Flexible sensors are highly desirable for tactile sensing and wearable devices. Previous researches of smart elements have focused on flexible pressure or temperature sensors. However, realizing material identification remains a challenge. Here, we report a multifunctional sensor composed of hydrophobic films and graphene/polydimethylsiloxane sponges. By engineering and optimizing sponges, the fabricated sensor exhibits a high-pressure sensitivity of >15.22 per kilopascal, a fast response time of <74 millisecond, and a high stability over >3000 cycles. In the case of temperature stimulus, the sensor exhibits a temperature-sensing resolution of 1 kelvin via the thermoelectric effect. The sensor can generate output voltage signals after physical contact with different flat materials based on contact-induced electrification. The corresponding signals can be, in turn, used to infer material properties. This multifunctional sensor is excellent in its low cost and material identification, which provides a design concept for meeting the challenges in functional electronics.

Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

As one important subclass of piezoelectric materials, pyroelectric materials have caused increasing attention owing to the unique pyroelectric effect induced by spontaneous polarization, showing broad promising application prospects due to various electrical responses induced by time‐dependent temperature variation. This review systematically introduces the pyroelectric effect and evaluation of pyroelectric materials and follows by analyzing and concluding the novel properties corresponding to four kinds of main pyroelectric materials. The emphasis of this review focuses on several significant and practical applications of pyroelectric materials in thermal energy harvesting from the external environment, pyroelectric sensing, and imaging, even some electrochemical applications including hydrogen generation, wastewater treatment, sterilization, and disinfection. Finally, the development direction of pyroelectric materials, potential challenges and opportunities in the future are all discussed and proposed. Through systematical conclusion and analysis of the latest research progress in the recent two decades, this review may provide significant guide and inspiration in the development of pyroelectric materials.