Controllable synthesis of porous Co3O4 nanorods and their ethanol-sensing performance

A simple strategy for synthesizing porous Co₃O₄ nanostructures through a hydrothermal process with subsequent thermal decomposition of the obtained Co(CO3)0·35Cl0·20(OH)1.10 precursors was introduced. To understand the growth mechanism of the Co(CO₃)_0·35Cl_0·20(OH)_1.10 precursors and realize morphology control of the resultant Co₃O₄ nanomaterials, a series of controlled experiments were carried out by varying CO(NH₂)₂ dosages, hydrothermal temperatures and time. The Co₃O₄ nanorods obtained under optimized synthesis conditions demonstrated porous structural features, which were constructed by well-connected nanograins, leaving many pores composed of the space between nanograins. The ethanol-sensing behaviors of these Co₃O₄ nanostructures were evaluated, showing the highest response (19.581) and a short response and recovery time (1 s/10 s) to 100 ppm ethanol. Moreover, the Co₃O₄ sensor demonstrated excellent anti-interference ability toward several interfering gases such as methanol, benzene hexane, and dichloromethane. The stability of the Co₃O₄ sensor was further confirmed by 14 days of continuous testing. Compared with previously reported works, this Co₃O₄ sensor still demonstrated outstanding gas sensing properties due to its unique advantages such as 1D porous nanostructures, high BET surface area, abundant oxygen vacancies, and active cobalt sites.     

Effects of hydrogen enhancement on mesoscale burner array flame stability under acoustic perturbations

Partial substitution of hydrocarbon fuel with hydrogen can effectively improve small-scale combustion system stability and performance, potentially opening the way for novel compact power generation and/or propulsion systems in the future. In this study, the effects of hydrogen enhancement between 0% and 40% hydrogen volumetric fractions in methane fuel were experimentally observed in a mesoscale burner array subjected to external acoustic perturbations. The mesoscale burner array utilizes an array of swirl-stabilized burner elements and their interactions with neighboring elements to improve the overall flame stability and simultaneously reduces the combustor length scale. OH1 chemiluminescence and OH planar laser-induced fluorescence (OH-PLIF) were used to image various hydrogen-enriched flames at an equivalence ratio of 0.7, subjected to transverse acoustic perturbations at 320 Hz. Two acoustic modes were imposed by controlling the phase difference between two speakers perturbing the flow. OH1 chemiluminescence images exhibited flame length scale reduction, leading to a denser flame array. Also, flame arrays with higher hydrogen enrichment were found to be more robust against transverse acoustic perturbations, demonstrated by reduced fluctuations in the global heat release rate. OH-PLIF images showed that flames with higher hydrogen enrichment initiated V- to M-shaped flame shape transition even under fuel lean conditions, thereby improving the combustion stability. OH-PLIF images were also used for flame stability analysis through spectral proper orthogonal decomposition (SPOD). The SPOD analysis showed hydrogen enrichment diminished flame fluctuation structures under fuel lean operation.     

High temperature sulfuric acid decomposition in iodine-sulfur process --thermodynamics,concentrator and reactor,product separation,materials, and energy analysis

A successful demonstration of closed-loop thermochemical Iodine–Sulfur (IS) cycle for hydrogen production remains challenging owing to the lack of detailed data on thermodynamic estimates, recuperator system, reactor designs, product separation, and materials of construction. This review identifies and details the technological challenges in the most energy-consuming sulfuric acid decomposition step with a focus on the thermodynamic behavior of the fluids, acid concentration system, reactor designs, SO₂–O₂ separation, energy consumption, and materials of construction. The state-of-art and directions for future development are also discussed in detail. The results show that the thermodynamic properties can be estimated accurately using an electrolyte NRTL model. The compression-cooling process is found to be a better alternative for separating SO₂–O₂. However, the high energy requirement for cooling remains a tall challenge. Hastelloy C-276 and SiC are suitable materials for the construction of the equipment in the high temperature range while, SiC, Teflon, and glass are suitable in the low temperature range.     

Proper Orthogonal Decomposition and Statistical Analysis of 2D Confined Impinging Jets Chaotic Flow

Proper orthogonal decomposition (POD) is shown to be a statistical operation that identifies the main characteristics of chaotic flows and separates them into a few modes. The dynamic chaotic flow is obtained from two‐dimensional (2D) computational fluid dynamics simulations, for different Reynolds numbers, of a confined impinging jets mixer. POD enables reconstruction of the dynamic flow from a few modes that are related to coherent flow structures. The POD flow reconstruction enables a large compression of the flow data set. The decomposition of the flow field into orthogonal modes related to coherent structures provides direct insight into the mixing dynamics and scales which are not accessible from flow dynamics statistic quantities, which were introduced in the context of turbulence and are here applied to chaotic flow.     

Mechanochemically assisted fabrication of ultrafine Pd nanoparticles on natural waste-derived nitrogen-doped porous carbon for the efficient formic acid decomposition

Utilizing natural waste as carbon source to prepare porous carbon with ultrahigh surface area and developing a facile protocol to synthesize supported metal nanoparticles toward an efficient formic acid (FA) decomposition are vital but remains challenging. Here, discarded ginkgo leaves were utilized as carbon source to prepare ginkgo leaf-derived porous carbon (GLPC) with an ultrahigh surface area of 3851 m²/g. Based on the as-prepared nitrogen-doped GLPC (N-GLPC) after “soft” nitriding, a facile solid-state reduction strategy with mortar-pestle grinding and without the use of any organic solvent and stabilizing ligand was developed to synthesize ultrafine and well-distributed Pd nanoparticles (NPs) with a diameter of 2.7 ± 0.7 nm. The “soft” nitriding temperature and addition of base during preparation played vital roles in the activity of the fabricated catalysts. The Pd/N-GLPC-350 exhibited the highest catalytic activity toward decomposing FA, achieving a high turnover frequency of 2952 h^?1 at 333 K. The Pd/N-GLPC-350 was quite stable and could be reused at least five times without evident activity loss. This study provides a facile solid-state reduction protocol with mortar-pestle grinding to synthesize metal NPs by using natural waste-derived porous carbon as support toward efficient FA decomposition.     

Low-light image enhancement via deep Retinex decomposition and bilateral learning

Low-light images enhancement is a challenging task because enhancing image brightness and reducing image degradation should be considered simultaneously. Although existing deep learning-based methods improve the visibility of low-light images, many of them tend to lose details or sacrifice naturalness. To address these issues, we present a multi-stage network for low-light image enhancement, which consists of three sub-networks. More specifically, inspired by the Retinex theory and the bilateral grid technique, we first design a reflectance and illumination decomposition network to decompose an image into reflectance and illumination maps efficiently. To increase the brightness while preserving edge information, we then devise an attention-guided illumination adjustment network. The reflectance and the adjusted illumination maps are fused and refined by adversarial learning to reduce image degradation and improve image naturalness. Experiments are conducted on our rebuilt SICE low-light image dataset, which consists of 1380 real paired images and a public dataset LOL, which has 500 real paired images and 1000 synthetic paired images. Experimental results show that the proposed method outperforms state-of-the-art methods quantitatively and qualitatively.     

Microstructure evolution and decomposition behavior of an novel core-multi-shell structure TiH2 foaming agent

Al foams have received an increasing attention due to their light weight, high specific strength, energy absorption, sound absorption, damping and electromagnetic shielding. However, the issue of low mechanical property is still a challenge due to the mismatch between the melting point of Al and Al alloys and the decomposition temperature of TiH₂ blowing agent. In this work, an novel TiH₂ foaming agent with core-multi-shell structure (TiH₂/Ti₃O@TiO₂/BPR) was prepared by thermal oxidation and coating boron phenolic resin (BPR) treatment. The results showed that the composite layers of Ti₃O@TiO₂ with a thickness about 70 nm and BPR with a thickness about 200 nm were formed on the surface of TiH₂ particles which can act as an excellent thermal and diffusional barrier to retard the heat transfer and delay the release of hydrogen. Compared with as-received and pre-oxidized TiH₂ particles, TiH₂/Ti₃O@TiO₂/BPR particles exhibited an excellent slow release property and its peak temperature of hydrogen release and corresponding time are about 647 °C and 1801 s, which increased by about 83 °C and 176 s, 44 °C and 59 s, respectively. The hydrogen release temperature matched well with the melting point of Al and Al alloys, which is very suitable for preparing high quality Al foams.     

Trudinger-Moser不等式的两种证明方法

Trudinger-Moser不等式在研究带有临界指数增长非线性项的偏微分方程解的存在性问题上有着重要的应用.在径向空间中利用施瓦兹对称重排，基于单位分解的技巧取截断函数是证明Trudinger-Moser不等式的两种主要方法.     

基于Massive MIMO及频谱叠加的5G SA网络上行优化方法

大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)可以有效提升5G SA网络的上行链路数据传输速率以及可靠性。针对5G SA网络上行链路速率和覆盖不均衡的情况,提出了基于大规模MIMO的分组算法,将发送信号矢量进行分组,组内采用最大似然检测,组外采用基于正交三角分解(QR分解)的干扰消除检测,并且结合5G频谱的叠加策略,在降低算法复杂度的同时,有效提升网络覆盖和速率。通过5G SA现网实测,通过MIMO降低分组数量能够提升分组检测性能,结合上行低频段频谱叠加策略能够有效提升5G SA网络上行覆盖30%,提升5G SA网络上行平均速率40%~80%,特别是弱覆盖边缘的网络速率,最高可达600%。     

A customized low-rank prior model for structured cartoon–texture image decomposition

The mathematical characterization of the texture component plays an instrumental role in image decomposition. In this paper, we are concerned with a low-rank texture prior based cartoon–texture image decomposition model, which utilizes a total variation norm and a global nuclear norm to characterize the cartoon and texture components, respectively. It is promising that our decomposition model is not only extremely simple, but also works perfectly for globally well-patterned images in the sense that the model can recover cleaner texture (or details) than the other novel models. Moreover, such a model can be easily reformulated as a separable convex optimization problem, thereby enjoying a splitting nature so that we can employ a partially parallel splitting method (PPSM) to solve it efficiently. A series of numerical experiments on image restoration demonstrate that PPSM can recover slightly higher quality images than some existing algorithms in terms of taking less iterations or computing time in many cases.