Effects of incineration and pyrolysis on removal of organics and liberation of cathode active materials derived from spent ternary lithium-ion batteries.

Removing organics via thermal treatment to liberate active materials from spent cathode sheets is essential for recovering lithium-ion batteries. In this study, the effects of incineration, N2 pyrolysis, and CO2 pyrolysis on the removal of organics and liberation of ternary cathode active materials (CAMs) were compared. The results indicated that the organics in the spent ternary cathode sheets comprised a residual electrolyte and polyvinylidene fluoride (PVDF) binder. Moreover, the organics could be removed to promote the liberation of CAMs via incineration, N2 pyrolysis, and CO2 pyrolysis. When the temperature was <200 °C, the chemical properties of the volatilized ester electrolyte remained unchanged during both N2 and CO2 pyrolysis, indicating that the electrolyte can be collected by controlling the pyrolysis temperature and condensation. Furthermore, PVDF binder decomposition occurred at 200-600 °C. The optimal temperatures of incineration, N2 pyrolysis, and CO2 pyrolysis were 550, 500, and 450 °C, respectively, and these treatments increased the liberation efficiency of CAMs from 81.49 % to 98.75 %, 99.26 %, and 97.98 %, respectively. In addition, heat-treated CAMs required less time to achieve adequate liberation. Following three thermal treatment processes, the sizes of the CAM particles were mainly concentrated in the ranges of 0.075-0.1 mm and <0.075 mm. Furthermore, for all types of CAMs examined, the Al concentration decreased from 1.09 % to <0.35 %, which increased the separation efficiency and improved the chemical metallurgical performance.

Process parameters and biological mechanism of efficient removal of Cd(II) ion from wastewater by a novel Bacillus subtilis TR1.

The safe treatment of heavy metals in wastewater is directly related to the human health and social development. In this paper, a new biological strain has been isolated from electroplating wastewater, which can effectively remove metal ions in wastewater. The results of 16 S rDNA sequencing analysis and NCBI GenBank database comparison show that the strain belongs to a novel Bacillus genus and names Bacillus subtilis TR1 with the accession number of OL441606. The removal rate of Cd(II) reaches to 85.68% with the conditions of pH = 7, C0Cd(II) = 20 mg L-1, t = 48 h, m = 0.1 g, and T = 35 °C. The biological removal mechanism of Cd(II) is in-depth studied by FTIR and XRD combined with third-generation sequencing. The results indicate that Bacillus subtilis TR1 removes Cd(II) mainly through two synergistic pathways, namely, extracellular chemisorption and intracellular bioaccumulation: 1) The groups carried on the surface of the strain, such as -COOH, -NH, -OH and C-H, have good chemisorption properties for Cd(II) and easily form cadmium containing chelation (-COO-Cd(II), -N-Cd(II), etc.) with these groups. The appearance of TR1 strain changes from cylindrical to spherical after Cd(II) adsorption, which is due to the biotoxicity of Cd(II); 2) Cd(II) exchanges on the surface of TR1 strain with K and Na ions released from the intracellular cytoplasm and enters the cytoplasm under the transfer of biological transport medium. This part of Cd(II) is converted into its own components by anabolic enzymes and accumulates in the cytoplasm. These data provide a new biological agent for the efficient treatment of heavy metal ions in wastewater and enrich relevant theoretical knowledge.

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography.

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L 2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L 1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L 1/2 norm framework for feature extraction, and uses L 2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46-21.81%.

Novel joint algorithm based on EEG in complex scenarios.

At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition.

Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.