From mechanism to design: Thermodynamic–metabolic coupling and data-driven optimization of Fe-S substrates in constructed wetlands

Constructed wetlands enable sustainable nitrogen removal but are constrained by electron donor scarcity. Although iron–sulfur (Fe-S) substrates provide sustained redox energy, the mechanisms linking their thermodynamic properties with microbial metabolism remain unresolved, hindering rational design. Here, we propose an energy–metabolism coupling framework, where mechanistic insights yield a guiding rule and data-driven modeling translates it into F-S substrate optimization. Results demonstrate that strongly reductive environments accelerate early nitrogen removal but cause redox imbalance and ion accumulation, whereas moderate redox potentials activate the TCA cycle and enrich denitrifies such as Pseudomonas and Shewanella, supporting long-term stability. Functional gene analysis further shows that electron oversupply upregulates iron transporter genes, imposing energetic burdens. These insights point to a central guideline: effective Fe-S regulation requires balancing electron availability with metabolic adaptability. Building on these insights, we developed a machine-learning model that predicts denitrification performance across Fe-S ratios, enabling rational optimization. In 200-day validation experiments, the optimized system consistently maintained nitrogen removal efficiencies above 90 % with minimal fluctuation (standard deviation <5 %). Taken together, this study establishes a scalable Fe-S regulation strategy and provides a blueprint for designing efficient and resilient constructed wetland systems.