MXenes for Wearable Multifunctional Sensing and Artificial Intelligence Devices
Year: 2025
Type: book-chapter
Abstract: The exponential growth of artificial intelligence (AI) has led to an escalating demand for energy-efficient, data-intensive computing solutions. Conventional von Neumann architectures, constrained by inherent memory-processor bottlenecks, struggle to meet these requirements. Neuromorphic devices enable energy-efficient, scalable, and high-speed neuromorphic computing, potentially addressing the von Neumann bottleneck and the limits of Moore’s Law. Two-dimensional MXene materials, with their excellent mechanical and electrical properties, have become a transformative platform for developing neuromorphic devices, providing unparalleled advantages in sensing, nonvolatile memory, and bio-inspired computation. This chapter systematically summarizes recent advances in MXene-based flexible neuromorphic memristor devices. First, we delineate materials engineering strategies for synthesizing MXene thin films with tailored electronic and mechanical properties. Next, we classify MXene-derived neuromorphic materials and elucidate their switching mechanisms, including ion migration and charge trapping. A critical analysis of MXene-enabled devices highlights breakthroughs in-memory, artificial synapses, neuromorphic circuits, and multimodal in-sensor computing. Finally, we discuss persistent challenges in stability, scalability, and interfacial engineering, while projecting future directions for MXene-integrated sensing-memory-processing systems. This chapter provides a potential pathway for leveraging MXenes to transcend the limitations of conventional computing paradigms.
Source: IntechOpen eBooks
Author: Long Chen
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409040536