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Deep-Learning-Based Deconvolution of Mechanical Stimuli with Ti3C2Tx MXene Electromagnetic Shield Architecture via Dual-Mode Wireless Signal Variation Mechanism

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    Deep-Learning-Based Deconvolution of Mechanical Stimuli with Ti3C2Tx MXene Electromagnetic Shield Architecture via Dual-Mode Wireless Signal Variation Mechanism
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    • Gun-Hee Lee
      Gun-Hee Lee
      Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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    • Gang San Lee
      Gang San Lee
      National Creative Research Initiative Center for Multi-dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, KAIST Institute for Nanocentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
      More by Gang San Lee
    • Junyoung Byun
      Junyoung Byun
      School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
      More by Junyoung Byun
    • Jun Chang Yang
      Jun Chang Yang
      Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
      More by Jun Chang Yang
    • Chorom Jang
      Chorom Jang
      School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
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    • Seongrak Kim
      Seongrak Kim
      Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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    • Hyeonji Kim
      Hyeonji Kim
      Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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    • Jin-Kwan Park
      Jin-Kwan Park
      School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
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    • Ho Jin Lee
      Ho Jin Lee
      National Creative Research Initiative Center for Multi-dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, KAIST Institute for Nanocentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
      More by Ho Jin Lee
    • Jong-Gwan Yook
      Jong-Gwan Yook
      School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
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    • Sang Ouk Kim*
      Sang Ouk Kim
      National Creative Research Initiative Center for Multi-dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, KAIST Institute for Nanocentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
      *Email: [email protected]
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    • Steve Park*
      Steve Park
      Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
      *Email: [email protected]
      More by Steve Park
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    ACS Nano

    Cite this: ACS Nano 2020, 14, 9, 11962–11972
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    https://doi.org/10.1021/acsnano.0c05105
    Published August 19, 2020
    Copyright © 2020 American Chemical Society
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    Abstract

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    Passive component-based soft resonators have been spotlighted in the field of wearable and implantable devices due to their remote operation capability and tunable properties. As the output signal of the resonator-based wireless communication device is given in the form of a vector (i.e., a spectrum of reflection coefficient), multiple information can, in principle, be stored and interpreted. Herein, we introduce a device that can deconvolute mechanical stimuli from a single wireless signal using dual-mode operation, specifically enabled by the use of Ti3C2Tx MXene. MXene’s strong electromagnetic shielding effect enables the resonator to simultaneously measure pressure and strain without overlapping its output signal, unlike other conductive counterparts that are deficient in shielding ability. Furthermore, convolutional neural-network-based deep learning was implemented to predict the pressure and strain values from unforeseen output wireless signals. Our MXene-integrated wireless device can also be utilized as an on-skin mechanical stimuli sensor for rehabilitation monitoring after orthopedic surgery. The dual-mode signal variation mechanism enabled by integration of MXene allows wireless communication systems to efficiently handle various information simultaneously, through which multistimuli sensing capability can be imparted into passive component-based wearable and implantable electrical devices.

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    • Figures S1–S24: schematic illustration of overall fabrication process of MXWiDi; schematic illustration of two-step deposition for fabrication of 2D coil; visualized simulation of strain distribution; schematic illustration of anchoring for strain sensing; schematic illustration of porous PDMS fabrication using microfluidic channel; schematic illustration of MXene coating on porous PDMS; SEM image of nanowire deposited on PI film; simulation result of S11 variation; schematic depiction of effective dielectric constant (εeff) variation-based pressure sensing mechanism; relative capacitance variation of Ppy-coated porous PDMS according to pressure; SEM image of AFM tip coated with MXene; relative capacitance variation under continuous loading and unloading of pressure; mechanical property variation according to pore size; schematic illustration for discussion about linear relation between pressure and capacitance; relative capacitance variation under continuous loading and unloading of strain; S11 spectrum variation according to pressure using small pore based MXene-coated porous PDMS pressure sensor; S11 magnitude variation during 100 cycles of 30 kPa pressure loading and unloading; equivalent circuit model for analyzing relation between S11 and capacitance; resonant frequency variation during 100 cycles of 20% strain loading and unloading; schematic diagram of convolutional neural network architecture; predicted output values after training with 100 data and actual input values of test data; visualized simulation of magnetic flux induction on stacked 2D coils; S11 spectra variation at various distance or angles between the coil and the reader; visualized plot comparing the actual mechanical input and predicted one (PDF)

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    ACS Nano

    Cite this: ACS Nano 2020, 14, 9, 11962–11972
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acsnano.0c05105
    Published August 19, 2020
    Copyright © 2020 American Chemical Society
    Request reuse permissions

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