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Conjugated polymers that can efficiently transport both ionic and electronic charges have shown broad applications in next-generation optoelectronic, bioelectronic, and energy storage devices. To date, almost all the conjugated polymers have hydrophobic backbones, which impedes efficient ion diffusion/transport in aqueous media. Here, we design and synthesize a novel hydrophilic polymer building block, 4a-azonia-naphthalene (AN). Because of the strong electron-withdrawing ability of AN, the traditional p-type polymer, thiophene diketopyrrolopyrrole (TDPP) polymer, turns into an n-type polymer. More importantly, AN enhances the hydrophilicity and ionic-electronic coupling of the polymer, leading to a fast device response (1.00 ms/0.18 ms) and a high figure-of-merit (μC*) of 62.3 F cm−1 V−1 s−1 in organic electrochemical transistor (OECT). We find that cationic aromatics exhibit interesting cation-π interactions, leading to smaller π-π stacking distances and interesting ion diffusion behavior. Our work demonstrates that ionic polymer backbones can provide efficient ionic-electronic coupling and charge transport for organic electronics working in aqueous media.

This paper proposes a variable-weight distributed expectation maximization (VWDEM) filtering method to achieve accuracy improvement and power reduction of MEMS sensors array under unknown disturbance. First, the local measurement data of each sensor node is used to obtain local state estimates via expectation step(E-step). Next, the weight coefficient is adaptively adjusted based on the above state estimate differences between this local sensor node and its neighbor nodes. Through iterative consensus, the local estimate of sensor node can be then updated quickly due to adaptive weight coefficients. The updated local estimate is finally utilized to identify the sensor bias by maximum step(M-step). The results of numerical calculation relying on experimental tests are given. Comparing with the average consensus distributed expectation maximization (ADEM), the bias variance and power consumption of the MEMS sensors array is reduced to 12% and 15.5% by the proposed method, respectively.