Design of atomically-thin-body field-effect sensors and pattern recognition neural networks for ultra-sensitive and intelligent trace explosive detection

Abstract

There has been enormous demand for detecting trace concentrations of chemical molecules and in particular low-volatile explosives through electronic instrumentation, which however, still faces significant shortcomings in both detectability and selectivity to date. In this work, we propose a novel sensor strategy that incorporates arrays of atomically-thin-body field-effect sensors for highly-scalable, ultra-sensitive trace explosive sensors with fast response to ultra-low analyte concentrations. Sensor performance and functionalization are theoretically simulated through system-level considerations using various kinetic, electrostatic, quantum mechanics, and drift-diffusion models. Moreover, by implementing custom-built neural network models for pattern recognition, we successfully achieved automatic, accurate detection of four different types of analytes with concentrations down to 0.02 part per trillion. The intelligent sensors have the capability to differentiate analyte types with 100% accuracy and predict the concentration values with ~10% of relative error simultaneously. We envision the proposed sensor platform, design metrics, deep learning methods and the combination of these innovations will be a promising yet practical solution towards ultra-sensitive trace explosive detection and can be extended to a wide range of molecular sensing applications.