microwave absorption performance with a minimum reflection loss of up to − 57.6 dB at a thin thickness of only 1.4 mm under a low filling rate of 10%. Density functional theory calculations deeply reveal the polarization loss mechanism triggered by heterogeneous interfaces. The heterostructure coating displays a remarkable radar cross section reduction of 31.9 dB m2, demonstrating a great electromagnetic microwave scattering ability and radar stealth capability.

Benefiting from the ultra-high specific energy that is even comparable to gasoline, metal-air batteries (MABs) showcase great promising practical prospects in next-generation battery technologies that beyond the commercial Li-ion batteries. Even though the advances witnessed in MABs in the last decades, there are still numerous critical scientific challenges that need to be solved for their future practical applications. Especially, ascribed to the sluggish reaction kinetics and high overpotentials of air cathodes, the performances of MABs are less than ideal, which also makes its industrialization prospect unclear. Noble metal-based catalysts often show the superior catalytic activity in MABs, but the corresponding systematic review is still not given. Our review mainly focuses on the relationship between the MABs performances and the composite and structure of the noble metal catalysts regarding their size from nano to atom levels. In this review, we first introduced the synthesis strategies of the noble metal-based nano-catalysts followed up by discussing the influences of the size, morphology, and support of noble metal-based catalysts as well as their applications in a variety of MABs. In the following sections, the main advanced characterization techniques for single atom catalysts (SACs) and the roles and recent progress of the noble metal-based SACs were also discussed in detail. Finally, we provided personal outlook and perspectives towards the challenges and future development of MABs and noble metal-based catalysts.

Microglia-mediated chronic neuroinflammation has been associated with cognitive decline induced by rotenone, a well-known neurotoxic pesticide used in agriculture. However, the mechanisms remain unclear. This work aimed to elucidate the role of complement receptor 3 (CR3), a highly expressed receptor in microglia, in cognitive deficits induced by rotenone. Rotenone up-regulated the expression of CR3 in the hippocampus and cortex area of mice. CR3 deficiency markedly ameliorated rotenone-induced cognitive impairments, neurodegeneration and phosphorylation (Ser129) of α-synuclein in mice. CR3 deficiency also attenuated rotenone-stimulated microglial M1 activation. In microglial cells, siRNA-mediated knockdown of CR3 impeded, while CR3 activation induced by LL-37 exacerbated, rotenone-induced microglial M1 activation. Mechanistically, CR3 deficiency blocked rotenone-induced activation of nuclear factor κB (NF-κB), signal transducer and activator of transcription 1 (STAT1) and STAT3 signaling pathways. Pharmacological inhibition of NF-κB or STAT3 but not STAT1 was confirmed to suppress microglial M1 activation elicited by rotenone. Further study revealed that CR3 deficiency or knockdown also reduced rotenone-induced expression of C3, an A1 astrocyte marker, and production of microglial C1q, TNFα and IL-1α, a cocktail for activated microglia to induce neurotoxic A1 astrocytes, via NF-κB and STAT3 pathways. Finally, a small molecule modulator of CR3 efficiently mitigated rotenone-elicited cognitive deficits in mice even administered after the establishment of cognitive dysfunction. Taken together, our findings demonstrated that CR3 is a key factor in mediating neurotoxic glial activation and subsequent cognitive impairments in rotenone-treated mice, giving novel insights into the immunopathogenesis of cognitive impairments in pesticide-related Parkinsonism.

Abstract Three crystal complexes were designed and synthesised through the solvothermal method, with Cu2+, Zn2+, and Cd2+ ions as the metal centres and 2,4,6‐tri(2‐pyridyl)‐s‐triazine (TPTZ) and terephthalate (BDC2−) as the ligands. Their compositions were determined to be Cd(TPTZ)Cl2 (Cd‐MOF), {[Zn(TPTZ)(BDC)] ⋅ 3H2O}n (Zn‐MOF), and Cu2(PCA)2(BDC)(H2O)2 (Cu‐MOF) (PCA−=2‐pyridinium amide), respectively. Cd‐MOF can adsorb 90 % of Congo red (CR) in 10 s at room temperature and atmospheric pressure, and CR removal was complete at 20 s over a wide pH range. The adsorption capacity for CR reached 1440 mg g−1 in 5 min. Selective adsorption was demonstrated in mixed dyes. The adsorption kinetic data agree well with the pseudo‐second‐order kinetic model. The Temkin model was successfully used to evaluate the adsorption isotherms of CR on Cd‐MOF at room temperature, suggesting that adsorption occurs through a hybrid of monolayer and multilayer mechanisms.

The radial artery reflects the largest amount of physiological and pathological information about the human body. However, ultrasound signal processing involves a large number of complex functions, and traditional digital signal processing can hardly meet the requirements of real-time processing of ultrasound data. The research aims to improve computational accuracy and reduce the hardware complexity of ultrasound signal processing systems. Firstly, this paper proposes to apply the coordinate rotation digital computer (CORDIC) algorithm to the whole radial artery ultrasound signal processing, combines the signal processing characteristics of each sub-module, and designs the dynamic filtering module based on the radix-4 CORDIC algorithm, the quadrature demodulation module based on the partitioned-hybrid CORDIC algorithm, and the dynamic range transformation module based on the improved scale-free CORDIC algorithm. A digital radial artery ultrasound imaging system was then built to verify the accuracy of the three sub-modules. The simulation results show that the use of the high-performance CORDIC algorithm can improve the accuracy of data processing. This provides a new idea for the real-time processing of ultrasound signals. Finally, radial artery ultrasound data were collected from 20 volunteers using different probe scanning modes at three reference positions. The vessel diameter measurements were averaged to verify the reliability of the CORDIC algorithm for radial artery ultrasound imaging, which has practical application value for computer-aided clinical diagnosis.

Aqueous zinc-ion batteries (ZIBs) have received much attention because of their high safety, low pollution, and satisfactory energy density (840 mAh g−1), which is important for the research of new energy storage devices. However, problems such as short cell cycle life and low coulombic efficiency (CE) of zinc (Zn) anodes due to disorderly growth of Zn dendrites and side reactions of hydrogen corrosion have delayed the practical application of ZIBs. In this work, a new “self-growth” method is proposed to build a robust and homogeneous three-dimensional (3D) nanoporous structure of tin (Sn)-coated Zn anodes (ZSN) in just 10 min by a simple and fast reaction, which can largely raise the surface area of the electrode plate. The ZSN not only provides abundant Zn nucleation sites, but also reduces the corrosion current, thus alleviating the self-corrosion of the electrolyte, reducing the occurrence of hydrogen precipitation side reactions, and effectively inhibiting the growth of Zn dendrites during cycling. Thus, a symmetric cell with a ZSN anode can be stabilized with very low voltage hysteresis (30 mV) for 480 h of stable plating/stripping cycles and can operate well for 200 h even at high current densities of 10 mA cm−2. Supercapacitors and button cells were assembled, respectively, to verify the performance of ZSN electrodes in different energy storage tools. The ZSN||AC supercapacitor exhibited superior capacity (75 mAh g−1) and high reversibility (98% coulombic efficiency) at a current density of 2 A g−1. With a MnVO (MVO) electrode as the cathode, the ZSN||MVO full cell presents excellent cycling stability with a capacity retention of 95.4% after 500 cycles at 2 A g−1, which far exceeds that of the bare Zn cell.

Hypersepctral unmixing (HU) has been one of the most challenging tasks in hyperspectral image research. Recently, nonnegative matrix factorization (NMF) has shown its superiority in hyperspectral unmixing due to its flexible modeling and little prior requirement. But most NMF algorithms tend to use least square function as the objective, which is sensitive to outliers and different kinds of noise. In this article, we propose a modified Huber (mHuber) NMF model to achieve robustness to outliers and different kinds of noise. Under this robust model, we accelerate the half-quadratic optimization algorithm by replacing multiplicative updating rule with a projected nonlinear conjugated gradient rule, which achieves much faster convergence rate. Moreover, a new tuning parameter, rather than a fixed one, is given to adapt to mHuber loss function. Finally, we perform algorithm analysis and experiments in the synthetic and real-world datasets, which confirms the effectiveness and superiority of the proposed method when compared with several state-of-the-art NMF methods in HU.

Hyperspectral image (HSI) classification is a challenging task due to subtle interclass difference and large intraclass variability, especially when the available training samples are scarce. To overcome this barrier, this article proposes a novel deep similarity network (DSN) for HSI classification, which not only ensures enough samples for training but also extracts more discriminative features. Unlike other classification methods, our essential idea is to approach the classification task by learning a new similarity measure of pixel pairs under a two-branch neural network. Specifically, a binary classification dataset with same-class and different-class pixel pairs is first constructed, which can significantly increase the number of training samples. Then, the DSN utilizes two subnetworks to extract deep features from the pixel pairs, and computes the similarity between the extracted deep features by a fusion subnetwork. Finally, the output of the DSN is used to measure the similarity to each class and the similarity determines the class label. To make full use of the spatial information, the extended multiattribute profile is incorporated to the DSN. Moreover, a joint loss function is proposed to enhance the discrimination and alleviate the challenge caused by the spatial variability of spectral signatures. Experiments on real HSI datasets verify the superiority of the DSN over several state-of-the-art methods in HSI classification. For instance, the overall accuracy of the DSN on Houston2013 dataset is 89.07%, which achieves a marked improvement of at least 4.2% over all compared methods like convolutional neural network, deep learning with attribute profiles and so on.

Sustainable development goals (SDGs) were proposed for harmonious development for future, this aim is highly consisted with the idea of circular economy (CE) which can be achieved by applying green chemistry principles (GCPs). In this work the relationship between GCPs and SDGs are discussed from the perspective of input selection and reduction, sustainable design and safety management. And it also indicated the benefits of CE which creates a kind of green business model. By integrating GCPs and SDGs, the specific applications of renewable resources towards sustainable development are introduced. Afterwards, it explores the utilization of bio-derived resources and energy in bamboo industry, pulping industry and carbon neutrality for CE. Finally, the challenges and perspectives are proposed in governance, industry and academic aspects.

A pulse-coupled neural network (PCNN) is a promising image segmentation approach that requires no training. However, it is challenging to successfully apply a PCNN to medical image segmentation due to common but difficult scenarios such as irregular object shapes, blurred boundaries, and intensity inhomogeneity. To improve this situation, a novel framework incorporating fuzzy connectedness (FC) is proposed. First, a comparative study of the traditional PCNN models is carried out to analyze the framework and firing mechanism. Then, the characteristic matrix of fuzzy connectedness (CMFC) is presented for the first time. The CMFC can provide more intensity information and spatial relationships at the pixel level, which is helpful for producing a more reasonable firing mechanism in the PCNN models. Third, by integrating the CMFC into the PCNN framework models, a construction scheme of FC-PCNN models is designed. To illustrate this concept, a general solution that can be applied to different PCNN models is developed. Next, the segmentation performances of the proposed FC-PCNN models are evaluated by comparison with the traditional PCNN models, the traditional segmentation methods, and deep learning methods. The test images include synthetic and real medical images from the Internet and three public medical image datasets. The quantitative and visual comparative analysis demonstrates that the proposed FC-PCNN models outperform the traditional PCNN models and the traditional segmentation methods and achieve competitive performance to the deep learning methods. In addition, the proposed FC-PCNN models have favorable capability to eliminate inference from surrounding artifacts.

Hashing has been widely used for large-scale remote sensing image retrieval due to its outstanding advantages in storage and search speed. Recently, deep hashing methods, which produce discriminative hash codes by building end-to-end deep convolutional networks, have shown promising results. However, training these networks requires numerous labeled images, which are scarce and expensive in remote sensing datasets. In order to solve this problem, we propose a deep unsupervised hashing method, namely deep contrastive self-supervised hashing (DCSH), which uses only unlabeled images to learn accurate hash codes. It eliminates the need for label annotation by maximizing the consistency of different views generated from the same image. More specifically, we assume that the hash codes generated from different views of the same image are similar, and those generated from different images are dissimilar. On the basis of the hypothesis, we can develop a novel loss function containing the temperature-scaled cross-entropy loss and the quantization loss to train the developed deep network end-to-end, resulting in hash codes with semantic similarity preserved. Our proposed network contains four parts. First, each image is transformed into two different views using data augmentation. After that, they are fed into an encoder with the same shared parameters to obtain deep discriminate features. Following this, a hash layer converts the high-dimensional image representations into compact binary codes. Lastly, a novel hash function is introduced to train the proposed network end-to-end and thus guide generated hash codes with semantic similarity. Extensive experiments on two popular benchmark datasets of the UC Merced Land Use Database and the Aerial Image Dataset have demonstrated that our DCSH has significant superiority in remote sensing image retrieval compared with state-of-the-art unsupervised hashing methods.

We aimed to study the long-term sinus reversion rate and recovery of left atrial function after modified surgical radiofrequency ablation for permanent atrial fibrillation caused by mitral valve disease. From March 2014 to May 2020, 35 patients who underwent modified surgical radiofrequency ablation during cardiac valve surgery in our hospital were selected as the study group, and 25 normal individuals without cardiac structural changes were selected as the control group. The time of modified surgical radiofrequency ablation and long-term sinus reversion rate were measured, and left atrial anteroposterior, superoinferior, left and right diameters, left atrial ejection fraction, left atrial filling index, and left atrial ejection force were measured before and 6 months after surgery. The mean ablation time was 23.2 min, and the long-term sinus reversion rate was 80.0%. The left atrium diameter decreased and the left atrium ejection fraction increased after the operation (P

Recent studies have demonstrated that radiation activates in situ antitumor immunity and consequently induced a synergistic effect of radiotherapy and immunotherapy. However, studies related to radiation-induced changes in immune system of tumor-bearing mice are limited, which are of great significance to improve the efficacy of radioimmunotherapy. In this study, we first established a primary lung tumor mouse model using urethane. Then part of the right lung of the mouse was exposed to X-ray irradiation with a computed tomography–guided small animal irradiator and the changes of immune cells in both peripheral blood and spleen were determined by flow cytometry. Besides, the levels of both cytokines and immunoglobulins in mouse serum were detected by a protein chip. We found that B lymphocytes increased while CD8 + T lymphocytes reduced significantly. Interleukin-3 (IL-3), IL-6, regulated upon activation, normally T-expressed, and presumably secreted factor (RANTES), and vascular endothelial growth factor (VEGF) were found to be decreased after tumor formation, and the similar results have also been observed with kappa, IgG3, IgE, IgM, and IgG2a. After irradiation, lower concentrations of IgD, kappa, and IgM were found in the serum. Our findings indicate that localized tumor irradiation caused some obvious changes like inhibiting the ability of innate immunity, and these changes may be useful in predicting prognosis.

In this study, effects of pretreatment of corn stover (CS) with sodium hydroxide (NaOH) combined with calcium oxide (CaO) on anaerobic co-digestion of swine manure and CS for biogas production were investigated. Different pretreated-CSs were prepared by adding different doses of NaOH and CaO to CS: Treat-CSA (0.10 g NaOH/g CS), Treat-CSB ((0.075 g NaOH + 0.05 g CaO)/g CS), Treat-CSC ((0.05 g NaOH + 0.05 g CaO)/g CS), and Treat-CSD ((0.025 g NaOH + 0.1 g CaO)/g CS). Lignin removal rate, biomass recovery, reduced sugar, methane yield, DT80 (digestion time when biogas achieved 80% of the total biogas), composition of residues, and cost-efficiency were measured to characterize CS after pretreatment and to evaluate the performance of co-digestors fed with swine manure and differently-pretreated CS. The results showed that Treat-CSB showed an excellent lignin removal efficiency and biomass recovery, resulting in the highest methane yield in its co-digestion with swine manure. Since the net benefit of Treat-CSB was calculated to be the highest (i.e., $1.89/ton total solids), therefore, we believe the co-digestion of Treat-CSB and swine manure for biogas production be an effective valorization option for the wastes.