Electric-controlled pressure relief valve for enhanced safety in liquid-cooled lithium-ion battery packs

The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.

Hypoxia-induced reactive oxygen species in organ and tissue fibrosis

Fibrosis is the end-stage change of damaged tissues in various human diseases,which can lead to permanent scarring or organ malfunction.Hypoxia leads to oxidative stress,mitochondrial dysfunction,and inflammation in dysfunctional organs and tissues.Oxidative stress resulting from the overproduction of reactive oxygen species plays a central role in the fibrosis of injured organs.This review addresses the updated knowledge of the relationship between hypoxia and tissue fibrosis mediated by the reactive oxygen species pathway.Moreover,novel anti-fibrotic strategies are discussed,which may suppress reactive oxygen species and organ fibrosis.

Effect of Nitrogen Application Rates on the Nitrogen Utilization, Yield and Quality of Rice

Research on the effect of the nitrogen application rate on the balance of the nitrogen utilization, yield and quality of rice is common in South China but is relatively lacking in Northeast China, especially in the Liaohe Delta. In this study, Yanfeng 47 rice was planted in Panjin city, China, to explore the effect of six nitrogen rates (0, 160, 210, 260, 315 and 420 kg N/ha) on the nitrogen use efficiency, rice quality and grain yield of rice plants. The results showed that the application of an appropriate nitrogen rate (210-260 kg N/ha) remarkably increased the nitrogen use efficiency of rice plants, grain yield, rice milling quality and nutritional quality and resulted in a moderate rice eating quality. Although low nitrogen rates (160 kg N/ha) maintained a high rice eating quality, they decreased grain yield and other rice qualities, and excessive nitrogen (315 kg N/ha) increased rice appearance quality but significantly reduced the nitrogen use efficiency (p < 0.05), yield and eating quality of rice. Therefore, to produce rice in the Liaohe Delta by an environmentally friendly method and guarantee rice with high quality and yield, the recommended nitrogen application rate is 210 kg N/ha.

Plasmonic nanostructure characterized by deep-neuralnetwork-assisted spectroscopy[Invited]

The lateral geometry and material property of plasmonic nanostructures are critical parameters for tailoring their optical resonance for sensing applications.While lateral geometry can be easily observed by a scanning electron microscope or an atomic force microscope,characterizing materials properties of plasmonic devices is not straightforward and requires delicate examination of material composition,cross-sectional thickness,and refractive index.In this study,a deep neural network is adopted to characterize these parameters of unknown plasmonic nanostructures through simple transmission spectra.The network architecture is established based on simulated data to achieve accurate identification of both geometric and material parameters.We then demonstrate that the network training by a mixture of simulated and experimental data can result in correct material property recognition.Our work may indicate a simple and intelligent characterization approach to plasmonic nanostructures by spectroscopic techniques.