Based on MnO₂/carbon cloth (CC) composite materials, an Ag-doped MnO₂ nanowire, self-assembled, urchin-like structure was synthesized in situ on the surface of CC using a simple method, and a novel and efficient flexible electrode material for supercapacitors was developed. The morphology, structure, elemental distribution, and pore distribution of the material were analyzed using SEM, TEM, XRD, XPS, and BET. The electrochemical performance was tested using cyclic voltammetry (CV) and galvanostatic charge/discharge (GCD). In the three-electrode system, GCD testing showed that the specific capacitance of the material reached 520.8 F/g at 0.5 A/g. After 2000 cycles at a current density of 1 A/g, the capacitance retention rate was 90.6%, demonstrating its enormous potential in the application of supercapacitor electrode materials.

With the increasing demand for effective electromagnetic wave (EMW) absorbers due to the proliferation of electronic devices and 5G communication systems, traditional wave-absorbing materials can no longer meet the current requirements. Thus, this research introduces a three-dimensional (3D) composite material consisting of PMMA@Mxene@Co₃O₄ microspheres, prepared through in situ self-assembly and hydrothermal growth. The strong electrical conductivity of Mxene, combined with the magnetic loss of Co₃O₄, ensures enhanced dielectric–magnetic synergy, leading to excellent EMW absorption. The study investigates the influence of varying Co₃O₄ content on the electromagnetic properties of the composite. Experimental results show that the optimal sample, with a thickness of 2.5 mm, achieves a minimum reflection loss (RL_min) of −52.88 dB at 6.88 GHz and an effective absorption bandwidth (EAB) of 5.28 GHz. This work highlights the potential of 3D PMMA@Mxene@Co₃O₄ composites as high-performance microwave absorbers, providing a promising solution to EMW pollution. The findings offer valuable insights into material design strategies, demonstrate a promising pathway for developing lightweight, high-performance EMW absorbing materials by optimizing impedance matching and utilizing advanced microstructure design techniques.

The subcellular localization of mRNAs determines the spatial context of protein translation and plays a critical role in the precise regulation of protein function. However, existing computational approaches for predicting eukaryotic mRNA localization are often limited by inadequate exploitation of sequence-derived information and suboptimal performance under highly imbalanced data distributions. To address these challenges, we propose mRNALocator-imb, an imbalance-aware ensemble framework for mRNA subcellular localization prediction. The proposed model integrates physicochemical property–based features with k-mer frequency vectors, and adopts a hybrid architecture that combines Random Forest and Transformer networks to capture both global sequence patterns and contextual dependencies. To explicitly mitigate class imbalance, Random Oversampling and Label-Distribution-Aware Margin (LDAM) loss are incorporated during Transformer training, while the Synthetic Minority Over-sampling Technique (SMOTE) is employed to rebalance the data for Random Forest learning. Extensive experiments demonstrate that mRNALocator-imb consistently outperforms conventional machine learning methods and state-of-the-art predictors, particularly in scenarios characterized by severe class imbalance. Overall, this study presents a robust and generalizable framework for sequence-based subcellular localization prediction, with broad applicability to other imbalanced learning problems in bioinformatics.