Graphene stratiform membrane materials have been recently applied to heavy metal removal in aqueous systems via adsorption due to their high mechanical strength, chemical stability, and other properties. We applied reduced graphene oxide (rGO) alone as an adsorbent to remove heavy metal ions from wastewater. Self-supported rGO membrane was prepared using a green reduction method with sodium hydrosulfite. We used the Raman spectra to observe the structure of the rGO membrane. The morphology of the self-supported membrane was measured by a scanning electron microscope. The Cu²⁺ adsorption performance was measured in terms of pH, reaction time, metal ion concentration, and temperature. The maximum Cu²⁺ adsorption capacity of the rGO membrane was found to be 149.25 mg/g. The adsorption process followed a pseudo-second-order kinetic model, and adsorption isotherms were simulated by the Freundlich model.

In recent years, with the continuous improvement of computer computing power and the emergence of big data processing, artificial intelligence has become an important research field for researchers. The current research on emotion and consciousness is an important part of artificial intelligence research, and the research on speech emotion recognition technology is receiving more and more attention from researchers. Speech emotion recognition has a very broad application prospects. For example, it can be used for student education to identify students’ emotions in time and make appropriate processing,for intelligent human-computer interaction to discover the speaker’s emotional changes in time. It can also be used for criminal investigation to detect the psychological state of the suspect and assist in polygraph detection.This paper mainly studies the MFCC parameters and KNN algorithm of speech signals, and realizes MFCC feature extraction based on MATLAB, and realizes emotion classification through KNN algorithm. The CASIA corpus was used for training and testing, and finally reached an accuracy of 78.00%.

In this work, a personal thermal management (PTM) device based on single walled carbon nanotubes (SWCNTs) functionalized polyester fabrics had been studied. Polyester fabrics were functionalized with SWCNTs through coating method with poly (butyl acrylate) emulsion as the adhesive. The SEM images exhibited that SWCNTs formed high-efficiently conductive networks due to the large aspect ratio and uniform dispersion. A steady-state temperature of 40 °C was achieved at the input voltage of 2.5 V within 7 s, which exhibited excellent electro-thermal performance. Even under periodic heating-cooling conditions, heating system still displayed relatively stable temperature and relative resistance, which could have potential application for wearable clothes.

In this work, cognitive behaviour and electroencephalography (EEG) of 16 subjects applied colour matching and contrast colour matching to silk fabrics had been measured. The results showed that, the exogenous components P1 (100 ms) and P2 (200 ms) were mainly produced in the frontal, temporal, apical, and occipital regions, while the endogenous component P3 (300 ms) was mainly produced in the front-top area, the front-central area, the central-top area, and the top-pillow area. Regarding P1, the information transmission speed was faster than contrasting colours with matching process. Regarding P2, external and internal factors and their interactions significantly affected amplitude, whereas only interactions affected latency, and the interaction was stronger with colour matching. Regarding P3, colour matching type, involved brain area, and interactions had no significant effect on amplitude and latency. The information processing of same colour system in brain has shorter information processing time and faster information transmission. That indicated the EEG technology could provide objective visual cognitive aesthetic rules for silk fabric colour matching, and provide reference value for fabric manufacturers to timely understand consumers’ preference for fabric colour categories.