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Advanced strain sensors based on CNT and AgNP-MWCNT for biomedical applications: A review of mechanisms and performance evaluation

IMPACT SIGNAL74/100
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Information from the abstract

Carbon nanotube (CNT) and silver-nanoparticle-decorated multi-walled carbon nanotube (AgNP-MWCNT) strain sensors have progressed through three generations in roughly two decades, from randomly dispersed films sensitive only at small strains to aligned and percolating composites with a tunable gage factor (GF) to engineered-crack sensitive or hierarchical hybrids that report GFs above 10 4 . Yet the field has not converged on a shared evaluation framework: published GF values are reported across incommensurate strain windows, hysteresis is more often shown qualitatively than quantified, and durability tests range from 10 2 to 10 5 cycles without justification. In this review, we consider that the central problem of AgNP-MWCNT strain-sensor design is not maximizing any one metric but managing the GF, stretchability, hysteresis, and linearity trade-offs dictated by piezoresistance, tunneling, or crack-mediated conduction that dominates at the operating strain. We critically compare the three competing mechanistic models, propose a normalized reporting standard for performance comparison, and reframe AgNP-MWCNT strain sensors as nodes in intelligent material systems, where model-based calibration, sensor fusion, and embedded health-monitoring, not recorded GF values alone, will determine practical deployment in wearable and structural applications.

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Why this record is monitored

This record has an Impact Signal of 74/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.

Related topics: Advanced Sensor and Energy Harvesting Materials · Carbon Nanotubes in Composites · Nanomaterials and Printing Technologies

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Thai researcher and institutional participation

Aamir Sada Khan · Chutiwat Likitaporn · Peerawat Prathumrat · Tawan Wongsalam · Nattapon Tanalue · Nichakan Nichakornpong · Manunya Okhawilai · Chulalongkorn University

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Data limitations

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