Kim, Juhui Lee, Junseo Choi, Kwang-hun Im, Seongil Kim, Jae Hong Choi, Changsoon Byun, Junghwan Shin, Changhwan Jang, Ho Won Ju, Hyunsu Lim, Jung Ah 2026-01-13T06:00:15Z 2026-01-13T06:00:15Z 2026-01-12 2026-04 1616-301X https://pubs.kist.re.kr/handle/201004/153975 Despite significant advances, flexible pressure sensors still face critical limitations, including pixelated architecture and extensive wiring, which hinder their scalability for large-area applications. Here, we present a pixelation-free, monolithic iontronic pressure sensor that simultaneously detects pressure and position over a large area with high sensitivity (4.16 kPa−1), a wide detection range (<455 kPa), and excellent durability. The device, consisting of a single ionogel film bridging multiple peripheral electrodes, eliminates the structural complexity of conventional designs. Under AC bias, mechanical pressure releases ions from polymer chains inside the ionogel, increasing ionic conductivity and establishing spatially varying impedance pathways to each electrode. This enables the sensor to capture both spatial and pressure information in the output current. Continuous ion oscillation under AC bias facilitates sustained signal generation under static pressure (>3300 s) without significant degradation, thereby overcoming key limitations of conventional piezoionic pressure sensors. Machine learning algorithms effectively decouple overlapping pressure–position signals from the multichannel outputs, achieving high accuracy in pressure prediction across different spatial positions. Real-time demonstrations, including handwriting recognition with pressure-dependent color variation and simultaneous multi-point detection producing piano sounds of varying tones, highlight the versatility of the device for next-generation large-area and flexible human–machine interfaces. English John Wiley & Sons Ltd. Pixelation-Free, Monolithic Iontronic Pressure Sensors Enabling Large-Area Simultaneous Pressure and Position Recognition via Machine Learning Article 10.1002/adfm.202527178 1 Advanced Functional Materials, v.36, no.32 Advanced Functional Materials 36 32 Y scie scopus 2-s2.0-105026036301 Chemistry, Multidisciplinary Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Physics, Condensed Matter Chemistry Science & Technology - Other Topics Materials Science Physics Article; Early Access MULTILAYER FEEDFORWARD NETWORKS flexible pressure sensor human-machine interface (HMI) ionogel machine learning piezoionic tactile sensor