锂离子电池中环保型富镍正极浆料系统的最新进展与挑战OA

Ni-rich layered cathodes have become the mainstream choice to meet the growing demand for high-energy lithium-ion batteries(LIBs),which typically involves the use of highly polar N-methyl-2-pyrrolidone(NMP)to dissolve polymeric binders and form rheologically stable slurries for strong mechanical adhesion within the electrode.However,growing health and environmental concerns over NMP have triggered increasingly stringent regulations for sustainable development of LIB industries,thereby accelerating a long-overdue paradigm shift toward greener and safer solvent systems.In this context,this review first establishes a comprehensive theoretical framework for green solvent selection and slurry evaluation,including key concepts of solvent-binder compatibility,such as solubility theory,Hansen solubility parameters,Flory-Huggins interactions,and rheological characterization.Subsequently,the review highlights recent research progress in the development of green solvent-based slurries,covering a variety of solvent systems such as lactones,sulfoxides,phosphates,amides,and bio-based alternatives.Special emphasis is placed on elucidating how the processing behavior of green slurry influences the architecture of electrodes and determines their key performance indicators.Binder solubility,dispersion stability,rheological properties,and drying dynamics are analyzed in relation to their effects on electrode morphology,mechanical cohesion,capacity retention,and cycling stability.Despite encouraging laboratory results,these green slurry systems still face several practical barriers,including incomplete binder dissolution,binder migration during drying,and limited adaptability to high-solid-content formulations and accelerated drying protocols.To address these challenges,this review also proposes corresponding mitigation strategies and design recommendations,including thermodynamic-based solvent screening,rheological optimization,and drying kinetics control tailored to Ni-rich electrode systems.Finally,by integrating the latest advances in artificial intelligence,this review outlines future directions for predictable green slurry systems enabled by techniques such as machine learning-assisted solubility prediction,data-driven rheology modeling,and numerical model-enhanced drying simulations.By combining classical theoretical insights with advanced computational strategies,this review is expected to provide new perspectives for the sustainable manufacturing of next-generation high-energy batteries.