Force-driven ionic transfer in dual-charge MXene membranes for self-powered linear pressure sensing
Source abstract
Abstract
Traditional pressure sensors often lack the required linear characteristics in their force-electric transfer functions, which limits the practicality in numerous fields such as the Internet of Things (IoT) and artificial intelligence. This study has developed a self-powered pressure sensor based on Ti 3 C 2 T x -MXene membranes, which are modified with cellulose nanofibrils (CNF) and poly diallyl dimethyl ammonium chloride (PDDA) to create composite membranes with negative and positive charges. Owing to their two-dimensional nanofluidic channels and surface charge properties, these composite membranes can efficiently convert mechanical pressure into electrical signals. Notably, the output signals exhibit a linear relationship with applied pressure, significantly simplifying signal processing. To optimize the sensing performance, the nanofluidic channel structures of the composite membranes were fine-tuned to enhance the sensitivity and response speed. Furthermore, the influence of electrolyte concentrations on sensing performance, including detection range, sensitivity, and stability, was systematically investigated. Experimental results demonstrate that the sensor exhibits excellent linear response, high sensitivity, fast response/recovery times, good stability, and repeatability. This research not only provides new ideas for the design of self-powered sensors but also advances the application of 2D materials in the field of intelligent sensing. • MXene-based PN junction nanofluidic system facilitates efficient ion transport through charge-selective membranes. • Unprecedented 86.3% stabilized efficiency under pressure. • Delivers optimal sensitivity (142.2 μV/kPa) in 0.05 mol/L KCl solution. • Excellent durability (10,000 cycles) with minimal performance degradation. • Rapid response (60.1 ms)/recovery (70.0 ms) times for real-time applications.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · MXene and MAX Phase Materials · Dielectric materials and actuators
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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