Since the desire for real-time human health monitoring as well as seamless human-machine interaction is increasing rapidly, plenty of research efforts have been made to investigate wearable sensors and implantable devices in recent years. As a novel 2D material, graphene has aroused a boom in the field of sensor research around the world due to its advantages in mechanical, thermal, and electrical properties. Numerous graphene-based sensors used for human health monitoring have been reported, including wearable sensors, as well as implantable devices, which can realize the real-time measurement of body temperature, heart rate, pulse oxygenation, respiration rate, blood pressure, blood glucose, electrocardiogram signal, electromyogram signal, and electroencephalograph signal, etc. Herein, as a review of the latest graphene-based sensors for health monitoring, their novel structures, sensing mechanisms, technological innovations, components for sensor systems and potential challenges will be discussed and outlined.

Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system’s classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field.

染料污染是水体污染的一个重要方面, 吸附法因具有效率高, 简单易操作等优点, 被认为优于其他的染料废水处理技术. 本文采用一种简单的方法制备了具有高吸附、易分离特性的氧化石墨烯基功能纸, 研究其对水中阳离子染料的吸附, 考察了吸附时间、染料初始浓度、吸附剂用量及温度对吸附性能的影响. 利用扫描电子显微镜、拉曼光谱分析、热重分析、紫外可见吸收光谱仪等测试方法, 对氧化石墨烯基功能纸的结构、形貌和吸附性能进行表征分析. 结果表明: 氧化石墨烯基功能纸对阳离子染料亚甲基蓝和罗丹明B有良好的吸附效果, 当亚甲基蓝和罗丹明B的初始浓度分别为40和30 mgL-1, 功能纸对两种染料的吸附量分别达到了54.84 和21.74 mgg-1. 而且这种氧化石墨烯基吸附材料很好地解决了吸附剂与水体的分离问题. 另外, 实验还发现, 在氧化石墨烯基功能纸吸附染料的过程中, 氧化石墨烯对染料的吸附作用远远大于纸巾本身对染料的吸附. 例如, 对于罗丹明B, 纸巾的吸附量几乎为零, 当罗丹明B 的初始浓度为30 mgL-1时, 以纸巾上负载的氧化石墨烯质量计算的吸附量达到了183 mgg-1. 动力学研究结果表明吸附过程较好地符合伪二级动力学模型, 热力学数据分析, 氧化石墨烯基功能纸对两种染料的吸附行为是自发吸热的. 研究结果对基于氧化石墨烯的吸附材料的制备和应用提供了参考依据.

The continuous scanning laser Doppler vibrometry (CSLDV) technique is usually used to evaluate the vibration operational deflection shapes (ODSs) of structures with continuous surfaces. In this paper, an extended CSLDV is demonstrated to measure the non-continuous surface of the bladed disk and to obtain the ODS efficiently. For a bladed disk, the blades are uniformly distributed on a given disk. Although the ODS of each blade can be derived from its response data along the scanning path with CSLDV, the relative vibration direction between different blades cannot be determined from those data. Therefore, it is difficult to reconstruct the complete vibration mode of the whole blade disk. In order to measure the complete ODS of the bladed disk, a method based on ODS frequency response functions (ODS FRFs) has been proposed. While the ODS of each blade is measured by designing the suitable scanning paths in CSLDV, an additional response signal is obtained at a fixed point as the reference signal to identify the relative vibration phase between the blade and the blade of the bladed disk. Finally, a measurement is performed with a simple bladed disk and the results demonstrate the feasibility and effectiveness of the proposed extended CSLDV method.