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   Low efficiency in recovering low-grade heat remains unresolved despite decades of attempts. In this research, we designed and fabricated a novel thermo-osmotic ionogel (TOI) composite to recover low-grade heat to generate electric power through a thermo-induced ion gradient and selective ion diffusion. The TOI composite was assembled with a crystalline ionogel (polymer-confined LiNO 3 –3H 2 O) film, ion selective membrane, and hydrogel film. With a 90 °C heat supply, the single TOI composite produced a high open-circuit voltage of 0.52 V, a differential thermal voltage of ∼26 mV K −1 , a peak power density of 0.4 W m −2 , and a ground-breaking peak energy conversion efficiency of 11.17%. Eight pieces of such a TOI composite were connected in series, demonstrating an open-circuit voltage of 3.25 volts. Such a TOI system was also demonstrated to harvest body temperature for powering a LED, opening numerous opportunities in powering wearable devices. 

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4. Zwitterionic Copolymer-Supported Ionogel Electrolytes Featuring a Sodium Salt/Ionic Liquid Solution

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5. Unobtrusive Swallow Monitoring Enabled by Conformal IONOGEL Biopotential Electrodes and Machine Learning

   https://doi.org/10.1002/admt.202500229  

   Dong, Penghao; Ives, Jasmine; Garcia, Ethan; Caporusso, Christopher; Bragina, Polina; Laguatan, Cassandra; Yao, Shanshan (May 2025, Advanced Materials Technologies)

   Abstract Dysphagia or difficulty swallowing is caused by the failure of neurological pathways to properly activate swallowing muscles. Current electromyography (EMG) systems for dysphagia monitoring are bulky and rigid, limiting their potential for long‐term and unobtrusive use. To address this, a machine learning‐assisted wearable EMG system is presented, utilizing self‐adhesive, skin‐conformal, semi‐transparent, and robust ionic gel electrodes. The presented electrodes possess good conductivity, superior skin contact, and good transmittance, ensuring high‐fidelity EMG sensing without impeding daily activities. Moreover, the optimized material and structural designs ensure wearing comfort and conformable skin‐electrode contact, allowing for long‐term monitoring with high accuracy. Machine learning and mel‐frequency cepstral coefficient techniques are employed to classify swallowing events based on food types and volumes. Through an analysis of electrode placement on the chin and neck, the proposed system is able to effectively distinguish between different food types and water volumes using a small number of channels, making it suitable for continuous dysphagia monitoring. This work represents an advancement in machine learning assisted EMG systems for the classification and regression of swallowing events, paving the way for more efficient, unobtrusive, and long‐term dysphagia monitoring systems. 

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