All Resistive Pressure–Temperature Bimodal Sensing E-Skin for Object Classification

Electronic skin (E-skin) with multimodal sensing ability demonstrates huge prospects in object classification by intelligent robots. However, realizing the object classification capability of E-skin faces severe challenges in multiple types of output signals. Herein, a hierarchical pressure–temperature bimodal sensing E-skin based on all resistive output signals is developed for accurate object classification, which consists of laser-induced graphene/silicone rubber (LIG/SR) pressure sensing layer and NiO temperature sensing layer. The highly conductive LIG is employed as pressure-sensitive material as well as the interdigital electrode. Benefiting from high conductivity of LIG, pressure perception exhibits an excellent sensitivity of −34.15 kPa⁻¹. Meanwhile, a high temperature coefficient of resistance of −3.84%°C⁻¹ is obtained in the range of 24–40 °C. More importantly, based on only electrical resistance as the output signal, the bimodal sensing E-skin with negligible crosstalk can simultaneously achieve pressure and temperature perception. Furthermore, a smart glove based on this E-skin enables classifying various objects with different shapes, sizes, and surface temperatures, which achieves over 92% accuracy under assistance of deep learning. Consequently, the hierarchical pressure–temperature bimodal sensing E-skin demonstrates potential application in human-machine interfaces, intelligent robots, and smart prosthetics.     

Ultraminiature and Flexible Sensor Based on Interior Corner Flow for Direct Pressure Sensing in Biofluids

Conventional pressure sensing devices are well developed for either indirect evaluation or internal measuring of fluid pressure over millimeter scale. Whereas, specialized pressure sensors that can directly work in various liquid environments at micrometer scale remain challenging and rarely explored, but are of great importance in many biomedical applications. Here, pressure sensor technology that utilizes capillary action to self‐assemble the pressure‐sensitive element is introduced. Sophisticated control of capillary flow, tunable sensitivity to liquid pressure in various mediums, and multiple transduction modes are realized in a polymer device, which is also flexible (thickness of 8 µm), ultraminiature (effective volume of 18 × 100 × 580 µm³), and transparent, enabling the sensor to work in some extreme situations, such as in narrow inner spaces (e.g., a microchannel of 220 µm in width and 100 µm in height), or on the surface of small objects (e.g., a 380 µm diameter needle). Potential applications of this sensor include disposables for in vivo and short‐term measurements.     

一种可同时测量温度和压力的新型SAW传感器

针对汽车轮胎压力监测系统（TPMS）的典型应用,提出了一种基于声表面波（SAW）的新型传感器.这用SAW延迟线理论,温度和压力对于传感器的影响能够通过射频回波信号的变化反映出来.通过在数据处理中引入权重因子,实现对于温度和压力的准确测量.在一定压力（0-200 kPa）和温度（20-100℃）范围内的测试结果表明,该传感器能够同时准确测量温度和压力.SAW传感器对温度的测量精度可以达到0.05℃,对压力的测量精度可以达到7.2 kPa.在对于可靠性和耐久性具有特殊要求的汽车轮胎压力监测等领域,该传感器简单的结构和无线无源的测量方式,进一步提高了其实用性和推广价值.     

用于压/拉检测的机器人皮肤柔/弹性传感器阵列设计

基于炭黑/硅橡胶复合材料的压力以及拉伸敏感特性,设计了一种可用于检测机器人皮肤接触压力和拉伸特性的新型传感器阵列。该传感器阵列的弹性电极结构克服了传统传感器不可拉伸的缺点,实现了传感器阵列的柔/弹性;阵列中设计了9个检测压力的传感器单元和2个检测拉伸的传感器单元,通过传感器结构设计和拉/压干扰特性分析以及补偿算法解决了拉伸和压力同时测量时的干扰问题,并构建了求解压力与拉伸的数学模型。实验结果表明:该传感器阵列实现了对压力和拉伸的同步检测,可用于机器人柔性皮肤中关节等部位。     

用于机器人关节的柔性压力触觉传感器设计

分析了碳纤维/硅橡胶导电复合材料的拉伸、弯曲及压敏特性,基于上述特性设计了一种新型的应用于机器人关节等活动部位的柔性压力触觉传感器。通过解耦算法解决了拉伸、弯曲引起的干扰问题,构建了求解接触压力的数学模型。实验结果表明,该柔性触觉传感器具有拉伸和弯曲性,并消除了拉伸和弯曲对接触压力的检测的干扰,可应用于机器人关节。     

一种压力传感器零位温漂的补偿方法

介绍了一种解决微型动态压阻式压力传感器温补问题的方法,导出了计算公式,其特点是补偿元件少,方法更简便易行。     

光电化学传感器及其在生物分析中的应用研究进展

光电化学传感器是近年来发展起来的一种基于化学或生物识别过程的分析设备,因具有响应快速、灵敏度高、设备简单、价格低廉且易于微型化等优点,在生命分析和环境分析等领域受到了广泛关注。首先介绍了光电化学传感器的基本原理、分类及用于构建该类传感器的光电活性纳米材料,在此基础上进一步综述了光电化学传感器在生物分析中的应用,如用于DNA检测、免疫传感及酶分析等。     

嵌入式大气数据传感系统压力传感器设计研究

嵌入式大气数据传感(FADS)系统较之传统的大气数据系统有很大的优势,它依靠飞行器前端的压力传感器间接得到飞行大气数据.在本文中,首先简要介绍了FADS系统的压力模型;然后通过分析压力传感器几何外形设计对系统性能的影响,建立了压力传感器的动力学模型,并提出了FADS系统中的压力传感器的设计准则;最后给出了仿真结果.     

A Behavior-Learned Cross-Reactive Sensor Matrix for Intelligent Skin Perception

Mimicking human skin sensation such as spontaneous multimodal perception and identification/discrimination of intermixed stimuli is severely hindered by the difficulty of efficient integration of complex cutaneous receptor-emulating circuitry and the lack of an appropriate protocol to discern the intermixed signals. Here, a highly stretchable cross-reactive sensor matrix is demonstrated, which can detect, classify, and discriminate various intermixed tactile and thermal stimuli using a machine-learning approach. Particularly, the multimodal perception ability is achieved by utilizing a learning algorithm based on the bag-of-words (BoW) model, where, by learning and recognizing the stimulus-dependent 2D output image patterns, the discrimination of each stimulus in various multimodal stimuli environments is possible. In addition, the single sensor device integrated in the cross-reactive sensor matrix exhibits multimodal detection of strain, flexion, pressure, and temperature. It is hoped that his proof-of-concept device with machine-learning-based approach will provide a versatile route to simplify the electronic skin systems with reduced architecture complexity and adaptability to various environments beyond the limitation of conventional “lock and key” approaches.     

光纤压力传感器微机控制与数据处理系统

本文介绍的光纤压力传感器微机控制与数据处理系统是以8098单片机为核心,配上适量的外围电路所构成。该系统直接利用8098单片机芯片内的A/D模拟输入通道和脉宽调制(PWM)功能来进行数据采集和输出控制信号。该系统还方便地实现与微型打印机的联接,以便将采集的数据保存。整个系统结构简单,成本低,可靠性高,实时控制和测试功能较强,有良好的工业应用价值。