Inkjet Printing of MXene Gas Sensors for Agricultural Monitoring

Abstract

Inkjet printing enables scalable fabrication of nanomaterial-based devices, but stable droplet formation and reproducible deposition remain challenging for emerging nanosheet inks. In this work, we develop a model-guided inkjet printing framework that monitors and optimizes droplet generation based on fluid properties and nozzle dynamics. Unlike previous MXene inkjet printing studies that primarily rely on empirical parameter tuning and focus on ink formulation or device performance, the proposed framework employs a physics-based analytical model that quantitatively links waveform parameters, fluid properties, nozzle dynamics, and droplet behavior. This predictive approach enables rational waveform design for stable single-droplet ejection, minimizes satellite droplet formation, and reduces the need for extensive experimental calibration. MXene (Ti3C2Tx) nanosheets dispersed in ethanol are used as a representative conductive ink to demonstrate the capability of the optimized printing approach. Under model-guided printing conditions, the system achieves reliable pattern formation and enables fabrication of printed MXene devices that exhibit measurable responses to several gases relevant to agricultural environments. These results demonstrate that analytical-model-guided monitoring and optimization of the inkjet printing process provides a predictive and generalizable strategy for reproducible printing of nanomaterial-based devices.