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  5. Bath Electrospinning of Continuous and Scalable Multifunctional MXene‐Infiltrated Nanoyarns

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Article
en
2020

Bath Electrospinning of Continuous and Scalable Multifunctional MXene‐Infiltrated Nanoyarns

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0 Files

en
2020
Vol 16 (26)
Vol. 16
DOI: 10.1002/smll.202002158

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Yury Gogotsi
Yury Gogotsi

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Ariana Levitt
Shayan Seyedin
Jizhen Zhang
+4 more

Abstract

Electroactive yarns that are stretchable are desired for many electronic textile applications, including energy storage, soft robotics, and sensing. However, using current methods to produce these yarns, achieving high loadings of electroactive materials and simultaneously demonstrating stretchability is a critical challenge. Here, a one-step bath electrospinning technique is developed to effectively capture Ti3 C2 Tx MXene flakes throughout continuous nylon and polyurethane (PU) nanofiber yarns (nanoyarns). With up to ≈90 wt% MXene loading, the resulting MXene/nylon nanoyarns demonstrate high electrical conductivity (up to 1195 S cm-1 ). By varying the flake size and MXene concentration, nanoyarns achieve stretchability of up to 43% (MXene/nylon) and 263% (MXene/PU). MXene/nylon nanoyarn electrodes offer high specific capacitance in saturated LiClO4 electrolyte (440 F cm-3 at 5 mV s-1 ), with a wide voltage window of 1.25 V and high rate capability (72% between 5 and 500 mV s-1 ). As strain sensors, MXene/PU yarns demonstrate a wide sensing range (60% under cyclic stretching), high sensitivity (gauge factor of ≈17 in the range of 20-50% strain), and low drift. Utilizing the stretchability of polymer nanofibers and the electrical and electrochemical properties of MXene, MXene-based nanoyarns demonstrate potential in a wide range of applications, including stretchable electronics and body movement monitoring.

How to cite this publication

Ariana Levitt, Shayan Seyedin, Jizhen Zhang, Xuehang Wang, Joselito M. Razal, Geneviève Dion, Yury Gogotsi (2020). Bath Electrospinning of Continuous and Scalable Multifunctional MXene‐Infiltrated Nanoyarns. , 16(26), DOI: https://doi.org/10.1002/smll.202002158.

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Publication Details

Type

Article

Year

2020

Authors

7

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/smll.202002158

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