HPCL法测定左亚叶酸钠的异构体

目的:常用的亚叶酸类制剂包括亚叶酸钙、亚叶酸钠及其他们的左旋单体左亚叶酸钙、左亚叶酸钠,亚叶酸类制剂在体内真正起活性作用的部分为左亚叶酸.在制备中一般通过2种单体成盐时不同溶解度进行分离,但现有测定含量的检测方法均无法分离左旋或右旋单体,容易造成含量测定误差.结论:因此我们用HPLC法对注射用左亚叶酸钠异构体测定方法进行了研究,该法测定重复性好,结果准确可靠.     

Self-healing,elastic and deformable novel composite phase change polymer based on thermoplastic elastomer SEBS for wearable devices

Journal of Materials Science - Form-stable composite phase change materials (C-PCMs) prepared by microencapsulation method and porous matrix adsorption method need for compression molding after...     

盾构隧道施工物料无人运输发展现状及关键技术

盾构隧道施工物料无人运输有助于提升运输效率、降低运输成本、减少运输安全事故率。与公路汽车无人驾驶、港口车辆无人驾驶、轨道交通无人驾驶等逐步成熟的无人驾驶技术不同，在隧道场景下实施施工物料无人运输存在运输物料种类繁多、运输调度困难、地下定位信号拒止、狭窄车道频繁会车、行车路面工况复杂、地上地下联动响应慢等诸多挑战。从实现盾构隧道施工物料无人运输的关键问题分析入手，综述当前盾构隧道施工物料运输方式、运输需求、无人运输发展现状及存在的挑战，提出盾构隧道施工物料无人运输的五大关键技术：多种类物料智能货控能效管理技术，无人化垂直装卸门机控制技术，多传感器融合同步定位与地图构建技术，隧道复杂环境路径规划与自主避障技术，隧-地一体化联动响应的高可靠性无线通信技术。     

Optimized decomposition and two-step nonlinear integration model with error correction strategy coupled interval prediction for digital currency price forecast

Digital currency price prediction is vital to both sellers and purchasers. Over these years, decomposition and integration models have been applied more and more to realize the goal of precise prediction, however, many of them tend to neglect the reconstruction of features or the residual series. Altogether, one of the biggest drawbacks of the decomposition and integration framework is the method applied requires manual parameter setting whether it is for decomposition or integration. Still, for the results, they are merely satisfied with the point prediction which brings high uncertainty. In this paper, an optimized feature reconstruction decomposition and two-step nonlinear integration method is proposed which gives consideration to feature reconstruction, nonlinear integration, optimization and interval prediction. The original data series is decomposed through improved variational mode decomposition based approximate entropy feature reconstruction system. Then, improved particle swarm optimization-gated recurrent unit (iPSO-GRU) is utilized in the first and second nonlinear integration part separately. Meanwhile, the residual series is given attention, if it is not a white noise series, the residual will be the input of iPSO-GRU whose result will be added back to the second integration result to form the point prediction result. Based on the point prediction result, interval prediction estimate will be generated as well via maximum likelihood function. This study chooses three kinds of digital currency as cases and the results show that the MAPE values of point prediction are all below 3.5%, and CP values of interval prediction are all 1 with suitable MWP. In addition, compared with other benchmark models, the proposed model shows better performance.

     

Improved thermal stability of the near-infrared Al-modulated Zn3Ga2GeO8: Cr3+ phosphors for plant growth applications

The exploration of the high thermal stability near-infrared (NIR) phosphors is significantly crucial for the development of plant lighting. However, NIR phosphors suffer from the poor chemical and thermal stability, which severely limits their long-term operation. Here, the successful improvement of luminous intensity (149.5%) and thermal stability at 423 K of Zn₃Ga₂GeO₈ (ZGGO): Cr³⁺ phosphors is achieved for the introduction of Al³⁺ ions into the host. The release of carriers in deep traps inhibits the emission loss for the thermal disturbance. Furthermore, an NIR light emitting diodes (LEDs) lamp is explored by combining the optimized Zn₃Ga_1.1675Al_0.8GeO₈: 0.0325Cr³⁺ phosphors with a commercial 460 nm blue chip, and the emission band can match well with the absorption bands of photosynthetic pigments and the phytochrome (P_R and P_FR) of plants. The explored LEDs lamp further determines the growth and the pheromone content of the involved plants for the participation of the NIR emission originated from Cr³⁺ ions. Our work provides a promising NIR lamp as plant light with improved thermal stability for long-term operation.     

Modification of the wood-plastic composite for enhancement of formaldehyde clearance and the 3D printing application

As the formaldehyde is one of the main indoor pollutants, the purpose of this study is to effectively remove indoor formaldehyde pollution by using environmentally friendly 3D printing ornaments. The wood 3D printing filaments cellulose/polylactic acid composite (Cellu/P) was selected as the starting material, and 3-aminopropyltriethoxysilane (APTES) was used for chemical modification to obtain a series of cellulose composite materials with amino groups. The modified composite materials (APTES@Cellu/P) were characterized by Fourier transform infrared, X-ray diffraction, scanning electron microscope, energy dispersive spectroscopy, thermogravimetric analysis, and mechanical tests, and a formaldehyde removal experiment was performed. The feasibility of 3D printing was evaluated, and the process of 3D printing-functionalized customized ornaments was proposed, and then a school emblem was used for modeling, printing, and surface modification. Compared with the commercially traditional activated carbon, 3D printing-customized ornaments of APTES@Cellu/P material has a better formaldehyde removal effect, and can even avoid the secondary pollution that is common to the activated carbon.