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June 29, 2026ACS Applied Materials & Interfaces0 citations

Micro-Cluster Engineering Enables Hybrid Channel-Network Cracks for Highly Sensitive, Linear, and Robust Strain Sensors

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WTWenteng TangJDJiemeng DingJHJunlei Han

Key Points

  • This work aims to develop a crack-based strain sensor with high sensitivity, wide strain range, and reliable linearity.
  • Fabrication of a carbon nanotube-polydimethylsiloxane composite film using screen printing.
  • Control of cluster density and size to optimize crack morphology.
  • Testing the sensor's performance and stability across multiple cycles of stretching and twisting.
  • Achieved a gauge factor of 149.51 with a strain range of up to 80%.
  • Demonstrated excellent linearity with R² = 0.979.
  • After 100 cycles of stretching and twisting, the sensor showed less than 2% degradation in sensitivity and linearity.

Abstract

Crack-based strain sensors offer substantial potential for health monitoring, motion detection, and human-machine interaction. Yet their practical use is constrained by inherent performance trade-offs that make it difficult to combine high sensitivity, broad working range, and reliable linearity, as well as by the mechanical instability of brittle conductive layers. This work reports a crack sensor based on an adjustable micron-cluster structure. It is fabricated through screen printing, which enables the production of a structurally tunable carbon nanotube-polydimethylsiloxane (CNT-PDMS) composite film. By controlling the cluster density and size, we successfully guide the formation of high-density, alternating long–short channel-network crack morphology, thereby synergistically optimizing the sensor performance. A high gauge factor (GF) of 149.51 and excellent linearity ( R 2 = 0.979) over a strain range up to 80% were achieved by the fabricated sensor. After 100 cycles of 100% stretching and 360° twisting, the sensor exhibits less than 2% degradation in both sensitivity and linearity. Demonstrations in cardiomyocyte contractile force detection and wearable human-machine interaction confirm its strong potential for applications in biomedical monitoring and intelligent interactive systems.

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Cite This Study

Tang et al. (2026) studied this question.

synapsesocial.com/papers/6a420b7af91bb43ea9192889https://doi.org/10.1021/acsami.6c06885
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