In order to solve the problems of long-time consumption and high energy consumption in existing capacity detection methods of retired Ni-MH batteries, a fast and reliable capacity prediction method for retired Ni-MH batteries by multi-parameter driven analysis was proposed in this paper. This method mainly obtains several parameters through short-time measurement and pulse rapid nondestructive testing. Then, Pearson correlation coefficient and KS-test were used to analyze the correlation between the two parameters and verify the same distribution. Finally, SVR was used to predict the battery discharge capacity. The results show that the volume expansion thickness difference Δd, AC internal resistance R, terminal voltage U of the battery, charge and discharge polarization internal resistance R_f1 and R_f2 and pulse charging power P₂ of the battery are strongly negatively correlated with the discharge capacity, and these characteristic parameters can effectively and reliably reflect the internal structural characteristics of the battery. Additionally, the mean relative error of the established capacity model is 5.87%, and the lowest error is 1.32%. The prediction effect is good, which provides a certain reference value for the subsequent consistent sorting method.

Silicon nanochannels possess adjustable structural characteristics and excellent mechanical properties, and have great application potential in nanoelectromechanical systems, seawater desalination, and nanoreactors. However, achieving controllable synthesis and precise structural modulation remains highly challenging. Metal-assisted chemical etching (MACE), with its advantages of simple operation and low cost, has been widely used in the fabrication of silicon nanochannels. This study systematically investigates the morphological regulation mechanism of silicon nanochannels prepared via MACE, with a focus on analyzing the influence of etching solution composition, gold nanoparticle microstructure, and reaction time on the formation of helical structures. By combining thermomechanical molding for replicating nanochannel structures with electron microscopy characterization, comprehensive morphological analysis was achieved. The results indicate that an etching solution ratio of HF:H₂O₂:H₂O = 6:4:5 (v/v/v) yields helical nanochannels with greater length and higher yield. Increasing the HF proportion promotes the formation of helical structures; however, excessively high HF concentration leads to severe lateral etching, inhibiting deep channel propagation. The study further reveals an externally-driven mechanism for helical structure formation: the hydrogen bubbles generated during the reaction and the flow of the etching solution exert thrust on the nanoparticles, causing them to move and carve helical pathways.

End-of-life (EoL) electric vehicle (EV) batteries comprise numerous interconnected components with complex topological and precedence relationships. These constraints significantly increase the difficulty of disassembly sequence planning (DSP), as feasible sequences must satisfy multiple dependency requirements. Moreover, the large number of possible disassembly alternatives creates a vast search space, making sequence optimisation susceptible to combinatorial explosion and convergence to local optima. Therefore, effective DSP requires both robust constraint-handling mechanisms to ensure sequence feasibility and efficient optimisation strategies to identify high-quality solutions. To address these challenges, this paper proposes a disassembly sequence optimisation method that integrates a hard-constraint rule base, the linear upper confidence bound (LinUCB) algorithm, and the Bees Algorithm (BA). First, a disassembly-oriented hard-constraint rule base is developed to standardise the identification of component topological relationships and precedence constraints, thereby ensuring the generation of feasible disassembly sequences. A LinUCB-based contextual adaptive operator-selection mechanism is subsequently introduced to dynamically select neighbourhood operators according to the current search state. A weighted multi-criteria evaluation function incorporating disassembly time, payment cost, and human–robot utility is integrated into the BA. Two representative EoL-EV battery case studies with different levels of structural complexity are used for validation. Across 50 independent runs, LinUCB-BA reduced the mean normalised weighted objective value by 39.01% and 28.12% relative to simplified swarm optimisation (SSO) and teaching–learning-based optimisation (TLBO), respectively, in the 27-component case, and by 7.19% and 2.33% in the 16-component case. Compared with the enhanced discrete Bees Algorithm (EDBA) ablation baseline, further reductions of 1.98% and 0.43% were achieved, together with lower run-to-run variability. These results indicate that the proposed framework is effective for the two investigated battery disassembly scenarios, while broader validation across additional battery architectures and operating conditions remains necessary.

Coal-based reduction roasting is a widely used processing unit for recycling valuable metals (Li, Ni, Co, and Mn) from Mn-rich spent NCM black mass. However, solid carbon reductants require high temperatures (≥650 °C) and promote the formation of water-insoluble LiAlO₂ via reactions between Li₂O and Al-/F-bearing impurities. Herein, Mn-rich spent NCM black mass after mild NaOH leaching (1 wt%) was subjected to self-reduction, CO reduction, and H₂ reduction, and the three routes were systematically compared. Thermodynamic calculations, XRD, and XPS were used to elucidate phase evolution and element migration. Among the three routes, CO reduction achieved the highest lithium recovery of 87.4% (relative to the lithium remaining in the NaOH-pretreated alkaline leaching residue; the lithium originally present in the Mn-rich spent NCM black mass is the denominator for the overall mass-balance basis) at 10% CO, 600 °C and 90 min, outperforming self-reduction (60.4%) and H₂ reduction (60.7%). The superior performance of CO reduction is attributed to a synergistic effect between externally supplied CO and residual organics/graphite, which generates a controlled atmosphere that promotes Li₂CO₃ formation while suppressing LiAlO₂ crystallization. Subsequent sulfuric acid leaching of the water-leaching residue enabled nearly complete recovery of Ni, Co, and Mn, with leaching efficiencies exceeding 99.9%. This work provides mechanistic insight and comparative process data for optimizing reduction roasting–water leaching routes for selective metal recovery from Mn-rich spent NCM black mass.