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Black phosphorous-based human-machine communication interface

Author

Listed:
  • Jayraj V. Vaghasiya

    (University of Chemistry and Technology Prague)

  • Carmen C. Mayorga-Martinez

    (University of Chemistry and Technology Prague)

  • Jan Vyskočil

    (University of Chemistry and Technology Prague)

  • Martin Pumera

    (University of Chemistry and Technology Prague
    Yonsei University
    VSB—Technical University of Ostrava
    China Medical University Hospital, China Medical University)

Abstract

Assistive technology involving auditory feedback is generally utilized by those who are visually impaired or have speech and language difficulties. Therefore, here we concentrate on an auditory human-machine interface that uses audio as a platform for conveying information between visually or speech-disabled users and society. We develop a piezoresistive tactile sensor based on a black phosphorous and polyaniline (BP@PANI) composite by the facile chemical oxidative polymerization of aniline on cotton fabric. Taking advantage of BP’s puckered honeycomb lattice structure and superior electrical properties as well as the vast wavy fabric surface, this BP@PANI-based tactile sensor exhibits excellent sensitivity, low-pressure sensitivity, reasonable response time, and good cycle stability. For a real-world application, a prototype device employs six BP@PANI tactile sensors that correspond to braille characters and can convert pressed text into audio on reading or typing to assist visually or speech-disabled persons. Overall, this research offers promising insight into the material candidates and strategies for the development of auditory feedback devices based on layered and 2D materials for human-machine interfaces.

Suggested Citation

  • Jayraj V. Vaghasiya & Carmen C. Mayorga-Martinez & Jan Vyskočil & Martin Pumera, 2023. "Black phosphorous-based human-machine communication interface," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-34482-4
    DOI: 10.1038/s41467-022-34482-4
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    References listed on IDEAS

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    1. Shumao Cui & Haihui Pu & Spencer A. Wells & Zhenhai Wen & Shun Mao & Jingbo Chang & Mark C. Hersam & Junhong Chen, 2015. "Ultrahigh sensitivity and layer-dependent sensing performance of phosphorene-based gas sensors," Nature Communications, Nature, vol. 6(1), pages 1-9, December.
    2. Subramanian Sundaram & Petr Kellnhofer & Yunzhu Li & Jun-Yan Zhu & Antonio Torralba & Wojciech Matusik, 2019. "Learning the signatures of the human grasp using a scalable tactile glove," Nature, Nature, vol. 569(7758), pages 698-702, May.
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    Cited by:

    1. Radu Valentin & Croitoru Ionut Marius & Tabirca Alina Iuliana & Stoica Silviu-Ionel, 2023. "Ai Components For Performance Measurement - A Bibliometric Approach," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 286-300, December.

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