多孔SiO2分子巢制备多功能涤纶纤维试验研究

针对传统PET材料不具备抗菌、不耐洗等问题,以煎煮法为基础,以草珊瑚、艾叶和薄荷为原料,制备含植物活性成分的溶液,其具有抗菌、杀菌的作用;以溶胶-凝胶法为多孔材料制备方法,用十六烷基三甲基溴化铵、十二烷基苯磺酸钠表面活性剂为模板剂,正硅酸乙酯为有机硅源,氨水为催化剂,乙醇和乙醚为助溶剂,在水-乙醇-乙醚体系中合成多孔二氧化硅微球;然后,多孔二氧化硅微球与提取液混合制备含植物活性成分的多孔二氧化硅分子巢;最后以制备的多孔二氧化硅分子巢与普通的聚酯切片用熔融纺丝工艺进行造粒、纺丝,得到具有抗菌、杀菌和耐洗的多功能涤纶纤维.通过SEM微观观察和力学性能测试、抗菌试验、耐洗性测试,对上述制备的多功能涤纶纤维性能进行验证.结果表明:在模板剂总浓度为0.029 mol·L-1、V醇:V醚=20:20、两种表面活性剂比为4:1时,得到的多孔SiO2微球排列规整;当多孔二氧化硅分子巢掺量(质量分数)在0.5％～1％时,通过熔融共混纺丝得到的新型多功能涤纶纤维力学性能表现最优;当多孔二氧化硅分子巢掺量(质量分数)在1％时,得到的新型多功能涤纶纤维的抗菌性能达到87.9％.而二氧化硅分子巢掺量越高,纤维材料越耐洗.以上结果说明本试验制备涤纶纤维的方案可行.     

独立学院《环境监测实验》课程教学改革

从独立学院"应用型专业人才"的培养目标出发,结合我院近年来开展《环境监测实验》课程积累的经验,对本实验课程的教学内容、教学方法和课程评价体系等进行了改革。强调理论与实验内容有机结合,通过激发学生的学习兴趣,提高学生的实践应用能力,以满足社会对环境科学与工程专业应用型专业人才的需求。     

井工转露天采区顶板厚度不足诱发滑坡问题研究

井工转露天开采是安全生产遇到的新问题。以紫金山金铜矿为背景，利用数值模拟法系 统研究了井 工转露天开采工况下顶板厚度从 68 m 逐渐减小至 44 m 时顶板的安全性，界定出顶板最小安 全厚度为 45 m。同时 系统研究了顶板厚度变化对上部边坡应力场的影响特点，得出随着顶板变薄坡脚处塑性破坏区 增大，导致边坡稳 定性变小的规律，并通过边坡稳定性分析计算验证其正确性。因此在井工转露天开采过程中， 为了确保安全生产 需要确保顶板有足够的厚度，避免作业设备和工作人员坠入老井采区及滑坡灾害发生。     

Decorative near-infrared transmission filters featuring high-efficiency and angular-insensitivity employing 1D photonic crystals

We present a new scheme for visibly-opaque but near-infrared-transmitting filters involving 7 layers based on one-dimensional ternary photonic crystals, with capabilities in reaching nearly 100% transmission efficiency in the near-infrared region. Different decorative reflection colors can be created by adding additional three layers while maintaining the near-infrared transmission performance. In addition, our proposed structural colors show great angular insensitivity up to ±60° for both transverse electric and transverse magnetic polarizations, which are highly desired in various fields. The facile strategy described here involves a simple deposition method for the fabrication, thereby having great potential in diverse applications such as image sensors, anti-counterfeit tag, and optical measurement systems.

     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

Study on wax precipitation characteristics of wax deposit in pipes

Wax deposit properties are a significant concern in pipeline pigging during waxy crude oil transportation. In the present work, the impacts of flow conditions and oil properties on the wax precipitation characteristics of wax deposits are investigated. A flow loop apparatus was developed to conduct wax deposition experiments using four crude oils collected from different field pipes. The differential scanning calorimetry (DSC) technique was employed to observe the wax precipitation characteristics of crude oil and wax deposit. The results show that the wax content and the wax appearance temperature (WAT) of the deposits increase with shear stress and radial temperature gradient, and decrease with radial wax molecule concentration gradient near the pipe wall. The DSC tests on the wax deposits revealed that the deposit wax content is strongly correlated to the oil wax content. Furthermore, an empirical correlation was developed to predict the wax content and the WAT of the wax deposit. Verification of the empirical correlation using the different oils indicated that the average relative error of the wax content prediction and average absolute error of WAT prediction were 13.2% and 3.6°C, respectively.     

四川盆地高石梯-磨溪地区下二叠统气源示踪

以天然气、烃源岩及储层沥青的地球化学特征为主要依据,通过天然气-天然气、储层沥青-烃源岩、天然气-烃源岩3个方面对比,深入探讨四川盆地高石梯-磨溪地区下二叠统天然气来源。研究表明,四川盆地高石梯-磨溪地区下二叠统天然气主要来源于筇竹寺组泥岩。其5项证据分别为:下二叠统天然气乙烷碳同位素小于-30.5‰,甲、乙烷碳同位素倒转是由高演化程度烃源岩所致;多数下二叠统天然气与龙王庙组天然气特征最为相似;GS18,NC1井下二叠统天然气特征与寒武系相似,说明上下层气源一致;高石梯-磨溪地区下二叠统储层沥青地球化学特征与筇竹寺组泥岩最为相似;筇竹寺组泥岩主要为下二叠统供气,这一认识符合干酪根油气生成理论和同位素分馏规律。     

Facile construction of composition-tuned ruthenium-nickel nanoparticles on g-C3N4 for enhanced hydrolysis of ammonia borane without base additives

Developing high-efficiency and low-cost catalysts for hydrogen evolution from hydrolysis of ammonia borane (AB) is significant and critical for the exploitation and utilization of hydrogen energy. Herein, the in-situ fabrication of well-dispersed and small bimetallic RuNi alloy nanoparticles (NPs) with tuned compositions and concomitant hydrolysis of AB are successfully achieved by using graphitic carbon nitride (g-C₃N₄) as a NP support without additional stabilizing ligands. The optimized Ru₁Ni_7.5/g-C₃N₄ catalyst exhibits an excellent catalytic activity with a high turnover frequency of 901 min^?1 and an activation energy of 28.46 kJ mol^?1 without any base additives, overtaking the activities of many previously reported catalysts for AB hydrolysis. The kinetic studies indicate that the AB hydrolysis over Ru₁Ni_7.5/g-C₃N₄ is first-order and zero-order reactions with respect to the catalyst and AB concentrations, respectively. Ru₁Ni_7.5/g-C₃N₄ has a good recyclability with 46% of the initial catalytic activity retained even after five runs. The high performance of Ru₁Ni_7.5/g-C₃N₄ should be assigned to the small-sized alloy NPs with abundant accessible active sites and the synergistic effect between the composition-tuned Ru–Ni bimetals. This work highlights a potentially powerful and simple strategy for preparing highly active bimetallic alloy catalysts for AB hydrolysis to generate hydrogen.     

A new way for multidimensional medical data management: volume of interest (VOI)-based retrieval of medical images with visual and functional features.

The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis.     

井下电视检测系统在水井施工中的应用

简要介绍了井下电视在大港油田水源井水井结构检测、井管故障检测、指导落物打捞、水源井验收、地层结构检测及水源井地层受力分析等方面的应用情况，及应用井下电视检测所取得的经济效益和社会效益。