Low voltage operating organic light emitting transistors with efficient charge blocking layer

Charge injection/blocking layers play important roles in the performances of organic electronic devices. Their incorporation into organic light emitting transistors has been limitted, due to generally high operating voltages (above 60 V) of these devices. In this work, two hole blocking molecules are integrated into tris-(8-hydroxyquinoline) aluminum (Alq₃) based light emitting transistors under operating voltage as low as 5 V. The effects of hole blocking and electron injection are decoupled through the differences in the energy levels. Significantly improved optical performance is achieved with the molecule of suitable energy level for electron injection. Surprisingly, a decreased performance is observed in the case of another hole blocking molecule evidencing that charge injection overweighs charge blocking in this device architecture.     

仪器仪表产品脉冲耐压试验要求和保护器件的选择

仪器仪表产品的脉冲耐压试验是产品型式试验、例行试验中的基本内容。通过对GB4793.1标准的整理,归纳了仪器仪表产品的脉冲耐压试验要求。通过对试验方法和设备特性的梳理,总结了特性参数,并介绍了应对脉冲耐压试验可采取的保护器件的类别及选用方法。     

Co?N graphene encapsulated cobalt catalyst for H2O2 decomposition under acidic conditions

Hydrogen peroxide (H₂O₂) has been listed as one of the 100 most important chemicals in the world. However, huge amount of residual H₂O₂ is hard to timely decomposed into O₂ and H₂O under acidic condition, easily resulting in explosion hazard. Here, we reported a core–shell structure catalyst, that is graphene with Co N structure encapsulated Co nanoparticles. Co N graphene shell serves as the active site for the H₂O₂ decomposition, and Co core further enhance this decomposition. Benefiting from it, the H₂O₂ decomposition were close to 100% after 6 cycles without pH adjustment, which increased 6 orders of magnitude compared with no catalyst. At the same time, the O₂ generation reached 99.67% in 2 h with little metal leaching, and ·OH has been greatly inhibited to only 0.08%. This work can cleanly remove H₂O₂ with little deep oxidation and protect the process of H₂O₂ utilization to achieve a safer world.     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.     

Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.     

分级加载电压下考虑有效电势变化的软黏土电渗固结理论

分级加载电压技术能够有效延长电渗处理的时间,改善地基的处理效果并能有效降低能耗。在利用分级加载电压技术开展电渗试验时,随着土体的排水固结,土体各点的电势会发生明显的变化,这与现有的电渗固结理论假设土体各点电势保持不变有所差异。利用自主设计的电渗试验仪器开展了分级加载电压下的电渗室内试验,分析了电渗过程中土体有效电势变化的规律。基于该规律并结合Esrig固结理论建立了在阳极处不排水、阴极处排水条件下考虑土体有效电势变化的电渗固结理论,得到了分级加载电压条件下考虑土体有效电势变化的超静孔隙水压力和平均固结度的解析表达式。研究表明,在电压分级加载过程中,土体有效电势变化规律表现为随时间先基本保持不变,后呈抛物线状先增后减。通过试验案例验证了考虑土体有效电势变化所求得解析解的合理性,且该数值计算结果相较于Esrig数值计算结果,前者更加接近实测值,该固结方程的建立为后续分级加载电压技术的推广应用提供了理论依据,也为实际工程提供了参考。