颗粒增强铝基复合材料的电磁连铸研究

用原位反应合成法制备(Al2O3 Al3Zr)p/Al颗粒增强铝基复合材料,在半连铸过程施加低频交变电磁场进行搅拌,改善增强颗粒在基体内的分布状态,并在结晶器初始凝固区域施加高频电磁场实现软接触连铸以改善铸坯的表面质量.SEM分析显示:施加电磁搅拌后凝固组织致密,颗粒增强相的数量增加,颗粒细化、分布趋于均匀.施加的高频磁场使得单纯使用电磁搅拌产生的表面粗糙现象消失,铸坯表面质量显著改善.     

耐温抗盐调堵剂研究与应用进展

探讨了高温高盐油藏对堵剂性能的要求以及常规堵剂耐温抗盐堵剂改性的途径,综述了近年来国内对耐温抗盐堵剂的室内研究进展和矿场应用情况,包括聚合物凝胶、体膨颗粒、盐沉析技术、聚合物微球等适合高温高盐油藏的调堵技术,并指出了在堵剂的研究与应用中存在的一些问题。表1参42     

不同杀菌剂防治对马铃薯主要营养成分的影响

目的 以喷施4种杀菌剂防治马铃薯Y病毒病的样品为试验原料,研究防效与干物质、淀粉、还原糖和维生素C含量之间关系,分析各营养成分之间的相关性。方法 在国家标准基础上稍加改动测定营养成分,采用矩阵散点图、标准化残差P-P图、一元线性回归分析防效与营养成分及各营养成分之间的相关性。结果 防效与干物质、维生素C和淀粉含量之间呈正相关,干物质含量与淀粉含量、维生素C呈正相关,维生素C含量与淀粉含量呈正相关,还原糖含量与维生素C含量、淀粉含量、干物质含量和平均防效四者之间无显著相关性。结论 4种杀菌剂中防效最好的为喷施5%氨基寡糖素水剂中剂量,且采收该小区马铃薯干物质含量、淀粉含量和维生素C含量最高。研究结果可为种植马铃薯防治Y病毒病筛选最佳药剂,为马铃薯病害防治与营养成分之间相关性提供数据支撑。     

Elastic structural analysis based on graph neural network without labeled data

Artificial intelligence is gaining increasing popularity in structural analysis. However, at the structural system level, the appropriateness of data representation, the paucity of data, and the physical interpretability of results are rarely studied and remain profound challenges. To fill such gaps, a physics-informed model named StructGNN-E (i.e., structural analysis based on graph neural network [GNN]–elastic) based on the GNN architecture, which is capable of implementing the elastic analysis of structural systems without labeled data, is proposed in this study. The systems with structural topologies and member configurations are organized as graph data and later processed by a modified graph isomorphism network. Moreover, to avoid dependence on big data, a novel physics-informed paradigm is proposed to incorporate mechanics into deep learning (DL), ensuring the theoretical correctness of the results. Numerical experiments and ablation studies demonstrate the unique effectiveness of StructGNN-E against common DL models, with an average accuracy of 99% and excellent computational efficiency. Due to its differentiability, StructGNN-E is promising for bidirectionally linking structural parameters and analysis results, paving the way for a new end-to-end structural optimization method in the future.     

Novel Semiconductive Ternary Hybrid Heterostructures for Artificial Optoelectronic Synapses

Synaptic devices that mimic biological synapses are considered as promising candidates for brain-inspired devices, offering the functionalities in neuromorphic computing. However, modulation of emerging optoelectronic synaptic devices has rarely been reported. Herein, a semiconductive ternary hybrid heterostructure is prepared with a D-D’-A configuration by introducing polyoxometalate (POM) as an additional electroactive donor (D’) into a metalloviologen-based D-A framework. The obtained material features an unprecedented porous 8-connected bcu -net that accommodates nanoscale [α-SiW₁₂O₄₀]⁴⁻ counterions, displaying uncommon optoelectronic responses. Besides, the fabricated synaptic device based on this material can achieve dual-modulation of synaptic plasticity due to the synergetic effect of electron reservoir POM and photoinduced electron transfer. And it can successfully simulate learning and memory processes similar to those in biological systems. The result provides a facile and effective strategy to customize multi-modality artificial synapses in the field of crystal engineering, which opens a new direction for developing high-performance neuromorphic devices.