H. Choi, Y.M. Kim, H.C. Moon, D.H. Kim
North Carolina State University,
United States
Keywords: ionic diode, copolymer strategy, synaptic tactile sensing, ionotronics
Summary:
Ionotronic devices, which utilize ion migration as charge carriers, are emerging as key platforms for low-power, flexible, and biologically integrated electronics. Among these, ionic diodes are particularly attractive for neuromorphic and tactile systems due to their ability to enable spontaneous, directional ion transport. To advance the performance of these systems, tailoring the characteristics of ionic diodes is critical, as their widespread application has been hindered by unstable ion depletion layers (IDLs), poor rectification performance, and the lack of active control mechanisms. Conventional strategies have focused on adjusting ion concentration, incorporating high ionic strength polyelectrolytes, or engineering electrode interfaces. While these approaches enhanced rectification behavior, they inherently suffered from limited ion mobility, sluggish response times, and instability under dynamic mechanical conditions. Here, we present a mechano-gated ionic diode with balanced ionic conductivity between cationic and anionic polymer layers, achieved through copolymer engineering. This conductivity matching enables the formation of well-defined IDLs, yielding a rectification ratio of 23.5 and pressure-sensitive piezo-ionic behavior. Consequently, the ionic diode exhibits mechano-gated tunable ionic rectification, remaining electrically dormant under ambient conditions and becoming activated only when a mechanical threshold is surpassed, thereby mimicking biological threshold behavior. Leveraging this threshold-gated conduction, the device transduces mechanical stimuli into discrete ionic spikes, consuming as little as 0.41 nJ per spike at rest and 1.49 nJ under pressure, while achieving a 24-fold enhancement in signal-to-noise ratio. When integrated into a tactile interface, the diode displays synaptic-like plasticity and activity-dependent signal encoding. These findings establish a materials-driven strategy for real-time, low-power ionic sensing and neuromorphic functionality.