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Abstract Skin‐like robust materials with prominent sensing performance have potential applications in flexible bioelectronics. However, it remains challenging to achieve mutually exclusive properties simultaneously including low interfacial impedance, high stretchability, sensitivity, and electrical resilience. Herein, a material and structure design concept of mixed ion‐electron conduction and mechanical interlocking structure is adopted to fabricate high‐performance mechanical‐bioelectrical dual‐modal composites with large stretchability, excellent mechanoelectrical stability, low interfacial impedance, and good biocompatibility. Flower‐like conductive metal‐organic frameworks (cMOFs) with enhanced conductivity through the overlapped level of metal‐ligand orbital are assembled, which bridge carbon nanotubes (denoted as cMOFs‐b‐CNTs). Then, precursor of poly(styrene‐block‐butadiene‐block‐styrene)/ionic liquid penetrates the pores and cavities in cMOFs‐b‐CNTs‐based network fabricated via filtration process, creating a semi‐embedded structure via mechanical interlocking. Thus, the mixed ion‐electron conduction and semi‐embedded structure endow the as‐prepared composites with a low interfacial impedance (51.60/28.90 kΩ at 10/100 Hz), wide sensing range (473%), high sensitivity (2195.29), rapid response/recovery time (60/85 ms), low limit of detection (0.05%), and excellent durability (>5000 cycles to 50% strain). Demonstrations of multifunctional mechanical‐bioelectrical dual‐modal sensors for in vivo/vitro monitoring physiological motions, electrophysiological activities, and urinary bladder activities validate the possibility for practical uses in biomedical research areas. This concept creates opportunities for the construction of durable skin‐like sensing materials. 

Perovskite oxides have a wide variety of physical properties that make them promising candidates for versatile technological applications including nonvolatile memory and logic devices. Chemical tuning of those properties has been achieved, to the greatest extent, by cation-site substitution, while anion substitution is much less explored due to the difficulty in synthesizing high-quality, mixed-anion compounds. Here, nitrogen-incorporated BaTiO₃thin films have been synthesized by reactive pulsed-laser deposition in a nitrogen growth atmosphere. The enhanced hybridization between titanium and nitrogen induces a large ferroelectric polarization of 70 μC/cm²and high Curie temperature of ~1213 K, which are ~2.8 times larger and ~810 K higher than in bulk BaTiO₃, respectively. These results suggest great potential for anion-substituted perovskite oxides in producing emergent functionalities and device applications. 

Charging processes are the key to promoting electric taxis and improving their operational efficiency due to frequent charging activities and long charging time. Nevertheless, optimizing charging resource allocation in real time is extremely challenging because of uneven charging demand/supply distributions, heuristic-based charging behaviors of drivers, and city-scale of the fleets. The existing solutions have utilized real-time contextual information for charging recommendation, but they do not consider the much-richer fleet information, leading to the suboptimal individual-based charging recommendation. In this paper, we design a data-driven fleet-oriented charging recommendation system for charging resource allocation called ForETaxi for electric taxis , which aims to minimize the overall charging overhead for the entire fleet, instead of individual vehicles. ForETaxi considers not only current charging requests but also possible charging requests of other nearby electric taxis in the near future by inferring their status in real time. More importantly, we implement ForETaxi with multiple types of sensor data from the Chinese Shenzhen city including GPS data, and taxi transaction data from more than 13,000 electric taxis, combined with road network data and charging station data. The data-driven evaluation results show that compared to the state-of-the-art individual-based recommendation methods, our fleet-oriented ForETaxi outperforms them by 16% in the total charging time reduction and 82% in the queuing time reduction.