Modular Assembly of MXene Frameworks for Noninvasive Disease Diagnosis via Urinary Volatiles

Xuyin Ding, Yecheng Zhang, Yue Zhang, Xufa Ding, Hanxin Zhang, Tian Cao, Zhi Bei Qu, Jing Ren, Lei Li, Zhijun Guo, Feng Xu, Qi Xian Wang, Xing Wu, Guoyue Shi, Hossam Haick, Min Zhang

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

Volatile organic compounds (VOCs) in urine are valuable biomarkers for noninvasive disease diagnosis. Herein, a facile coordination-driven modular assembly strategy is used for developing a library of gas-sensing materials based on porous MXene frameworks (MFs). Taking advantage of modules with diverse composition and tunable structure, our MFs-based library can provide more choices to satisfy gas-sensing demands. Meanwhile, the laser-induced graphene interdigital electrodes array and microchamber are laser-engraved for the assembly of a microchamber-hosted MF (MHMF) e-nose. Our MHMF e-nose possesses high-discriminative pattern recognition for simultaneous sensing and distinguishing of complex VOCs. Furthermore, with the MHMF e-nose being a plug-and-play module, a point-of-care testing (POCT) platform is modularly assembled for wireless and real-time monitoring of urinary volatiles from clinical samples. By virtue of machine learning, our POCT platform achieves noninvasive diagnosis of multiple diseases with a high accuracy of 91.7%, providing a favorable opportunity for early disease diagnosis, disease course monitoring, and relevant research.

Original languageEnglish
Pages (from-to)17376-17388
Number of pages13
JournalACS Nano
Volume16
Issue number10
DOIs
StatePublished - 25 Oct 2022

Keywords

  • MXene frameworks
  • e-nose
  • machine learning
  • noninvasive disease diagnosis
  • urinary volatiles

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