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Emergence of MXene–Polymer Hybrid Nanocomposites as High‐Performance Next‐Generation Chemiresistors for Efficient Air Quality Monitoring.

Authors :
Chaudhary, Vishal
Ashraf, Naveed
Khalid, Mohammad
Walvekar, Rashmi
Yang, Ya
Kaushik, Ajeet
Mishra, Yogendra Kumar
Source :
Advanced Functional Materials. 8/15/2022, Vol. 32 Issue 33, p1-44. 44p.
Publication Year :
2022

Abstract

Air contamination is one of the foremost concerns of environmentalists worldwide, which has elevated global public health concerns for monitoring air contaminants and implementing appropriate safety policies. These facts have generated nascent global demand for exploring sustainable and translational strategies required to engineer affordable, intelligent, and miniaturized sensors because commercially available sensors lack lower detection limits, room temperature operation, and poor selectivity. The state‐of‐the‐art sensors are concerned with architecting advanced nanomaterials to achieve desired sensing performance. Recent studies demonstrate that neither pristine metal carbides/nitrides (MXenes) nor polymers (P) can address these practical challenges. However, synergistic combinations of various precursors as hybrid‐nanocomposites (MXP‐HNCs) have emerged as superior sensing materials to develop next‐generation intelligent environmental, industrial, and biomedical sensors. The expected outcomes could be manipulative due to optimizing physicochemical and morphological attributes like tunable interlayer‐distance, optimum porosity, enlarged effective surface area, rich surface functionalities, mechanical flexibility, and tunable conductivity. This review intends to detail a comprehensive summary of the advancements in state‐of‐the‐art MXP‐HNCs chemiresistors. Moreover, the underlying sensing phenomenon, chemiresistor architecture, and their monitoring performance are highlighted. Besides, an overview of challenges, potential solutions, and prospects of MXP‐HNCs as next‐generation intelligent field‐deployable sensors with the integration of IoT and AI are outlined. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1616301X
Volume :
32
Issue :
33
Database :
Academic Search Index
Journal :
Advanced Functional Materials
Publication Type :
Academic Journal
Accession number :
158551010
Full Text :
https://doi.org/10.1002/adfm.202112913