Heavy metals immobilization of ternary geopolymer based on nickel slag, lithium slag and metakaolin.

To solve heavy metals leaching problem in the utilization of various industrial solid wastes, this work investigated the heavy metals immobilization of ternary geopolymer prepared by nickel slag (NS), lithium slag (LS), and metakaolin (MK). Compressive strength was measured to determine the optimum and appropriate mix proportions. The leaching characteristics of typical heavy metals (Cu (Ⅱ), Pb (Ⅱ), and Cr (Ⅲ)) in acid, alkali, and salt environments were revealed by Inductively Coupled Plasma (ICP). The heavy metals immobilization mechanism was explored by Mercury Intrusion Porosimetry (MIP), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscopy (SEM) tests. The experimental results show that the group with a mass ratio of NS, LS and MK of 1:1:8 exhibits the highest compressive strength, which reaches 69.1 MPa at 28 d. The ternary geopolymer possesses a desirable capacity for immobilizing inherent heavy metals, where the immobilization rates of Cu and Pb reach 96.69 %, and that of Cr reaches 99.97 %. The leaching concentrations of Cr and Pb increase when the samples are exposed to acidic and alkaline environments. Cu and Pb are mainly physically encapsulated in geopolymer. Additionally, immobilization of Cr mainly involves physical encapsulation and chemical bonding.

Prediction of mechanical solutions for a laminated LCEs system fusing an analytical model and neural networks.

This paper presents a convenient and efficient method to predict the mechanical solutions of a laminated Liquid Crystal Elastomers (LCEs) system subjected to combined thermo-mechanical load, based on a back propagation (BP) neural network which is trained by machine learning from a database established by analytical solutions. Firstly, the general solutions of temperature, displacement, and stress of any single layer in the LCEs system are obtained by solving the two-dimensional (2D) governing equations of both heat conduction and thermoelasticity. Then, the unknown coefficients in above general solutions are determined by a transfer-matrix method based on the continuity condition at the interface of adjacent layers and the combined thermo-mechanical loads condition at the surface of the LCEs system. The formula derivation and calculator program are verified through convergence studies and comparisons with FEM results. Finally, a database with displacements of LCEs system in a temperature field subjected to 561 sets of mechanical loads is established based on the presented analytical model. The BP neural network based on above database is further applied to establish the relationship between deformation and mechanical load to predict the elastic deformation of the LCEs system in a temperature field subjected to a mechanical load. Moreover, the BP network can also inverse the coefficients of mechanical load which induces the specific deformation in a temperature field. The numerical examples show that: (1) The deformation of a laminated LCEs system due to thermal load is limited within the range of human temperature changes from 36 °C to 40 °C. (2) The thickness of the LCE is a sensitive parameter on the deformation at the bottom surface of the system. (3) The accuracy of predicted displacements induced by the thermo-mechanical load and the inversed mechanical load based on deformation of the LCEs system in a temperature field using BP neural network reaches 99.6% and 98.5% respectively.