The threat of antimicrobial resistance (AMR) and the need for sustainable disinfectants have spurred interest in natural antimicrobials such as essential oils (EOs). However, their application is limited by volatility, poor water solubility, and cytotoxicity. Herein, we present the development of bio-based core–shell sub-micro-/nanocapsules (NCs) with encapsulated oregano (OO), thyme (TO), eucalyptus (EuO), and tea tree (TTO) oils to enhance antimicrobial (AM) performance and reduce cytotoxicity. NCs were synthesized via a nanoencapsulation method using chemically modified zein or poly(methyl vinyl ether-co-maleic anhydride) (GZA) as shell polymers, with selected EOs encapsulated in their core (encapsulation efficacy > 98%). Chemical modification of zein with vanillin (VA) and GZA with either dodecyl amine (DDA) or 3-(glycidyloxypropyl)trimethoxysilane (EPTMS) resulted in improvement in particle size distributions, polydispersity indices (PDIs) of synthesized NCs, and in the stability of the NC-dispersions in water. Antibacterial testing against Staphylococcus aureus and cytotoxicity assays showed that encapsulation significantly reduced toxicity while preserving their antibacterial activity. Among the formulations, GZA-based NCs modified with EPTMS provided the best balance between safety and efficacy. Despite this, life cycle assessment revealed that zein-based NCs were more environmentally sustainable due to lower energy use and material impact. Overall, the approach offers a promising strategy for developing sustainable, effective, and safe EO-based antibacterial agents for AM applications.

Background Wind turbine blade manufacturing by vacuum resin infusion is prone to defects that trigger repair operations, consuming additional resin, glass fibre and energy beyond the material embedded in the blade. The Horizon Europe Innovation Action TURBO (Grant Agreement No. 101058054) develops technologies to reduce these repair-related impacts. This study reports a defect-level carbon footprint assessment of three manufacturing configurations tested on a blade demonstrator. Methods Three physical castings of a 16 metre section of a 108 metre blade mould were produced and inspected: a standard-process baseline, an optimised-infusion configuration, and a sensors-plus-machine-learning configuration. Repair-related resin, glass fibre and grinding-energy consumption were quantified from post-manufacturing defect inventories and converted to carbon dioxide equivalent (CO 2 eq) emissions using reference factors. Results were extrapolated to a full blade using a surface-area scaling factor, and to annual fleet production. Defects were grouped into four families to assess whether mitigation acted uniformly across defect types. Results Optimised infusion reduced defect-repair CO 2 eq by 30.4% relative to baseline; the machine-learning configuration reduced it by 19.5%, a smaller benefit than its 32.1% reduction in defect count would suggest, because the defects it fails to prevent are on average larger. Both mitigation scenarios almost completely removed root-end air pockets, delamination and miscellaneous defects (50 to 100% reduction), but left dry-spot defects, the largest baseline family, nearly unaffected (4.0% reduction). The optimised-infusion estimate, cross-checked against an independent method, agreed within 3.1%. Scaled to a realistic annual production of 490 blades, avoided impacts of optimised infusion and the machine-learning configuration represent approximately 900 and 580 tonnes CO 2 eq per year. Conclusions These results indicate that both TURBO technologies already deliver measurable reductions in defect-repair carbon footprint, identifying dry-spot defects as the main remaining target for further improvement, with extension to a full impact category suite identified as future work.