Phase-Modified Strongly Coupled δ/ε-MnO2 Homojunction Cathode for Kinetics-Enhanced Zinc-Ion Batteries.

Rechargeable Zn-MnO2 batteries using mild water electrolytes have garnered significant interest owing to their impressive theoretical energy density and eco-friendly characteristics. However, MnO2 suffers from huge structural changes during the cycles, resulting in very poor stability at high charge-discharge depths. Briefly, the above problems are caused by slow kinetic processes and the dissolution of Mn atoms in the cycles. In this paper, a 2D homojunction electrode material (δ/ε-MnO2) based on δ-MnO2 and ε-MnO2 has been prepared by a two-step electrochemical deposition method. According to the DFT calculations, the charge transfer and bonding between interfaces result in the generation of electronic states near the Fermi surface, giving δ/ε-MnO2 a more continuous distribution of electron states and better conductivity, which is conducive to the rapid insertion/extraction of Zn2+ and H+. Moreover, the strongly coupled Mn-O-Mn interfacial bond can effectively impede dissolution of Mn atoms and thus maintain the structural integrity of δ/ε-MnO2 during the cycles. Accordingly, the δ/ε-MnO2 cathode exhibits high capacity (383 mAh g-1 at 0.1 A g-1), superior rate performance (150 mAh g-1 at 5 A g-1), and excellent cycling stability over 2000 cycles (91.3% at 3 A g-1). Profoundly, this unique homojunction provides a novel paradigm for reasonable selection of different components.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil.

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Ice-template-induced highly ionic conductive PVA/PEG-SiO2 gel polymer electrolyte for zinc-ion batteries.

In this work, a SiO2 doped polyvinyl alcohol/polyethylene glycol (PVA/PEG) gel polymer electrolyte (PVA/PEG-SiO2) was constructed via an ice-crystal template for zinc-ion batteries. The SiO2 and the three-dimensional porous skeleton make it have excellent ionic conductivity and mechanical strength, and inhibit the growth of dendrites. The assembled ZIBs exhibit excellent rate performance and cycle stability, making it a promising electrolyte membrane candidate for flexible wearable electronics.