Molecular and biophysical foundations of nano-enabled seed priming and smart nanocarriers for crop stress alleviation

Agricultural productivity is increasingly constrained by interacting abiotic stresses, yet translation of nano-enabled seed treatments from controlled experiments to field agriculture remains limited. Seed nanopriming can initiate physiological and molecular priming before radicle emergence, but outcomes depend strongly on nanomaterial identity, particle size and morphology, surface charge, colloidal stability, cargo chemistry, exposure regime, and crop genotype. This review critically examines mesoporous silica nanoparticles, chitosan-based systems, metal–organic frameworks, hybrid/core–shell platforms, and catalytic nanozymes, distinguishing true cargo-delivery carriers from catalytic functional nanomaterials. We integrate interfacial physics with seed-coat and cell-wall barriers, clarifying how zeta potential, surface charge density, hydrodynamic size, porosity, functionalization, and material transformation influence adhesion, uptake, release, and translocation. At the molecular level, we connect nanoparticle–surface interactions with ROS and Ca2+ signaling, membrane-potential changes, MAPK/CDPK cascades, phytohormone cross-talk, transcription-factor activation, antioxidant defenses, and stress-memory processes. We also distinguish transient physiological memory from mitotically persistent and experimentally demonstrated transgenerational inheritance, and critically examine bio–nano combinations in soil, including dissolution, redox transformation, heteroaggregation, and eco-corona formation. Particular emphasis is placed on dose- and material-dependent phytotoxicity, multi-stress cross-tolerance, regulatory status of transgene-free editing, and practical barriers involving reproducibility, cost, scalability, field stability, and environmental safety. Finally, we identify realistic research priorities, including standardized nanoformulation reporting, field-validated dose windows, multi-omics and mechanistic imaging, Nano-QSAR/graph-learning workflows, and safer-by-design criteria for precision seed treatment.