Degradation mechanisms of bio-enhanced geopolymers for acid-resistant sewer infrastructure

Abstract

The long-term deterioration of sewer infrastructure in acidic environments presents a significant challenge for water utilities, as traditional cementitious materials tend to degrade rapidly when exposed to biogenic and industrial acids. To address this, this study investigates the durability of fly ash-based geopolymer concrete (GPC) reinforced with Shewanella bacteria, subjected to controlled sulphuric acid (H₂SO₄) immersions at concentrations of 0%, 3%, 5%, and 10% (by volume) over a duration of 60 days. A comprehensive experimental framework, including pH monitoring, mass change evaluation, capillary absorption tests, chloride migration analysis, permeability measurements, SEM/EDS, XRF, XRD, and FTIR spectroscopy, was employed to characterise chemical, mineralogical, and microstructural modifications. Results show that bio-modified GPC exhibited consistently improved performance under mild to moderate acidity, associated with enhanced Ca retention, reduced transport coefficients, and partial pore refinement, supporting a biomineralisation hypothesis pending direct bacterial activity verification. A clear mechanistic threshold emerged at 5% acid concentration, where bio‑GPC demonstrated the greatest stabilisation against gel depolymerisation through secondary mineral precipitation. At 10% acidity, both mixes underwent advanced degradation, and the benefits of biomodification were largely overwhelmed, with both systems showing dominant gypsum formation, severe gel dissolution, and substantially increased permeability, indicating that extreme acid concentrations exceed the stabilisation capacity of the bio-modified matrix. These lab-scale findings are consistent with microbial biomineralisation enhancing acid resistance within the pH ranges typical of sewer environments (3–5% H₂SO₄), supporting further investigation of bio-enhanced geopolymers as candidate sustainable materials for wastewater infrastructure, pending validation under dynamic field conditions.