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101.
A new route of utilization of α-olefin rich hydrocarbon fractions obtained by waste polymer pyrolysis was investigated. α-olefin-succinic-anhydride intermediate-based pour point depressant additives for diesel fuel were synthesized, in which reactions needed α-olefins were obtained by pyrolysis of waste high-density polyethylene (HDPE). Fraction of α-olefins was produced by the de-polymerization of plastic waste in a tube reactor at 500℃ in the absence of catalysts and air. C17~22 range of mixtures of olefins and paraffins were separated for synthesis and then, these hydrocarbons were reacted with maleic-anhydride (MA) for formation of α-olefin-succinic-anhydride intermediates. The olefin-rich hydrocarbon fraction contained approximately 60% of olefins, including 90%~95% α-olefins. Other intermediates were produced in the same way by using commercial C20 α-olefin instead of C17~22 olefin mixture. The two different experimental intermediates with number average molecular weights of 1850g/mol and 1760g/mol were reacted with different alcohols: 1-butanol, 1-hexanol, 1-octanol, i-butanol, and c-hexanol to produce their ester derivatives. The synthesized ten experimental pour point depressants were added in different concentrations to conventional diesel fuel, which had no other additive content before. The structure and efficiency of experimental additives were followed by different standardized and non-standardized methods. Results showed that the experimental additives on the basis of the product of waste pyrolysis were able to decrease not only the pour but also the cloud point and cold filter plugging point (CFPP) of diesel fuel, whose effects could be observed even if the concentration of additives was low. Furthermore, all additives had anti-wear and anti-friction effects in diesel fuel. 相似文献
102.
纳米钯膜电极的制备、结构表征和特殊反应性能 总被引：11，自引：0，他引：11
用循环伏安方法制备纳米钯膜电极,运用扫描隧道显微镜和原位红外光谱等方法研究其结构和反应性能.STM图像表明,制备的纳米钯膜具有特殊的层状结构,纳米级厚度的层状晶体由直径6nm左右的Pd微晶聚集而成.发现当钯膜厚度为几个纳米时,CO的吸附表现出异常红外效应,即红外谱峰反向和红外吸收显著增强(增强因子可达42.6).纳米钯膜电极对氢的反应也具有特殊的性能,与氢向钯晶格扩散吸收过程相比较,氢吸脱附的表面过程成为主要反应.研究结果还指出,纳米钯膜电极的异常红外效应和对氢反应的特殊性能与钯膜厚度密切关联,并可归结为钯膜材料的纳米尺度效应. 相似文献
103.
建立了液相色谱-串联质谱测定小麦中9种氨基甲酸酯类农药的多残留检测方法．样品经乙腈提取,中性氧化铝填充柱净化,然后采用HPLC-ESI（＋）-MS／MS测定．着重探讨了液质联用体系的稳定性、测定中普遍存在的基体效应及其校正的可能方法．结果表明,本研究建立的方法简便、快捷、准确,适合大批量样品的测定． 相似文献
104.
105.
纳米尺度物质的生物效应研究是近年来在纳米科技发展过程中派生出来的一个崭新的、发展很快的且多学科高度交叉的领域,需要把纳米科学、物理学、化学以及生物医学等多学科的研究手段结合起来,进行综合研究.核磁共振波谱与成像,作为一种原位、无损、动态、实时、多信息的检测手段,在此领域的研究中将发挥不可或缺的重要作用.文章分3个方面简要介绍核磁共振波谱与成像技术在纳米尺度物质生物效应研究中的应用：(1)纳米尺度物质在生物组织及个体内的检测与分析;(2)纳米尺度物质与生物大分子相互作用的核磁共振波谱研究;(3)纳米尺度物质生物效应的核磁共振代谢组学研究. 相似文献
106.
107.
108.
商继芳;孙军;李清连;吴婧;张玲;窦飞飞;董潮涌;许京军 《光子学报》2018,47(5):514001-514001
研制了一种基于铌酸锂（LN）电光调Q的高重复频率窄脉宽短腔激光器.通过测量激光穿过置于正交偏振镜间的电光晶体后,透射强度随晶体上施加的脉冲高压的变化情况,探究了不同尺寸LN晶体中的压电振铃效应,并与磷酸钛氧铷（RTP）晶体中的压电振铃效应进行了比较.实验发现,块状LN晶体中的压电振铃效应严重,而小尺寸LN晶体中的压电振铃效应和RTP晶体中的相似,基本可以忽略.结合压电效应理论得出,压电振铃效应的强弱与外加电压大小及晶体固有的压电共振频率有关,电压越低,压电共振频率越大,压电振铃效应越弱.在此基础上,制备了可高重频应用的尺寸为1.2mm×9mm×9.4mm的LN调Q开关,并实现了LN晶体的高重频调Q运转.激光增益介质采用具有较大受激发射截面和较短荧光寿命的Nd∶YVO 相似文献
109.
按WHO抗癌药急性及亚急性毒性分级标准 ,对 650例化、放疗肿瘤患者分别作了治疗前后毒性反应的总分值统计。结果加服PSP组 ( 557例 )的疗后总分值为837,低于疗前的 962 ;而 93例阳性对照组 (加服鲨肝醇 )疗前总分为 157,疗后为 2 4 1,疗后总分高于疗前。用Wilcoxon两样本秩和统计法检验 ,PSP组和对照组间具有极显著差异 ,表明PSP对比、放疗药物引起的毒副反应有明显降低效应。 相似文献
110.
HUANG Hong;YANG Yichuan;WANG Long;ZHENG Fujian;WU Jian 《光子学报》2024,53(1):78-90
In clinical practice, segmentation and quantitative evaluation of target objects in pathological images provide valuable information for histopathological analysis, which is of great significance to auxiliary diagnosis and subsequent treatment. However, due to the dense distribution of cells and great morphological similarities between the cancer cells and normal cells, there are some challenges such as difficulty in feature extraction and unclear segmentation boundaries in the segmentation task of pathological images. At the same time, the traditional image segmentation methods are time-consuming and labor-intensive. They can only extract low level manual features, and the expression ability of deep discrimination features is insufficient, resulting in limited performance of traditional methods. Meanwhile, previous deep learning algorithms still suffer from two significant problems. Firstly, most networks ignore pixels that are difficult to segment, such as the boundaries of targets, which is particularly important for accurate segmentation. In addition, the problem of inconsistent semantic levels between different features are not solved, leading to low training efficiency. To address the above-mentioned problems, an end-to-end histopathological image segmentation network called Boundary Perception Network （BPNet） is proposed for improving the segmentation accuracy of histopathological images. Based on encoder-decoder structure, the encoder performs the convolutional downsampling operation to extract the feature information of the image through the Convolutional Neural Network （CNN）. And the encoding process uses the feature encoder based on the EfficientNet-B4 network which is specifically used for pathological image segmentation. The decoder mainly consists of decooding blocks, Boundary Perception Module （BPM） and Adative Shuffle Channel Attention Moudule （ASCAM）. In detail, the decoding block performs deconvolution operation to complete the decoding process of the feature information. Then, the BPM in the decoder stage aims to strengthen the ability of mining for difficult segmentation regions, so that the network focuses on the higher uncertainty as well as more complex edge regions, achieving feature complementarity and precision prediction results. For implementation, the BPM extracts the edge from the decoder output of each layer, and superimposes the edge information onto the encoded feature to strengthen the boundary feature information extracted from pathological images, outputting the enhanced edge perception feature map. Subsequently, the ASCAM is an improved chanel attention moudule which is used to make up the semantic gap between different levels of features, extrated by encoder, decoder and BPM, so as to further strengthens the feature understanding ability of the BPNet. This module exploits adaptive kernel size one-dimensional convolusion to capture the interactive information of local channels, at the same time ensures the efficiency and effectiveness of the training process. The obtained channel attention coefficient is multiplied by the module input feature layer to obtain the fusion feature, helping effectively learn the channel interaction information between features to improve the feature representation ability. Furthermore, a joint loss function based on structure and boundary is designed to optimize the targeting and detail processing capabilities of this method, achieving the better segmentation result of pathological images. Experiments are carried out on the Gland segmentation （GlaS） and MoNuSeg dataset, respectively. Both of the two datasets are devided into 4∶1 for training and validation. At the same time, in order to make up for the overfitting caused by the lack of training data, two kinds of online data enhancement methods of horizontal flipping and vertical flipping were carried out on the training set data in the experiment. And the four evaluation index, the Dice coefficient score, Intersection Over Union （IoU）, Accuracy （ACC） and Precision （PRE）, are used to evaluate the performance of this method propsed in this paper. The Dice coefficient score of the proposed method is 92.21% and 81.18%, the IoU is 85.55% and 68.34%, the ACC is 92.14% and 92.50%, the PRE is 92.07% and 75.46% on the GlaS and MoNuSeg datasets, respectively. Compared with the previous classical methods, such as U-Net, UNet++, MultiResUNet, TransUNet, UCTransNet and so on, the BPNet proposed gets the best segmentation result, especially retains more details in the segmentation boundary. Moreover, ablation experiments are carried out on the same two datasets for indicating the impacts of BPM and ASCAM. The results shows that the proposed BPM significantly optimizes the segmentation effect of the network for the edge, as well as the ASCAM makes up the semantic gap between features at different levels and further strengthens the feature understanding ability of the network. In conclusion, the BPNet proposed in this paper exploits BPM to generate edge enhancement feature maps, and uses ASCAM to seize crucial features. Finally, a joint loss function is used to capture the information of features at different levels in the output layer to achieve optimal segmentation performance. The experimental results have demonstrated that the effectiveness of each part of proposed method in the segmentation task of pathological images. 相似文献