近日，国际知 名顶刊《Science Advances》上演了一场令人瞩目的"中国时刻"—— 同一天内，5篇来自中 国科研团队的柔性电子领域重磅研究集中登刊，其中1篇更直接拿下当期封面！ 放眼全球科研圈，如此高密 度、高含金量的成果爆发，实属罕见。 作为一门新兴交叉科学与技术，柔性电子凭 "轻薄柔透 "的核心特性，在 航空航天、公共安全、国防军工、 健康医疗等国计民生关键领域 展现出广阔的应用前景，并为培育新质生产力提供了重要技术支撑。特别是在 未来信息芯片、高端智能装备、新型电子器件等战略方向 ，该技术有望催生突破性创新，开拓全新的产业空 间。预计到2028年，中国制造的柔性电子在泛物联网领域的应用规模将突破 3000万美元 ，预计占未来10 —15年柔性电子整体市场的 40%份额 ，将逐步发展为国家战略性新兴产业的重要支柱，在未来产业格局中 占据主导地位。 下面为大家具体呈现这 5 篇同一日登上《Science Advances》顶刊的中国团队重磅研究。 ▍清华大学团队在磁驱动柔性电池集成机器人上取得新进展 近日，清华大学深圳国际研究生院智能感知与机器人（Smart Sensing and Robotic ...

Core Viewpoint - The article discusses the emergence of "soft human-machine interfaces" based on flexible electronic technology, which aims to enhance the interaction between humans and machines through intuitive and natural means. The technology faces challenges such as accurately interpreting physiological signals and achieving cost-effective, scalable manufacturing [1][2]. Group 1: Flexible Electronic Technology - A research team from Shanghai University of Science and Technology has developed a printed human-machine interface that includes electronic skin for surface electromyography (sEMG) collection and feedback, multimodal tactile sensing soft robots, and machine learning algorithms for gesture classification and material recognition [2]. - The core breakthrough of this technology is the electronic skin (e-skin), a thin electronic sensing device that can be attached to the human body to monitor various physiological signals in real-time [5]. Group 2: Production Techniques - The research team utilized an efficient integrated printing technology, including direct ink writing (DIW), infrared laser engraving, and laser cutting, to achieve large-scale production of multi-material, high-density sensor arrays [7]. - Various functional inks, such as silver ink, carbon ink, and PDMS/C, were used to print electrical circuits as narrow as 40 micrometers on flexible substrates [7]. Group 3: Intelligent Algorithms - The challenge of enabling machines to accurately understand human intentions is addressed through an adaptive machine learning method that combines linear mapping networks (LMN) and initial time models (ITM) [11]. - The LMN adjusts the weights of signals from different channels to adapt signals from various users to a unified standard distribution, while the ITM captures local features in time series with low latency and high accuracy [12]. Group 4: Multimodal Sensing - The soft human-machine interface integrates a "sensory system" for robots, allowing them to recognize object characteristics through touch by incorporating temperature, pressure, thermal conductivity, and electrical conductivity sensors [14]. - The pressure sensor features a capacitive design with a sensitivity of 10.5 pF/kPa, maintaining stable performance over 2000 tests, while the combination of thermal and electrical conductivity sensors improved material recognition accuracy from 63.99% to 98.03% [16]. Group 5: Application Prospects - This technology has broad application prospects, establishing a complete interactive ecosystem that includes signal collection, intention recognition, action execution, and sensory feedback, forming a closed-loop human-machine interaction cycle [18]. - In the medical field, it offers new hope for upper limb amputees, achieving an average accuracy of 94.36% in recognizing 11 hand and finger gestures, even with significant time delays and reduced signal strength [18].