两室气体互通对光合微生物燃料电池性能的影响

以斜生栅藻生长产生氧为电子受体的光合微生物燃料电池(PMFC)和外加CO2光合微生物燃料电池(AC-PMFC)联合构建成微生物碳捕获电池(MCC).研究MCC在不同运行条件下的产电性能及影响因素.测量MCC,PMFC和AC-PMFC三种系统中的电压、溶解氧和pH.结果表明,产电压趋势与所有系统中的藻类阴极的氧浓度相关,...     

Superhydrophobic and ultraviolet-blocking cotton textiles

Cotton textile was coated with ZnO@SiO(2) nanorods in order to obtain superhydrophobic and ultraviolet (UV)-blocking properties. The coating process was conducted in mild conditions, which involved the low-temperature preparation of ZnO seeds, hydrothermal growth of ZnO nanorods, bioinspired layer-by-layer deposition of a SiO(2) shell on the surface of ZnO nanorods, and hydrophobic modification of ZnO@SiO(2) nanorods with octadecyltrimethoxysilane. Despite the highly curved morphology of cotton fibers, the ZnO@SiO(2) nanorods coated the textile densely and uniformly. The treated cotton textile was found to have a large UV protection factor (UPF = 101.51) together with UV-durable superhydrophobicity, as determined by contact-angle measurement under long-term UV irradiation. The good UV-blocking property can be ascribed to the high UV absorbance and scattering properties of ZnO nanorods, and the UV-durable superhydrophobicity is a result of suppression of the photoactivity of ZnO nanorods by a SiO(2) shell.     

Photocatalytic Reduction of Graphene Oxide–TiO2 Nanocomposites for Improving Resistive‐Switching Memory Behaviors

Graphene oxide (GO)‐based resistive‐switching (RS) memories offer the promise of low‐temperature solution‐processability and high mechanical flexibility, making them ideally suited for future flexible electronic devices. The RS of GO can be recognized as electric‐field‐induced connection/disconnection of nanoscale reduced graphene oxide (RGO) conducting filaments (CFs). Instead of operating an electrical FORMING process, which generally results in high randomness of RGO CFs due to current overshoot, a TiO₂‐assisted photocatalytic reduction method is used to generate RGO‐domains locally through controlling the UV irradiation time and TiO₂ concentration. The elimination of the FORMING process successfully suppresses the RGO overgrowth and improved RS memory characteristics are achieved in graphene oxide–TiO₂ (Go‐TiO₂) nanocomposites, including reduced SET voltage, improved switching variability, and increased switching speed. Furthermore, the room‐temperature process of this method is compatible with flexible plastic substrates and the memory cells exhibit excellent flexibility. Experimental results evidence that the combined advantages of reducing the oxygen‐migration barrier and enhancing the local‐electric‐field with RGO‐manipulation are responsible for the improved RS behaviors. These results offer valuable insight into the role of RGO‐domains in GO memory devices, and also, this mild photoreduction method can be extended to the development of carbon‐based flexible electronics.     

基于改进Faster R CNN的光伏电池内部缺陷检测

光伏电池近红外图像中复杂异构背景使内部缺陷检测成为一项极具挑战性的问题,为此,提出了一种基于深度学习的目标检测框架-残差通道注意力Faster R-CNN(residual-channel-attention-faster R-CNN,RCA-Faster R-CNN),该网络通过卷积层-池化层提取图像特征,再送入新颖...     

带动压反馈的质心测量系统电液伺服控制方法

质心测量系统用于大型重载车辆的质量及质心位置的测量,具有俯仰和横滚两个运动自由度.系统采用三点支撑加双缸驱动方案,驱动系统采用液压伺服控制,保证了平台运动的平稳性与准确度.建立了非对称阀控非对称缸的动力机构数学模型以及系统仿真模型.分析了采用动压反馈校正前后单通道控制系统的闭环频率特性和响应特性,仿真结果验证了动压反馈校正是有效的.     

具有输入数据注意力机制的卷积神经网络用于氟化工产品质量预测

在氟化工等复杂的化工过程中,具有不同时间尺度的时变特性同时存在并作用于系统运行。这类复杂的强时变特性严重制约着各种先进控制策略的广泛应用。为了克服关键质量变量测量滞后所带来的不利因素,进一步提高氟化工过程先进控制系统的控制精度,本文提出了一种具有输入数据注意力机制的卷积神经网络（ACNN）并用于产品质量预测。通过引入注意力机制自适应地提取不同时间跨度输入数据的时间特性,来克服常规卷积神经网络因输入数据窗口固定而无法充分利用各类时变尺度特性的弊端,从而更为精准地提取氟化工过程复杂的强时变特性,更加准确地预测产品质量,辅助工业生产。应用氟化工过程真实数据和TE（Tennessee Eastman）模拟数据验证了方法的有效性和泛化性,结果表明对于强时变或同时具有长时间跨度的漂移波动而言,ACNN的质量预测模型具有更高的可靠性。     

干涉型光纤传感器直流相位跟踪检测系统的改进

对干涉型光纤传感器直流相位跟踪检测系统进行了分析和改进，通过在检测系统中加入简单的电路模块，使系统的幅频响应特性得到改善。建立了Michelson光纤干涉型传感系统，实验结果与理论分析相吻合。     

吴江市水功能区纳污能力及限制排污总量研究

依据《江苏省水功能区划》,结合吴江市水资源质量及污染现状,采用一维非稳态模型确定水质参数,计算吴江市水功能区纳污能力,核定限排总量,与吴江市现状污染物入河量对比,确定污染物削减量,为吴江市水污染防治与污染减排工作提供方法和依据。结果表明:吴江市水功能区水域纳污能力CODCr和氨氮分别为17 344和1 672 t/a,为入河量的56%和57%,CODCr和氨氮的平均削减率分别为54%和56%,该结果与污染物入河量、水质超标率结果基本吻合,说明纳污能力与限排总量计算结果合理。     

基于分形和概率神经网络的水电机组故障诊断

水电机组振动信号属于非线性、非平稳信号，在不同尺度下呈现一定的相似性，是典型的分形信号。本文运用多重分形方法分析机组振动信号，提取信号的广义维数谱特征，并应用人工鱼群算法优化的概率神经网络进行故障诊断。诊断实例表明，多重分形和概率神经网络结合，能够准确辨别故障类型。与BP和RBF网络相比，该方法诊断识别率更高，速度更快，为机组运行维护人员提供更为可靠的参考依据。     

溶剂型木器涂料中挥发性有机化合物释放规律

小型气候箱模拟典型室内空气条件，不同时间用椰子壳活性炭吸附管收集一定体积硝基、多组分聚氨酯清漆涂膜释放出的VOC与空气的混合气体，气相色谱法测定二硫化碳提取液中VOC组分浓度的实验方法，研究了溶剂型木器涂料成膜后VOC的气相浓度由迅速衰减、过渡和缓慢衰减三个过程构成并受液-气界面压力差和解吸的控制，基于数据分析建立定量描述涂料涂装后气相浓度与涂膜中VOC组分量、空气交换率、实验仓容积相互关系的数学模型。气相浓度的衰减率R，可表征VOC释放特性，温度、湿度、空气交换率条件对气相浓度衰减率影响研究表明其与温度、组分的色谱保留时间存在函数关系。