济阳坳陷下古生界潜山油气藏特征及成藏模式

济阳坳陷下古生界潜山具有多样性、复杂性的特点，潜山差异性的形成演化、油气成藏主控因素和控藏模式不明确，严重制约了该区潜山油气勘探。在潜山分类的基础上，综合利用系统恢复、分类对比和典型解剖等方法，揭示了济阳坳陷下古生界不同类型潜山的形成演化过程和油气成藏主控因素差异性，分类建立了油气成藏模式。研究表明，济阳坳陷下古生界主要发育高位新盖侵蚀残丘潜山、中位古盖拉张断块潜山、中位新古盖拉张剪切断块潜山、中位中古盖挤压拉张断块潜山和低位古盖拉张滑脱断块潜山5种潜山类型。不同类型潜山的形成演化和油气成藏各具特色，其中，高位新盖侵蚀残丘潜山的发育受隆升、侵蚀作用控制，油气成藏主要受控于油源和盖层条件，表现为"单向供烃、砂体-不整合岩溶体联合输导、残丘控藏"的成藏模式；中位古盖拉张断块潜山的发育受掀斜、断裂作用控制，油气成藏主要受控于储集条件，表现为"单向供烃、顺向断层输导、反向断层控藏"的成藏模式；中位新古盖拉张剪切断块潜山的发育受反转、翘倾和走滑切割作用控制，油气成藏主要受控于输导条件，表现为"多源供烃、断溶体立体输导、断裂控藏"的成藏模式；中位中古盖挤压拉张断块潜山的形成受强烈挤压、拉张滑脱作用控制，油气成藏主要受控于储集条件，表现为"多源供烃、断缝体输导、断褶控藏"的成藏模式；低位古盖拉张滑脱断块潜山的形成受强烈拉张滑脱作用控制，油气成藏主要受控于输导条件，表现为"顶部供烃、断缝体输导、断裂控藏"的成藏模式。     

Spatiotemporal variations in soil CO2 fluxes under a winter wheat-summer maize cropping system in the North China Plain

Nutrient Cycling in Agroecosystems - Most studies on soil CO2 fluxes focus on the upper soil layers (i.e., 0–200 mm); however, there is a lack of investigation into soil layers below...     

氮对镀锡钢板的强化作用

通过物理化学相分析研究了3种不同氮含量镀锡板中固溶氮、化合氮的含量,结合相关理论计算了镀锡板中氮的固溶强化和析出强化对强度的贡献。结果表明:随着总氮含量从0.0021%提高到0.0103%,钢中化合氮含量下降10.1%,但是AlN析出相中的氮的含量上升10.7%。氮在提高镀锡板强度方面发挥重要作用,钢中总氮含量提高0.0082%,其对屈服强度的贡献值提升约30 MPa。     

时效工艺对纳米结构2297铝锂合金力学性能与微观组织的影响

采用535 ℃×2 h固溶制度,将热锻态2297铝锂合金固溶水淬后冷轧,冷轧压下量为95%,然后将轧制样品在不同温度(120~190 ℃)和时间(0~80 h)范围内进行时效处理。采用拉伸、扫描电镜(SEM)和透射电镜(TEM)等测试方法,分析时效温度和时间对铝锂合金组织与性能的影响。结果表明:时效前的大塑性变形能获得纳米结构组织,能促进T₁相均匀细小地析出,缩短合金达到峰时效的时间,最终成功制备了高强高塑性铝锂合金。在120~140 ℃温区内时效时,时效温度越高,达到峰时效的时间越短、强度越高。140 ℃达到峰时效时间缩短为40 h,此时合金的抗拉强度、屈服强度和伸长率分别为525 MPa、478 MPa和7.7%,主要强化相为细小的T₁相。在170~190 ℃温区内时效时,时效温度越高,达到峰时效的时间越短,但抗拉强度与屈服强度迅速下降。170 ℃时效8 h达到峰时效状态,此时合金的抗拉强度、屈服强度和伸长率分别是503 MPa、462 MPa和5.0%,主要强化相仍为T₁相,但已经明显粗化。     

CO2对油田地下水环境中Q235钢和X70钢腐蚀行为的影响

为了探明辽河油田地下水环境中CO₂对Q235钢和X70管线钢的腐蚀规律,采用动电位极化技术及交流阻抗技术研究了不同CO₂浓度对该环境下两种钢腐蚀行为的影响,并利用光学显微镜对试样腐蚀界面进行表征。结果表明:随着CO₂浓度的增加,Q235和X70钢的交流阻抗图谱的半径均减小,腐蚀电流密度增大,腐蚀坑数量、深度和面积都增加,腐蚀敏感性增大。在低浓度CO₂环境下(CO₂含量为10%时),Q235钢的腐蚀敏感性比X70钢更高;而在高浓度CO₂环境下(CO₂含量为20%、40%、60%时),Q235钢的腐蚀速率反而较小,耐腐蚀性更好。     

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.