Search

A series of Cu-X/γ-Al2O3 (X = Fe, Co, Ni) catalysts were synthesized by a rapid intermittent microwave irradiation method for hydrogen generation from dimethyl ether steam reforming. Different parameters, such as the promoters of X (X = Fe, Co, Ni), microwave irradiation procedure and the ratio of metal to γ-Al2O3, were investigated. The results show that 2Cu-Fe/72γ-Al2O3 has the best performance, for which the agglomeration is prevented, CuO is well dispersed and the catalytic activity is improved. Promoter iron oxide in 2Cu-Fe/9γ-Al2O3 facilitates the watergas shift reaction, which lead to an increase in the conversion of CO to CO2 and hydrogen yield. Particularly, the 2Cu-Fe/72γ-Al2O3 catalyst, with the best molar ratio of metal to γ-Al2O3, shows a dimethyl ether conversion of >99% and a hydrogen yield of >98% and produces the lowest CO content of 1.4%, indicating that the synergism between dimethyl ether hydrolysis and methanol reforming requires an appropriate balance between the metallic Cu-Fe and the acid γ-Al2O3. The intermittent microwave irradiation technique provides a simple but effective method of the Cu-Fe/γ-Al2O3 synthesis with a good catalyst performance for the dimethyl ether steam reforming.

Spiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural systems work by simulating the transmission of information through discrete spiking signals between neurons. Influenced by the great potential shown by the attention mechanism in convolutional neural networks, Therefore, we propose a Spiking Attention Neural Network (Sa-SNN). The network includes a novel Spiking-Efficient Channel Attention (SECA) module that adopts a local cross-channel interaction strategy without dimensionality reduction, which can be achieved by one-dimensional convolution. It is implemented by convolution, which involves a small number of model parameters but provides a significant performance improvement for the network. The design of local inter-channel interactions through adaptive convolutional kernel sizes, rather than global dependencies, allows the network to focus more on the selection of important features, reduces the impact of redundant features, and improves the network's recognition and generalisation capabilities. To investigate the effect of this structure on the network, we conducted a series of experiments. Experimental results show that Sa-SNN can perform image classification tasks more accurately. Our network achieved 99.61%, 99.61%, 94.13%, and 99.63% on the MNIST, Fashion-MNIST, N-MNIST datasets, respectively, and Sa-SNN performed well in terms of accuracy compared with mainstream SNNs. [ABSTRACT FROM AUTHOR]

In recent years, the transformer model has been widely used in computer-vision tasks and has achieved impressive results. Unfortunately, these transformer-based models have the common drawback of having many parameters and a large memory footprint, causing them to be difficult to deploy on mobiles as lightweight convolutional neural networks. To address these issues, a Vision Transformer (ViT) model, named the lightweight Vision Transformer (LW-ViT) model, is proposed to reduce the complexity of the transformer-based model. The model is applied to offline handwritten Chinese character recognition. The design of the LW-ViT model is inspired by MobileViT. The lightweight ViT model reduces the number of parameters and FLOPs by reducing the number of transformer blocks and the MV2 layer based on the overall framework of the MobileViT model. The number of parameters and FLOPs for the LW-ViT model was 0.48 million and 0.22 G, respectively, and it ultimately achieved a high recognition accuracy of 95.8% on the dataset. Furthermore, compared to the MobileViT model, the number of parameters was reduced by 53.8%, and the FLOPs were reduced by 18.5%. The experimental results show that the LW-ViT model has a low number of parameters, proving the correctness and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Mesoporous Pt-containing nanocomposites with well-organized pores are desirable for fuel cells as well as sensors, electronics, and various chemical reactions. However, it remains challenging to construct three-dimensional (3D) ordered honeycomb-like (OHC) nanostructures with Pt species anchored in the mesopores. Herein, we show for the first time an in situ strategy of developing an N-doped ordered honeycomb (N-OHC) nanopattern with well-dispersed Pt–N2 moieties by using the self-assembly of a block copolymer (BCP). The as-mentioned Pt including Pt single atoms (SAs) and Pt ∼2.5 nm ultrafine nanoparticles (NPs) was hierarchically located on the inner walls and the outer surfaces of the N-OHC mesopores (Pt/N-OHC), forming well-dispersed Pt–N active sites and showing efficient catalytic activity towards the oxygen reduction reaction (ORR). By changing the film thickness of the pristine Pt/BCP template, double-layered Pt/N-OHC could be designed and the ORR activity could be correspondingly improved, for which a current density of 1.60 A cm−2@0.6 V and a peak power density of 1.07 W cm−2 were observed at a very low Pt-loading of 0.04 mg cm−2, better than 1.21 A cm−2@0.6 V and 0.79 W cm−2 of the commercial Pt/C catalyst at a Pt-loading of 0.15 mg cm−2. The electron donating behavior of Pt–N2 and the pathway for O2 reduction were investigated via the density functional theory (DFT) computation. The electron transfer from Pt to N gave rise to the formation of Pt–N covalent bonds, which resulted in a lower d band center and a weaker O adsorption energy and endowed the Pt/N-OHCs with enhanced ORR activity. [ABSTRACT FROM AUTHOR]

Background. Polygonum Multiflorum Radix Preparata (PMP), prepared from Polygonum multiflorum Thunb. (PM), is traditionally valued for its liver and kidney-tonifying effects. However, the previous studies showed that PMP was hepatotoxic, which limited its clinical use. Unfortunately, the potential hepatotoxic ingredients and the molecular mechanism are still uncertain. Objective. The aim of this study was to find out potential biomarkers of hepatotoxicity using metabolomics profile. Materials and Methods. 60% ethanol extract of PMP (PMPE) was prepared. Subsequently, an untargeted metabolomics technology in combination with ROC curve analysis method was applied to investigate the alteration of plasma metabolites in rats after oral administration of PMPE (40 g/kg/d) for 28 days. Results. Compared to the control group, the significant difference in metabolic profiling was observed in the PMPE-induced liver injury group, and sixteen highly specific biomarkers were identified. These metabolites were mainly enriched into bile acids, lipids, and energy metabolisms, indicating that PMPE-induced liver injury could be related to cholestasis and dysregulated lipid metabolism. Conclusions. This study is contributed to understand the potential pathogenesis of PMP-induced liver injury. The metabonomic method may be a valuable tool for the clinical diagnosis of PMP-induced liver injury. [ABSTRACT FROM AUTHOR]

We present a facile approach for the direct attachment of cross-linked poly(2-thiopheneacetic acid) onto the surface of magnetite nanoparticles with one single carbon layer (Fe3O4@1C NPs) via self-assembly oxidative polymerization. The functional Fe3O4@1C NPs combined with poly(2-thiopheneacetic acid) [Fe3O4@1C@ poly(Th) NPs] was successfully prepared and used for magnetic solid-phase extraction (MSPE). The influence of the preparation conditions and extraction conditions were investigated in detail. The morphology and size distribution of the monolithic materials were characterized by transmission electron microscopy. Under the optimized experimental conditions, the prepared Fe3O4@1C@ poly(Th) NPs showed good extraction efficiency for canthin-6-one alkaloids. Furthermore, the results indicated that the binding specificity and selectivity of the Fe3O4@1C@ poly(Th) NPs might be attributed to electrostatic interactions and pore structure. A MSPE–HPLC method was also developed for quantitative analysis of canthin-6-one alkaloids. The developed method showed good linearity, low limit of detections, low limit of quantifications, and good reproducibility over a wide linear range. To further prove the utility, Fe3O4@1C@ poly(Th) NPs-MSPE was also used to extract canthin-6-one alkaloids from Eurycoma longifolia stems, with a recovery rate of 87.9–114%. Thus, this method is potentially applicable for the extraction of similar alkaloid compounds from medicinal herb samples. [ABSTRACT FROM AUTHOR]

The low-cost sodium (Na) metal anode has high specific capacity and is one of the most promising electrode materials for next generation high-energy density batteries. Unfortunately, it is also extremely reactive and suffers from severe side reactions with almost all electrolytes, which results in detrimental failures including uncontrollable growth of mossy structures, fast electrolyte depletion and short cycle life. We report here the in situ synthesis of composite sponges with sodiated NaxSnSb particles embedded in N-doped graphene (NG) frameworks for modulating Na anodes with significantly improved stability and fast charging capability. The sodiated NaxSnSb composites function as seeding particles that guide low-overpotential and smooth growth of Na metal whereas the NG wrapping layers protect the freshly plated Na metal from detrimental side reactions with the battery electrolyte. As a result, as high as 95% of the freshly plated Na metal can be reversibly stripped during battery operation and this efficiency can last hundreds of cycles. We further show that the Na3Sb phase in the NaxSnSb composites played critical roles as sponges with only the Na15Sn4 phase have much lower performance. Overall, the synergy of guided growth from NaxSnSb and shielding from NG allows multifunctional hosts that support Na anodes to cycle with nearly unchanged overpotential and charge transfer resistance for >500 cycles in both symmetric cells and full cells. The Na metal remained dense without measurable formation of unsafe dendrite or inactive mossy structures in these novel sponges even after aggressive cycling. [ABSTRACT FROM AUTHOR]

For the advanced surface-to-air missile, the accurate information of missile-target encounter is required. Based on the adaptive Kalman filter algorithm, an unbiased converted tracking method of radar seeker with velocity information is proposed. According to the current statistical model, the current acceleration is estimated by recursive least square algorithm with forgetting factor, and the state equation is obtained. By analyzing the unbiased conversion relations of position and velocity information from polar coordinates to Cartesian coordinates, the true unbiased converted measurement covariance and its estimation using measurements are deduced. And the measurement equation is acquired. With the state and error estimation result of filter, the estimation algorithm of the true unbiased converted measurement covariance is improved. The simulation results prove the good performance of the tracking method in both filtering accuracy and tracking speed. [ABSTRACT FROM AUTHOR]

Bimetallic CuAg nanoparticles were synthesized using uric acid as a stabilizer, Fe3O4 loaded CuAg nanoparticles (Fe3O4–CuAg NPs) were also fabricated, and the morphology and content of both CuAg NPs and Fe3O4–CuAg NPs were investigated. The bimetallic CuAg NPs can be used as a catalyst and exhibited superior catalytic performance in the hydrogenation of nitrophenols and potassium ferricyanide(III). Notably, the bimetallic CuAg NPs and Fe3O4–CuAg NPs showed better catalytic activity compared with the same amount of Ag NPs in the hydrogenation of 4-nitrophenol, with rate constants k of 20.02 × 10−3 s−1 and 20.19 × 10−3 s−1, respectively. The magnetic Fe3O4–CuAg NPs can be re-used as a catalyst at least five times, promoting the concept of recycling by improving the deficiency in the disposable use of colloidal nanoparticles. [ABSTRACT FROM AUTHOR]

Introduction: The effect of comorbid cardiometabolic diseases (CMDs), including diabetes, heart diseases, and stroke, on dementia remains unclear. Methods: A cohort of 2648 dementia‐free adults aged ≥60 years was followed up for 12 years. An active lifestyle was defined in accordance with the engagement in leisure activities and/or a social network. Cox models were used in data analysis. Results: The multiadjusted hazard ratio (HR, 95% confidence interval) of dementia was 1.41 (1.07–1.86) for one, 2.38 (1.58–3.59) for two, and 4.76 (2.04–11.13) for three CMDs. In joint exposure analysis, the HR of dementia was 3.36 (2.14–5.30) for participants with CMDs plus an inactive lifestyle and 1.32 (0.95–1.84) for those with CMDs plus an active lifestyle (reference: no CMDs plus active lifestyle). An active lifestyle delayed dementia onset by 3.50 years in people with CMDs. Discussion: CMDs, especially when comorbid, are associated with increased dementia risk; however, leisure activities and social integration mitigate this risk. [ABSTRACT FROM AUTHOR]

The practical applications of high capacity metallic Li electrodes is severely hindered by the uncontrollable Li nucleation and growth that lead to poor cycle life and growth of unsafe dendrites. Here we demonstrate the design of porous but robust sponges with Cu nanoparticles and graphene and their applications for enabling Li electrodes with stable cycling under high capacity and high current conditions. We discovered that the Cu nanoparticles decorated on graphene effectively direct the heterogeneous nucleation of Li whereas the graphene frameworks provide ample spaces for confining Li growth. These sponges are able to host at least 60 mAh cm−2 Li without forming dendrites and allow stable Li plating/stripping with low overpotentials for hundreds of cycles even at a high current of 7 mA cm−2. The sponges have remarkable mechanical strengths and exhibited minimal structural deformation after aggressive plating/stripping cycles. These superior properties enable Li metal batteries with substantially improved high current cyclic stability and rate performance, as demonstrated with both LiNi 0.4 Co 0.2 Mn 0.4 O 2 and LiFePO 4 cathodes. • Light weight but strong sponges were synthesized with simple and scalable methods. • The sponges directly controlled heterogeneous nucleation and confined growth of Li. • Cu NPs reduced the overpotential required to initiate heterogeneous nucleation of Li. • The sponges inhibited formation of dendrites after depositing of 60 mAh cm-2 Li. [ABSTRACT FROM AUTHOR]

A series of Ni-modified Mo2C catalysts with La2O3-stabilized ZrO2 supports were synthesized via template method coupled with incipient wetness impregnation and temperature-programmed carbonization. The catalyst was tested in dry reforming of methane (DRM) reaction at the temperature range from 973 to 1173 K in a fixed-bed quartz reactor under atmospheric pressure. XRD, Raman, BET, H2-TPD, SEM, EDS and TEM were conducted to characterize the phase constitution, the pore structure, the morphology and the crystal structure of the catalysts. Ni-Mo2C nanoparticles of ~ 3 nm diameter were obtained for these catalysts, and the La2O3-ZrO2 supports exhibited a cubic lattice structure with a sheet shape, which is believed to contain an abundant of oxygen vacancies. And the synergistic interaction between the oxygen vacancy in La-Zr-O solid solution and the Ni-Mo2C particles could decrease the apparent activation energy of CO2. Both of them are beneficial for accelerating DRM reaction. CH4 conversion of the best specimen catalyst reaches 94% at 1173 K, and the catalyst maintains its stability after 100-h reaction. The superior catalytic activity of NMLZ-7.5 is attributed to the improved BET surface area (33.2 m2/g), smaller crystallites, grain boundaries and oxidation-carbonization cycle of Ni-Mo2C. [ABSTRACT FROM AUTHOR]

The bridge deck sections used for long-span suspension bridges have evolved through the years, from the compact box deck girders geometrical configurations to twin-box and three-box bridge decks sections. The latest generation of split and multiple-box bridge decks proved to have better aerodynamic behavior; thus further optimization methods are sought for such geometrical configurations. A new type of multibox bridge deck, consisting of four aerodynamically shaped deck boxes, two side decks for the traffic lanes and two middle decks for the railway traffic, connected between them by stabilizing beams, was tested in the wind tunnel for identifying the flutter derivatives and to verify the aerodynamic performance of the proposed multibox deck. Aerodynamic static force coefficients were measured for the multibox bridge deck model, scaled 1 : 80, for Reynolds numbers up to 5.1 × 105, under angles of attack between −8° and 8°. Iterative Least Squares (ILS) method was employed for identifying the flutter derivatives of the multibox bridge deck model, based on the results obtained from the free vibration tests and based on the frequency analysis the critical flutter wind speed for the corresponding prototype of the multibox bridge was estimated at 188 m/s. [ABSTRACT FROM AUTHOR]

ABSTRACT Mechanism of the morphological changes between toroidal and rod-like nanostructures of P4VP-b-PS-b-P4VP amphiphilic triblock copolymer micelles has been investigated in aqueous solution. This transition is proved to be highly reversible and tunable upon changing temperature. The toroidal structure, evolving from fibers at 20 °C, can transform to rod-like morphology as the temperature either gradually or directly increases to 80 °C, and vice versa. However, the transition mechanisms are quite different in different temperature-changing processes. The structure and thickness of the micelles are dependent on the specific temperature, whereas the transition mechanism is related to the method of the temperature change. These morphological changes are considered as a result from the interaction parameter between the solvent and the copolymer blocks, especially the hydrophobic block. Our research complements the external control over the reversible morphological transition of block copolymer micelles without changing the composition of the system or introducing additional influencing factors. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 1450-1457 [ABSTRACT FROM AUTHOR]

Block copolymer (BCP) is a promising precursor to design ordered mesoporous carbon (OMC) materials for proton exchange membrane fuel cells due to its accessibility and controllability. However, enhancing the performance of OMCs through the integration of heteroatoms during the self-assembly process remains a challenging endeavor. Herein, an ammonia-assisted pyrolysis method is used to deposit N atoms in self-assembled OMC (N-OMC). The resulting N-OMC exhibits a well-ordered mesoporous structure with the N content up to 6.43 at.%. Upon PtCo nanoparticles loading (PtCo/N-OMC), the PtCo/N-OMC demonstrates exceptional electrocatalytic activity, evidenced by a half-wave potential of 0.876 V RHE and an electrochemical surface area of 98.19 m2·g−1. The single-cell test confirms the superiority of the N-OMC structure, revealing a current density of 1.53 A·cm−2 at 0.6 V and a peak power density of 1.17 W·cm−2. Theoretical calculations affirm that the pyridinic N-doping reduces oxygen binding energy, thereby enhancing oxygen reduction reaction activity. The confinement effect of the mesopores avoids aggregation of nanoparticles, while doping-N-induced defects further bolster their electrochemical activities. This work highlights the synergistic effect of N-doping defects and ordered mesoporous architectures, illustrating the exceptional potential of the BCP-derived OMC as an ideal support for electrocatalysis applications. [Display omitted] • Synthesis of efficient block copolymer-derived 3D ordered mesoporous carbon support for Pt-based electrocatalyst with synchronized ammonia-assisted pyrolysis. • Coordination environment and stability of N atoms in the block copolymer-derived carbon. • Synergistic enhancement of catalytic performance by N doping and confinement effect of ordered mesoporous structures. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis and treatment of cardiovascular diseases. Precise segmentation is challenging due to high costs and the need for specialized knowledge, as a large amount of accurately annotated data is required, demanding significant time and medical resources.In order to reduce the burden of data annotation while maintaining the high accuracy of segmentation tasks, this paper introduces a semi‐supervised learning method to solve the limitations of current PEAT segmentation methods.In this paper, we propose a difference‐guided collaborative mean teacher (DCMT) semi‐supervised method, designed for the segmentation of PEAT from DCMT consists of two main components: a semi‐supervised framework with a difference fusion strategy and a backbone network MCM‐UNet using Mamba‐CNN mixture (MCM) blocks. The differential fusion strategy effectively utilizes the uncertain areas in unlabeled data, encouraging the model to reach a consensus in predictions across these difficult‐to‐segment yet information‐rich areas. In addition, considering the sparse and scattered distribution of PEAT in cardiac MR images, which makes it challenging to segment, we propose MCM‐UNet as the backbone network in our semi‐supervised framework. This not only enhances the processing ability of global information, but also accurately captures the detailed local features of the image, which greatly improves the accuracy of PEAT segmentation.Our experiments conducted on the MRPEAT dataset show that our DCMT method outperforms existing state‐of‐the‐art semi‐supervised methods in terms of segmentation accuracy. These findings underscore the effectiveness of our approach in handling the specific challenges associated with PEAT segmentation.The DCMT method significantly improves the accuracy of PEAT segmentation in cardiac MR images. By effectively utilizing uncertain areas in the data and enhancing feature capture with the MCM‐UNet, our approach demonstrates superior performance and offers a promising solution for semi‐supervised learning in medical image segmentation. This method can alleviate the extensive annotation requirements typically necessary for training accurate segmentation models in medical imaging. [ABSTRACT FROM AUTHOR]

Non-precious metal catalysts (NPMCs) doped with nitrogen were derived from MOFs. Appropriate ligand and framework order in the precursor ensured the presence of various nitrogenous species and high surface area in the NPC, Ni-NPC, Co-NPC, and Fe-NPC electrocatalysts. Electrochemical properties of the as-prepared electrocatalysts were examined in 0.1 M KOH solution to investigate the effects of metal and doped nitrogen on the oxygen reduction reaction (ORR). The free metal electrocatalyst (NPC) displayed poor ORR activity with an onset potential of 0.87 V RHE. The presence of nickel in the Ni-NPC catalyst could not create more active sites in comparison with the NPC catalyst, while onset potential of the Co-NPC catalyst shifted to 21 mV more positive (Compare to NPC). The investigation of doping active transient metals indicated that Fe-doping could be effective. It was because of the improved ORR activity of the Fe-NPC electrocatalysts with an onset potential of 0.99 V RHE. Furthermore, in a long-term stability test, this optimum electrocatalyst could retain more than 92% of its initial current density and experienced just 57 mV loss in its half-wave potential in an accelerated degradation test. Direct ethanol fuel cell test reveals maximum power density of 19.2 mW cm−2 at ambient temperature. • New M-NPS electrocatalysts were synthesized by pyrolysis of effective MOF precursors. • The synthesized M-N-C electrocatalysts had high surface area and a variety in nitrogenous species. • The effect of the metal on the activity and stability of the M-N-C electrocatalysts was evaluated for ORR. • The sequence of the catalysts activity in ORR are: FeN x > CoN x > NiN x ∼ metal free-N x. [ABSTRACT FROM AUTHOR]