Ordered compartmentalization in colloidal microspheres: Design strategies and biomedical applications

The rational design of colloidal microspheres with ordered internal compartments represents a frontier challenge in colloid and interface science, offering unique opportunities to mimic the structural and functional complexity of biological cells. Orange-flap shaped multicompartmental microspheres (OFMM) exemplify this platform by featuring radially separated, geometrically uniform compartments that enable precise spatial control over multi-enzyme cascade reactions, directional inter-compartmental signal transduction, and programmed payload release. Unlike conventional core-shell or randomly distributed multi-core colloidal carriers, OFMM provide structural regularity and functional synergy, establishing them as a novel class of soft colloidal materials with biomimetic capabilities. The gas-shearing method has emerged as a green, oil-free, and high-throughput fabrication technology that overcomes the limitations of traditional microfluidic approaches by harnessing gas-liquid interfacial shear forces and Rayleigh-Plateau instability to drive controlled droplet breakup. Rational manipulation of nozzle geometry, gas flow dynamics, solution viscoelasticity, and ionic crosslinking enables precise control over compartment number (2-10), particle size (50-900 μm), and inter-compartment volume ratios. These parameters collectively dictate multi-active payload loading, cascade reaction efficiency, and release kinetics. This review systematically integrates the fundamental principles of OFMM structural design, the interfacial physicochemical processes underlying gas-shearing fabrication, bio-based material strategies for compartmental functionalization, and their conceptually novel applications in glucose-responsive drug delivery, reactive oxygen species scavenging, multichannel fluorescence encoding, and magnetically actuated microrobots. This review further outlines future directions encompassing nanoscale OFMM (<500 nm), machine learning-assisted multiphysics modeling of interfacial dynamics, and dynamically adaptive colloidal systems, thereby advancing the theoretical framework for next-generation intelligent biomimetic microsystems.