Synaptic Sensors for Development of Potentiometric NO2 Sensors by Using WSe2/PPy Composite Nanoarchitectonics

In this study, we report the design of a bioinspired synaptic potentiometric NO2 sensor based on a WSe2/polypyrrole (PPy) nanocomposite, synthesized via a facile liquid-phase exfoliation coupled with an in situ oxidation polymerization process. The resulting core-shell heterostructure exhibits dynamic charge transfer and plasticity behavior, mimicking biological synaptic functions such as potentiation and depression under repetitive gas stimuli. The optimized WSe2/PPy (1:1) device displayed an excellent synaptic sensing response of 7.33 at 100 ppb NO2, with ultrafast response (19 s), recovery (249 s), and an ultralow detection limit of 8.74 ppb at room temperature achieved without any post-annealing treatment. The synergistic interaction between defect-rich WSe2 and nitrogen-active PPy sites enhanced selective NO2 adsorption, while DFT analysis confirmed substantial interfacial charge redistribution and band modulation. Moreover, neural network-assisted learning accurately reproduced and predicted synaptic response patterns (R2 > 0.99), validating the sensor's adaptive recognition behavior. This work demonstrates a hybrid material platform bridging neuromorphic sensing and gas detection, opening pathways toward self-learning, low-power synaptic gas sensors for next-generation environmental and health-monitoring systems. The synergistic effects lead to improved sensitivity and rapid response, highlighting its potential for advanced gas sensing applications.