Interfacial coronas of green-energy nanomaterials: A life-cycle eco-to-protein corona framework and molecular fingerprints

Green-energy technologies (GETs)-including solar cells, batteries, and biomass systems-underpin climate-change mitigation, but their performance increasingly depends on engineered nanoscale interfaces, making the energy transition also an interface transition. The ligand shells, coatings, and conductive networks that optimize a device can be inherited by nanomaterials (NMs) released during manufacturing, operation, aging, accidents, and end-of-life recycling, thereby programming how this debris behaves in the environment and the body. Released fragments are seldom pristine cores with fixed identities; instead, natural organic matter, proteins, and metabolites rewrite their surfaces into eco-coronas (ECs) and protein coronas (PCs) that reset charge, aggregation, dissolution, transport, and biological recognition. These successive coronas form a life-cycle EC-PC continuum linking interfacial evolution to environmental fate, bioaccessibility, and biological effects. Yet mechanistic corona evidence comes largely from model NMs such as silver, gold, and titania, leaving the release states of deployed GET materials-perovskite (PVSK) residues and quantum dots (QDs), high-nickel (high-Ni) cathodes and black-mass residues, carbon conductors, MXenes, and metal-organic framework (MOF)-derived fragments-scattered across disconnected literatures and unable to support prediction. Here we develop a life-cycle eco-to-protein-corona framework that organizes these materials along a single interfacial sequence and recasts the corona as reportable state variables-composition, enrichment, stability, transformation, exposure context, and outcome-that make heterogeneous systems comparable. This framework exposes a structured evidence gap: GET materials are increasingly characterized for environmental transformation, persistence, and ecotoxicity, whereas matched EC/PC and health-outcome evidence remains scarce. Corona fingerprints thus offer a computable bridge from surface history to predictive, scenario-specific, and safer-by-design assessment of green-energy NMs.