润滑油中磨损颗粒在线监测传感器研究进展*

油液监测是判断设备健康状况和提供设备故障隐患预警的有效方法。综述了用于监测润滑油中磨损颗粒的最新各类在线传感器的基本原理,包括批量磁吸附式传感器、微流体电容传感器、金属扫描传感器、双层平面线圈传感器、超声波传感器和光学传感器等,并讨论各种传感方法的优缺点以及未来发展面临的挑战;指出了高通量、高灵敏度、能区别金属与非金属磨粒、可采集磨粒图像信息的集成式传感器是未来研究的重点,集成式、智能化、无线传输的传感器是未来研发方向。     

Changes in the content and antioxidative activity of β-carotene and its metabolite vitamin A during gastrointestinal digestion and absorption and optimisation of HPLC-based detection

The β-Carotene (BC), an important precursor of vitamin A (VA), possesses antioxidant activity but is fat-soluble and has low bioavailability. In previous in-vitro assays evaluating antioxidant and 2,2′-azobis(2-amidinopropane) dihydrochloride (AAPH) free radical scavenging, both BC and VA showed a strong ability to scavenge radicals and protected cells from oxidative stress. Here, we used artificially simulated gastrointestinal digestion and Caco-2 cell absorption models to evaluate the bioavailability of the BC during gastrointestinal digestion and absorption using high-performance liquid chromatography (HPLC) analysis. We observed high absorptive and transfer rates of BC and detected retinol metabolites (Vitamin A). Therefore, BC can be detected in the acidic gastrointestinal environment using HPLC. Optimised method provided better separation of BC and VA in the column, improving the accuracy of the test results.     

疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Effects of non-thermal plasma treating wheat kernel on the physicochemical properties of wheat flour and the quality of fresh wet noodles

The effects of non-thermal plasma (NTP) on the physicochemical properties of wheat flour and the quality of fresh wet noodles ( FWN) were investigated. The results showed that NTP effectively decreased the total plate count (TPC), yeast and mould count (YMC) and Bacillus spp. in wheat flour. Wet gluten contents and the stability time reached the maximum when treated for 20 s. The viscosity of starch increased significantly after treatment due to the increased of damaged starch. The contents of secondary structure were altered to some extent, which was because that the ordered network structure of gluten protein broken. Furthermore, compared with the control, texture properties of FWN were enhanced significantly at 20 s, and the darkening rate of FWN was greatly inhibited due to the low polyphenol oxidase (PPO) activity. Consequently, the most suitable treatment was 500 W for 20 s, providing a basis for the application of NTP in flour products.