Schematic diagram of theoretical models and applications of single atom catalysts. A review on the theoretical models, intrinsic properties, and the related application of SACs.

Self-healing hydrogel plays an important role in flexible sensors. However, the development of high-performance hydrogel-based strain sensors with both high sensitivity and large sensing range remains a key challenge. Herein, we prepare a dual conductive network (DCN) hydrogel based on carbon nanotube (CNT) film and conductive hydrogel that exhibits high-conductivity, self-healing, anti-freezing, and non-drying features. The tolerance of this hydrogel to extreme temperatures is improved via a simple solvent replacement, enabling the DCN hydrogel to maintain high flexibility and stretchability under arduous conditions such as temperatures ranging from −85 to 50 °C. Additionally, owing to the dual conductive percolation network structure, the strain sensor based on DCN hydrogel exhibits a gauge factor as high as 343 at a strain of 110%, indicating high sensitivity. The mechanical and electrical performances of the hydrogel would be efficiently self-repaired after a simple heating–cooling treatment. The self-healing sensor can be mounted on the human body to detect biosignals in real time. Our work shows a method of fabricating high-performance self-healing hydrogel for future flexible electronics.

No abstract

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ / operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions—so-called operando modeling—is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.