ABSTRACT Flexible sensors that interface the physical world with digital intelligence have faced a trade‐off among sensing accuracy, response selectivity, and signal decoupling. This key challenge arises from the design of high‐performance sensitive materials. In this study, a dual‐mode flexible sensor is assembled by a new‐style dual‐gradient HEC‐CNT/Bi 2 Te 3 nanowire aerogel prepared via the synchronous processes of directional freeze casting and gravity‐induced self‐sedimentation. This cooperative top‐to‐bottom pore‐size and bottom‐to‐up compositional dual‐gradient architecture enables the effective spatial partitioning of pressure and temperature sensing behaviors via generating two independent response patterns for piezoresistive and thermoelectric mechanisms, respectively. As a result, the derived device possesses the ultra‐low pressure and temperature detection limit/resolution of 5 Pa/2 Pa (sensitivity of −7.97% kPa −1 ) and 0.1 K/0.1 K (Seebeck coefficient of −20.79 µV K −1 ) without any mutual interference, respectively, which is perfectly superior to that of a non‐gradient device. Furtherly, this capability of the device is leveraged for robust pattern recognition and predictive analytics, achieving the ultrahigh identification accuracies of 100% and 97% for the effective warning capacities of battery thermal runaway and skin inflammation stages via using the latest convolutional neural network algorithm with transfer learning, respectively. This dual‐gradient strategy establishes a feasible design paradigm for high‐precision multimodal perception.
Xia et al. (Wed,) studied this question.