Data sharing

Inspired by the natural mechanism of taste perception, artificial bionic electronic tongues have successfully enabled the detection and classification of various tastes. The liquid-solid contact electrification (LSCE) effect has emerged as a highly effective approach for developing self-inducted electronic tongues. However, droplet-based sensing structures often face challenges related to internal and environmental interferences, compromising their stability and repeatability. In this work, we developed a monolithically integrated self-inducted microfluidic bionic electronic tongue (SMET), combining the LSCE effect with deep learning algorithms to achieve highly reliable and intelligent sample identification and concentration detection. The incorporation of a multiplexed microchannel structure significantly reduced the required liquid sample volume while simultaneously increasing the electrical output amplitude (up to 10 V at multitone wave excitation), thereby enhancing sensitivity. Instead of micropumps, miniaturized exciters were employed as SMET drivers to generate multiple excitation waveforms, producing various signal types to improve specific algorithmic accuracy. The SMET achieved over 93% classification accuracy for five taste element samples (glacial acetic acid, anhydrous dextrose, quinine, edible chili essence, sodium chloride) and five concentrations of sodium chloride solutions using a single waveform signal, reaching 100% accuracy with the fusion of multiple waveform signals. Furthermore, the SMET was used to detect more than ten different taste samples, each exhibiting distinct signal variations. Thus, due to its ultra-high sensitivity to the electrical properties of liquids, SMET enables accurate and rapid analysis of liquid samples with high reliability, positioning it as a promising tool in the field of rapid liquid detection.

受味觉感知的自然机制启发，人工仿生电子舌已成功实现多种味觉的检测与分类。液固接触起电（Liquid-Solid Contact Electrification, LSCE）效应已成为开发自感应电子舌的高效途径。然而，基于液滴的传感结构常面临内部与环境干扰的挑战，损害其稳定性与重复性。本研究开发了一种单片集成自感应微流控仿生电子舌（monolithically integrated self-inducted microfluidic bionic electronic tongue, SMET），将液固接触起电效应与深度学习算法相结合，实现了高可靠性的智能样本识别与浓度检测。多路微通道结构的引入大幅降低了所需液体样本体积，同时提升了电输出幅值（多音波激励下可达10 V），进而增强了检测灵敏度。本装置未采用微型泵，而是以微型激励器作为SMET的驱动源，可生成多种激励波形，产生多样化信号类型以提升特定算法的识别精度。仅使用单一波形信号时，SMET对五种味觉样本（冰乙酸、无水葡萄糖、奎宁、食用辣椒精、氯化钠）以及五种浓度的氯化钠溶液的分类准确率已超过93%；融合多波形信号后，分类准确率可达100%。此外，SMET还可检测十余种不同味觉样本，各类样本均呈现出独特的信号变化特征。鉴于其对液体电学特性的超高灵敏度，SMET可实现准确、快速且高可靠性的液体样本分析，有望成为快速液体检测领域极具应用前景的工具。