Abstract

The growing use of lithium-ion batteries (LIBs) underscores the need for sustainable end-of-life strategies, particularly in pre-treatment processes such as mechanical activation. This study evaluates the influence of grinding frequency and time on dry comminution performance, a key step in reducing particle size and liberating critical metals (Co, Ni, Mn) from mixed black mass containing lithium nickel manganese cobalt oxide (NMC), lithium cobalt oxide (LCO), and lithium manganese oxide (LMO) cathodes with graphite anode material. A randomized 3(2) factorial design (18 samples; frequencies: 10, 20, 30 Hz; times: 3, 5, 7 min), analysed through response surface methodology (RSM), identified frequency as the dominant factor controlling D80 value reduction. The second-order regression model predicted an optimal D-80 value of 62.81 mu m at 35 Hz and 7 min. Structural analysis by X-ray Diffraction (XRD) confirmed distinct associations between lithium metal oxides and secondary crystalline phases. Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy (SEM-EDS) mapping revealed heterogeneous spatial distribution, with Co exhibiting different patterns from Ni and Mn, reflecting the influence of cathode chemistry on comminution response. Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) results showed Co liberation of 80-70 % in the <= 425 mu m range, decreasing at intermediate sizes but improving again below 38 mu m. Ni liberation was maximized under intermediate conditions (sample M2: 10 Hz - 3 min - D-80 237.40 mu m), which also achieved the highest average Co-Ni-Mn liberation (similar to 39 %) while minimizing fines generation and energy demand. This highlights the importance of defining key performance indicators that integrate energy efficiency, fines control, and metal recovery. Overall, intermediate grinding regimes emerge as a promising pretreatment route to enhance the sustainable recovery of valuable metals from spent lithium-ion batteries (SLIBs) within circular economy frameworks.