3-硝基-1,2,4-三唑-5-酮银盐的合成和热分解性能

将3-硝基-1,2,4-三唑-5-酮(NTO)的钠盐水溶液与硝酸银水溶液反应,制备了3-硝基-1,2,4-三唑-5-酮银盐(AgNTO·2H2O).用DSC、TG分析了其热行为,AgNTO·2H2O盐的热分解过程分为4个阶段:脱水、硝基的断裂和分解、三唑环的断裂分解和无机物的初步生成、最终分解为无机物.最大分解温度在2...     

重结晶碳化硅烧成中碳化硅的分解现象、热力学条件及对蒸发凝聚的影响

重结晶碳化硅陶瓷烧成试验采用国内碳化硅微粉,对其烧成后的现象与结果进行分析检测,在热力学分析和试验的基础上得出含有较多杂质的国内碳化硅微粉在重结晶烧成中有显著分解挥发,而且影响碳化硅的蒸发凝聚过程。     

挥发性有机化合物(VOCs)的低温等离子体-催化协同净化

低温等离子体-催化协同净化技术是一种理想的环境污染治理技术。催化剂的加入可提高等离子体反应中污染物的脱除效率和二氧化碳的选择性，减少副产物的产生，并进一步降低能耗。分析了低温等离子体-催化协同净化挥发性有机化合物的效果与净化原理，并从影响污染物降解率的因素、产物分析和反应动力学等机理性研究方面概括了目前国内外在应用该技术去除挥发性有机污染物方面取得的成果，最后提出了该项技术在环境保护领域的应用前景以及研究方向。     

基于自动特征工程的飞行器轴承故障诊断

针对飞行器轴承信号单一且噪声多、需要针对性特征以及需要高可解释性的问题,开发了涵盖具有自适应噪声的完全集合经验模态分解（CEEMDAN）、自动特征工程以及随机森林的故障诊断模型,模型核心为自动进行特征生成以及提取的特征工程。通过该特征工程能够根据不同对象的信号差异,自动提取出不同对象的有效特征,具备对象间的通用性,且该特征工程可根据样本量的不同调整有效特征的数量,丰富特征空间,具备灵活的可扩展性。验证表明,该涵盖自动特征工程的模型的故障分类准确率为95.32%,可较好地在大样本量下区分压缩机轴承上的不同故障。     

DFTNAN/B的热分解行为及相容性

为了进一步研究DFTNAN及其与晶体硼混合后的热分解行为,采用差示扫描量热法(DSC)和热重法(TG)对DFTNAN、B和DFTNAN/B进行测试,分析了DFTNAN含量对B热分解的影响,并通过DSC法和真空安定性试验综合考察了二者的化学相容性。结果表明,在密闭环境下,DFTNAN在289.7℃发生热分解反应并出现明显的分解峰,而在敞开环境中没有分解峰存在;DFTNAN与B混合会导致DFTNAN的熔融峰温提前2℃,而硼粉分解峰温却发生滞后;当DFTNAN/B质量比为9∶1时,硼粉放热峰消失,说明其与DFTNAN反应完全;由DSC法计算得到DFTNAN与硼粉的相容性等级为2级,而在真空安定性试验中测得反应净增放气量为2.04mL,均表明二者有良好的相容性,可以直接进行混合熔铸。     

两类复合无机相变储热材料高温热稳定性和安全性研究

以适合工业储热的复合无机相变储热材料硝酸盐（KNO₃、NaNO₃）和碳酸盐（Li₂CO₃、K₂CO₃、Na₂CO₃和CaCO₃）为相变组分,研究了4种不同配比硝酸盐相变组分和6种不同配比碳酸盐相变组分的热性能（熔点、潜热）差异,分别优选出一种熔融盐相变组分配比。利用多孔载体吸附原理,制备出两种最佳配比的复合熔融盐类储热材料,分析了储热材料在不同介质气氛中（Ar和air）分解难易程度,TG-DSC-MS联用测试的高温热分解产物表明,复合硝酸盐类储热材料在中温（300℃）储热时,物理化学性能稳定,安全性较好,但在高温（500℃以上）时易分解成NO和NO₂,且air气氛中更易生成有毒气体;而复合碳酸盐类储热材料在air中比在Ar中更易生成CO而影响储热过程中的安全性。     

纳米金属钴粒子的制备及对高氯酸铵热分解的影响

The monodispersed Co nanoparticles were successfully prepared by means of hydrogen plasma method in inert atmosphere. The particle size, specific surface area, crystal structure and morphology of the samples were characterized by transmission electron microscopy （TEM）, BET equation, X-ray diffraction （XRD）, and the corresponding selected area electron diffraction （SAED）. The catalytic effect of Co nanoparticles on the decomposition of ammonium perchlorate （AP） was investigated by differential thermal analyzer （DTA）. Compared with the thermal decomposition of pure AP, the addition of Co nanoparticles （2%-10%, by mass） decreases the decomposition temperature of AP by 145.01-155.72℃. Compared with Co3O4 nano-particles and microsized Co particles, the catalytic effect of Co nanoparticles for AP is stronger. Such effect is attributed to the large specific surface area and its interaction of Co with decomposition intermediate gases. The present work provides useful information for the application of Co nanoparficles in the AP-based propellant.     

The Effect of Different Active Sites on the Catalytic Activity of Fe-ZSM-5 Zeolite for N2O Direct Decomposition

Fe-modified ZSM-5 zeolites (Si/Al = 25) were prepared by adopting the liquid ion-exchange method with nitrate and oxalate of iron as Fe precursors and their catalytic performance was studied in the N₂O decomposition reaction. The results of FT-IR and H₂-TPR investigations indicated that (i) part of the iron ions could replace Brönsted acid protons at the straight channel wall (α sites), intersection of straight and sinusoidal channels (β sites), and sinusoidal channel wall (γ sites) within the ZSM-5 zeolite; and (ii) different Fe precursors gave rise to various distributions of α, β, and γ sites. We observed that the Fe-ZSM-5 catalyst prepared with iron oxalate as Fe precursor outperformed the ones prepared with iron nitrate as Fe precursor in the direct decomposition of N₂O. Furthermore, the catalytic activity of iron ions located at the α sites was higher than those of iron ions located at the β and γ sites.     

Multivariate statistical process monitoring of batch‐to‐batch startups

In batch processes, multivariate statistical process monitoring (MSPM) plays an important role for ensuring process safety. However, despite many methods proposed, few of them can be applied to batch‐to‐batch startups. The reason is that, during the startup stage, process data are usually nonstationary and nonidentically distributed from batch to batch. In this article, the trajectory signal of each process variable is decomposed into a series of components corresponding to different frequencies, by adopting a nonparametric signal decomposition technique named ensemble empirical mode decomposition. Then, through instantaneous frequency calculation, these components can be divided into two groups. The first group reflects the long‐term trend between batches, which extracts the batch‐wise nonstationary drift information. The second group corresponds to the short‐term intrabatch variations. The variable trajectory signals reconstructed from the latter fulfills the requirements of conventional MSPM. The feasibility of the proposed method is illustrated using an injection molding process. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3719–3727, 2015