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The purpose of this paper is to synthesize SnO₂–ZnO hollow nanofibers, study their sensing properties and introduce an attractive candidate for formaldehyde detection in practice.

Pure and SnO₂–ZnO hollow nanofibers were synthesized by electrospinning method and characterized via X-ray diffraction, field-emission scanning electron microscopy and Fourier transform infrared spectroscopy. The formaldehyde-sensing properties were investigated.

The optimum performance was obtained at 260°C by the 14 at.% SnO₂–ZnO hollow nanofiber sensor. The sensor could detect formaldehyde down to 0.1 ppm with rapid response–recovery time (4-6 s and 7-9 s, respectively), high sensitivity, good selectivity and stability. The relationship between the sensor’s sensitivity and formaldehyde concentration suggests that the adsorbed oxygen species on the sensor’s surface is O2−. The prominent sensing properties are attributed to the one dimensional hollow nanofiber structures and the promoting effects of SnO₂.

The sensor fabricated from 14 at.% SnO₂–ZnO fibers exhibits excellent formaldehyde-sensing characteristics. It can be used for formaldehyde detection in practice.

The electrospinning method is a very simple and convenient method for fabricating hollow nanofibers and the sensing material is of low cost.

To the best of the authors’ knowledge, studies on formaldehyde sensing of SnO₂–ZnO hollow nanofibers have not been reported before.

The rapid evacuation of personnel in emergency situations is of great significance to the safety of pedestrians. In order to further improve the evacuation efficiency in emergency situations, this paper proposes a pedestrian evacuation model based on improved cellular automata based on microscopic features.

First, the space is divided into finer grids, so that a single pedestrian occupies multiple grids to show the microscopic behavior between pedestrians. Second, to simulate the velocity of pedestrian movement under different personnel density, a dynamic grid velocity model is designed to establish a linear correspondence relationship with the density of people in the surrounding environment. Finally, the pedestrian dynamic exit selection mechanism is established to simulate the pedestrian dynamic exit selection process.

The proposed method is applied to single-exit space evacuation, multi-exit space evacuation, and space evacuation with obstacles, respectively. Average speed and personnel evacuation decisions are analyzed in specific applications. The method proposed in this paper can provide the optimal evacuation plan for pedestrians in multiple exit and obstacle environments.

In fire and emergency situations, the method proposed in this paper can provide a more effective evacuation strategy for pedestrians. The method proposed in this paper can quickly get pedestrians out of the dangerous area and provide a certain reference value for the stable development of society.

This paper proposes a cellular automata pedestrian evacuation method based on a fine grid velocity model. This method can more realistically simulate the microscopic behavior of pedestrians. The proposed model increases the speed of pedestrian movement, allowing pedestrians to dynamically adjust the speed according to the specific situation.