Iron-carbon composites for anammox enhancement: a critical review of electron transfer mechanisms, ferroptosis regulation and scale-up challenges.

Anaerobic ammonium oxidation (anammox) has emerged as a sustainable wastewater nitrogen removal pathway; however, its full-scale application is impeded by slow autotrophic growth and environmental sensitivity. Iron-carbon (Fe-C) composites have been demonstrated to enhance performance by accelerating extracellular electron transfer (EET) and fostering robust aggregates. Departing from traditional descriptive summaries, this review establishes a multi-scale framework evaluating the cellular stress limits, material lifecycles, and techno-economic boundaries of Fe-C-anammox systems. A systematic elucidation of the competitive and synergistic kinetics governing anammox and Feammox substrate cross-feeding is presented. Methodologically, our paper categorized literature evidence for solid-state direct interspecies electron transfer (DIET) and liquid-phase redox shuttling into direct proof versus indirect inference to resolve a reported three-order-of-magnitude discrepancy in optimal dosages. At the cellular level, this work delineates the quantitative boundaries of reactive oxygen species (ROS) toxicity and the structural evidence limitations of applying eukaryotic "ferroptosis-like" cell death models to prokaryotes. It identifies explicit validation biomarkers (lipid peroxide accumulation, glutathione depletion, and lipophilic antioxidant sensitivity). In order to address the discrepancy between laboratory and full-scale practices, a comprehensive evaluation of unaddressed operational constraints is necessary. These include composite cost-benefit dynamics, material recyclability, mixing-induced particle abrasion, and in-situ regeneration. Downstream environmental and processing implications, specifically effluent iron-nanoparticle leaching and the handling of iron-rich waste biomass must be critically quantified. In conclusion, we have identified the essential input parameters and on-site deployment barriers for data-driven, predictive machine learning dosing frameworks. As a result, it delivers a rigorous, authoritative optimization roadmap for next-generation anammox intensification technologies.