LC-4D化学需氧量测定仪的研制

介绍了自行设计的LC-4D化学需氧量测定仪的工作原理（基于闭管消解-微库仑滴定法）,仪器组成,主要电路结构,仪器特点,主要工作参数及实验结果。目前该仪器已用于环保、水处理、石油化工等部门,使用结果令人满意。     

LC-4原油水含量测定仪的研制

基于无吡啶卡尔费休试剂作电解液的微库仑滴定法的工作原理,研制了一台LC-4原油水含量测定仪。实验表明,该仪器具有操作简单、节省时间和试剂、测量下限低的优点,既可用于原油水含量测定,又可用于原油盐含量测定。     

含裂纹故障齿轮系统的非线性动力学研究

考虑时变啮合刚度、间隙非线性及传动误差的影响,针对试验齿轮箱中的单对齿轮传动建立齿轮副扭转振动的参数化动力学模型,对裂纹故障的非线性动力学机理进行研究。采用平均法分析齿轮裂纹模型的主共振及1/2亚谐共振的动力学响应;给出裂纹演化过程对齿轮系统啮合刚度及动力学行为的影响;通过幅频特性曲线、时域图、相轨迹图、Poincaré截面图及频谱图综合分析含有裂纹故障齿轮的振动特征;通过奇异性理论分析裂纹程度及传动误差所产生的内部激励与系统动力学分岔的关系,从而揭示了不同裂纹程度和传动误差所引起的不同分岔模式;最后通过试验提取含有裂纹故障齿轮的振动特征,试验结果验证了理论分析的结果,从而为齿轮系统裂纹故障的识别提供理论依据。     

堆用中子探测器校验装置设计

本文采用MCNP4c程序,对影响堆用中子探测器校验装置的各项参数进行计算,确定了装置慢化体厚度、引出孔径等一系列参数.对装置的屏蔽性能进行了计算,结果表明装置屏蔽性能满足标准要求.     

带填充墙钢框架结构抗侧力性能试验及理论研究

介绍了7榀钢框架及带填充墙钢框架结构的水平静力及低周反复加载试验。通过测试有墙和无墙钢框架试验模型在侧向力作用下的变形全过程,得到了墙体对钢框架结构强度和刚度的影响,了解了节点的破坏特征及墙体本身的工作性能,得到了有墙及无墙钢框架结构的滞回性能。试验中发现,填充墙体和钢框架之间的连接性能很好,墙体和框架可以共同工作。通过参数分析,给出了结构抗震弹性层间角位移的建议取值为1/350。在理论分析的基础上,提出了填充墙框架体系抗侧刚度简化公式,得到了试验结果的验证。     

钢筋混凝土框架结构屋盖温度效应的量测与研究

本文介绍两座钢筋混凝土屋面结构温度效应观测研究成果,分析了日照温度引起的框架结构内力分布一般规律,提出了屋面结构温度效应的计算可参照铁路桥梁设计规范方法,以及有关裂缝控制的设计建议。     

基于实时路况的交通拥堵时空特征及其影响因素分析——以苏州古城区为例

城市化水平提高的同时交通问题也日趋严重,交通拥堵已然成为制约大中城市发展的"瓶颈"。新数据环境下我们可以获得更加丰富的城市交通特征信息来分析交通拥堵特征。研究采集连续一周的实时路况数据,通过ArcGIS操作平台对苏州古城区范围内的常发性交通拥堵时空特征进行分析,并剖析交通拥堵发生的主要原因。研究显示:时间上,苏州古城区工作日和休息日均有两个出行高峰,但高峰时间段和峰值有所差异;工作日路况拥堵变化程度较大,潮汐现象更为明显;空间上,交通拥堵呈现"点、线"并存的特征,并有向周围区域扩散形成"面"拥堵的趋势。研究表明,常发性拥堵时空分布规律与交通供需矛盾、职住分离、路网结构、用地布局等因素存在一定相关性。     

Phase- and size-controllable synthesis with efficient photocatalytic activity of ZnS nanoparticles

We report a simple solution-based method to synthesize phase- and size-controllable ZnS nanoparticles at low temperature. Cubic ZnS (c-ZnS) and hexagonal ZnS nanoparticles (h-ZnS) were obtained by heating an aqueous solution of Zn(NO₃)₂·6H₂O and Na₂S₂O₃·5H₂O at different temperatures. When the system was heated at 65 °C for 24 h, hexagonal crystal structure of ZnS nanoparticles, with size of 50–350 nm, was obtained, as confirmed by X-ray diffraction and selected-area electron diffraction. When the reaction temperature was 100 °C under hydrothermal condition, c-ZnS nanoparticles were obtained and exhibited monodisperse nanoparticles with average size of 4 nm. Proper rate of S releasing tuned by the variation of pH value is believed to be critical to stabilize the hexagonal ZnS nanoparticles. Compared with large size of h-ZnS nanoparticles, c-ZnS nanoparticles show higher photocatalytic activity in degrading methyl orange (MO). The degradation efficiency of c-ZnS nanoparticles reaches 97% under UV irradiation for 120 min. The good ultraviolet absorbing ability, charge separation property, and large surface area of c-ZnS nanoparticles are believed to have a positive impact on improving the degradation rate and degradation efficiency of MO.     

Concurrent optimal allocation of distributed manufacturing resources using extended Teaching-Learning-Based Optimization

The optimal allocation of distributed manufacturing resources is a challenging task for supply chain deployment in the current competitive and dynamic manufacturing environments, and is characterised by multiple objectives including time, cost, quality and risk that require simultaneous considerations. This paper presents an improved variant of the Teaching-Learning-Based Optimisation (TLBO) algorithm to concurrently evaluate, select and sequence the candidate distributed manufacturing resources allocated to subtasks comprising the supply chain, while dealing with the trade-offs among multiple objectives. Several algorithm-specific improvements are suggested to extend the standard form of TLBO algorithm, which is only well suited for the one-dimensional continuous numerical optimisation problem well, to solve the two-dimensional (i.e. both resource selection and resource sequencing) discrete combinatorial optimisation problem for concurrent allocation of distributed manufacturing resources through a focused trade-off within the constrained set of Pareto optimal solutions. The experimental simulation results showed that the proposed approach can obtain a better manufacturing resource allocation plan than the current standard meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimisation and Harmony Search. Moreover, a near optimal resource allocation plan can be obtained with linear algorithmic complexity as the problem scale increases greatly.     

Modulating the Interlayer Stacking of Covalent Organic Frameworks for Efficient Acetylene Separation

Controllable modulation of the stacking modes of 2D (two-dimensional) materials can significantly influence their properties and functionalities but remains a formidable synthetic challenge. Here, an effective strategy is proposed to control the layer stacking of imide-linked 2D covalent organic frameworks (COFs) by altering the synthetic methods. Specifically, a modulator-assisted method can afford a COF with rare ABC stacking without the need for any additives, while solvothermal synthesis leads to AA stacking. The variation of interlayer stacking significantly influences their chemical and physical properties, including morphology, porosity, and gas adsorption performance. The resultant COF with ABC stacking shows much higher C₂H₂ capacity and selectivity over CO₂ and C₂H₄ than the COF with AA stacking, which is not demonstrated in the COF field yet. Furthermore, the outstanding practical separation ability of ABC stacking COF is confirmed by breakthrough experiments of C₂H₂/CO₂ (50/50, v/v) and C₂H₂/C₂H₄ (1/99, v/v), which can selectively remove C₂H₂ with good recyclability. This work provides a new direction to produce COFs with controllable interlayer stacking modes.