Interlayer Engineering of Molybdenum Trioxide toward High‐Capacity and Stable Sodium Ion Half/Full Batteries

Orthorhombic molybdenum trioxide (MoO₃) is one of the most promising anode materials for sodium‐ion batteries because of its rich chemistry associated with multiple valence states and intriguing layered structure. However, MoO₃ still suffers from the low rate capability and poor cycle induced by pulverization during de/sodiation. An ingenious two‐step synthesis strategy to fine tune the layer structure of MoO₃ targeting stable and fast sodium ionic diffusion channels is reported here. By integrating partially reduction and organic molecule intercalation methodologies, the interlayer spacing of MoO₃ is remarkably enlarged to 10.40 Å and the layer structural integration are reinforced by dimercapto groups of bismuththiol molecules. Comprehensive characterizations and density functional theory calculations prove that the intercalated bismuththiol (DMcT) molecules substantially enhanced electronic conductivity and effectively shield the electrostatic interaction between Na⁺ and the MoO₃ host by conjugated double bond, resulting in improved Na⁺ insertion/extraction kinetics. Benefiting from these features, the newly devised layered MoO₃ electrode achieves excellent long‐term cycling stability and outstanding rate performance. These achievements are of vital significance for the preparation of sodium‐ion battery anode materials with high‐rate capability and long cycling life using intercalation chemistry.     

A case-deletion diagnostic for penalized calibration estimators and BLUP under linear mixed models in survey sampling

The penalized calibration technique in survey sampling combines usual calibration and soft calibration by introducing a penalty term. Certain relevant estimates in survey sampling can be considered as penalized calibration estimates obtained as particular cases from an optimization problem with a common basic structure. In this framework, a case deletion diagnostic is proposed for a class of penalized calibration estimators including both design-based and model-based estimators. The diagnostic compares finite population parameter estimates and can be calculated from quantities related to the full data set. The resulting diagnostic is a function of the residual and leverage, as other diagnostics in regression models, and of the calibration weight, a singular feature in survey sampling. Moreover, a particular case, which includes the basic unit level model for small area estimation, is considered. Both a real and an artificial example are included to illustrate the diagnostic proposed. The results obtained clearly show that the proposed diagnostic depends on the calibration and soft-calibration variables, on the penalization term, as well as on the parameter to estimate.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

GOS+MU:一种查询对象选择新方法

在分析单一MU(Most Uncertainty)采样缺陷的基础上,提出一种"全局最优搜寻"方法 GOS(Global Optimal Search),并结合MU共同完成查询选择。GOS+MU方法中,GOS着眼全局寻找目标,在应用环境能提供的训练样本数量有限、分类器受训不充分时,该方法选择的对象学习价值高,能快速推进分类器学习进程;MU则能够在GOS采样失效情形下,利用分类器当前训练成果,选择查询不确定性最强的样本补充训练集。通过对网络商品的用户评论进行分类仿真,并比较其他采样学习方法的效果,证明了GOS+MU方法在压缩学习成本、提高训练效率方面的有效性。     

高应力区沿空留巷围岩现状监测研究与分析

陈四楼矿计划实施沿空留巷的2802运输巷，断层较多，直接顶厚度较大，巷道矿压显现较为明显。针对顶板下沉、两帮外鼓、顶板淋水较大、巷道顶板受压破碎、金属网锈蚀严重、压脱锚杆较多、两帮位移较大等现象，提出在高应力区采用矿压监测的方法，通过对巷道变形现状进行的监测分析，对巷道破坏变形程度有了清晰的认识，对造成巷道破坏变形原因和影响因素有了详细的分析，根据注浆加固可改善围岩结构，提高围岩承载能力及锚杆、锚索锚固性能，是一种较为有效的围岩控制措施，确定了需要采用注浆加固的方式对进行巷道围岩破坏变形治理，并根据现场实际情况对注浆材料的性能提出了要求。     

新型低GWP高温热泵工质HFO-1234ze(Z)的研究进展

高温热泵可以有效回收工业余热，达到节能减排和保护环境的目的。目前，有关高温热泵技术的研究热点在于寻找一种全球变暖潜能值(GWP)低、使用性能良好的工质，以替代现有CFC-114、HFC-245fa工质。对新型环境友好型工质HFO-1234ze(Z)进行了综述，其GWP＜1，临界温度高于423 K，是一种潜在的高温热泵替代工质。总结了近年来国内外学者对HFO-1234ze(Z)的合成技术、热力学性质、输运性质、传热性能等方面的研究，并分析了HFO-1234ze(Z)在高温热泵系统中应用的可行性，认为HFO-1234ze(Z)在高温热泵中具有较好的工作性能和发展前景。     

SBBR反应器中耐冷微生物的驯化与识别

以模拟生活污水为处理对象，三种不同填料为生物膜载体，运行 A、B、C三个SBBR，通过逐步降温（25、20、15、10、6和5℃）培养驯化处理低温污水的生物膜。运行251周期，三个反应器COD出水浓度均低于50 mg/L；B反应器对氨氮去除效果优于A和C。特别在低温5℃时，A、B和C三个反应器氨氮平均出水浓度分别为14.1、3.79和14.1 mg/L。高通量测序结果显示，5℃时，三个反应器中均以降解有机物的微生物为主；只有B反应器中驯化富集出了嗜冷硝化菌（Candidatus Nitrotoga）；而其他有硝化作用的菌属在B反应器中的丰度也高于A和C反应器；不利于脱氮的类固氮菌（Elstera）在A和C反应器中丰度较高。此结果可从微生物角度解释三个反应器处理效果的差异性，表明B反应器处理低温污水有显著优势，其所用填料可结合低温菌进一步开发利用。     

罩式退火温度对高强IF钢组织及性能的影响

为研究不同退火温度下高强IF钢的组织性能及织构的变化规律,采用温箱式电阻炉加热模拟罩式退火工艺,研究了不同退火温度下高强IF钢210P1冷轧板力学性能;对不同退火温度钢板的r90进行了统计并对其进行显微组织观察;采用X射线衍射仪及热场发射扫描电镜对不同退火温度的罩式退火成品板进行了织构分析。结果表明,在高强IF钢210P1冷轧板的罩式退火过程中,提高退火温度将使晶粒明显长大。随着退火温度的升高,屈服强度及抗拉强度下降,伸长率升高,n值略有上升,板材横向r值增加较明显,有利织构{111}取向密度增加,不利织构{100}取向密度降低。     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.