生物质电厂废弃物草木灰成分分析及成形

生物质热电厂焚烧废弃物的主要成分是草木灰，其产生量大、密度小。大量草木灰不经处理会造成环境污染问题。本文首先分析草木灰的化学成分，确定草木灰的肥料特征，通过添加NH₄H₂PO₄使草木灰的pH达到国家复合肥的相关标准。选用聚天冬氨酸（30%水溶液）作为复合肥的黏结剂，应用于草木灰复合肥的成形造粒，以增加其抗压强度和肥效。研究不同烘干时间、烘干温度、聚天冬氨酸添加量对草木灰复合肥抗压强度的影响规律，通过分析植物的形态及生理指标，进而研究草木灰复合肥的肥效。结果表明：最佳烘干条件为10h、140℃，聚天冬氨酸对草木灰的抗压强度有明显的增强作用，并且草木灰与聚天冬氨酸的复配比为6g∶1mL时肥效最好。此方法解决了元素循环中断、草木灰环境污染，在运输过程中草木灰复合肥易碎，纯草木灰肥效低等问题。     

起重机自重应力对安全评估中应力测试实验结果的影响

应力测试是起重机安全评估中必须进行的一项检测。目前在安全评估工作中常采用应变电测法来采集起重机工作过程中的应力时程数据,由于该方法只能采集到结构由于载荷变化产生的应力,而无法采集到结构因自重产生的应力,导致应力测试无法获取结构的完整应力。利用起重机金属结构自重应力在总应力中占有的权重,分析其对应力测试实验结果的影响大小,试验结果表明在危险截面自重应力大小占总应力大小可达60%,此时不考虑自重应力进行结构安全性的判定是不全面的。可考虑利用仿真技术获取自重应力[1,2],从而使得安全评估应力测试实验结论更加准确。     

利用固液扩散偶研究Mg-40Al与Mg-20Ce的界面反应

本文主要研究了Mg-40Al与Mg-20Ce固液界面在475°C, 500°C和525°C下保温5min至30min的界面反应和扩散层的生长动力学。结果发现，在扩散层中由于Al元素和Ce元素反应生成Al₁₁Ce₃, Al₃Ce and Al₂Ce金属化合物。金属化合物的体积分数随着扩散温度的升高而增加。扩散层的生长满足抛物线生长规律，扩散层的扩散激活能为42±3.7kJ/mol。实验研究的固液扩散为理解合金熔炼过程中金属化合物的形成提供了理论基础。     

基于信息熵—集对分析的煤矿隐患排查治理风险评估模型

以煤矿安全隐患排查治理风险为研究对象，分析隐患排查闭环管理全过程，并基于此建立了隐患排查治理能力纵向评估指标体系，融合熵值法和集对分析理论，建立了煤矿安全隐患排查治理风险纵向评估模型，为企业更好地了解自身隐患排查治理能力及变化发展趋势提供参考，最后进行了实例分析。     

An easy and scalable approach to synthesize three-dimensional sandwich-like Si/Polyaniline/Graphene nanoarchitecture anode for lithium ion batteries

A new three-dimensional (3D) sandwich-like Si/Polyaniline/Graphene nanoarchitecture anode for lithium ion batteries (LIBs) is successfully fabricated through an easy approach. In this nanoarchitecture, the in-situ polymerized electronic conductive polyaniline (PAni) hydrogel, acting as “glue”, agglutinates tightly to both the silicon nanoparticles (SiNPs) and graphene sheets, forming efficient conductive networks with high elastic modulus and high tensile strength. This mechanically robust nanoarchitecture can endure the great volume change of silicon and retain structural stability during Li-ion insertion/extraction. The electrodes consisting of this 3D sandwich-like Si/Polyaniline/Graphene nanoarchitecture reveal excellent electrochemical performance. The progress made in this work provides an easy and scalable route for preparing Si-based anode materials with high performance for advanced LIBs.     

Bend,Twist, and Turn: First Bendable and Malleable Toughened PLA Green Composites

The future of green electronics possessing great strength and toughness proves to be a promising area of research in this technologically advanced society. This work develops the first fully bendable and malleable toughened polylactic acid (PLA) green composite by incorporating a multifunctional polyhydroxybutyrate rubber copolymer filler that acts as an effective nucleating agent to accelerate PLA crystallization and performs as a dynamic plasticizer to generate massive polymer chain movement. The resultant biocomposite exhibits a 24‐fold and 15‐fold increment in both elongation and toughness, respectively, while retaining its elastic modulus at >3 GPa. Mechanism studies show the toughening effect is due to an amalgamation of massive shear yielding, crazing, and nanocavitation in the highly dense PLA matrix. Uniquely distinguished from the typical flexible polymer that stretches and recovers, this biocomposite is the first report of PLA that can be “bend, twist, turn, and fold” at room temperature and exhibit excellent mechanical robustness even after a 180° bend, attributes to the highly interconnected polymer network of innumerable nanocavitation complemented with an extensively unified fibrillar bridge. This unique trait certainly opens up a new horizon to future sustainable green electronics development.     

长管拖车缠绕气瓶纤维层冲击损伤的数字射线检测

采用缠绕气瓶替代钢制无缝气瓶是长管拖车轻量化的重要手段之一,但对于缠绕气瓶缠绕层因冲击造成的内部损伤,目前仍缺少成熟有效的检测手段。采用数字射线检测方法,针对玻璃纤维缠绕层冲击损伤造成的分层和危害较大的纤维断裂进行了试验研究。试验表明,采用数字射线检测能够有效检测最小深度为0.5 mm模拟纤维环向断裂刻槽和0.25 mm的模拟纤维轴向断裂刻槽;此外,还通过试验验证增加补偿块的方式,能极大地改善缠绕层分层部位的一次切向透照成像质量,进而提高检测效率。     

Automatic sorting of fresh tea leaves using vision-based recognition method

Mechanical tea harvesting using plucking machines is highly efficient, but harvested raw fresh tea leaves (FTLs) are always low quality because they contain a mixture of old leaves and leaf debris. To address this problem, this study developed an automatic sorting machine with a vision-based recognition method to extract high-quality FTLs from plucked raw FTLs. First, the raw FTLs were separated one by one after passing through three sequential conveyor belts with increasing speed, and were then classified into four grades using a vision-based recognition method. Finally, the FTLs were blown by air nozzles into collection boxes according to their specific grade. In the recognition method, the shape-based feature of each FTL is extracted by establishing the FTL's topological structure, and the support vector machine model is used for classification. The experimental results revealed that the vision-based recognition method performed satisfactorily with an accuracy rate of 94% and precision rate of 85%. The sorting success rate and efficiency of the automatic sorting machine were approximately 80% and 15 kg hr⁻¹, respectively. The results indicate that the developed automatic sorting machine can effectively and efficiently sort raw FTLs, which may improve the profitability and promote the automation of tea processing.