1成果簡介
隨著仿生設備和智能可穿戴設備的快速發展，電子皮膚（E-skin）在太空垃圾回收、醫療診斷和康復領域的應用潛力日益凸顯。本文，同濟大學袁光傑 副教授、李智軍等在《Cell Reports Physical Science》期刊發表名為「Highly stretchable and microporous hybrid film-based electronic skins for biomimetic devices and smart wearables」的論文，研究報道了基於高伸展性微孔碳納米管/石墨烯/熱塑性聚氨酯複合薄膜的伸展/溫度敏感型（S/T-S）和壓力敏感型（P-S）電子皮膚。在 ε 變形率為 0%–340% 和 340%–480% 的範圍內，S/T-S 電子皮膚的應變係數（GF）分別為 12.19 和 635.03。此外，預拉伸處理顯著提升了其在拉伸-釋放循環中的重複性。壓力-拉伸型電子皮膚在5–20,005 Pa壓力範圍內具有1.79 kPa⁻¹的應變係數。憑藉卓越性能，該電子皮膚被集成至仿生蛙舌結構，實現環境溫度、伸縮及觸覺信號檢測。同時提出基於機器學習的足部姿勢分析系統，其識別準確率高達96.2%。
2圖文導讀
圖1. Preparation process and morphology of the CNT/G/TPU hybrid film and structural schematics of the resulting E-skins
(A) Schematic illustration of the preparation process of the CNT/G/TPU hybrid film.
(B) Optical images of the original TPU and hybrid films (scale bars, 1 cm and 50 μm).
(C) Optical images showing the flexibility of the hybrid film under twisting and bending.
(D) Optical images of the hybrid film at ε values of 0% and 480% (scale bar, 80 mm).
(E) Structural schematic of the S/T-S E-skin.
(F) Structural schematic of the P-S E-skin.
圖2. Stretching performance and mechanism of S/T-S E-skins with different G contents
(A) Relationship between ε and ΔR/R0 of the E-skins with varying G contents.
(B) Dependency of the maximum GF and ε on the G content.
(C) FE-SEM image of the CNT/TPU film (scale bars, 1 μm and 100 nm).
(D) FE-SEM image of the G/TPU film (scale bars, 1 μm and 100 nm).
(E) FE-SEM image of the CNT/G/TPU film with a G content of 60 wt % (scale bars, 1 μm and 100 nm).
圖3. Stretching sensing performance of the S/T-S E-skin
(A) Schematic diagram of the sensing mechanism during the stretching-releasing process (scale bars, 10 μm and 100 nm).
(B) I-V curves with various ε values.
(C) ΔR/R0 variation as a function of time stepwise.
(D) Dependency of ΔR/R0 on ε.
(E) Response and recovery times as ε varied from 0% to 10%.
(F) Hysteresis curves with ε values of 20%, 50%, and 100%.
(G) Long-term durability test with a maximum ε of 200% and 3000 stretching-releasing cycles after 5 prestretching cycles under 210% and typical cycles of the test.
圖4. Temperature sensing performance of the S/T-S E-skin
(A) Schematic diagram of the sensing mechanism during the cooling-heating process.
(B) I-V curves at various temperatures.
(C) Relationship between ΔI/I0 and temperature.
(D) Response and recovery times with the temperature varying from 25°C to 85°C.
(E) Hysteresis curves for temperatures ranging from 25°C to 85°C.
(F) ΔI/I0 with the temperature varying from 25°C to 45°C, 65°C, or 85°C during 5 repeated cooling-heating cycles.
(G) Long-term durability test at temperatures varying from 25°C to 45°C during 50 cooling-heating cycles.
圖5. Pressure sensing performance of the P-S E-skin
(A) Schematic diagram of the sensing mechanism during the loading-unloading process.
(B) I-V curves at various pressures.
(C) Relationship between ΔI/I0 and pressure.
(D) Response and recovery times under pressures ranging from 5 to 50,005 Pa.
(E) ΔI/I0 with pressures varying from 5 to 105, 1,505, 10,005, 15,005, 20,005, and 50,005 Pa during 5 loading-unloading cycles.
(F) Long-term durability test at pressures varying from 5 to 50,005 Pa during 3,000 loading-unloading cycles and typical cycles of the test.
圖6. Application of the E-skins in biomimetic devices
(A) Schematic diagram of a biomimetic frog tongue and its potential application in the field of space debris retrieval.
(B) Variations in the ΔI/I0 of the P-S E-skin and the ΔR/R0 of the S/T-S E-skin after prestretching during the predatory process of the biomimetic tongue.
(C) Variations in the ΔI/I0 of the S/T-S E-skin with different environmental temperatures.
圖7. Application of the E-skins in smart wearables
(A) Schematic diagram of an injured athlete with an intelligent kneepad and smart insoles during the telediagnosis process.
(B) Schematic diagram of the detection platform.
(C) Schematic diagram of the intelligent kneepad and its electrical curves with different degrees of knee flexion as well as different temperatures of thermotherapy.
(D) Schematic diagram of the smart insole and its ΔI/I0 as well as surface pressure distributions with different foot postures.
(E) Data processing of machine learning based on the KNN algorithm.
(F) Confusion matrix of machine learning outcomes.
3小結
在此研究中，基於高伸展性微孔碳納米管/聚烯烴/熱塑性聚氨酯複合薄膜，成功製備了S/T-S和P-S電子皮膚，並實現了包括ε值、溫度和壓力在內的多種信號感知。S/T-S 電子皮膚在 0%–340% 和 340%–480% 的拉伸範圍內具有 12.19 和 635.03 的 GF 值，在 25°C–85°C 的溫度範圍內具有 3.66 × 10−3°C −1 的值。P-S電子皮膚在5–20,005 Pa和20,005–50,005 Pa壓力範圍內分別具有1.79和0.2 kPa−1的GF值。此外，預拉伸工藝顯著提升了電子皮膚的重複性，使其在拉伸-釋放循環中保持固定ε值時最大ΔR/R0值基本恆定，解決了電子皮膚在大ε值下常見的不穩定問題，提高了應用可行性。此外，將S/T-S型與P-S型電子皮膚集成於仿生蛙舌裝置，成功監測其伸展狀態、環境溫度及觸覺信號。同時分別將兩類電子皮膚集成於智能護膝與智能鞋墊。通過監測膝關節運動與熱療溫度，同時提出結合機器學習的足部姿勢分析系統，實現96.2%的高識別準確率。
文獻：
來源：材料分析與應用
