Effect of Dietary Protein Level and Origin on the Redox Status in the Digestive Tract of Mice

The present study was undertaken to evaluate the effects of high protein (soybean protein or casein) on the balance between production of free radicals and antioxidant level in digestive organs of mice. For this purpose, male (C57BL/6J) mice were adapted to experimental diets containing soybean protein or casein with 20% (normal protein diets, NPDs) or 60% (high protein diets, HPDs), and HPDs supplemented with 0.06g/kg cysteamine. After two weeks of feeding, oxidative and antioxidative parameters in duodenum, liver and pancreas were measured. The results show that ingestion of high protein markedly increased contents of superoxide anion and malondialdehyde (MDA), decreased activities of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT) and Na+ K+-ATPase, and content of reduced glutathione (GSH) in digestive organs of mice (P<0.05). Levels of oxidative parameters were lower and antioxidant capacity of both enzyme and non-enzyme was higher in mice fed with soybean protein than those fed with casein. In groups fed HPDs supplemented with cysteamine, oxidative stress was mitigated. However, oxidative parameter levels were still higher than those of NPD-fed groups. The present study indicates that ingestion of high protein diets could result in an imbalance between oxidant and antioxidant, and thus induce oxidative stress in digestive organs of mice. The oxidative damage was smaller in mice fed with high level of soy protein in comparison with casein

A linear recursive state of power estimation for fusion model component analysis with constant sampling time.

The state of power of lithium-ion batteries, as the main product of choice for electric and hybrid electric vehicle energy storage systems, is one of the precise feedback control parameters for the battery management system. The proposed research establishes a method for the analysis of charging and discharging constitutive factors under the sampling time, realizes the online identification of parameters by building an adaptive forgetting factor recursive least-squares method based on the Thevenin model, and uses the online parameters to achieve an effective characterization of the power state under voltage and current limitations. The results demonstrate that the accuracy error of online parameter identification is less than 0.03 V. Combining the analysis of charging and discharging constitutive factors under-sampling time with the fusion model of voltage and current limitation makes the power state estimation more reliable and accurate. The results demonstrate that the power state estimation error in the discharging state is less than 8%

Online full-parameter identification and SOC estimation of lithium-ion battery pack based on composite electrochemical-dual circuit polarization modeling.

A new composite electrochemistry-dual circuit polarization model (E-DCP) is proposed by combining the advantages of various electrochemical empirical models in this paper. Then, the multi-innovation least squares (MILS) algorithm is used to perform online full parameter identification for the E-DCP model in order to improve data usage efficiency and parameter identification accuracy. In addition, on the basis of the E-DCP model, the MILS and the extended Kalman filter (EKF) are combined to enhance the state estimation accuracy of the battery management system (BMS). Finally, the model and the algorithm are both verified through urban dynamometer driving schedule (UDDS) and the complex charge-discharge loop test. The results indicate that the accuracy of E-DCP is relatively high under different working conditions, and the errors of state of charge (SOC) estimation after the combination of MILS and EKF are all within 2.2%. This lays a concrete foundation for practical use of the BMS in the future

Adaptive iterative working state prediction based on the double unscented transformation and dynamic functioning for unmanned aerial vehicle lithium-ion batteries.

In lithium-ion batteries, the accuracy of estimation of the state of charge is a core parameter which will determine the power control accuracy and management reliability of the energy storage systems. When using unscented Kalman filtering to estimate the charge of lithium-ion batteries, if the pulse current change rate is too high, the tracking effects of algorithms will not be optimal, with high estimation errors. In this study, the unscented Kalman filtering algorithm is improved to solve the above problems and boost the Kalman gain with dynamic function modules, so as to improve system stability. The closed-circuit voltage of the system is predicted with two non-linear transformations, so as to improve the accuracy of the system. Meanwhile, an adaptive algorithm is developed to predict and correct the system noises and observation noises, thus enhancing the robustness of the system. Experiments show that the maximum estimation error of the second-order Circuit Model is controlled to less than 0.20V. Under various simulation conditions and interference factors, the estimation error of the unscented Kalman filtering is as high as 2%, but that of the improved Kalman filtering algorithm are kept well under 1.00%, with the errors reduced by 0.80%, therefore laying a sound foundation for the follow-up research on the battery management system

Integrating a dual-silicon photoelectrochemical cell into a redox flow battery for unassisted photocharging

Solar rechargeable flow cells (SRFCs) provide an attractive approach for in situ capture and storage of intermittent solar energy via photoelectrochemical regeneration of discharged redox species for electricity generation. However, overall SFRC performance is restricted by inefficient photoelectrochemical reactions. Here we report an efficient SRFC based on a dual-silicon photoelectrochemical cell and a quinone/bromine redox flow battery for in situ solar energy conversion and storage. Using narrow bandgap silicon for efficient photon collection and fast redox couples for rapid interface charge injection, our device shows an optimal solar-to-chemical conversion efficiency of similar to 5.9% and an overall photon-chemical-electricity energy conversion efficiency of similar to 3.2%, which, to our knowledge, outperforms previously reported SRFCs. The proposed SRFC can be self-photocharged to 0.8V and delivers a discharge capacity of 730 mAhl(-1). Our work may guide future designs for highly efficient solar rechargeable devices