Melting process and melt structure of fly ash/magnesium slag blends for the production of inorganic fibers

Fiber production from inorganic industrial solid wastes is an effective waste management strategy. Because of cost considerations, most enterprises generally use local solid wastes as raw materials to produce fibers. In this study, we explored the feasibility of producing fibers using fly ash and magnesium slag. The results show that the melting temperature of the blends composed of fly ash, magnesium slag, and a small amount of calcined dolomite first decreased and then increased with an increase in acidity coefficient (M_k) from 1.0 to 2.4_. The samples could form a eutectic system in the M_k range of 1.4–1.8, and therefore have a relatively low melting temperature in this M_k range. Fly ash could react with magnesium slag and calcined dolomite to form akermanite, gehlenite-magnesium, and anorthite at temperatures close to the melting temperature; therefore, these crystalline phases were the main reaction products formed in the samples with M_k values lower than 1.80. Anorthite reacted further with some Na-containing and Si-containing spieces to produce labradorite. Thus, the content of anorthite and labradorite rapidly increased and they became the major crystal phases in the blend samples with M_k values greater than 1.80. MAS-NMR spectroscopic analysis revealed that the network structure of the melts depended on the ratio of bridging oxygen to non-bridging oxygen; a high ratio of bridging oxygen to non-bridging oxygen could lead to the formation of a dense network structure in the melt. The blends of fly ash and magnesium slag can be used to produce wool fibers and continuous fibers. In addition, the suitable temperature ranges for the production of both types of fibers were determined. The drawing temperature for continuous fiber production depended on the degree of polymerization and structure of the melt.     

Effect of phosphogypsum on saline-alkalinity and aggregate stability of bauxite residue

A column experiment was conducted to investigate the effect of phosphogypsum (PG) on the saline- alkalinity, and aggregate stability of bauxite residue. Results showed that: with increasing leaching time, the concentrations of saline-alkali ions decreased while the concentration increased in bauxite residue leachate; compared with CK (control group) treatment, pH, electric conductivity (EC), exchangeable sodium percentage (ESP), sodium absorption ratio (SAR), and exchangeable Na⁺ content of bauxite residue were reduced following PG treatment; average particle sizes in aggregates following CK and PG treatments were determined to be 155 and 193 nm, respectively. SR-μCT test results also confirmed that bauxite residue following PG treatment acquired larger aggregates and larger pore diameter. These results indicate that the PG treatment could significantly modulate the saline-alkalinity, and simultaneously enhance aggregate stability of bauxite residue, which provides a facile approach to reclaim bauxite residue disposal areas.     

土壤中有机污染物的检测技术研究进展

土壤污染防治法的实施促进了国内土壤监测业务的发展.对比分析了目前土壤中有机污染物的采样、前处理技术.挥发性有机污染物(VOCs)对采样环节的要求较高,需使用采样器和保存剂来减少挥发损失,进样前提取过程宜选用吹扫捕集等具有封闭系统的高效自动化方式.半挥发性有机污染物(SVOCs)的回收率主要受萃取、净化方式的影响,二氯甲烷和加速溶剂萃取的组合方式既快速又节约溶剂;净化材料宜选择弗罗里硅土、氧化铝类物质.选择合适的采样、前处理方法才能保证回收率、质量控制的要求.     

Effects of Cu on the corrosion behavior and microstructure of Mg–Zn–Ca bulk metallic glass

For the purpose of developing biodegradable magnesium alloys with suitable properties for biomedical applications, Mg–Zn–Ca–Cu metallic glasses were prepared by copper mold injection methods. In the present work, the effect of Cu doping on mechanical properties, corrosion behavior, and glass-forming ability of Mg₆₆Zn₃₀Ca₄ alloy was studied. The experimental findings demonstrated that the incorporation of Cu decreases the corrosion resistance of alloys, but increases the microhardness and degradation rate slightly. However, the addition of a trace amount of Cu can make the samples have antibacterial properties. Therefore, Mg–Zn–Ca–Cu has great advantages in clinical implantation and is the potential implant material.     

50型螺旋式钢板仓卷边设备理论分析与试验

结合已有卷边设备的生产工艺和特点,研发了国内首台50型螺旋式钢板仓卷边设备,并完成了4次样机调试试验.分析了新型卷边设备关键机构的工作原理,对下轧辊调节机构进行了力学计算分析,完成了卷边孔型设计.利用显示动力学软件数值模拟了5 mm钢板的冷弯成形(卷边)过程,分析了成形过程中的等效应力和等效塑性应变.通过4次样机调试试验,将调试结果与模拟结果对比.结果 表明,折弯5 mm钢板比折弯3 mm的钢板力载荷大很多,有必要研发新型的卷边设备;卷边设备的后3个工位的应力峰值比前3个工位大,其中工位6应力峰值最大;文中的卷边孔型设计合理;卷边过程的模拟结果与试验结果一致.     

CuO-SiO2气凝胶@TiO2纳米纤维无牺牲剂光催化还原CO2

采用溶胶-凝胶法制备CuO-SiO2复合气凝胶,通过在气凝胶孔道内填充TiCl4,然后将其气相水解,得到了在CuO-SiO2气凝胶表面生长了高结晶度的TiO2纳米纤维(CuO-SiO2@TiO2),纤维直径～16 nm.通过XPS、UPS、UV-Vis DRS、荧光光谱(PL)等表征了材料的结构及光电性能.结果表明,制备的CuO-SiO2@TiO2对可见光有明显吸收,且荧光强度较商用TiO2(P25)大幅降低,光生电子-空穴对更加稳定.再在纳米纤维上负载CuO,所得CuO-SiO2@TiO2/CuO在可见光区的荧光强度进一步增强.以300 W氙灯为光源,分别以CuO-SiO2@TiO2及CuO-SiO2@TiO2/CuO为催化剂,无牺牲剂条件下光催化还原CO2,4 h后甲醇产率分别为1304.0及1589.0μmol/g-cat,转换频率(TOF)分别为0.038及0.046 h–1.循环实验表明,纳米纤维具有较好的光催化稳定性,经过4次光催化循环实验后,CuO-SiO2@TiO2/CuO的保留率～94％,甲醇产率可达1472.0μmol/g-cat,TOF为0.042 h–1.     

Additive Manufacturing of Stable Energy Storage Devices Using a Multinozzle Printing System

The development of the Internet of things has prompted an exponential increase in the demand for flexible, wearable devices, thereby posing new challenges to their integration and conformalization. Additive manufacturing facilitates the fabrication of complex parts via a single integrated process. Herein, the development of a multinozzle, multimaterial printing device is reported. This device accommodates the various characteristics of printing materials, ensures high-capacity printing, and can accommodate a wide range of material viscosities from 0 to 1000 Cp. Complete capacitors, inclusive of the current collector, electrode, and electrolyte, can be printed without repeated clamping to complete the preheating, printing, and sintering processes. This method addresses the poor stability issue associated with printed electrode materials. Furthermore, after the intercalation of LiFePO₄ with Na ions, X-ray photoelectron spectroscopy and X-ray diffraction results reveal that the Na ions permeate the interlayer structure of LiFePO₄, enhancing the ion migration channels by increasing the ion transmission rate. A current rate of 2.5 mAh ensures >2000 charge/discharge cycles, while retaining a charge/discharge efficiency of 96% and a discharge capacity of 91.3 mAh g⁻¹. This manufacturing process can provide conformal power modules for a diverse range of portable devices with various shapes, improving space utilization.     

Self-Adaptive Control System for Additive Manufacturing Using Double Electrode Micro Plasma Arc Welding

Wire arc additive manufacturing(WAAM)has been investigated to deposit large-scale metal parts due to its high deposition efficiency and low material cost.However,in the process of automatically manufacturing the high-quality metal parts by WAAM,several problems about the heat build-up,the deposit-path optimization,and the stability of the process parameters need to be well addressed.To overcome these issues,a new WAAM method based on the double electrode micro plasma arc welding(DE-MPAW)was designed.The circuit principles of different metal-transfer models in the DE-MPAW deposition process were analyzed theoretically.The effects between the parameters,wire feed rate and torch stand-off distance,in the process of WAAM were investigated experimentally.In addition,a real-time DE-MPAW control system was developed to optimize and stabilize the deposition process by self-adaptively changing the wire feed rate and torch stand-off distance.Finally,a series of tests were performed to evaluate the con-trol system's performance.The results show that the capability against interferences in the process of WAAM has been enhanced by this self-adaptive adjustment system.Further,the deposition paths about the metal part's layer heights in WAAM are simplified.Finally,the appearance of the WAAM-deposited metal layers is also improved with the use of the control system.     

基于堆叠稀疏去噪自动编码网络与多隐层反向传播神经网络的铣刀磨损预测模型

刀具磨损状态是机械加工过程中需考虑的重要因素之一.针对铣刀磨损的在线预测问题,建立了一种基于深度学习的铣刀磨损预测模型.首先,将采集到的铣刀切削时的振动信号进行小波去噪后,利用快速傅里叶变换和小波包分解等技术提取时域、频域及时频域上的特征参数,并根据相关性分析从中筛选出合格的特征参数合并为特征向量,以此作为堆叠稀疏去噪自动编码网络(SSDAE)的含噪样本.其次,利用特征后处理的方式对已经筛选出的特征参数进行单调不递减及平滑处理,并将其作为SSDAE的无噪样本来训练该网络.然后,将经过SSDAE降维后的特征向量作为多隐层反向传播神经网络(BPNN)的输入,以这些特征对应的实际铣刀的磨损量作为标签对该网络进行拟合训练.最后,对训练好的模型进行实验验证,通过测试数据集和人为加入噪声的测试数据集的对比,结果显示所提模型不仅具有较高的预测精度,还具有较高的鲁棒性.