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[Display omitted] • ZIF-67 was etched to produce CoCo-LDH while coated by organic polydopamine (PDA) concomitantly. A PDA modified hollow cage lamellar LDH nanostructure loaded with TPP was constructed (TPP@LDH@Co-PDA). • Polydopamine enhanced compatibility between flame retardants and the matrix, boosting mechanical strength. • An inspiration for enhancing the thermal stability and compatibility of LDH, and to solve the migration problem of phosphorous small molecules. Layered double hydroxides (LDH) have irreplaceable advantages in the field of polymer flame retardancy, but their thermal stability and compatibility with matrix still need to be improved. In this paper, the bottom-up method is adopted, and the phosphorus series flame retardant triphenyl phosphate (TPP) was first encapsulated inside ZIF-67. On this basis, ZIF-67 was etched to produce LDH while modified by polydopamine (PDA) concomitantly. An organic coated polydopamine hollow cage lamellar LDH microstructure loaded with TPP was constructed, and its structure-performance relationship was verified. When 2 wt% TPP@LDH@Co-PDA was added to the epoxy resin, the LOI value of the composite was increased to 29.4 %, the peak heat release was reduced by 43.1 %, and the smoke release was significantly reduced. The unique microstructure endows epoxy composites with good flame retardancy, improves mechanical properties, and provides a new solution to the migration problem of phosphorous based flame retardants. [ABSTRACT FROM AUTHOR]

The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models. This limitation hampers the robustness and generalizability of the models. Therefore, multi-source geographical data were used in this study to determine three temporal indices for characterizing the enrichment process of soil PTEs in temporal dimensions, and additionally to construct the temporal-spatial-spectral (TSS) covariate combinations. The random forest (RF) algorithm was used to map soil PTEs at a regional scale. Results showed that: (1) When using spatial parameters or spectral parameters as predictor variables and measured Pb content as the dependent variable, the values of the model performance indicator RPIQ (ratio of performance to inter-quartile range) were 2.66 and 2.27, respectively. After incorporating the temporal parameters into the model input, values of RPIQ for the RF model reached 3.55 (using spatial-temporal covariates) and 3.21 (using spectral-temporal covariates), representing performance improvements of 33.46% and 41.41%, respectively. (2) The RF model constructed with the temporal-spatial-spectral covariates achieved satisfactory mapping accuracy (R 2 = 0.85; RMSE = 0.80 mg kg−1; RPIQ = 4.09). (3) The soil Pb content in the western and northeastern regions was relatively high, while the remaining areas exhibited lower Pb levels, mainly due to industrial activities. (4) The mapping results of Pb obtained in this study were superior to other mapping methods, such as ordinary kriging, artificial neural networks, and multivariate linear regression methods. The soil PTE mapping technique employed in this study that combined TSS covariates with the RF provided an effective methodological approach for preventing soil pollution, controlling environmental risk, and improving soil management. [Display omitted] • A remote sensing-based strategy for mapping regional PTE was developed. • A mapping model was constructed using TSS covariates and RF. • Three temporal indices were designed to characterize soil PTEs. • Satisfactory mapping results for soil PTEs were achieved with RF/TSS (RPIQ = 4.09). [ABSTRACT FROM AUTHOR]