发表机构
University College London; UCL Medical Physics and Biomedical Engineering, University College London; University College London Hospitals NHS; Royal Free Hospital, University College London（伦敦大学学院; 伦敦大学学院医学物理与生物医学工程; 伦敦大学学院医院NHS; 伦敦大学学院皇家自由医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用丝绸纤维模板生长MOF纳米带，构建超质子界面，实现高电导率柔性纤维传感器，用于高精度呼吸监测和动态湿度映射。
AI 中文摘要
将纺织纤维转化为传感器可以促进连续生理监测，从而改善人类医疗保健。用电子导体涂覆纤维有助于检测生理刺激，但其灵敏度随厚度增加而提高，且常常降低纤维的机械柔韧性。质子导体是潜在的替代方案；然而，它们存在脆弱的物理界面和缓慢的动力学问题。在此，我们报告了一种利用天然丝绸纤维创建超质子界面的生物模板化、可扩展策略。我们利用丝绸纤维上有序的纳米原纤维，选择性生长连续的金属有机框架（MOF）纳米带。这种图案化的、直径约10微米的纤维结构为超快质子传输提供了低缺陷界面路径，实现了高达40 S cm⁻¹的电导率——比现有技术水平高出两个数量级——并具有毫秒级响应和高机械鲁棒性。单个微线传感器在志愿者测试中实现了呼吸监测，平均偏差为0.01次呼吸/分钟，不受人体运动影响，并能轻松捕获高流量氧疗设备中的呼吸特征。我们将微线传感器阵列集成到纺织品中，并利用深度学习展示了动态流场中高分辨率时空湿度映射，以及解析传统单点探测器无法捕捉的不对称呼吸模式。我们的结果应具有广泛的意义，例如在可穿戴健康监测、生物电子学和人机界面领域。
英文摘要
Transforming textile fibres into sensors can facilitate continuous physiological monitoring for improving human healthcare. Coating fibres with electronic conductors can help detect physiological stimuli but their sensitivity scales with thickness and often lowers the fibre mechanical flexibility. Proton conductors are potential alternatives; however, they suffer from fragile physical interfaces, and sluggish kinetics. Here, we report a bio-templated, scalable strategy to create superprotonic interfaces using natural silk fibres. We exploit ordered nanofibrils on silk fibres to selectively grow continuous metal-organic framework (MOF) nanostrips. This patterned, ca. $10\,μ\mathrm{m}$-diameter fibre-structure provides low-defect interfacial pathways for ultrafast proton transport, yielding conductivities up to 40 $S\,cm^{-1}$ - two orders of magnitude above the state of the art - with millisecond response and high mechanical robustness. The individual micro-thread sensors facilitate respiratory monitoring with a mean bias of 0.01 breaths $min^{-1}$ in volunteer testing, unaffected by human movement and readily capture breathing signature in high-flow oxygen therapy devices. We integrate arrays of micro-thread sensors in textiles and exploit deep learning to demonstrate high-resolution spatiotemporal humidity mapping in dynamic flow-fields and resolve asymmetric respiratory patterns that elude conventional single-point detectors. Our results should have broad-ranging implications, e.g. in wearable health monitoring, bioelectronics, and human-machine interfaces.
Comments69 pages, 32 figures,
