Three-Dimensional Printing of High-Performance Moisture Power Generators

Water-enabled electricity generation technologies that are highly accessible and fundamentally clean are promising for next-generation green energy. However, the challenge of scalability in both material processing and device fabrication greatly limits their practical applications. A high-performance polyelectrolyte moist-electric generator (MEG), which can be directly 3D printed for massive production and efficient integration, is reported. The printed MEG (p-MEG) generates a high open-circuit voltage of 0.8 V and a short-circuit-current density of 0.12 mA cm⁻² by actively harvesting moisture from humid conditions. The synergistic effects of moisture gradient, ionic concentration gradient, and ion diffusion gradient, which remarkably enhance the driving force to separate ion pairs and notably facilitate the directional ion transport, are responsible for the high power generation performance of p-MEG, as further backed up by in situ ion dynamics investigations and molecular simulations. When connected in serial and parallel, hundreds of p-MEGs can deliver a high voltage of more than 180 V and a current of more than 1 mA. A constructed “moisture-powered cup lamp” that lights up for hours further demonstrates the practicability of p-MEG. This work provides a feasible and scalable 3D printing approach for the next-generation environment-adaptive self-powered system.     

Charge-Programmable Photopolymers for 3D Electronics via Additive Manufacturing

Charge-programmed 3D printing enables the fabrication of 3D electronics with lightweight and high precision via selective patterning of metals. This selective metal deposition is catalyzed by Pd nanoparticles that are specifically immobilized onto the charged surface and promises to fabricate a myriad of complex electronic devices with self-sensing, actuation, and structural elements assembled in a designed 3D layout. However, the achievable property space and the material-performance correlation of the charge-programmed printing remain unexplored. Herein, a series of photo-curable resins are designed for unveiling how the charge and crosslink densities synergistically impact the nanocatalyst-guided selective deposition in catalytic efficiency and properties of the 3D printed charge-programmed architectures, leading to high-quality 3D patterning of solid and liquid metals. The findings offer a wide tunability of the structural properties of the printed electronics, ranging from stiff to extreme flexibility. Capitalizing on these results, the printing and successful application of an ultralight-weight and deployable 3D multi-layer antenna system operating at an ultrahigh-frequency of 19 GHz are demonstrated.     

LIF技术与ELM算法的电力变压器故障诊断研究

电力变压器油的检测分析是电力变压器故障诊断的有效方法,快速识别电力变压器油的油样对电力变压器故障诊断工作有重大意义.常规的电力变压器油的检测技术主要是气相色谱法,此方法操作比较复杂,且不适合在线检测,不能及时发现变压器的故障隐患.提出一种激光诱导荧光光谱(L IF)技术与极限学习机(ELM)算法的电力变压器故障诊断研究...     

不同手机屏幕对青少年生理节律和蓝光危害的影响

针对市场上三款常见的不同材质的手机屏幕（LED背光LCD、OLED、SUPER AMOLED）,在黑暗中、屏幕距离瞳孔15 cm处4种不同色温的条件下,研究不同手机对青少年生理节律和蓝光危害的影响。在充分考虑年龄、人眼受光面积、透射率的情况下,改进传统计算蓝光危害因子的公式,并计算节律因子和蓝光危害因子。结果表明,不管何种手机,节律因子和蓝光危害因子都随色温升高而增大,并且均呈现出良好的线性关系。在7 425~9 201 K色温下,节律因子由大到小依次为:OLED > SUPER AMOLED > LED背光LCD;而蓝光危害因子由大到小依次为:LED背光LCD > SUPER AMOLED > OLED,由此可以根据不同需求,选择相对更健康合适的手机。     

铯原子nP_(3/2)(n=70—94)里德伯态的紫外单光子激发及量子亏损测量

基于成熟的光纤激光器、光纤放大器及高效激光频率转换技术,我们在实验中研制了一套瓦级输出的窄线宽连续波单频可调谐318.6 nm紫外激光系统,并在室温铯原子气室中实现了6S_(1/2)—nP_(3/2)(n=70—94)单光子跃迁里德伯激发.借助由铯原子6S_(1/2)(F=4)基态、6P_(3/2)(F′=5)激发态和nP_(3/2)(n=70—94)里德伯态构成的V型三能级系统,通过频率锁定于铯原子6S_(1/2)(F=4)—6P_(3/2)(F′=5)超精细跃迁的852.3 nm探测光束的吸收减弱信号获得了里德伯态的信息,并利用高精度波长计测量了铯原子nP_(3/2)(n=70—94)里德伯态的量子亏损值.经过与理论计算值的变化趋势进行对比,我们认为由于原子气室的里德伯屏蔽效应并不能完全屏蔽外部直流电场,铯原子气室内存在残余的直流电场,影响了对里德伯态的量子亏损值的实验测量.利用残余直流电场的Stark效应理论模型及其与有效主量子数n*的依赖关系,对铯原子里德伯态的量子亏损实验测量值进行了修正.修正后的铯原子nP_(3/2)(n=70—94)态量子亏损测量值为3.5591±0.0007,与理论计算值相吻合.     

高光谱成像的土壤剖面水分含量反演及制图

传统土壤水分的获取方法仅可获得离散的土壤水分点位数据,难以获得剖面上精细且连续的水分含量分布图。研究了野外条件下利用近红外高光谱（882～1 709 nm）成像反演剖面土壤水分含量（SMC）,并实现精细制图的可行性。研究剖面位于江苏省东台市,我们利用近红外高光谱成像仪对剖面进行了5天原位连续观测,共采集了280个土样用于烘干法测定SMC。原始高光谱图像经数字量化值（DN）校正、黑白校正、拼接、几何校正、剪切和掩膜等一系列预处理后,提取各采样点的平均光谱反射率。提取光谱（Raw）经吸光度[LOG₁₀(1/R)],Savitzky-Golay平滑（SG）、一阶微分（FD）、二阶微分（SD）、多元散射校正（MSC）和标准正态变量（SNV）转换后,采用偏最小二乘回归（PLSR）和最小二乘支持向量机（LS-SVM）方法建立SMC预测模型,并对比分析不同光谱预处理方法与建模方法组合条件下SMC的预测精度。结果表明,光谱反射率随SMC增加逐渐降低,不同光谱预处理方法的预测精度有所差异,除MSC方法外,同一光谱预处理方法的LS-SVM模型预测精度均高于PLSR模型,并且基于LOG₁₀(1/R)光谱的LS-SVM模型对SMC预测精度最高,其建模集的决定系数（R2_c）和均方根误差（RMSE_c）分别为0.96和0.65%,预测集的决定系数（R2_p）、均方根误差（RMSE_p）和相对分析误差（RPD_p）分别为0.88,1.05%和2.88。利用最优模型进行剖面SMC的高空间分辨率精细制图,通过比较SMC反演图中提取的预测值与实测值关系发现预测精度较高（R2: 0.85～0.95, RMSE: 0.94%～1.02%）,且两者在剖面中的变化趋势基本一致,说明SMC反演图不仅能很好地反映出土壤水分在整个剖面中毫米级的含量分布信息,也可反映出同一位置处不同天数间的含量差异。因此,利用近红外高光谱成像结合优化的预测模型,能够实现土壤剖面SMC的定量预测及精细制图,有助于快速、有效监测田间剖面土壤水分状况。     

Extrapolation for Aeroengine Gas Path Faults with SVM Bases on Genetic Algorithm

Mining aeroengine operational data and developing fault diagnosis models for aeroengines are to avoid running aeroengines under undesired conditions. Because of the complexity of working environment and faults of aeroengines, it is unavoidable that the monitored parameters vary widely and possess larger noise levels. This paper reports the extrapolation of a diagnosis model for 20 gas path faults of a double-spool turbofan civil aeroengine. By applying support vector machine (SVM) algorithm together with genetic algorithm (GA), the fault diagnosis model is obtained from the training set that was based on the deviations of the monitored parameters superimposed with the noise level of 10%. The SVM model (C = 24.7034; γ = 179.835) was extrapolated for the samples whose noise levels were larger than 10%. The accuracies of extrapolation for samples with the noise levels of 20% and 30% are 97% and 94%, respectively. Compared with the models reported on the same faults, the extrapolation results of the GASVM model are accurate.