Henan Normal University

About: Henan Normal University is a education organization based out in Xinxiang, China. It is known for research contribution in the topics: Catalysis & Ionic liquid. The organization has 10863 authors who have published 11077 publications receiving 166773 citations.

Recycling and synthesis of LiNi1/3Co1/3Mn1/3O2 from waste lithium ion batteries using D,L-malic acid

Abstract: If waste LIBs are disposed of in landfill sites, soil contamination will ensue from leakage of the organic electrolyte, and the heavy metals ions contained in the batteries would pose a threat to the environment. A new process for recycling valuable metal ions from waste lithium-ion batteries (LIBs) is introduced herein. D,L-malic acid was used as both a leaching reagent and chelating agent. By adjusting the metal ion ratio and pH of leachate, a new cathode material of LiNi1/3Co1/3Mn1/3O2 for lithium ion batteries through a sol–gel process without other chelating reagents was synthesized. Electrochemical tests showed the initial charge and discharge capacity of the regenerated material to be 152.9 mA h g−1 and 147.2 mA h g−1 (2.75–4.25 V, 0.2C), respectively. The capacity retention at the 100th cycle remains 95.06% of the original value (2.75–4.25 V, 0.5C). Results indicated that the LiNi1/3Co1/3Mn1/3O2 produced from waste LIBs possessed good electrochemical properties.

Predicting Thermophilic Proteins by Machine Learning

Abstract: Thermophilic proteins can maintain good activity under high temperature, therefore, it is important to study thermophilic proteins for the thermal stability of proteins. In order to solve the problem of low precision and low efficiency in predicting thermophilic proteins, a prediction method based on feature fusion and machine learning was proposed in this paper. For the selected thermophilic data sets, firstly, the thermophilic protein sequence was characterized based on feature fusion by the combination of g-gap dipeptide, entropy density and autocorrelation coefficient. Then, Kernel Principal Component Analysis (KPCA) was used to reduce the dimension of the expressed protein sequence features in order to reduce the training time and improve efficiency. Finally, the classification model was designed by using the classification algorithm. A variety of classification algorithms was used to train and test on the selected thermophilic dataset. By comparison, the accuracy of the Support Vector Machine (SVM) under the jackknife method was over 92%. The combination of other evaluation indicators also proved that the SVM performance was the best. Because of choosing an effectively feature representation method and a robust classifier, the proposed method is suitable for predicting thermophilic proteins and is superior to most reported methods.

Adsorption sensitivity of metal atom decorated bilayer graphene toward toxic gas molecules (CO, NO, SO2 and HCN)

Abstract: Based on the first-principles calculations, the sensing performances of Fe embedded graphene sheets (including monolayer Fe-MG and bilayer Fe-BG) toward toxic gases (NO, CO, HCN and SO2) are comparably investigated. Compared with the Fe-MG, the stable configuration of Fe-BG sheet exhibits the stronger affinity toward the gas molecules. The adsorbed NO has the largest energy difference between Fe-MG and Fe-BG substrate as compared with the other gases, as well as inducing the change in electronic structure and magnetic property of Fe-graphene systems. In addition, the supported Pt(111) substrate can effectively regulate the strength of interaction between gas molecule and Fe-graphene substrates. As a result, the increased layer of graphene substrate can be utilizing as good sensor for toxic gas molecules, yet the metal Pt supported substrate can enhance the magnetic property of adsorbed gas on the Fe-graphene systems. These results could provide important information for controlling the adsorption sensoring of gas molecules, which opens up a new avenue for the design and fabrication of the graphene-based gas sensors or spintronic devices.