Recent Progress on Modification Strategies of Alloy-based Anode Materials for Alkali-ion Batteries

Alkali-ion batteries,including lithium-ion batteries(LIBs),sodium-ion batteries(NIBs)and potassium-ion batteries(KIBs),with alloy-based anodes exhibit huge potential in high energy density due to the natural abundance,high theoretical capacity as well as suitable operating voltages.However,the practical application is severely hindered by the huge volume variation based on the alloying mechanism and inferior conductivity,especially for red phosphorus(P)and silicon(Si)anodes,which induces poor rate capability and fast capacity decay.Herein,we will briefly review fundamental advantages and challenges of alloy-based anode materials.Then,effective modification strategies of alloy-based anode materials for boosting the performance would be emphasized and discussed.Finally,we will share our perspectives and some opportunities to obtain high-performance alloy-based anode materials for further application.     

光热材料在太阳能海水脱盐中应用的研究进展

用于海水脱盐的太阳能界面蒸发装置因其绿色环保、简单高效以及适用范围广等优点,受到了广泛关注。与传统的体积式蒸发装置不同,太阳能界面蒸发装置将太阳光的收集和蒸汽的产生锁定在空气-水的界面,无需从底部加热整体水来产生蒸汽,极大提高了能源利用效率。本文详细介绍了太阳能界面水蒸发装置的重要组成部分——光热材料的光热转换机理、材料种类以及材料的性能;探讨了高效海水净化太阳能蒸发装置的设计策略(增强光吸收、充足水供应、耐盐排盐等)。在此基础上,总结了基于界面蒸发中的太阳能蒸发装置的研究进展,展望了新型太阳能蒸发装置在海水净化领域的发展前景。     

激光诱饵最佳布设策略研究

探讨了在无合作靶条件下,以波动海面为载体的激光诱骗布设策略.通过建立基于镜面反射的最佳对抗空间角度模型进行模拟计算,给出了布设激光诱饵的空间角度与激光诱饵空间位置及激光目标指示器投影距离的关系曲线,利用这些曲线可以导出在各种情况下进行激光诱骗对抗的最佳策略.     

Preferences in quantum games

A quantum game can be viewed as a state preparation in which the final output state results from the competing preferences of the players over the set of possible output states that can be produced. It is therefore possible to view state preparation in general as being the output of some appropriately chosen (notional) quantum game. This reverse engineering approach in which we seek to construct a suitable notional game that produces some desired output state as its equilibrium state may lead to different methodologies and insights. With this goal in mind we examine the notion of preference in quantum games since if we are interested in the production of a particular equilibrium output state, it is the competing preferences of the players that determine this equilibrium state. We show that preferences on output states can be viewed in certain cases as being induced by measurement with an appropriate set of numerical weightings, or payoffs, attached to the results of that measurement. In particular we show that a distance-based preference measure on the output states is equivalent to a having a strictly-competitive set of payoffs on the results of some measurement.     

Phytoplankton Temporal Strategies Increase Entropy Production in a Marine Food Web Model

We develop a trait-based model founded on the hypothesis that biological systems evolve and organize to maximize entropy production by dissipating chemical and electromagnetic free energy over longer time scales than abiotic processes by implementing temporal strategies. A marine food web consisting of phytoplankton, bacteria, and consumer functional groups is used to explore how temporal strategies, or the lack thereof, change entropy production in a shallow pond that receives a continuous flow of reduced organic carbon plus inorganic nitrogen and illumination from solar radiation with diel and seasonal dynamics. Results show that a temporal strategy that employs an explicit circadian clock produces more entropy than a passive strategy that uses internal carbon storage or a balanced growth strategy that requires phytoplankton to grow with fixed stoichiometry. When the community is forced to operate at high specific growth rates near 2 d⁻¹, the optimization-guided model selects for phytoplankton ecotypes that exhibit complementary for winter versus summer environmental conditions to increase entropy production. We also present a new type of trait-based modeling where trait values are determined by maximizing entropy production rather than by random selection.     

A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy

Quantum machine learning is a promising application of quantum computing for data classification. However, most of the previous research focused on binary classification, and there are few studies on multi-classification. The major challenge comes from the limitations of near-term quantum devices on the number of qubits and the size of quantum circuits. In this paper, we propose a hybrid quantum neural network to implement multi-classification of a real-world dataset. We use an average pooling downsampling strategy to reduce the dimensionality of samples, and we design a ladder-like parameterized quantum circuit to disentangle the input states. Besides this, we adopt an all-qubit multi-observable measurement strategy to capture sufficient hidden information from the quantum system. The experimental results show that our algorithm outperforms the classical neural network and performs especially well on different multi-class datasets, which provides some enlightenment for the application of quantum computing to real-world data on near-term quantum processors.     

Intellectual property—The Foundation of Innovation: A scientist's guide to intellectual property

Increasingly, the activities of the practicing scientist inextricably intersect with the business world. Science is central to the development and delivery of many goods or services, and scientific discovery can directly, or indirectly, lead to substantial revenue generation. Scientists, however, have little training in the complex issues of intellectual property (IP), and often little understanding of their rights and obligations. Here, we define IP and disuss what it means to be an inventor or creator of IP. We define and differentiate between the primary forms of IP—trade secrets, copyright, trademarks, and patents—and discuss their implementation and implications for the practicing scientist.     

Multi-swarm UPSO algorithm based on seed strategy for atomic clusters structure optimization

Particle Swarm Optimization (PSO) algorithm is prone to get trapped in local optima and insufficient information exchange among particles. To solve this problem, this paper proposes a Multi-swarm Unified Particle Swarm Optimization algorithm based on Seed Strategy (SS-DMS-UPSO) to optimize the atomic clusters structure. In this algorithm, the population is divided into some sub-populations evolving randomly and evenly, and each sub-population uses UPSO algorithm with different unification factors to evolve independently in parallel. After a certain number of independent evolution, the particles of all sub-populations are merged into a new population, and the population is again randomly divided into average sub-populations. Iterate the algorithm repeatedly in this way. And finally the global best particle can be obtained. The experimental results show that the SS-DMS-UPSO algorithm can search for the optimal structure or extremely similar optimal structure for atomic clusters with atomic numbers between 2 and 31. For atomic clusters with atomic numbers between 32 and 35, the algorithm can find its approximate optimal structure. Compared with other algorithms, the difference between the lowest energy value and the ideal energy value obtained by the SS-DMS-UPSO algorithm is much smaller. It means that its optimal structure of the atomic clusters is closer to the stable structure, and the algorithm is more stable, which proves the effectiveness of the SS-DMS-UPSO algorithm.     

高校涉化类专业安全教育实效及提升策略研究——以天津理工大学为例

为考察高校涉化类专业安全教育实效,对天津理工大学化学化工学院670名学生进行问卷调查。问卷内容包括学生对化学专业的态度、兴趣、安全意识、安全经验、安全行为等。调查结果表明:安全意识方面,不同年级差异显著;化学恐惧和安全行为方面,不同性别差异显著;回归分析表明,安全意识/能力及化学兴趣与安全行为显著正相关。根据统计分析,提出以下提升策略:注重安全教育的持续性;关注学生心理安全;注意发挥学生的主观能动性。     

Structuring Metal–Organic Framework Materials into Hierarchically Porous Composites through One-Pot Fabrication Strategy

Controlled synthesis of metal–organic framework (MOF)-based materials with multiple levels of porous structures across different length scales is of great interest in various applications but it still remains challenging. Most of the current strategies are time consuming and labor intensive, and not readily scaled-up. In this work, we introduce a straightforward one-pot fabrication strategy to prepare a robust and flexible hierarchically macro-meso-micro porous HKUST-1/polyvinylidene fluoride (PVDF) composite through solvent evaporation, in which MOF crystallization and polymer precipitation are combined together. The effect of the MOF precursor and the polymer initial amount on the morphology of the final composite was thoroughly studied. The interaction between the MOF and the polymer during the evaporation process is the key factor, which would limit the mobility of the polymer chains and cause instability in the MOF growth, thus endowing the composite with a hierarchically macro-meso-micro porous structure. This “all-in-one” porous structure could enhance the mass transport property of molecules within the composite. The obtained HKUST-1/PVDF composite showed an enhanced CO₂ adsorption rate constant of 0.821 min⁻¹ (298 K, 1 bar), which was 3.5 times higher than that of the pristine MOF. In addition, the composite showed an equivalent gas adsorption capacity under all tested pressures and greatly improved water stability.