Peiru Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Peiru Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Hainan Zhang
Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
2 shared publications - Changqing Tian
Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, China
1 shared publication - Hongsheng Jiang
State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University 2 , Beijing 100084
1 shared publication - Huiming Zou
Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100045, China
1 shared publication - Ri Wang
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
1 shared publication - Sujun Dong
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
1 shared publication
Latest Publications
- Open Access
Experimental Investigation of Bacterial Inactivation of Beef Using Indirect Cold Plasma in Cold Chain and at Room Temperature
Pathogen contamination is a severe problem in maintaining food safety in the cold chain. Cold plasma (CP) is a novel non-thermal disinfection method that can be applied for the bacterial inactivation of food in appropriate contexts. Currently, research on CP used on food at cold chain temperatures is rare. This work investigated the bacterial inactivation effect of CP on beef at typical cold storage temperatures of 4 and −18 °C and room temperature (25 °C). The reactive species in CP were indirectly tested by evaluating O3, NO3− and NO2− in cold plasma-activated water (PAW), which indicated the highest concentrations of reactive species in CP at 25 °C and the lowest at −18 °C. The bactericidal efficacy of CP treatment against beef inoculated with Escherichia coli at −18 °C, 4 °C, and 25 °C was 30.5%, 60.1%, and 59.5%, respectively. The 4 °C environment was the most appropriate treatment for CP against beef, with the highest bactericidal efficacy and a minor influence on beef quality. The indirect CP treatment had no significant effect on the texture, color, pH, or cooking loss of beef at −18 °C. CP shows significant potential for the efficient decontamination of food at cold chain temperatures.
Open AccessExperimental Investigation of Bacterial Inactivation of Beef Using Indirect Cold Plasma in Cold Chain and at Room Temperature
Pathogen contamination is a severe problem in maintaining food safety in the cold chain. Cold plasma (CP) is a novel non-thermal disinfection method that can be applied for the bacterial inactivation of food in appropriate contexts. Currently, research on CP used on food at cold chain temperatures is rare. This work investigated the bacterial inactivation effect of CP on beef at typical cold storage temperatures of 4 and −18 °C and room temperature (25 °C). The reactive species in CP were indirectly tested by evaluating O3, NO3− and NO2− in cold plasma-activated water (PAW), which indicated the highest concentrations of reactive species in CP at 25 °C and the lowest at −18 °C. The bactericidal efficacy of CP treatment against beef inoculated with Escherichia coli at −18 °C, 4 °C, and 25 °C was 30.5%, 60.1%, and 59.5%, respectively. The 4 °C environment was the most appropriate treatment for CP against beef, with the highest bactericidal efficacy and a minor influence on beef quality. The indirect CP treatment had no significant effect on the texture, color, pH, or cooking loss of beef at −18 °C. CP shows significant potential for the efficient decontamination of food at cold chain temperatures.
- Open Access
Parameter-Matching Algorithm and Optimization of Integrated Thermal Management System of Aircraft
The integrated thermal management system of aircraft is essential to maintain a suitable environment for the cabin crew and devices. The system is composed of the air-cycle refrigeration subsystem, the vapor-compression refrigeration subsystem, the liquid-cooling subsystem and the fuel-cycle subsystem, which are coupled with each other through heat exchangers. Due to the complex structure and large number of components in the system, it is necessary to design a corresponding parameter-matching algorithm for its special structure and to select the appropriate optimization design method. In this paper, the structure of an integrated thermal management system is analyzed in depth. A hierarchical matching algorithm of system parameters was designed and realized. Meanwhile, a sensitivity analysis of the system was performed, where key parameters were selected. Besides, a variety of optimization algorithms was used to optimize the design calculations. The results show that the particle swarm optimization and genetic algorithm could effectively find the global optimal solution when taking the fuel penalty as the objective function. Furthermore, the particle swarm optimization method took less time.
Open AccessParameter-Matching Algorithm and Optimization of Integrated Thermal Management System of Aircraft
The integrated thermal management system of aircraft is essential to maintain a suitable environment for the cabin crew and devices. The system is composed of the air-cycle refrigeration subsystem, the vapor-compression refrigeration subsystem, the liquid-cooling subsystem and the fuel-cycle subsystem, which are coupled with each other through heat exchangers. Due to the complex structure and large number of components in the system, it is necessary to design a corresponding parameter-matching algorithm for its special structure and to select the appropriate optimization design method. In this paper, the structure of an integrated thermal management system is analyzed in depth. A hierarchical matching algorithm of system parameters was designed and realized. Meanwhile, a sensitivity analysis of the system was performed, where key parameters were selected. Besides, a variety of optimization algorithms was used to optimize the design calculations. The results show that the particle swarm optimization and genetic algorithm could effectively find the global optimal solution when taking the fuel penalty as the objective function. Furthermore, the particle swarm optimization method took less time.