Xiang Mianyi, Liu Yamin, Yang Ziyang, Jiang Jinlei, Wang Weicheng, Hu Yao, Cui Daxiang, Li Qichao
State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
Small. 2025 Sep;21(35):e2505098. doi: 10.1002/smll.202505098. Epub 2025 Jul 9.
Electronic noses (e-noses) have become indispensable analytical platforms for gas detection. However, conventional e-nose systems face significant limitations in portable and wearable implementations due to their bulk and high-power consumption. Herein, a single-sensor-based multifunctional e-nose system is reported by integrating a micro-electromechanical system (MEMS) gas sensor with a flexible printed circuit board (FPCB). Specifically, the ZnO-ZnSnO raspberry-like microspheres (ZZSRM) are utilized as the gas-sensitive materials, and the gas selectivity of the sensor is enhanced through a dual-temperature modulation strategy. Additionally, a gas classification model based on the MiniRocket algorithm has been developed, enabling efficient feature extraction and low-complexity classification of response signals, thereby satisfying the real-time processing demands of embedded devices. Moreover, a silent communication method is proposed, which maps breathing frequency to Morse code for information transmission in specific scenarios. Experimental results demonstrate that the wearable system achieves high-precision classification and concentration prediction for eight volatile organic compounds (VOCs), while simultaneously enabling robust recognition of exhaled signals and instantaneous conversion of Morse code into legible alphabetic characters. By combining the gas sensor with artificial intelligence (AI) technology, this work establishes a multifunctional flexible e-nose that merges portable gas detection and silent communication, offering a novel technological framework for environmental monitoring and human-machine interaction.
电子鼻已成为气体检测不可或缺的分析平台。然而，传统的电子鼻系统由于体积庞大和功耗高，在便携式和可穿戴应用中面临重大限制。在此，通过将微机电系统（MEMS）气体传感器与柔性印刷电路板（FPCB）集成，报道了一种基于单传感器的多功能电子鼻系统。具体而言，采用了ZnO-ZnSnO覆盆子状微球（ZZSRM）作为气敏材料，并通过双温度调制策略提高了传感器的气体选择性。此外，还开发了一种基于MiniRocket算法的气体分类模型，能够对响应信号进行高效特征提取和低复杂度分类，从而满足嵌入式设备的实时处理需求。此外，还提出了一种无声通信方法，该方法将呼吸频率映射为摩尔斯电码，以便在特定场景中进行信息传输。实验结果表明，该可穿戴系统对八种挥发性有机化合物（VOC）实现了高精度分类和浓度预测，同时能够可靠地识别呼出信号，并将摩尔斯电码即时转换为清晰的字母字符。通过将气体传感器与人工智能（AI）技术相结合，这项工作建立了一个融合便携式气体检测和无声通信的多功能柔性电子鼻，为环境监测和人机交互提供了一个新颖的技术框架。
