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Flexible Ionic Conductive Hydrogels with Wrinkled Texture for Flexible Strain Transducer with Language Identifying Diversity

自愈水凝胶 材料科学 传感器 纹理(宇宙学) 导电体 拉伤 离子键合 复合材料 纳米技术 计算机科学 声学 高分子化学 化学 人工智能 离子 物理 医学 有机化学 内科学 图像(数学)
作者
Mansoor Khan,Tanzil Ur Rahman,Muhammad Sher,Luqman Ali Shah,Hazizan Md Akil,Jun Fu,Hyeong-Min Yoo
出处
期刊:Chemistry of Materials [American Chemical Society]
卷期号:36 (9): 4703-4713 被引量:12
标识
DOI:10.1021/acs.chemmater.4c00456
摘要

Conductive hydrogels have garnered significant attention in the realm of flexible electronic strain transducers (FESTs). However, the development of such FEST hydrogels has been hindered by weak mechanical performance and low conductivity, sensitivity, and stability. In this study, we introduce a novel FEST hydrogel with a wrinkled surface possessing a unique ability to differentiate between different spoken and written languages. Our approach involved fabricating a robust, tough, and ionic conductive hydrogel with a wrinkled texture through a simple strategy utilizing the hydrophobic initiator benzophenone (BP). BP was incorporated into hydrophobically cross-linked hydrogels composed of hydrophobic lauryl methacrylate (LMA), acrylamide (Amm), and the cationic monomer 2-(dimethylamino) ethyl acrylate methochloride (DMAEAMC), reinforced with trimesic acid (TMA). Pluronic 123 (P123) served as a source of micelles, dynamically connecting polymer chains and facilitating the diffusion of BP to produce wrinkled textured hydrogels. Furthermore, LiCl salt induced ionic conductivity (0.18 S/m), while the synergistic effect of TMA enhanced the mechanical performance through electrostatic interactions with DMAEAMC chains. The combination of hydrophobic and electrostatic interactions enabled the hydrogels to stretch up to 1611% with high conductivity, remarkable sensitivity (GF = 4.98 at 500%), and a wide strain range (0.1 to 500%). These hydrogels are valuable candidates for integration into epidermal FEST devices. Moreover, the epidermal FEST has the capability to monitor various large joint movements as well as different physiological activities. Additionally, FEST can identify different spoken and written languages, including English, Urdu, and Pashto, and can respond to other handwriting styles such as alphabets, numbers, and signatures. This approach provides a promising roadmap for engineering wrinkled texture hydrogels for diverse applications, especially in the fields of flexible sensors, electronic skin, and biomedical devices.
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