热电池正极材料钒氧碳的性能研究

对热电池正极材料钒氧碳(VOC)的合成方法及性能特点作了评述。实验在不同工艺条件下的热反应制得不同成分的VOC材料,运用X射线衍射光谱法(XRD)和扫描电镜技术对所得产物进行了物相分析及形貌分析,并对其作为热电池正极材料的放电性能作了检测研究。实验结果表明:反应物配比、煅烧时间和温度对生成的VOC物相及性能都有重要的影响。粉末颗粒径向粒径小,比表面积较大的层片状及细棒状材料满足Li+快速扩散,使电池能够在较大电流密度下以较高的电压放电。对VOC材料进行放电性能测试发现,VOC能够提供较高的电压和平稳的放电平台,同时该化合物的重现性较好,容易加工成型,是一种较为理想的正极材料。     

基于神经网络的动态QFD方法

为了更迅速全面地将顾客需求转化为质量特性 ,提出了基于神经网络的动态 QFD方法 .该方法将神经网络与 QFD方法相结合 ,建立了 QFD神经网络模型 .通过网络的学习训练 ,获得最优的关系模型 .该方法能快速地将顾客需求变化传递给设计者 ,从而在第一时间内完成新产品的开发 .并以实例介绍了顾客需求模型以及顾客需求的获取方法     

未来环保涂料市场粉末涂料能否异军突起笑傲江湖

粉末涂料100%固体含量、零VOC,广泛应用于建筑铝型材、家用电器、管道防腐、钢筋防腐、金属家具等领域。在集装箱、汽车、卷材、家具、工程机械、船舶、钢结构这七大国家重点整治VOC排放领域,粉末涂料以其卓越的防腐与耐候特性,可实现真正的绿色涂装,其用量会大幅增长。但是在一些大型构件的现场施工中,粉末涂料涂装短期内难有用武之地。随着粉末涂料涂装技术的不断进步,粉末涂料将会有更多的应用空间。     

Emissions from sawdust in packed moving bed dryers and subsequent pellet production

This study quantifies emissions of hydrocarbon terpenes from the drying of sawdust in packed moving bed dryers, through the production chain to the finished pellets, and determines the parameters suitable for emission control. The terpene content in softwood sawdust and pellets was analyzed using gas chromatography. The distribution of VOC emissions over the bed was measured with a flame ionization detector. After drying, 30–40% of the initial terpenes remain in the wood, 20–30% remain after grinding, and 10–15% remain after pelleting. Dryer emissions correlate with residence time and final sawdust moisture content. Pellet press emissions correlate with pellet moisture content.     

家具厂挥发性有机物气体催化燃烧处理方案探讨

目前,在诸多行业生产中会产生大量的VOC废气,该气体严重造成了空气污染。通过催化燃烧技术对其进行处理,使其在催化剂的作用下在300℃左右燃烧氧化分解成CO2和H2O。燃烧过程产生的热能可用热交换器回收利用,达到废气利用和节能的目的。从测试数据来看,95%的VOC废气被去除,可知该方法的可行性。     

水性涂料VOG的气相色谱-质谱检测前处理方法研究

对水性涂料挥发性有机化合物（VOC）检测的前处理方法进行了研究.分别采用搅拌萃取法和超声波萃取法对水性涂料中28种VOC进行提取,采用离心方法提取清液,采用气相色谱-质谱法测定并计算回收率.通过比较,确定以二氯甲烷为萃取溶剂、搅拌时间为40 min的搅拌萃取法提取效率更高,采用该方法对28种VOC的加标回收率在94％～116％之间.     

Advanced oxidant reactivity pertaining to granular activated carbon beds for air pollution control

The Pennsylvania State University is researching an advanced oxidation system, which includes an air-phase photolytic chamber, an air/water stripping tower, and granular activated carbon (GAC) beds, for controlling volatile organic compounds (VOCs).A laboratory-scale experimental procedure has been employed that simulated certain aspects of several full-scale installations. The apparatus has been used to characterize the loading capacity and mass transfer zone of selected VOCs on coconut shell GAC. The GAC bed has then been placed in series with an ultraviolet reactor, which generates ozone and advanced oxidants in order to regenerate the loaded GAC at intensities of advanced oxidants that were higher than full-scale installations.VOC loading tests revealed that the adsorption of methyl isobutyl ketone (MIBK) was characterized by a well-defined mass transfer zone. Upon exposure to UV/O₃, desorption and/or destruction of the MIBK and other VOCs occurred most prominently within the first inch of the GAC bed. This correlated with the penetration of advanced oxidants into the GAC bed, which also occurred most significantly in the bed's first inch. However, the amount of oxidant penetration increased with time. The removal of oxidants from air by GAC was accompanied by a decrease in mass of the GAC. The ability of oxidants to penetrate a GAC bed was altered when the bed was loaded with a VOC.     

DF-SSD:一种基于反卷积和特征融合的单阶段小目标检测算法

针对经典的单阶段多目标检测算法SSD对小目标物检测效果差的问题,提出DF-SSD算法,其核心技术贡献包括基于反卷积与特征融合的方法和改进后的先验框尺寸计算算法.反卷积与特征融合能够增加浅层特征层的语义信息.改进后的先验框尺寸计算引入了数据集的特点,能有效利用每一个先验框进行训练和预测.改进后的方法DF-SSD与基于SS...     

基于YOLOv3的油茶果视觉定位系统

随着科技的进步,采摘机器人各个部分的系统也日益完善.其中,机器人视觉定位的系统设计很大程度影响了其工作效率,尤其是在目标检测速率、采摘果实准确率以及采摘目标环境适应度方面.本次研究提出利用双目立体视觉系统获取油茶果目标图像,并采集计算深度信息,制作自己的油茶果VOC数据集,采用YOLOv3目标检测算法来实现复杂环境下油...     

Novel e-nose for the discrimination of volatile organic biomarkers with an array of carbon nanotubes (CNT) conductive polymer nanocomposites (CPC) sensors

We report the original design of a new type of electronic nose (e-nose) consisting of only five sensors made of hierarchically structured conductive polymer nanocomposites (CPC). Each sensor benefits from both the exceptional electrical properties of carbon nanotubes (CNT) used to build the conductive architecture and the spray layer by layer (sLbL) assembly technique, which provides the transducers with a highly specific 3D surface structure. Excellent sensitivity and selectivity were obtained by optimizing the amount of CNT with five different polymer matrices: poly(caprolactone) (PCL), poly(lactic acid) (PLA), poly(carbonate) (PC), poly(methyl methacrylate) (PMMA) and a biobased polyester (BPR). The ability of the resulting e-nose to detect nine organic solvent vapours (isopropanol, tetrahydrofuran, dichloromethane, n-heptane, cyclohexane, methanol, ethanol, water and toluene), as well as biomarkers for lung cancer detection in breath analysis, has been demonstrated. Principal component analysis (PCA) proved to be an excellent pattern recognition tool to separate vapour clusters.