Electric fields (EFs) have emerged as effective, non-chemical tools for modulating microbial populations in complex matrices such as wastewater. This review consolidates current advances on EF-induced alterations in microbial structures and functions, focusing on both vegetative cells and spores. Key parameters affected include membrane thickness, transmembrane potential, electrical conductivity, and dielectric permittivity, with downstream impacts on ion homeostasis, metabolic activity, and viability. Such bioelectrical modifications underpin EF-based detection methods—particularly impedance spectroscopy and dielectrophoresis—which enable rapid, label-free, in situ microbial monitoring. Beyond detection, EFs can induce sublethal or lethal effects, enabling selective inactivation without chemical input. This review addresses the influence of field type (DC, AC, pulsed), intensity, and exposure duration, alongside limitations such as species-specific variability, heterogeneous environmental conditions, and challenges in achieving uniform field distribution. Emerging research highlights the integration of EF-based platforms with biosensors, machine learning, and real-time analytics for enhanced environmental surveillance. By linking microbiological mechanisms with engineering solutions, EF technologies present significant potential for sustainable water quality management. Their multidisciplinary applicability positions them as promising components of next-generation wastewater monitoring and treatment systems, supporting global efforts toward efficient, adaptive, and environmentally benign microbial control strategies.

This research aims to synthesize a novel hydrogel bio-composite based on natural clay, sodium alginate (Na-AL), and iota-carrageenan as adsorbents to remove phosphate ions from aqueous solutions. The adsorbents were characterized by a variety of techniques, such as Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy coupled with energy dispersive X-rays (SEM-EDX), and the determination of point zero charge (PZC). This research investigated how the adsorption process is influenced by parameters such as adsorbent dose, contact time, solution pH, and temperature. In this study, we used four isotherms and four kinetic models to investigate phosphate ion removal on the prepared bio-composite. The results showed that the second-order kinetic (PSO) model is the best model for describing the adsorption process. The findings demonstrate that the R² values are highly significant in both the Langmuir and Freundlich models (very close to 1). This suggests that Langmuir and Freundlich models, with a diversity of adsorption sites, promote the adsorption of phosphate ions. The maximum adsorbed amounts of phosphate ions by the bio-composite used were 140.84 mg/g for H₂PO₄⁻ ions and 105.26 mg/g for HPO₄²⁻ ions from the batch system. The positive ∆H° confirms the endothermic and physical nature of adsorption, in agreement with experimental results. Negative ∆G° values indicate spontaneity, while the positive ∆S° reflects increased disorder at the solid–liquid interface during phosphate uptake. The main parameters, including adsorbent dosage (mg), contact time (min), and initial concentration (mg/L), were tuned using the Box–Behnken design of the response surface methodology (BBD-RSM) to achieve the optimum conditions. The reliability of the constructed models is demonstrated by their high correlation coefficients (R²). An R² value of 0.9714 suggests that the model explains 97.14% of the variability in adsorption efficiency (%), which reflects its strong predictive capability and reliability. Finally, the adsorption behavior of phosphate ions on the prepared bio-composite beads was analyzed using an artificial neural network (ANN) to predict the process efficiency. The ANN model accurately predicted the adsorption of phosphate ions onto the bio-composite, with a strong correlation (R² = 0.974) between the predicted and experimental results.