Low-grade heat exists ubiquitously in the environment. Thermogalvanic cells (TGCs) are promising for converting the widespread low-grade heat directly into electricity owing to relatively high thermopowers of redox reactions. This work reports polarized electrolytes with ultrahigh thermopowers of −8.18 mV K −1 for n-type and 9.62 mV K −1 for p-type. The electrolyte consists of I − /I 3 − redox couple, methylcellulose, and KCl. Thermoresponsive methylcellulose leads to polarization switching from n-type to p-type above a transition temperature due to the strong hydrophobic interaction between methylcellulose and I 3 − ions. The giant thermopowers can be attributed to the simultaneously enhanced entropy change and concentration difference of redox couple enabled by the gelation of methylcellulose and KCl-induced complexation. The p-type TGC with the optimized electrolyte achieves a normalized maximum power density of 0.36 mW m −2 K −2 , which is far superior to other reported I − /I 3 − -based TGCs. This work demonstrates cost-effective, high-thermopower polarized electrolytes for low-grade heat harvesting.

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Ionic circuits using ions as charge carriers have demonstrated great potential for flexible and bioinspired electronics. The emerging ionic thermoelectric (iTE) materials can generate a potential difference by virtue of selective thermal diffusion of ions, which provide a new route for thermal sensing with the merits of high flexibility, low cost, and high thermopower. Here, ultrasensitive flexible thermal sensor arrays based on an iTE hydrogel consisting of polyquaternium‐10 (PQ‐10), a cellulose derivative, as the polymer matrix and sodium hydroxide (NaOH) as the ion source are reported. The developed PQ‐10/NaOH iTE hydrogel achieves a thermopower of 24.17 mV K−1, which is among the highest values reported for biopolymer‐based iTE materials. The high p‐type thermopower can be attributed to thermodiffusion of Na+ ions under a temperature gradient, while the movement of OH− ions is impeded by the strong electrostatic interaction with the positively charged quaternary amine groups of PQ‐10. Flexible thermal sensor arrays are developed through patterning the PQ‐10/NaOH iTE hydrogel on flexible printed circuit boards, which can perceive spatial thermal signals with high sensitivity. A smart glove integrated with multiple thermal sensor arrays is further demonstrated, which endows a prosthetic hand with thermal sensation for human–machine interaction.

Post-traumatic Stress Disorder (PTSD) is a common debilitating mental disorder, that occurs in some individuals following extremely traumatic events. Traditional identification of Genetic Markers (GM) for PTSD is mainly based on a statistical clinical approach by comparing PTSD patients with normal controls. However, these statistical studies present limitations, often generating inconsistent results. Few studies have yet examined thoroughly the role of somatic mutations, PTSD disease pathways and their relationships. Capitalizing on deep learning techniques, we have developed a novel hierarchical graph attention network to identify highly correlational GM (HGMs) of PTSD. The network presents the following novelties: First, both a hierarchical graph structure and a graph attention mechanism have been integrated into a model to develop a graph attention network (GAtN) model. Second, domain-specific knowledge, including somatic mutations, genes, PTSD pathways and their correlations have been incorporated into the graph structures. Third, 12 somatic mutations having high or moderate impacts on proteins or genes have been identified as the potential HGMs for PTSD. Fourth, our study is carefully guided by prominent PTSD literature or clinical experts of the field; any high saliency HGMs generated from our model are further verified by existing PTSD-related authoritative medical journals. Our study illustrates the utility and significance of a hybrid approach, integrating both AI and expert-guided/domain-specific knowledge for thorough identification of biomarkers of PTSD, while building on the nature of convergence and divergence of PTSD pathways. Our expert-guided AI-driven methodology can be extended to other pathological-based HGM identification studies; it will transform the methodology of biomarker identification for different life-threatening diseases to speed up the complex lengthy procedures of new biomarker identification.

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