Chapter 2.4 Adsorption kinetics on real surfaces

Accurate prediction and further dissection of neonicotinoid elimination in the water treatment by CTS@AgBC using multihead attention-based convolutional neural network combined with the time-dependent Cox regression model

Imidacloprid (IMI), as the most widely used neonicotinoid insecticide, poses a serious threat to the water ecosystem due to the inefficient elimination in the traditional water treatment. Chitosan (CTS)-stabilized biochar (BC)-supported Ag nanoparticles (CTS@AgBC) are applied to eliminate the IMI in the water treatment effectively. Batch experiments depict that the modification of BC by CTS and Ag nanoparticles remarkably improve its adsorption performance. The pseudo-second-order and Elovich models have good performance in simulating the adsorption processes of CTS@AgBC and BC. This indicates that the chemical adsorption on real surfaces plays the dominant role in the adsorption of IMI by CTS@AgBC and BC. In addition, the multihead attention (MHA)-based convolutional neural network (CNN) combined with the time-dependent Cox regression model are initially applied to predict and dissect the adsorption elimination processes of IMI by CTS@AgBC. The proposed MHA-CNN model achieves more accurate concentration prediction of IMI than traditional models. According to influence weights by MHA module, biochar category, pH, and treatment temperature are considered the three dominant environmental variables to determine the IMI elimination processes. This study provides insights into roles of environmental variables in the elimination of IMI by CTS@AgBC and the accurate prediction of IMI concentration.

Slow adsorption reaction between arsenic species and goethite (alpha-FeOOH): diffusion or heterogeneous surface reaction control

The slow stage of phosphate or arsenate adsorption on hydrous metal oxides frequently follows an Elovich equation. The equation can be derived by assuming kinetic control by either a diffusion process (either interparticle or intraparticle) or a heterogeneous surface reaction. The aim of this study is to determine whether the slow stage of arsenic adsorption on goethite is more consistent with diffusion or heterogeneous surface reaction control. Adsorption kinetics of arsenate and dimethylarsinate (DMA) on goethite (alpha-FeOOH) were investigated at different pH values and inert electrolyte concentrations. Their adsorption kinetics was described and compared using Elovich (Gamma vs ln time) plots. Desorption of arsenate and DMA was studied by increasing the pH of the suspension from pH 4.0 to pH 10.0 or 12.0. The effective particle sizes and zeta-potential of goethite were also determined. Effective particle size increased rapidly as the pH approached pH(IEP), both in the absence and presence of arsenic. Inert electrolyte concentrations and pH had no effect on the slow stage of arsenate adsorption on goethite, while the kinetics of DMA adsorption on goethite was influenced by both parameters. The slow stage of arsenate adsorption on goethite follows an Elovich equation. Since effective particle size changes with both pH and inert electrolyte concentrations, and effective particle size influences interparticle diffusion, the arsenate adsorption kinetics indicate that the slow adsorption step is not due to interparticle diffusion. DMA also has complex adsorption kinetics with a slow adsorption stage. DMA desorbed completely and rapidly when the pH was raised, in contrast to the slow adsorption kinetics, indicating that the slow adsorption step is not due to intraparticle diffusion. The slow adsorption is not the result of diffusion, but rather is due either to the heterogeneity of the surface site bonding energy or to other reactions controlling arsenic removal from solution.