Thin and soft Ti3C2Tx MXene sponge structure for highly sensitive pressure sensor assisted by deep learning

Guo, Wang; Ma, Zhiqiang; Chen, Zhou; Hua, Haojun; Wang, Dong; Elhousseini Hilal, Mohamed; Fu, Yatian; Lu, Pengyi; Lu, Jian; Zhang, Yuanting; Ho, Derek; Khoo, Bee Luan

doi:10.1016/j.cej.2024.149659

Cited by 16 publications

(1 citation statement)

References 68 publications

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“…Guo et al combined a pressure sensor and deep learning algorithms to develop a sound classification system with high measurement accuracy, which is helpful in robotics speech recognition. 61 Wang et al developed a gesture recognition system through a flexible memristor-based sensing system assisted by deep learning neural network calculations, presenting huge application potential in biomimetic robotics. 62 In our work, a gesture recognition system was developed by combining the DESL eutectogel-based wearable strain sensor with deep learning algorithms.…”

Section: Resultsmentioning

confidence: 99%

A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification

Liu,

Wang,

Wang

et al. 2024

J. Mater. Chem. B

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Section: Resultsmentioning

confidence: 99%

A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification

Liu,

Wang,

Wang

et al. 2024

J. Mater. Chem. B

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Deep‐Learning‐Based Prediction of Long‐Term Piezoresistive Sensing Performance of MXene/Aramid Nanofiber Sensors

Chen,

Qin,

Gong

et al. 2024

Adv Eng Mater

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Flexible compressible sensors are widely used in the human health monitoring field for their numerous advantages. However, the dynamic loads and possible injuries associated with long‐term living and exercise pose a challenge to the long‐term piezoresistive performance stability of these sensors. In this study, the application of deep learning for predicting the long‐term performance of these sensors is explored, aiming to enhance the assessment of sensor stability and ensure accurate and reliable long‐term monitoring. Samples with different Ti3C2Tx MXene/aramid nanofiber mass ratios (1:1, 1:2, 1:3) are prepared and piezoresistive characterization is conducted under long‐term loading cycles to obtain training data. Three distinct deep‐learning prediction models, convolutional neural network (CNN), long short‐term memory, and recurrent neural network (RNN), are utilized to assess their influence on prediction accuracy. To assess the effectiveness of the proposed method, its prediction of long‐term piezoresistive sensing performance with experimental data not used for training purposes is compared. The CNN model demonstrates optimal results with a mean absolute error of 0.0251 for the 1:3 mass ratio sample. Based on the experimental results, the model is expected to be integrated into human health monitoring systems, thus improving the assessment of sensor stability throughout its lifetime.

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Programmable Microfluidic‐Assisted Highly Conductive Hydrogel Patches for Customizable Soft Electronics

Liao,

Ma,

Liu

et al. 2024

Adv Funct Materials

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The utilization of hydrogels in soft electronics has led to significant progress in the field of wearable and implantable devices. However, challenges persist in hydrogel electronics, including the delicate equilibrium between stretchability and electrical conductivity, intricacies in miniaturization, and susceptibility to dehydration. Here, a lignin‐polyacrylamide (Ag‐LPA) hydrogel composite endowed with anti‐freeze, self‐adhesive, exceptional water retention properties, and high stretchability (1072%) is presented. Notably, this composite demonstrated impressive electrical conductivity at room temperature (47.924 S cm−1) and extremely cold temperatures (42.507 S cm−1). It is further proposed for microfluidic‐assisted hydrogel patches (MAHPs) to facilitate customizable designs of the Ag‐LPA hydrogel composite. This approach enhances water retention and offers versatility in packaging materials, making it a promising choice for enduring soft electronics applications. As a proof‐of‐concept, soft electronics across diverse applications and dimensions, encompassing healthcare monitoring, environmental temperature sensing, and 3D‐spring pressure monitoring electronics are successfully developed. The scenery of an extremely cold environment is further extended. The conductivity of the embedded Ag‐LPA hydrogel composite unveils the potential of MAHPs in polar rescue missions. It is envisioned that MAHPs will impact the development of sophisticated and tailored soft electronics, thereby forging new frontiers in engineering applications.

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Thin and soft Ti3C2Tx MXene sponge structure for highly sensitive pressure sensor assisted by deep learning

Cited by 16 publications

References 68 publications

A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification

A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification

Deep‐Learning‐Based Prediction of Long‐Term Piezoresistive Sensing Performance of MXene/Aramid Nanofiber Sensors

Programmable Microfluidic‐Assisted Highly Conductive Hydrogel Patches for Customizable Soft Electronics

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