With the rapidly developing wireless communication technology, electromagnetic pollution problems have become more prominent. Electromagnetic pollution has caused great harm to wireless equipment, precision instruments, military safety, etc., which urgently requires the development of lightweight, high-efficiency, broadband electromagnetic waves (EMW) absorbing materials. MXene is an emerging two-dimensional (2D) material with the advantages of lamellar structure, excellent conductivity, and abundant surface groups. At the same time, conducting polymers (CPs) have excellent performance in terms of conductivity, surface activity, quality, and electromagnetic loss, making them have excellent potential in EMW absorbing direction. This article examines the preparation, structure, and performance of MXene and CPs-based radar absorbing materials (RAM). A comprehensive summary and objective analysis of the nowaday study progress on the EMW absorbing performances of MXene and CPs, and a comprehension of the absorbing mechanism are reviewed. Finally, the research direction of absorbing materials has been prospected.

Summary The unique structures and foundations of a dam make its safety monitoring a complex task. As the most intuitive effect of dams, deformation contains important information on dam evolution. Actual response has the purpose of diagnosis and early warning compared with model prediction. Given the poor generalization ability of the conventional statistical model, establishing a dam deformation monitoring model is thus essential. The prediction of concrete dam deformation using statistical model and random forest regression (RFR) model is studied. To build an optimized RFR model, the statistical model is used to establish input variables, select the appropriate parameters Mtry and Ntree according to out‐of‐bag error, and extract strong explanatory variables. The model's advantage is that the influence factors can describe concrete dam deformation, and RF can serve as a sensible new data mining tool. The importance of variables for deformation prediction is measured by RF. The RFR method can extract representative influencing factors based on variable importance. The methods are applied to an actual concrete dam. Results indicate that the RFR model can be applied for analysis and prediction of other structural behavior.

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The ternary MXene/MnO 2 /polyaniline (PANI) nanostructure was successfully prepared through a simple, scalable, and reproducible two-step method. First, the interlaced layered MnO 2 is grown on the interlayer and surface of MXene through the hydrothermal reaction. And then the composite was covered with conducting polymer PANI through the redox reaction in the ice bath to obtain the ternary MXene/MnO 2 /PANI nanostructure. The accordion-like MXene substrate has good conductivity and provides electron conduction channels. MnO 2 nanosheets not only inhibit the re-stacking of the MXene layer but also form an effective pore space, which is conducive to ion diffusion and transfer. The outer PANI can further improve the electrochemical performance of the composite. The morphology, chemical structure, and crystal phase of the prepared nanostructure were measured in detail by SEM, FTIR, and XRD. In addition, the behavior of the supercapacitor was analyzed through cyclic voltammetry (CV) and galvanostatic charge and discharge (GCD) tests. Test analyses show that the specific capacitance of the MXene/MnO 2 /PANI nanostructure is about 216 F g −1 at a current density of 1 A g −1 . And the capacitance retention rate after 5000 cycles under the two-electrode test system is about 74%.